




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
__A								=       '''.'''

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







def      lowerCamelCase_						(			UpperCamelCase__							: Tuple					,	UpperCamelCase__							: Union[str, Any]					,	UpperCamelCase__							: Dict )	->			Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase									=       SavedModel()
				__lowerCamelCase									=       []

				with open(os.path.join(lowerCamelCase_					,	'utils'					,	'tf_ops'					,	'onnx.json' ) ) as f:
								__lowerCamelCase									=       json.load(lowerCamelCase_ )['opsets']

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

				with open(lowerCamelCase_					,	'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(lowerCamelCase_ )
				__lowerCamelCase									=       []

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

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


if __name__ == "__main__":
					__A								=       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)"
					)
					__A								=       parser.parse_args()

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








import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict





@pytest.mark.parametrize(
    'files'  ,							[
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     int  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tmp_path_factory.mktemp('dset_infos_dir'  )
			if "full:README.md" in files:
						with open(dataset_infos_dir / 'README.md'  ,							'w'  ) as f:
									f.write('---\ndataset_info:\n  dataset_size: 42\n---'  )
			if "empty:README.md" in files:
						with open(dataset_infos_dir / 'README.md'  ,							'w'  ) as f:
									f.write(''  )
    # we want to support dataset_infos.json for backward compatibility
			if "dataset_infos.json" in files:
						with open(dataset_infos_dir / 'dataset_infos.json'  ,							'w'  ) as f:
									f.write('{"default": {"dataset_size": 42}}'  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      DatasetInfosDict.from_directory(lowerCamelCase_  )
			assert dataset_infos
			assert dataset_infos["default"].dataset_size == 42





@pytest.mark.parametrize(
    'dataset_info'  ,							[
        DatasetInfo(),
        DatasetInfo(
            description='foo'  ,							features=Features({'a': Value('int32'  )}  )  ,							builder_name='builder'  ,							config_name='config'  ,							version='1.0.0'  ,							splits=[{'name': 'train'}]  ,							download_size=42  ,							),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     DatasetInfo  )       ->  Any:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Dict					  =      str(lowerCamelCase_  )
			dataset_info.write_to_directory(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[str]					  =      DatasetInfo.from_directory(lowerCamelCase_  )
			assert dataset_info == reloaded
			assert os.path.exists(os.path.join(lowerCamelCase_  ,							'dataset_info.json'  )  )





def        __UpperCAmelCase						(							)       ->  List[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : str					  =      DatasetInfo(
			    description='foo'  ,							citation='bar'  ,							homepage='https://foo.bar'  ,							license='CC0'  ,							features=Features({'a': Value('int32'  )}  )  ,							post_processed={}  ,							supervised_keys=()  ,							task_templates=[]  ,							builder_name='builder'  ,							config_name='config'  ,							version='1.0.0'  ,							splits=[{'name': 'train', 'num_examples': 42}]  ,							download_checksums={}  ,							download_size=13_37  ,							post_processing_size=4_42  ,							dataset_size=12_34  ,							size_in_bytes=13_37 + 4_42 + 12_34  ,							)
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      dataset_info._to_yaml_dict()
			assert sorted(lowerCamelCase_  ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML  )
			for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
						assert key in dataset_info_yaml_dict
						assert isinstance(dataset_info_yaml_dict[key]  ,							(list, dict, int, str)  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      yaml.safe_dump(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : int					  =      yaml.safe_load(lowerCamelCase_  )
			assert dataset_info_yaml_dict == reloaded





def        __UpperCAmelCase						(							)       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      DatasetInfo()
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      dataset_info._to_yaml_dict()
			assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    'dataset_infos_dict'  ,							[
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}  ),
        DatasetInfosDict({'my_config_name': DatasetInfo()}  ),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'  ,							features=Features({'a': Value('int32'  )}  )  ,							builder_name='builder'  ,							config_name='config'  ,							version='1.0.0'  ,							splits=[{'name': 'train'}]  ,							download_size=42  ,							)
            }  ),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42  ),
                'v2': DatasetInfo(dataset_size=13_37  ),
            }  ),
    ]  ,							)
def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[Any]  ,							lowerCamelCase_ :     DatasetInfosDict  )       ->  Tuple:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      str(lowerCamelCase_  )
			dataset_infos_dict.write_to_directory(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      DatasetInfosDict.from_directory(lowerCamelCase_  )

			# the config_name of the dataset_infos_dict take over the attribute
			for config_name, dataset_info in dataset_infos_dict.items():
						SCREAMING_SNAKE_CASE_   : Any					  =      config_name
						# the yaml representation doesn't include fields like description or citation
						# so we just test that we can recover what we can from the yaml
						SCREAMING_SNAKE_CASE_   : Any					  =      DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()  )
			assert dataset_infos_dict == reloaded

			if dataset_infos_dict:
						assert os.path.exists(os.path.join(lowerCamelCase_  ,							'README.md'  )  )



