






'''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
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    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			Optional[Any]:
      UpperCamelCase      = SavedModel()
      UpperCamelCase      = []

      with open(os.path.join(__UpperCamelCase		,						"""utils"""		,						"""tf_ops"""		,						"""onnx.json"""   )   ) as f:
            UpperCamelCase      = 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()   )

      UpperCamelCase      = 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
      UpperCamelCase      = sorted(__UpperCamelCase   )
      UpperCamelCase      = []

      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__":
   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=1_2, 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'''

from PIL import Image







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			Image:

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

      if not -255.0 <= level <= 255.0:
            raise ValueError("""level must be between -255.0 (black) and 255.0 (white)"""   )
      return img.point(__UpperCamelCase   )


if __name__ == "__main__":
     # Load image
     with Image.open('image_data/lena.jpg') as img:
          # Change brightness to 100
          SCREAMING_SNAKE_CASE__     =       change_brightness(img, 1_0_0)
          brigt_img.save('image_data/lena_brightness.png', format='png')
