




import os
import sys
import unittest


__UpperCamelCase   :			int          = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCamelCase   :			int          = os.path.join(git_repo_path, "src", "transformers")

__UpperCamelCase   :			Union[str, Any]          = "\n{0} = None\n"

__UpperCamelCase   :			Any          = "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


__UpperCamelCase   :			Any          = "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"

class 						__lowerCAmelCase      (      unittest.TestCase					):
							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								a									=				find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""							)
								self.assertIsNone(__magic_name__							)

								a									=				find_backend("""    if not is_tokenizers_available():"""							)
								self.assertEqual(__magic_name__						, """tokenizers"""							)

								a									=				find_backend("""    if not is_tensorflow_text_available():"""							)
								self.assertEqual(__magic_name__						, """tensorflow_text"""							)

								a									=				find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""							)
								self.assertEqual(__magic_name__						, """sentencepiece_and_tokenizers"""							)

								a									=				find_backend(
								    """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""							)
								self.assertEqual(__magic_name__						, """sentencepiece_and_tensorflow_text"""							)

								a									=				find_backend(
								    """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""							)
								self.assertEqual(__magic_name__						, """sentencepiece_and_tokenizers_and_vision"""							)
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								a									=				read_init()
								# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
								self.assertIn("""torch"""						, __magic_name__							)
								self.assertIn("""tensorflow_text"""						, __magic_name__							)
								self.assertIn("""sentencepiece_and_tokenizers"""						, __magic_name__							)

								# Likewise, we can't assert on the exact content of a key
								self.assertIn("""BertModel"""						, objects["""torch"""]							)
								self.assertIn("""TFBertModel"""						, objects["""tf"""]							)
								self.assertIn("""FlaxBertModel"""						, objects["""flax"""]							)
								self.assertIn("""BertModel"""						, objects["""torch"""]							)
								self.assertIn("""TFBertTokenizer"""						, objects["""tensorflow_text"""]							)
								self.assertIn("""convert_slow_tokenizer"""						, objects["""sentencepiece_and_tokenizers"""]							)
							def  lowerCamelCase__				(		self		:List[Any]							):







								'''simple docstring'''







								a									=				create_dummy_object("""CONSTANT"""						, """'torch'"""							)
								self.assertEqual(__magic_name__						, """\nCONSTANT = None\n"""							)

								a									=				create_dummy_object("""function"""						, """'torch'"""							)
								self.assertEqual(
								    __magic_name__						, """\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""							)

								a									=				"""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
								a									=				create_dummy_object("""FakeClass"""						, """'torch'"""							)
								self.assertEqual(__magic_name__						, __magic_name__							)





							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								a									=				"""# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
								a									=				create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}							)
								self.assertEqual(dummy_files["""torch"""]						, __magic_name__							)







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)

