296 lines
13 KiB
Python
296 lines
13 KiB
Python
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# coding=utf-8
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# Copyright 2023 Microsoft Research and 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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""" KOSMOS-2 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 KOSMOS2_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class Kosmos2TextConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Kosmos2TextModel`]. It is used to instantiate a
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KOSMOS-2 text decoder 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 text decoder of the KOSMOS-2
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[microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) 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 65037):
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Vocabulary size of the Kosmos2 model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`Kosmos2Model`].
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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embed_dim (`int`, *optional*, defaults to 2048):
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Dimensionality of the layers and the pooler layer.
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layers (`int`, *optional*, defaults to 24):
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Number of hidden layers in the Transformer encoder.
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ffn_dim (`int`, *optional*, defaults to 8192):
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Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
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attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer encoder.
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activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
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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"`, `"silu"` and `"gelu_new"` are supported.
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dropout (`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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attention_dropout (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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activation_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for activations inside the fully connected layer.
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layerdrop (`float`, *optional*, defaults to 0.0):
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The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
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for more details.
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layer_norm_eps (`float`, *optional*, defaults to 1e-5):
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The epsilon used by the layer normalization layers.
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init_std (`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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scale_embedding (`bool`, *optional*, defaults to `True`):
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Scale embeddings by diving by sqrt(embed_dim).
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use_cache (`bool`, *optional*, defaults to `True`):
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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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```"""
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model_type = "kosmos_2_text_model"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {
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"num_attention_heads": "attention_heads",
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"hidden_size": "embed_dim",
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"num_hidden_layers": "layers",
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}
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def __init__(
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self,
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vocab_size=65037,
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max_position_embeddings=2048,
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embed_dim=2048,
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layers=24,
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ffn_dim=8192,
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attention_heads=32,
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activation_function="gelu",
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dropout=0.1,
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attention_dropout=0.1,
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activation_dropout=0.0,
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layerdrop=0.0,
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layer_norm_eps=1e-5,
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init_std=0.02,
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scale_embedding=True,
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use_cache=True,
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pad_token_id=1,
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bos_token_id=0,
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eos_token_id=2,
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**kwargs,
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):
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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**kwargs,
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)
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.embed_dim = embed_dim
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self.layers = layers
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self.ffn_dim = ffn_dim
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self.attention_heads = attention_heads
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self.activation_function = activation_function
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.activation_dropout = activation_dropout
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self.layerdrop = layerdrop
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self.layer_norm_eps = layer_norm_eps
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self.init_std = init_std
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self.scale_embedding = scale_embedding
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self.use_cache = use_cache
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# get the text config dict if we are loading from Kosmos2Config
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if config_dict.get("model_type") == "kosmos-2":
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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 Kosmos2VisionConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Kosmos2VisionModel`]. It is used to instantiate a
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KOSMOS-2 vision encoder 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 vision encoder of the KOSMOS-2
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[microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) 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 1024):
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Dimensionality of the encoder layers and the pooler layer.
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intermediate_size (`int`, *optional*, defaults to 4096):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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num_hidden_layers (`int`, *optional*, defaults to 24):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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image_size (`int`, *optional*, defaults to 224):
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The size (resolution) of each image.
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patch_size (`int`, *optional*, defaults to 14):
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The size (resolution) of each patch.
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hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
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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"` ``"quick_gelu"` are supported.
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layer_norm_eps (`float`, *optional*, defaults to 1e-5):
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The epsilon used by the layer normalization layers.
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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 0.02):
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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):
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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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```"""
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model_type = "kosmos_2_vision_model"
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def __init__(
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self,
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hidden_size=1024,
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intermediate_size=4096,
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num_hidden_layers=24,
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num_attention_heads=16,
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num_channels=3,
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image_size=224,
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patch_size=14,
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hidden_act="quick_gelu",
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layer_norm_eps=1e-5,
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attention_dropout=0.0,
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initializer_range=0.02,
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initializer_factor=1.0,
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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.intermediate_size = intermediate_size
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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.num_channels = num_channels
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self.patch_size = patch_size
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self.image_size = image_size
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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.hidden_act = hidden_act
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
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cls._set_token_in_kwargs(kwargs)
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config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
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# get the vision config dict if we are loading from Kosmos2Config
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if config_dict.get("model_type") == "kosmos-2":
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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 Kosmos2Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Kosmos2Model`]. It is used to instantiate a
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KOSMOS-2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the KOSMOS-2
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[microsoft/kosmos-2-patch14-224](https://huggingface.co/microsoft/kosmos-2-patch14-224) architecture.
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Args:
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text_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`Kosmos2TextConfig`].
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vision_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize [`Kosmos2VisionConfig`].
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latent_query_num (`int`, *optional*, defaults to 64):
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The number of latent query tokens that represent the image features used in the text decoder component.
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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 Kosmos2Config, Kosmos2Model
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>>> # Initializing a Kosmos-2 kosmos-2-patch14-224 style configuration
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>>> configuration = Kosmos2Config()
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>>> # Initializing a model (with random weights) from the kosmos-2-patch14-224 style configuration
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>>> model = Kosmos2Model(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 = "kosmos-2"
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is_composition = True
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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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latent_query_num=64,
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**kwargs,
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):
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super().__init__(**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 `Kosmos2TextConfig` 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 `Kosmos2VisionConfig` with default values.")
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self.text_config = Kosmos2TextConfig(**text_config)
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self.vision_config = Kosmos2VisionConfig(**vision_config)
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self.latent_query_num = latent_query_num
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