


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


_lowerCAmelCase:						Union[str, Any]   				=       logging.get_logger(__name__)

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




class       lowercase_					(lowercase__   ):
				snake_case	    ='layoutlmv3'





				def __init__(				self       ,  lowercase_=50265       ,  lowercase_=768       ,  lowercase_=12       ,  lowercase_=12       ,  lowercase_=3072       ,  lowercase_="gelu"       ,  lowercase_=0.1       ,  lowercase_=0.1       ,  lowercase_=512       ,  lowercase_=2       ,  lowercase_=0.02       ,  lowercase_=1e-5       ,  lowercase_=1       ,  lowercase_=0       ,  lowercase_=2       ,  lowercase_=1024       ,  lowercase_=128       ,  lowercase_=128       ,  lowercase_=True       ,  lowercase_=32       ,  lowercase_=128       ,  lowercase_=64       ,  lowercase_=256       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_=True       ,  lowercase_=224       ,  lowercase_=3       ,  lowercase_=16       ,  lowercase_=None       ,  **lowercase_       ,  )  -> int:
											super().__init__(
											    vocab_size=lowercase_       ,  hidden_size=lowercase_       ,  num_hidden_layers=lowercase_       ,  num_attention_heads=lowercase_       ,  intermediate_size=lowercase_       ,  hidden_act=lowercase_       ,  hidden_dropout_prob=lowercase_       ,  attention_probs_dropout_prob=lowercase_       ,  max_position_embeddings=lowercase_       ,  type_vocab_size=lowercase_       ,  initializer_range=lowercase_       ,  layer_norm_eps=lowercase_       ,  pad_token_id=lowercase_       ,  bos_token_id=lowercase_       ,  eos_token_id=lowercase_       ,  **lowercase_       ,  )
											a__      =max_ad_position_embeddings
											a__      =coordinate_size
											a__      =shape_size
											a__      =has_relative_attention_bias
											a__      =rel_pos_bins
											a__      =max_rel_pos
											a__      =has_spatial_attention_bias
											a__      =rel_ad_pos_bins
											a__      =max_rel_ad_pos
											a__      =text_embed
											a__      =visual_embed
											a__      =input_size
											a__      =num_channels
											a__      =patch_size
											a__      =classifier_dropout







class       lowercase_					(lowercase__   ):
				snake_case	    =version.parse('1.12'   )





				@property
				def 						__UpperCamelCase    (				self)  -> Mapping[str, Mapping[int, 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 						__UpperCamelCase    (				self)  -> float:
											return 1e-5





				@property
				def 						__UpperCamelCase    (				self)  -> int:
											return 12





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_ = -1       ,  lowercase_ = -1       ,  lowercase_ = False       ,  lowercase_ = None       ,  lowercase_ = 3       ,  lowercase_ = 40       ,  lowercase_ = 40       ,  )  -> Mapping[str, Any]:
											setattr(processor.image_processor       ,  'apply_ocr'       ,  lowercase_)

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

											# Generate dummy bounding boxes
											a__      =[[[48, 84, 73, 128]]] * 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)
											a__      =self._generate_dummy_images(lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_)

											a__      =dict(
											    processor(
											        lowercase_       ,  text=lowercase_       ,  boxes=lowercase_       ,  return_tensors=lowercase_       ,  ))

											return inputs







'''simple docstring'''






import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
A_			      =		"."

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
A_			      =		[
    "Assert",
    "AssignVariableOp",
    "EmptyTensorList",
    "MergeV2Checkpoints",
    "ReadVariableOp",
    "ResourceGather",
    "RestoreV2",
    "SaveV2",
    "ShardedFilename",
    "StatefulPartitionedCall",
    "StaticRegexFullMatch",
    "VarHandleOp",
]






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Union[str, Any]:
      snake_case__       :   str	      =    SavedModel()
      snake_case__       :   Union[str, Any]	      =    []

      with open(os.path.join(__SCREAMING_SNAKE_CASE				,      'utils'				,      'tf_ops'				,      'onnx.json'		)		) as f:
            snake_case__       :   Optional[Any]	      =    json.load(__SCREAMING_SNAKE_CASE		)['opsets']

      for i in range(1				,      opset + 1		):
            onnx_ops.extend(onnx_opsets[str(__SCREAMING_SNAKE_CASE		)]		)

      with open(__SCREAMING_SNAKE_CASE				,      'rb'		) as f:
            saved_model.ParseFromString(f.read()		)

      snake_case__       :   List[str]	      =    set()

      # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
      for meta_graph in saved_model.meta_graphs:
            # Add operations in the graph definition
            model_op_names.update(node.op for node in meta_graph.graph_def.node		)

            # Go through the functions in the graph definition
            for func in meta_graph.graph_def.library.function:
                  # Add operations in each function
                  model_op_names.update(node.op for node in func.node_def		)

    # Convert to list, sorted if you want
      snake_case__       :   Optional[int]	      =    sorted(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   Optional[Any]	      =    []

      for op in model_op_names:
            if op not in onnx_ops and op not in INTERNAL_OPS:
                  incompatible_ops.append(__SCREAMING_SNAKE_CASE		)

      if strict and len(__SCREAMING_SNAKE_CASE		) > 0:
            raise Exception(f"Found the following incompatible ops for the opset {opset}:\n" + incompatible_ops		)
      elif len(__SCREAMING_SNAKE_CASE		) > 0:
            print(f"Found the following incompatible ops for the opset {opset}:"		)
            print(*__SCREAMING_SNAKE_CASE				,      sep='\n'		)
      else:
            print(f"The saved model {saved_model_path} can properly be converted with ONNX."		)


if __name__ == "__main__":
      A_			      =		argparse.ArgumentParser()
      parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
      parser.add_argument(
          "--opset", default=12, type=int, help="The ONNX opset against which the model has to be tested."
      )
      parser.add_argument(
          "--framework", choices=["onnx"], default="onnx", help="Frameworks against which to test the saved model."
      )
      parser.add_argument(
          "--strict", action="store_true", help="Whether make the checking strict (raise errors) or not (raise warnings)"
      )
      A_			      =		parser.parse_args()

      if args.framework == "onnx":
            onnx_compliancy(args.saved_model_path, args.strict, args.opset)
