





'''simple docstring'''




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
  from ...processing_utils import ProcessorMixin
  from ...utils import TensorType


__SCREAMING_SNAKE_CASE     :Optional[int]       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :Dict       =				{
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[int]           =							"""layoutlmv3"""




 def __init__(					self       :    int	,			snake_case_       :    List[Any]=5_0_2_6_5	,			snake_case_       :    int=7_6_8	,			snake_case_       :    Optional[int]=1_2	,			snake_case_       :    Dict=1_2	,			snake_case_       :    Union[str, Any]=3_0_7_2	,			snake_case_       :    Optional[Any]="gelu"	,			snake_case_       :    Union[str, Any]=0.1	,			snake_case_       :    Optional[Any]=0.1	,			snake_case_       :    List[Any]=5_1_2	,			snake_case_       :    Any=2	,			snake_case_       :    Any=0.0_2	,			snake_case_       :    Union[str, Any]=1e-5	,			snake_case_       :    Tuple=1	,			snake_case_       :    Any=0	,			snake_case_       :    Optional[int]=2	,			snake_case_       :    List[str]=1_0_2_4	,			snake_case_       :    int=1_2_8	,			snake_case_       :    List[Any]=1_2_8	,			snake_case_       :    List[str]=True	,			snake_case_       :    int=3_2	,			snake_case_       :    Union[str, Any]=1_2_8	,			snake_case_       :    Dict=6_4	,			snake_case_       :    int=2_5_6	,			snake_case_       :    Tuple=True	,			snake_case_       :    str=True	,			snake_case_       :    Tuple=True	,			snake_case_       :    Optional[Any]=2_2_4	,			snake_case_       :    List[str]=3	,			snake_case_       :    Tuple=1_6	,			snake_case_       :    Any=None	,			**snake_case_       :    Union[str, Any]	,			):
      super().__init__(
          vocab_size=snake_case_	,			hidden_size=snake_case_	,			num_hidden_layers=snake_case_	,			num_attention_heads=snake_case_	,			intermediate_size=snake_case_	,			hidden_act=snake_case_	,			hidden_dropout_prob=snake_case_	,			attention_probs_dropout_prob=snake_case_	,			max_position_embeddings=snake_case_	,			type_vocab_size=snake_case_	,			initializer_range=snake_case_	,			layer_norm_eps=snake_case_	,			pad_token_id=snake_case_	,			bos_token_id=snake_case_	,			eos_token_id=snake_case_	,			**snake_case_	,			)
      _UpperCAmelCase     							=   max_ad_position_embeddings
      _UpperCAmelCase     							=   coordinate_size
      _UpperCAmelCase     							=   shape_size
      _UpperCAmelCase     							=   has_relative_attention_bias
      _UpperCAmelCase     							=   rel_pos_bins
      _UpperCAmelCase     							=   max_rel_pos
      _UpperCAmelCase     							=   has_spatial_attention_bias
      _UpperCAmelCase     							=   rel_ad_pos_bins
      _UpperCAmelCase     							=   max_rel_ad_pos
      _UpperCAmelCase     							=   text_embed
      _UpperCAmelCase     							=   visual_embed
      _UpperCAmelCase     							=   input_size
      _UpperCAmelCase     							=   num_channels
      _UpperCAmelCase     							=   patch_size
      _UpperCAmelCase     							=   classifier_dropout







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Tuple           =							version.parse("""1.12"""			)




 @property
 def      lowercase	(					self       :    List[str]    ):
      # The order of inputs is different for question answering and sequence classification
      if self.task in ["question-answering", "sequence-classification"]:
           return OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "sequence"}),
                   ("attention_mask", {0: "batch", 1: "sequence"}),
                   ("bbox", {0: "batch", 1: "sequence"}),
                   ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
               ]    )
      else:
           return OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "sequence"}),
                   ("bbox", {0: "batch", 1: "sequence"}),
                   ("attention_mask", {0: "batch", 1: "sequence"}),
                   ("pixel_values", {0: "batch", 1: "num_channels"}),
               ]    )




 @property
 def      lowercase	(					self       :    Union[str, Any]    ):
      return 1e-5




 @property
 def      lowercase	(					self       :    Dict    ):
      return 1_2




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    "ProcessorMixin"	,			snake_case_       :    int = -1	,			snake_case_       :    int = -1	,			snake_case_       :    bool = False	,			snake_case_       :    Optional["TensorType"] = None	,			snake_case_       :    int = 3	,			snake_case_       :    int = 4_0	,			snake_case_       :    int = 4_0	,			):
      setattr(processor.image_processor	,			"apply_ocr"	,			snake_case_    )

      # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
      _UpperCAmelCase     							=   compute_effective_axis_dimension(
          snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_batch	,			num_token_to_add=0    )
      # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
      _UpperCAmelCase     							=   processor.tokenizer.num_special_tokens_to_add(snake_case_    )
      _UpperCAmelCase     							=   compute_effective_axis_dimension(
          snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_sequence	,			num_token_to_add=snake_case_    )
      # Generate dummy inputs according to compute batch and sequence
      _UpperCAmelCase     							=   [[" ".join([processor.tokenizer.unk_token]    ) * seq_length]] * batch_size

      # Generate dummy bounding boxes
      _UpperCAmelCase     							=   [[[4_8, 8_4, 7_3, 1_2_8]]] * batch_size

      # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
      # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
      _UpperCAmelCase     							=   self._generate_dummy_images(snake_case_	,			snake_case_	,			snake_case_	,			snake_case_    )

