
"""simple docstring"""

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase			:       List[Any]									=				logging.get_logger(__name__)

UpperCamelCase			:       Tuple									=				{
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"git_vision_model"





     def __init__(			self			,  __UpperCAmelCase=768			,  __UpperCAmelCase=3072			,  __UpperCAmelCase=12			,  __UpperCAmelCase=12			,  __UpperCAmelCase=3			,  __UpperCAmelCase=224			,  __UpperCAmelCase=16			,  __UpperCAmelCase="quick_gelu"			,  __UpperCAmelCase=1E-5			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0_2			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase   )

      __UpperCamelCase       						= hidden_size
      __UpperCamelCase       						= intermediate_size
      __UpperCamelCase       						= num_hidden_layers
      __UpperCamelCase       						= num_attention_heads
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= patch_size
      __UpperCamelCase       						= image_size
      __UpperCamelCase       						= initializer_range
      __UpperCamelCase       						= attention_dropout
      __UpperCamelCase       						= layer_norm_eps
      __UpperCamelCase       						= hidden_act







     @classmethod
     def 		UpperCAmelCase      (			cls			,  __UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




      cls._set_token_in_kwargs(__UpperCAmelCase   )

      __UpperCamelCase						,			__UpperCamelCase       						= cls.get_config_dict(__UpperCAmelCase			,  **__UpperCAmelCase   )

      # get the vision config dict if we are loading from GITConfig
      if config_dict.get('model_type'   ) == "git":
       __UpperCamelCase       						= config_dict['vision_config']

      if "model_type" in config_dict and hasattr(cls			,  'model_type'   ) and config_dict["model_type"] != cls.model_type:
       logger.warning(
           F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
           F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'   )

      return cls.from_dict(__UpperCAmelCase			,  **__UpperCAmelCase   )


class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"git"





     def __init__(			self			,  __UpperCAmelCase=None			,  __UpperCAmelCase=3_0522			,  __UpperCAmelCase=768			,  __UpperCAmelCase=6			,  __UpperCAmelCase=12			,  __UpperCAmelCase=3072			,  __UpperCAmelCase="gelu"			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=1024			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=1E-12			,  __UpperCAmelCase=0			,  __UpperCAmelCase="absolute"			,  __UpperCAmelCase=True			,  __UpperCAmelCase=False			,  __UpperCAmelCase=101			,  __UpperCAmelCase=102			,  __UpperCAmelCase=None			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      super().__init__(bos_token_id=__UpperCAmelCase			,  eos_token_id=__UpperCAmelCase			,  pad_token_id=__UpperCAmelCase			,  **__UpperCAmelCase   )

      if vision_config is None:
       __UpperCamelCase       						= {}
       logger.info('vision_config is None. initializing the GitVisionConfig with default values.'   )

      __UpperCamelCase       						= GitVisionConfig(**__UpperCAmelCase   )
      __UpperCamelCase       						= vocab_size
      __UpperCamelCase       						= hidden_size
      __UpperCamelCase       						= num_hidden_layers
      __UpperCamelCase       						= num_attention_heads
      __UpperCamelCase       						= hidden_act
      __UpperCamelCase       						= intermediate_size
      __UpperCamelCase       						= hidden_dropout_prob
      __UpperCamelCase       						= attention_probs_dropout_prob
      __UpperCamelCase       						= max_position_embeddings
      __UpperCamelCase       						= initializer_range
      __UpperCamelCase       						= layer_norm_eps
      __UpperCamelCase       						= position_embedding_type
      __UpperCamelCase       						= use_cache
      __UpperCamelCase       						= tie_word_embeddings
      __UpperCamelCase       						= num_image_with_embedding

      __UpperCamelCase       						= bos_token_id
      __UpperCamelCase       						= eos_token_id







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= copy.deepcopy(self.__dict__   )
      __UpperCamelCase       						= self.vision_config.to_dict()
      __UpperCamelCase       						= self.__class__.model_type
      return output





"""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
UpperCamelCase			:       Tuple									=				"."

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






def 							A		(       snake_case   :List[str]       ,      snake_case   :Optional[int]       ,      snake_case   :Optional[Any]	)   ->			Any:
 __UpperCamelCase       						= SavedModel()
 __UpperCamelCase       						= []

 with open(os.path.join(snake_case       ,      'utils'       ,      'tf_ops'       ,      'onnx.json'	)	) as f:
  __UpperCamelCase       						= json.load(snake_case	)['opsets']

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

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

 __UpperCamelCase       						= 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
 __UpperCamelCase       						= sorted(snake_case	)
 __UpperCamelCase       						= []

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

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


if __name__ == "__main__":
      UpperCamelCase			:       List[str]									=				argparse.ArgumentParser()
      parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
      parser.add_argument(
          "--opset", default=1_2, 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)"
      )
      UpperCamelCase			:       str									=				parser.parse_args()

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



