




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__ :     Optional[int]			       =   '.'

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

def    SCREAMING_SNAKE_CASE				(  __UpperCamelCase						,   __UpperCamelCase						,   __UpperCamelCase)     ->	Optional[Any]:
     a           = SavedModel()
     a           = []

     with open(os.path.join(__UpperCAmelCase						,   "utils"						,   "tf_ops"						,   "onnx.json")) as f:
          a           = json.load(__UpperCAmelCase)["opsets"]

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

     with open(__UpperCAmelCase						,   "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(__UpperCAmelCase)
     a           = []

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

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


if __name__ == "__main__":
   lowercase__ :     Dict			       =   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__ :     str			       =   parser.parse_args()

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






import logging
import os

from .state import PartialState
class    __UpperCamelCase			(       logging.LoggerAdapter				):

 """simple docstring"""



 @staticmethod
 def 						_UpperCAmelCase       (	SCREAMING_SNAKE_CASE      ) ->			Optional[Any]:
  a__												= PartialState()
  return not main_process_only or (main_process_only and state.is_main_process)



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			Optional[int]:
  if PartialState._shared_state == {}:
   raise RuntimeError(
       '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''      )
  a__												= kwargs.pop('''main_process_only'''		,				SCREAMING_SNAKE_CASE      )
  a__												= kwargs.pop('''in_order'''		,				SCREAMING_SNAKE_CASE      )

  if self.isEnabledFor(SCREAMING_SNAKE_CASE      ):
   if self._should_log(SCREAMING_SNAKE_CASE      ):
    a__							,			a__												= self.process(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
    self.logger.log(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

   elif in_order:
    a__												= PartialState()
    for i in range(state.num_processes      ):
     if i == state.process_index:
      a__							,			a__												= self.process(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
      self.logger.log(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
     state.wait_for_everyone()






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None       ):
 if log_level is None:
  a__												= os.environ.get('''ACCELERATE_LOG_LEVEL'''							,  __UpperCAmelCase       )
 a__												= logging.getLogger(__UpperCAmelCase       )
 if log_level is not None:
  logger.setLevel(log_level.upper()       )
  logger.root.setLevel(log_level.upper()       )
 return MultiProcessAdapter(__UpperCAmelCase							,  {}       )





