


from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset

class 							lowercase  (						_UpperCAmelCase			):







     def 							lowercase__       (						self :						Optional[int]      ):
           return [
               {"col_1": 3, "col_2": "a"},
               {"col_1": 2, "col_2": "b"},
               {"col_1": 1, "col_2": "c"},
               {"col_1": 0, "col_2": "d"},
           ]







     def 							lowercase__       (						self :						Union[str, Any]      ):
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		{'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
           return Dataset.from_dict(_lowercase      )







     def 							lowercase__       (						self :						Dict      ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		self._create_example_records()
           SCREAMING_SNAKE_CASE__       :  int						     =		Dataset.from_list(_lowercase      )
           self.assertListEqual(dset.column_names   ,   ['''col_1''', '''col_2''']      )
           for i, r in enumerate(_lowercase      ):
                 self.assertDictEqual(_lowercase   ,   example_records[i]      )







     def 							lowercase__       (						self :						Optional[Any]      ):
           SCREAMING_SNAKE_CASE__       :  Dict						     =		self._create_example_records()
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		Dataset.from_list(_lowercase      )
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}      )
           self.assertEqual(dset.info   ,   dset_from_dict.info      )







     def 							lowercase__       (						self :						List[Any]      ):  # checks what happens with missing columns
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		[{'''col_1''': 1}, {'''col_2''': '''x'''}]
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		Dataset.from_list(_lowercase      )
           self.assertDictEqual(dset[0]   ,   {'''col_1''': 1}      )
           self.assertDictEqual(dset[1]   ,   {'''col_1''': None}      )  # NB: first record is used for columns







     def 							lowercase__       (						self :						int      ):  # checks if the type can be inferred from the second record
           SCREAMING_SNAKE_CASE__       :  int						     =		[{'''col_1''': []}, {'''col_1''': [1, 2]}]
           SCREAMING_SNAKE_CASE__       :  int						     =		Dataset.from_list(_lowercase      )
           self.assertEqual(dset.info.features['''col_1''']   ,   Sequence(Value('''int64'''      )      )      )







     def 							lowercase__       (						self :						Optional[Any]      ):
           SCREAMING_SNAKE_CASE__       :  int						     =		Dataset.from_list([]      )
           self.assertEqual(len(_lowercase      )   ,   0      )
           self.assertListEqual(dset.column_names   ,   []      )












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
__UpperCamelCase   :			Tuple          = "."

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





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Union[str, Any]:
	a									=				SavedModel()
	a									=				[]

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

	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(__lowerCamelCase       )
	a									=				[]

	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__":
		__UpperCamelCase   :			Optional[Any]          = 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)"
		)
		__UpperCamelCase   :			str          = parser.parse_args()

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

