





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_					     =	"""."""

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




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     int:
  lowercase__			    =						SavedModel()
  lowercase__			    =						[]

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

  lowercase__			    =						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
  lowercase__			    =						sorted(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						[]

  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__":
     lowercase_					     =	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_					     =	parser.parse_args()

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









from PIL import Image




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Image:

  def brightness(_SCREAMING_SNAKE_CASE	) -> float:
    return 128 + level + (c - 128)

  if not -2_5_5.0 <= level <= 2_5_5.0:
    raise ValueError('level must be between -255.0 (black) and 255.0 (white)'	)
  return img.point(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      # Load image
      with Image.open("""image_data/lena.jpg""") as img:
            # Change brightness to 100
            lowercase_					     =	change_brightness(img, 100)
            brigt_img.save("""image_data/lena_brightness.png""", format="""png""")


