294 lines
13 KiB
Python
294 lines
13 KiB
Python
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# coding=utf-8
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# Copyright 2022 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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""" LayoutLMv3 model configuration"""
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from collections import OrderedDict
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from typing import TYPE_CHECKING, Any, Mapping, Optional
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from packaging import version
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from ...configuration_utils import PretrainedConfig
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from ...onnx import OnnxConfig
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from ...onnx.utils import compute_effective_axis_dimension
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from ...utils import logging
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if TYPE_CHECKING:
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from ...processing_utils import ProcessorMixin
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from ...utils import TensorType
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class LayoutLMv3Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`LayoutLMv3Model`]. It is used to instantiate an
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LayoutLMv3 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 LayoutLMv3
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[microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-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 50265):
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Vocabulary size of the LayoutLMv3 model. Defines the number of different tokens that can be represented by
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the `inputs_ids` passed when calling [`LayoutLMv3Model`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimension of the encoder layers and the pooler layer.
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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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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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hidden_act (`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"`, `"selu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`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_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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max_position_embeddings (`int`, *optional*, defaults to 512):
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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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type_vocab_size (`int`, *optional*, defaults to 2):
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The vocabulary size of the `token_type_ids` passed when calling [`LayoutLMv3Model`].
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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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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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max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
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The maximum value that the 2D position embedding might ever be used with. Typically set this to something
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large just in case (e.g., 1024).
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coordinate_size (`int`, *optional*, defaults to `128`):
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Dimension of the coordinate embeddings.
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shape_size (`int`, *optional*, defaults to `128`):
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Dimension of the width and height embeddings.
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has_relative_attention_bias (`bool`, *optional*, defaults to `True`):
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Whether or not to use a relative attention bias in the self-attention mechanism.
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rel_pos_bins (`int`, *optional*, defaults to 32):
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The number of relative position bins to be used in the self-attention mechanism.
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max_rel_pos (`int`, *optional*, defaults to 128):
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The maximum number of relative positions to be used in the self-attention mechanism.
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max_rel_2d_pos (`int`, *optional*, defaults to 256):
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The maximum number of relative 2D positions in the self-attention mechanism.
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rel_2d_pos_bins (`int`, *optional*, defaults to 64):
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The number of 2D relative position bins in the self-attention mechanism.
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has_spatial_attention_bias (`bool`, *optional*, defaults to `True`):
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Whether or not to use a spatial attention bias in the self-attention mechanism.
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visual_embed (`bool`, *optional*, defaults to `True`):
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Whether or not to add patch embeddings.
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input_size (`int`, *optional*, defaults to `224`):
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The size (resolution) of the images.
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num_channels (`int`, *optional*, defaults to `3`):
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The number of channels of the images.
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patch_size (`int`, *optional*, defaults to `16`)
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The size (resolution) of the patches.
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classifier_dropout (`float`, *optional*):
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The dropout ratio for the classification head.
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Example:
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```python
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>>> from transformers import LayoutLMv3Config, LayoutLMv3Model
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>>> # Initializing a LayoutLMv3 microsoft/layoutlmv3-base style configuration
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>>> configuration = LayoutLMv3Config()
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>>> # Initializing a model (with random weights) from the microsoft/layoutlmv3-base style configuration
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>>> model = LayoutLMv3Model(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 = "layoutlmv3"
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def __init__(
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self,
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vocab_size=50265,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=2,
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initializer_range=0.02,
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layer_norm_eps=1e-5,
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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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max_2d_position_embeddings=1024,
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coordinate_size=128,
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shape_size=128,
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has_relative_attention_bias=True,
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rel_pos_bins=32,
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max_rel_pos=128,
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rel_2d_pos_bins=64,
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max_rel_2d_pos=256,
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has_spatial_attention_bias=True,
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text_embed=True,
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visual_embed=True,
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input_size=224,
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num_channels=3,
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patch_size=16,
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classifier_dropout=None,
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**kwargs,
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):
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super().__init__(
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vocab_size=vocab_size,
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hidden_size=hidden_size,
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num_hidden_layers=num_hidden_layers,
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num_attention_heads=num_attention_heads,
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intermediate_size=intermediate_size,
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hidden_act=hidden_act,
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hidden_dropout_prob=hidden_dropout_prob,
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attention_probs_dropout_prob=attention_probs_dropout_prob,
