388 lines
17 KiB
Python
388 lines
17 KiB
Python
# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Pix2Struct model configuration"""
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import os
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from typing import Union
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class Pix2StructTextConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Pix2StructTextModel`]. It is used to instantiate
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a Pix2Struct text model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the Pix2Struct text decoder used by
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the [google/pix2struct-base](https://huggingface.co/google/pix2struct-base) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 50244):
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Vocabulary size of the `Pix2Struct` text model. Defines the number of different tokens that can be
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represented by the `inputs_ids` passed when calling [`Pix2StructTextModel`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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d_kv (`int`, *optional*, defaults to 64):
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Dimensionality of the key, query, value projections in each attention head.
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d_ff (`int`, *optional*, defaults to 2048):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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num_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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relative_attention_num_buckets (`int`, *optional*, defaults to 32):
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The number of buckets to use for each attention layer.
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relative_attention_max_distance (`int`, *optional*, defaults to 128):
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The maximum distance of the longer sequences for the bucket separation.
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dropout_rate (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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layer_norm_epsilon (`float`, *optional*, defaults to 1e-6):
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The epsilon used by the layer normalization layers.
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initializer_factor (`float`, *optional*, defaults to 1.0):
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A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
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testing).
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dense_act_fn (`Union[Callable, str]`, *optional*, defaults to `"gelu_new"`):
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The non-linear activation function (function or string).
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decoder_start_token_id (`int`, *optional*, defaults to 0):
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The id of the `decoder_start_token_id` token.
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use_cache (`bool`, *optional*, defaults to `False`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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pad_token_id (`int`, *optional*, defaults to 0):
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The id of the `padding` token.
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eos_token_id (`int`, *optional*, defaults to 1):
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The id of the `end-of-sequence` token.
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Example:
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```python
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>>> from transformers import Pix2StructTextConfig, Pix2StructTextModel
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>>> # Initializing a Pix2StructTextConfig with google/pix2struct-base style configuration
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>>> configuration = Pix2StructTextConfig()
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>>> # Initializing a Pix2StructTextModel (with random weights) from the google/pix2struct-base style configuration
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>>> model = Pix2StructTextModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "pix2struct_text_model"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {
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"hidden_size": "hidden_size",
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"num_attention_heads": "num_heads",
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"num_hidden_layers": "num_layers",
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}
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def __init__(
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self,
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vocab_size=50244,
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hidden_size=768,
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d_kv=64,
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d_ff=2048,
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num_layers=12,
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num_heads=12,
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relative_attention_num_buckets=32,
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relative_attention_max_distance=128,
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dropout_rate=0.1,
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layer_norm_epsilon=1e-6,
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initializer_factor=1.0,
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dense_act_fn="gelu_new",
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decoder_start_token_id=0,
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use_cache=False,
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pad_token_id=0,
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eos_token_id=1,
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tie_word_embeddings=False,
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is_decoder=True,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.d_kv = d_kv
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self.d_ff = d_ff
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self.num_layers = num_layers
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self.num_heads = num_heads
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self.relative_attention_num_buckets = relative_attention_num_buckets
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self.relative_attention_max_distance = relative_attention_max_distance
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self.dropout_rate = dropout_rate
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_factor = initializer_factor
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self.use_cache = use_cache
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self.eos_token_id = eos_token_id
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self.decoder_start_token_id = decoder_start_token_id
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# for backwards compatibility
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self.dense_act_fn = dense_act_fn
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super().__init__(
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pad_token_id=pad_token_id,
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eos_token_id=eos_token_id,
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decoder_start_token_id=decoder_start_token_id,
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tie_word_embeddings=tie_word_embeddings,
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is_decoder=is_decoder,
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**kwargs,
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)
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@classmethod
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def from_pretrained(
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cls, pretrainehidden_size_name_or_path: Union[str, os.PathLike], **kwargs
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) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrainehidden_size_name_or_path, **kwargs)
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# get the text config dict if we are loading from Pix2StructConfig
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if config_dict.get("model_type") == "pix2struct":
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config_dict = config_dict["text_config"]
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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)
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return cls.from_dict(config_dict, **kwargs)
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class Pix2StructVisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Pix2StructVisionModel`]. It is used to
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instantiate a Pix2Struct vision model according to the specified arguments, defining the model architecture.
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Instantiating a configuration defaults will yield a similar configuration to that of the Pix2Struct-base
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[google/pix2struct-base](https://huggingface.co/google/pix2struct-base) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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patch_embed_hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the input patch_embedding layer in the Transformer encoder.
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d_ff (`int`, *optional*, defaults to 2048):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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d_kv (`int`, *optional*, defaults to 64):
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Dimensionality of the key, query, value projections per attention head.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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dense_act_fn (`str` or `function`, *optional*, defaults to `"gelu_new"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` ``"gelu"` are supported.
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layer_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the layer normalization layers.
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dropout_rate (`float`, *optional*, defaults to 0.0):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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initializer_range (`float`, *optional*, defaults to 1e-10):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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initializer_factor (`float`, *optional*, defaults to 1.0):
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A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
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testing).
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seq_len (`int`, *optional*, defaults to 4096):
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Maximum sequence length (here number of patches) supported by the model.
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relative_attention_num_buckets (`int`, *optional*, defaults to 32):
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The number of buckets to use for each attention layer.