      _UpperCAmelCase     							=   dict(
          processor(
              snake_case_	,			text=snake_case_	,			boxes=snake_case_	,			return_tensors=snake_case_	,			)    )

      return inputs











'''simple docstring'''




from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


__SCREAMING_SNAKE_CASE     :Dict       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :int       =				{
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""perceiver"""




 def __init__(					self       :    Any	,			snake_case_       :    List[Any]=2_5_6	,			snake_case_       :    str=1_2_8_0	,			snake_case_       :    Optional[int]=7_6_8	,			snake_case_       :    int=1	,			snake_case_       :    List[Any]=2_6	,			snake_case_       :    Dict=8	,			snake_case_       :    List[Any]=8	,			snake_case_       :    Tuple=None	,			snake_case_       :    Tuple=None	,			snake_case_       :    Any="kv"	,			snake_case_       :    Any=1	,			snake_case_       :    List[str]=1	,			snake_case_       :    Optional[int]="gelu"	,			snake_case_       :    List[Any]=0.1	,			snake_case_       :    Dict=0.0_2	,			snake_case_       :    int=1e-12	,			snake_case_       :    List[str]=True	,			snake_case_       :    str=2_6_2	,			snake_case_       :    Optional[Any]=2_0_4_8	,			snake_case_       :    Union[str, Any]=5_6	,			snake_case_       :    Dict=[3_6_8, 4_9_6]	,			snake_case_       :    Tuple=1_6	,			snake_case_       :    Union[str, Any]=1_9_2_0	,			snake_case_       :    List[Any]=1_6	,			snake_case_       :    Tuple=[1, 1_6, 2_2_4, 2_2_4]	,			**snake_case_       :    List[Any]	,			):
      super().__init__(**snake_case_    )

      _UpperCAmelCase     							=   num_latents
      _UpperCAmelCase     							=   d_latents
      _UpperCAmelCase     							=   d_model
      _UpperCAmelCase     							=   num_blocks
      _UpperCAmelCase     							=   num_self_attends_per_block
      _UpperCAmelCase     							=   num_self_attention_heads
      _UpperCAmelCase     							=   num_cross_attention_heads
      _UpperCAmelCase     							=   qk_channels
      _UpperCAmelCase     							=   v_channels
      _UpperCAmelCase     							=   cross_attention_shape_for_attention
      _UpperCAmelCase     							=   self_attention_widening_factor
      _UpperCAmelCase     							=   cross_attention_widening_factor
      _UpperCAmelCase     							=   hidden_act
      _UpperCAmelCase     							=   attention_probs_dropout_prob
      _UpperCAmelCase     							=   initializer_range
      _UpperCAmelCase     							=   layer_norm_eps
      _UpperCAmelCase     							=   use_query_residual
      # masked language modeling attributes
      _UpperCAmelCase     							=   vocab_size
      _UpperCAmelCase     							=   max_position_embeddings
      # image classification attributes
      _UpperCAmelCase     							=   image_size
      # flow attributes
      _UpperCAmelCase     							=   train_size
      # multimodal autoencoding attributes
      _UpperCAmelCase     							=   num_frames
      _UpperCAmelCase     							=   audio_samples_per_frame
      _UpperCAmelCase     							=   samples_per_patch
      _UpperCAmelCase     							=   output_shape







class   A_	(  lowerCAmelCase_			):




 @property
 def      lowercase	(					self       :    int    ):
      if self.task == "multiple-choice":
           _UpperCAmelCase     							=   {0: "batch", 1: "choice", 2: "sequence"}
      else:
           _UpperCAmelCase     							=   {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("inputs", dynamic_axis),
              ("attention_mask", dynamic_axis),
          ]    )




 @property
 def      lowercase	(					self       :    Optional[Any]    ):
      return 1e-4




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]	,			snake_case_       :    int = -1	,			snake_case_       :    int = -1	,			snake_case_       :    int = -1	,			snake_case_       :    bool = False	,			snake_case_       :    Optional[TensorType] = None	,			snake_case_       :    int = 3	,			snake_case_       :    int = 4_0	,			snake_case_       :    int = 4_0	,			):
      # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

      if isinstance(snake_case_	,			snake_case_    ):
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           _UpperCAmelCase     							=   compute_effective_axis_dimension(
               snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_batch	,			num_token_to_add=0    )
           # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
           _UpperCAmelCase     							=   preprocessor.num_special_tokens_to_add(snake_case_    )
           _UpperCAmelCase     							=   compute_effective_axis_dimension(
               snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_sequence	,			num_token_to_add=snake_case_    )
           # Generate dummy inputs according to compute batch and sequence
           _UpperCAmelCase     							=   [" ".join(["a"]    ) * seq_length] * batch_size
           _UpperCAmelCase     							=   dict(preprocessor(snake_case_	,			return_tensors=snake_case_    )    )
           _UpperCAmelCase     							=   inputs.pop("input_ids"    )
           return inputs
      elif isinstance(snake_case_	,			snake_case_    ) and preprocessor.model_input_names[0] == "pixel_values":
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           _UpperCAmelCase     							=   compute_effective_axis_dimension(snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_batch    )
           _UpperCAmelCase     							=   self._generate_dummy_images(snake_case_	,			snake_case_	,			snake_case_	,			snake_case_    )
           _UpperCAmelCase     							=   dict(preprocessor(images=snake_case_	,			return_tensors=snake_case_    )    )
           _UpperCAmelCase     							=   inputs.pop("pixel_values"    )
           return inputs
      else:
           raise ValueError(
               "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."    )




