
"""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
lowercase_       :						List[str]     					=	'''.'''

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





def    _lowerCAmelCase    (     lowerCamelCase__							:							Union[str, Any],						lowerCamelCase__							:							int,						lowerCamelCase__							:							Tuple				)  ->    Tuple:
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = SavedModel()
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = []

  with open(os.path.join(snake_case__,						"utils",						"tf_ops",						"onnx.json"				)				) as f:
    _SCREAMING_SNAKE_CASE   :				Any							    = 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()				)

  _SCREAMING_SNAKE_CASE   :				List[Any]							    = 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
  _SCREAMING_SNAKE_CASE   :				Tuple							    = sorted(snake_case__				)
  _SCREAMING_SNAKE_CASE   :				str							    = []

  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__":
    lowercase_       :						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)'''
    )
    lowercase_       :						Any     					=	parser.parse_args()

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





"""simple docstring"""
import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str,						**lowerCamelCase__							:							Optional[Any]				)  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				Dict							    = AutoConfig.from_pretrained(lowerCamelCase__,						**lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = AutoModelForSeqaSeqLM.from_config(lowerCamelCase__				)
  model.save_pretrained(lowerCamelCase__				)
  AutoTokenizer.from_pretrained(lowerCamelCase__				).save_pretrained(lowerCamelCase__				)
  return model


if __name__ == "__main__":
      fire.Fire(save_randomly_initialized_version)



