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
_SCREAMING_SNAKE_CASE     				=		'.'

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






def 					snake_case    (			snake_case__     :List[Any]  ,      snake_case__     :Dict  ,      snake_case__     :Tuple)		->  Dict:
       _A	  =    SavedModel()
       _A	  =    []

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

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

       with open(a__  ,      """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(a__)
       _A	  =    []

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

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


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE     				=		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)'
    )
    _SCREAMING_SNAKE_CASE     				=		parser.parse_args()

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







'''simple docstring'''



import math







def   a__			(    a__		):




     """simple docstring"""


     return math.sqrt(a__		) * math.sqrt(a__		) == num







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = 0
     __SCREAMING_SNAKE_CASE      = n
     while left <= right:
          __SCREAMING_SNAKE_CASE      = (left + right) // 2
          if mid**2 == n:
               return True
          elif mid**2 > n:
               __SCREAMING_SNAKE_CASE      = mid - 1
          else:
               __SCREAMING_SNAKE_CASE      = mid + 1
     return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()