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max_position_embeddings=max_position_embeddings,
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type_vocab_size=type_vocab_size,
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initializer_range=initializer_range,
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layer_norm_eps=layer_norm_eps,
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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.max_2d_position_embeddings = max_2d_position_embeddings
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self.coordinate_size = coordinate_size
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self.shape_size = shape_size
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self.has_relative_attention_bias = has_relative_attention_bias
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self.rel_pos_bins = rel_pos_bins
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self.max_rel_pos = max_rel_pos
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self.has_spatial_attention_bias = has_spatial_attention_bias
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self.rel_2d_pos_bins = rel_2d_pos_bins
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self.max_rel_2d_pos = max_rel_2d_pos
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self.text_embed = text_embed
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self.visual_embed = visual_embed
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self.input_size = input_size
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self.num_channels = num_channels
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self.patch_size = patch_size
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self.classifier_dropout = classifier_dropout
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class LayoutLMv3OnnxConfig(OnnxConfig):
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torch_onnx_minimum_version = version.parse("1.12")
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@property
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def inputs(self) -> Mapping[str, Mapping[int, str]]:
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# The order of inputs is different for question answering and sequence classification
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if self.task in ["question-answering", "sequence-classification"]:
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return OrderedDict(
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[
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("input_ids", {0: "batch", 1: "sequence"}),
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("attention_mask", {0: "batch", 1: "sequence"}),
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("bbox", {0: "batch", 1: "sequence"}),
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("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
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]
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)
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else:
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return OrderedDict(
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[
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("input_ids", {0: "batch", 1: "sequence"}),
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("bbox", {0: "batch", 1: "sequence"}),
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("attention_mask", {0: "batch", 1: "sequence"}),
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("pixel_values", {0: "batch", 1: "num_channels"}),
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]
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)
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@property
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def atol_for_validation(self) -> float:
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return 1e-5
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@property
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def default_onnx_opset(self) -> int:
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return 12
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def generate_dummy_inputs(
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self,
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processor: "ProcessorMixin",
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batch_size: int = -1,
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seq_length: int = -1,
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is_pair: bool = False,
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framework: Optional["TensorType"] = None,
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num_channels: int = 3,
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image_width: int = 40,
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image_height: int = 40,
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) -> Mapping[str, Any]:
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"""
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Generate inputs to provide to the ONNX exporter for the specific framework
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Args:
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processor ([`ProcessorMixin`]):
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The processor associated with this model configuration.
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batch_size (`int`, *optional*, defaults to -1):
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The batch size to export the model for (-1 means dynamic axis).
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seq_length (`int`, *optional*, defaults to -1):
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The sequence length to export the model for (-1 means dynamic axis).
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is_pair (`bool`, *optional*, defaults to `False`):
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Indicate if the input is a pair (sentence 1, sentence 2).
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framework (`TensorType`, *optional*, defaults to `None`):
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The framework (PyTorch or TensorFlow) that the processor will generate tensors for.
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num_channels (`int`, *optional*, defaults to 3):
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The number of channels of the generated images.
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image_width (`int`, *optional*, defaults to 40):
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The width of the generated images.
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image_height (`int`, *optional*, defaults to 40):
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The height of the generated images.
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Returns:
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Mapping[str, Any]: holding the kwargs to provide to the model's forward function
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"""
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# A dummy image is used so OCR should not be applied
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setattr(processor.image_processor, "apply_ocr", False)
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# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
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batch_size = compute_effective_axis_dimension(
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batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0
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)
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# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
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token_to_add = processor.tokenizer.num_special_tokens_to_add(is_pair)
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seq_length = compute_effective_axis_dimension(
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seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add
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)
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# Generate dummy inputs according to compute batch and sequence
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dummy_text = [[" ".join([processor.tokenizer.unk_token]) * seq_length]] * batch_size
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# Generate dummy bounding boxes
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dummy_bboxes = [[[48, 84, 73, 128]]] * batch_size
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# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
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# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
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dummy_image = self._generate_dummy_images(batch_size, num_channels, image_height, image_width)
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inputs = dict(
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processor(
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dummy_image,
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text=dummy_text,
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boxes=dummy_bboxes,
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return_tensors=framework,
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)
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)
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return inputs
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