




'''simple docstring'''






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

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
      from ... import PreTrainedTokenizerBase, TensorType

A_			      =		logging.get_logger(__name__)






class        lowercase_      (						lowerCAmelCase_							):
     A_											=			"vision-encoder-decoder"
     A_											=			True





     def __init__(     self     :					str     ,							**__lowerCamelCase     :					Optional[Any]			):
           super().__init__(**__lowerCamelCase			)
           if "encoder" not in kwargs or "decoder" not in kwargs:
                 raise ValueError(
                     F"A configuraton of type {self.model_type} cannot be instantiated because "
                     F"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"			)

           snake_case__       :   str	      =    kwargs.pop('encoder'			)
           snake_case__       :   Union[str, Any]	      =    encoder_config.pop('model_type'			)
           snake_case__       :   List[str]	      =    kwargs.pop('decoder'			)
           snake_case__       :   str	      =    decoder_config.pop('model_type'			)

           snake_case__       :   Tuple	      =    AutoConfig.for_model(__lowerCamelCase     ,							**__lowerCamelCase			)
           snake_case__       :   Any	      =    AutoConfig.for_model(__lowerCamelCase     ,							**__lowerCamelCase			)
           snake_case__       :   Union[str, Any]	      =    True





     @classmethod
     def 			_lowerCAmelCase      (     cls     :					List[Any]     ,							__lowerCamelCase     :					PretrainedConfig     ,							__lowerCamelCase     :					PretrainedConfig     ,							**__lowerCamelCase     :					List[str]			):
           logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'			)
           snake_case__       :   Optional[int]	      =    True
           snake_case__       :   Optional[int]	      =    True

           return cls(encoder=encoder_config.to_dict()     ,							decoder=decoder_config.to_dict()     ,							**__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   List[Any]	      =    copy.deepcopy(self.__dict__			)
           snake_case__       :   Union[str, Any]	      =    self.encoder.to_dict()
           snake_case__       :   Any	      =    self.decoder.to_dict()
           snake_case__       :   Optional[Any]	      =    self.__class__.model_type
           return output






class        lowercase_      (						lowerCAmelCase_							):
     A_											=			version.parse("1.11"							)





     @property
     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           return OrderedDict(
               [
                   ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
               ]			)





     @property
     def 			_lowerCAmelCase      (     self     :					Dict			):
           return 1E-4





     @property
     def 			_lowerCAmelCase      (     self     :					int			):
           return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}			)






class        lowercase_      (						lowerCAmelCase_							):





     @property
     def 			_lowerCAmelCase      (     self     :					str			):
           snake_case__       :   Dict	      =    OrderedDict()
           snake_case__       :   Optional[Any]	      =    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
           snake_case__       :   Any	      =    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
           snake_case__       :   int	      =    {0: 'batch', 1: 'encoder_sequence'}

           return common_inputs





     def 			_lowerCAmelCase      (     self     :					str     ,							__lowerCamelCase     :					"PreTrainedTokenizerBase"     ,							__lowerCamelCase     :					int = -1     ,							__lowerCamelCase     :					int = -1     ,							__lowerCamelCase     :					bool = False     ,							__lowerCamelCase     :					Optional["TensorType"] = None     ,							):
           import torch

           snake_case__       :   Tuple	      =    OrderedDict()

           snake_case__       :   Dict	      =    super().generate_dummy_inputs(
               __lowerCamelCase     ,							batch_size=__lowerCamelCase     ,							seq_length=__lowerCamelCase     ,							is_pair=__lowerCamelCase     ,							framework=__lowerCamelCase			)

           snake_case__      ,							snake_case__       :   List[Any]	      =    dummy_input['input_ids'].shape
           snake_case__       :   int	      =    (batch, encoder_sequence, self._config.encoder_hidden_size)
           snake_case__       :   int	      =    dummy_input.pop('input_ids'			)
           snake_case__       :   Optional[int]	      =    dummy_input.pop('attention_mask'			)
           snake_case__       :   Optional[Any]	      =    torch.zeros(__lowerCamelCase			)

           return common_inputs






class        lowercase_      (						lowerCAmelCase_							):





     @property
     def 			_lowerCAmelCase      (     self     :					str			):
           pass





     def 			_lowerCAmelCase      (     self     :					str     ,							__lowerCamelCase     :					PretrainedConfig			):
           return VisionEncoderDecoderEncoderOnnxConfig(__lowerCamelCase			)





     def 			_lowerCAmelCase      (     self     :					str     ,							__lowerCamelCase     :					PretrainedConfig     ,							__lowerCamelCase     :					PretrainedConfig     ,							__lowerCamelCase     :					str = "default"			):
           snake_case__       :   Any	      =    encoder_config.hidden_size
           return VisionEncoderDecoderDecoderOnnxConfig(__lowerCamelCase     ,							__lowerCamelCase			)






'''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)
