






'''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      :       int      			=      """."""

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



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Union[str, Any],					SCREAMING_SNAKE_CASE__:	Dict,					SCREAMING_SNAKE_CASE__:	Any    )  ->			Optional[int]:

   """simple docstring"""
   __a				     =    SavedModel()
   __a				     =    []

   with open(os.path.join(SCREAMING_SNAKE_CASE__,					'utils',					'tf_ops',					'onnx.json'    )    ) as f:
      __a				     =    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()    )

   __a				     =    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
   __a				     =    sorted(SCREAMING_SNAKE_CASE__    )
   __a				     =    []

   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__":
 __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=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)"""
 )
 __UpperCamelCase      :       Union[str, Any]      			=      parser.parse_args()

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







'''simple docstring'''



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


__UpperCamelCase      :       List[str]      			=      logging.get_logger(__name__)

__UpperCamelCase      :       Tuple      			=      {
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="vit_msn"




      def __init__(  self ,   lowerCamelCase=768 ,   lowerCamelCase=12 ,   lowerCamelCase=12 ,   lowerCamelCase=3072 ,   lowerCamelCase="gelu" ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.02 ,   lowerCamelCase=1e-06 ,   lowerCamelCase=224 ,   lowerCamelCase=16 ,   lowerCamelCase=3 ,   lowerCamelCase=True ,   **lowerCamelCase ,   )							->Tuple:



         '''simple docstring'''
         super().__init__(**lowerCamelCase      )

         __a				     =    hidden_size
         __a				     =    num_hidden_layers
         __a				     =    num_attention_heads
         __a				     =    intermediate_size
         __a				     =    hidden_act
         __a				     =    hidden_dropout_prob
         __a				     =    attention_probs_dropout_prob
         __a				     =    initializer_range
         __a				     =    layer_norm_eps
         __a				     =    image_size
         __a				     =    patch_size
         __a				     =    num_channels
         __a				     =    qkv_bias