






"""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
a			:    Union[str, Any]				 =		"""."""

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





def 						lowercase__(A ,		A ,		A						)    ->Tuple:




						"""simple docstring"""



						lowercase__    :				Any=	SavedModel()
						lowercase__    :				List[Any]=	[]

						with open(os.path.join(A ,		"utils" ,		"tf_ops" ,		"onnx.json"						)						) as f:
												lowercase__    :				Optional[Any]=	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()						)

						lowercase__    :				List[Any]=	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
						lowercase__    :				Union[str, Any]=	sorted(A						)
						lowercase__    :				Union[str, Any]=	[]

						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__":
			a			:    int				 =		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			:    Optional[int]				 =		parser.parse_args()

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














"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a			:    List[str]				 =		{
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a			:    List[str]				 =		[
			    """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """XLMRobertaXLForCausalLM""",
			    """XLMRobertaXLForMaskedLM""",
			    """XLMRobertaXLForMultipleChoice""",
			    """XLMRobertaXLForQuestionAnswering""",
			    """XLMRobertaXLForSequenceClassification""",
			    """XLMRobertaXLForTokenClassification""",
			    """XLMRobertaXLModel""",
			    """XLMRobertaXLPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta_xl import (
			    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaXLConfig,
			    XLMRobertaXLOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaXLForCausalLM,
						    XLMRobertaXLForMaskedLM,
						    XLMRobertaXLForMultipleChoice,
						    XLMRobertaXLForQuestionAnswering,
						    XLMRobertaXLForSequenceClassification,
						    XLMRobertaXLForTokenClassification,
						    XLMRobertaXLModel,
						    XLMRobertaXLPreTrainedModel,
						)

else:
			import sys

			a			:    List[str]				 =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)






