'''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_     		= '''.'''

# 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__			(			a__			,      a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=SavedModel()
						_lowerCAmelCase										=[]

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

						_lowerCAmelCase										=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
						_lowerCAmelCase										=sorted(a__      )
						_lowerCAmelCase										=[]

						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__":
							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)






'''simple docstring'''

from PIL import Image



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						def brightness(a__      ) -> float:
												return 1_2_8 + level + (c - 1_2_8)

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


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''')





