





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             =							'.'

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






def 	_SCREAMING_SNAKE_CASE		(				SCREAMING_SNAKE_CASE		:Tuple   ,   SCREAMING_SNAKE_CASE		:Optional[Any]   ,   SCREAMING_SNAKE_CASE		:List[Any]   )      ->   List[Any]:
 __lowerCAmelCase					:	Optional[int]      =       SavedModel()
 __lowerCAmelCase					:	Tuple      =       []

 with open(os.path.join(SCREAMING_SNAKE_CASE   ,   """utils"""   ,   """tf_ops"""   ,   """onnx.json"""   )   ) as f:
  __lowerCAmelCase					:	Tuple      =       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()   )

 __lowerCAmelCase					:	Dict      =       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
 __lowerCAmelCase					:	Tuple      =       sorted(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	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__":
      _UpperCAmelCase             =							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             =							parser.parse_args()

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






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


_UpperCAmelCase             =							logging.get_logger(__name__)

_UpperCAmelCase             =							{
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class        snake_case_    (		__lowercase						):
  A_      							=   'biogpt'






  def __init__(							self			:					int       ,  _snake_case			:					Any=42384       ,  _snake_case			:					Any=1024       ,  _snake_case			:					List[Any]=24       ,  _snake_case			:					Any=16       ,  _snake_case			:					List[str]=4096       ,  _snake_case			:					Dict="gelu"       ,  _snake_case			:					Tuple=0.1       ,  _snake_case			:					str=0.1       ,  _snake_case			:					Tuple=1024       ,  _snake_case			:					Tuple=0.02       ,  _snake_case			:					Tuple=1E-12       ,  _snake_case			:					Optional[int]=True       ,  _snake_case			:					Optional[int]=True       ,  _snake_case			:					Any=0.0       ,  _snake_case			:					Tuple=0.0       ,  _snake_case			:					str=1       ,  _snake_case			:					Dict=0       ,  _snake_case			:					str=2       ,  **_snake_case			:					Union[str, Any]       ,  )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       vocab_size
   __lowerCAmelCase					:	Dict      =       max_position_embeddings
   __lowerCAmelCase					:	str      =       hidden_size
   __lowerCAmelCase					:	Dict      =       num_hidden_layers
   __lowerCAmelCase					:	List[Any]      =       num_attention_heads
   __lowerCAmelCase					:	Optional[Any]      =       intermediate_size
   __lowerCAmelCase					:	int      =       hidden_act
   __lowerCAmelCase					:	Any      =       hidden_dropout_prob
   __lowerCAmelCase					:	Any      =       attention_probs_dropout_prob
   __lowerCAmelCase					:	Any      =       initializer_range
   __lowerCAmelCase					:	int      =       layer_norm_eps
   __lowerCAmelCase					:	Optional[int]      =       scale_embedding
   __lowerCAmelCase					:	List[Any]      =       use_cache
   __lowerCAmelCase					:	str      =       layerdrop
   __lowerCAmelCase					:	Dict      =       activation_dropout
   super().__init__(pad_token_id=_snake_case       ,  bos_token_id=_snake_case       ,  eos_token_id=_snake_case       ,  **_snake_case      )