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relative_attention_max_distance (`int`, *optional*, defaults to 128):
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The maximum distance (in tokens) to use for each attention layer.
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Example:
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```python
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>>> from transformers import Pix2StructVisionConfig, Pix2StructVisionModel
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>>> # Initializing a Pix2StructVisionConfig with google/pix2struct-base style configuration
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>>> configuration = Pix2StructVisionConfig()
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>>> # Initializing a Pix2StructVisionModel (with random weights) from the google/pix2struct-base style configuration
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>>> model = Pix2StructVisionModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "pix2struct_vision_model"
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def __init__(
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self,
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hidden_size=768,
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patch_embed_hidden_size=768,
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d_ff=2048,
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d_kv=64,
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num_hidden_layers=12,
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num_attention_heads=12,
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dense_act_fn="gelu_new",
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layer_norm_eps=1e-6,
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dropout_rate=0.0,
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attention_dropout=0.0,
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initializer_range=1e-10,
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initializer_factor=1.0,
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seq_len=4096,
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relative_attention_num_buckets=32,
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relative_attention_max_distance=128,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.patch_embed_hidden_size = patch_embed_hidden_size
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self.d_ff = d_ff
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self.dropout_rate = dropout_rate
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.initializer_range = initializer_range
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self.initializer_factor = initializer_factor
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self.attention_dropout = attention_dropout
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self.layer_norm_eps = layer_norm_eps
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self.dense_act_fn = dense_act_fn
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self.seq_len = seq_len
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self.relative_attention_num_buckets = relative_attention_num_buckets
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self.relative_attention_max_distance = relative_attention_max_distance
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self.d_kv = d_kv
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@classmethod
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def from_pretrained(
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cls, pretrainehidden_size_name_or_path: Union[str, os.PathLike], **kwargs
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) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrainehidden_size_name_or_path, **kwargs)
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# get the vision config dict if we are loading from Pix2StructConfig
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if config_dict.get("model_type") == "pix2struct":
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config_dict = config_dict["vision_config"]
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if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
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logger.warning(
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f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
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f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
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)
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return cls.from_dict(config_dict, **kwargs)
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class Pix2StructConfig(PretrainedConfig):
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r"""
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[`Pix2StructConfig`] is the configuration class to store the configuration of a
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[`Pix2StructForConditionalGeneration`]. It is used to instantiate a Pix2Struct model according to the specified
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arguments, defining the text model and vision model configs. Instantiating a configuration with the defaults will
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yield a similar configuration to that of the Pix2Struct-base
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[google/pix2struct-base](https://huggingface.co/google/pix2struct-base) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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text_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`Pix2StructTextConfig`].
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vision_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`Pix2StructVisionConfig`].
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initializer_factor (`float`, *optional*, defaults to 1.0):
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Factor to multiply the initialization range with.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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is_vqa (`bool`, *optional*, defaults to `False`):
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Whether the model has been fine-tuned for VQA or not.
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kwargs (*optional*):
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Dictionary of keyword arguments.
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Example:
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```python
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>>> from transformers import Pix2StructConfig, Pix2StructForConditionalGeneration
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>>> # Initializing a Pix2StructConfig with google/pix2struct-base style configuration
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>>> configuration = Pix2StructConfig()
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>>> # Initializing a Pix2StructForConditionalGeneration (with random weights) from the google/pix2struct-base style configuration
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>>> model = Pix2StructForConditionalGeneration(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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>>> # We can also initialize a Pix2StructConfig from a Pix2StructTextConfig and a Pix2StructVisionConfig
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>>> # Initializing a Pix2Struct text and Pix2Struct vision configuration
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>>> config_text = Pix2StructTextConfig()
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>>> config_vision = Pix2StructVisionConfig()
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>>> config = Pix2StructConfig.from_text_vision_configs(config_text, config_vision)
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```"""
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model_type = "pix2struct"
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def __init__(
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self,
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text_config=None,
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vision_config=None,
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initializer_factor=1.0,
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initializer_range=0.02,
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is_vqa=False,
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tie_word_embeddings=False,
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is_encoder_decoder=True,
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**kwargs,
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):
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super().__init__(tie_word_embeddings=tie_word_embeddings, is_encoder_decoder=is_encoder_decoder, **kwargs)
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if text_config is None:
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text_config = {}
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logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values.")
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if vision_config is None:
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vision_config = {}
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logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values.")
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self.text_config = Pix2StructTextConfig(**text_config)
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self.vision_config = Pix2StructVisionConfig(**vision_config)
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self.decoder_start_token_id = self.text_config.decoder_start_token_id
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self.pad_token_id = self.text_config.pad_token_id
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self.eos_token_id = self.text_config.eos_token_id
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self.initializer_factor = initializer_factor
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self.initializer_range = initializer_range
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self.text_config.initializer_range = self.initializer_range
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self.vision_config.initializer_range = self.initializer_range
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self.is_vqa = is_vqa
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@classmethod
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def from_text_vision_configs(
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cls, text_config: Pix2StructTextConfig, vision_config: Pix2StructVisionConfig, **kwargs
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):
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r"""
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Instantiate a [`Pix2StructConfig`] (or a derived class) from pix2struct text model configuration and pix2struct
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vision model configuration.
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Returns:
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[`Pix2StructConfig`]: An instance of a configuration object
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"""
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return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)
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