



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
lowerCAmelCase__					  =  '''.'''

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


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Tuple													=					SavedModel()
				lowercase__					:   Optional[int]													=					[]

				with open(os.path.join(lowerCamelCase__					,			"utils"					,			"tf_ops"					,			"onnx.json"						)						) as f:
								lowercase__					:   List[str]													=					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()						)

				lowercase__					:   Tuple													=					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__					:   Optional[int]													=					sorted(lowerCamelCase__						)
				lowercase__					:   str													=					[]

				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__":
				lowerCAmelCase__					  =  argparse.ArgumentParser()
				parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
				parser.add_argument(
				    '''--opset''', default=1_2, 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)'''
				)
				lowerCAmelCase__					  =  parser.parse_args()

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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__					  =  {
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  ['''DebertaTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''DebertaForMaskedLM''',
				    '''DebertaForQuestionAnswering''',
				    '''DebertaForSequenceClassification''',
				    '''DebertaForTokenClassification''',
				    '''DebertaModel''',
				    '''DebertaPreTrainedModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  [
				    '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFDebertaForMaskedLM''',
				    '''TFDebertaForQuestionAnswering''',
				    '''TFDebertaForSequenceClassification''',
				    '''TFDebertaForTokenClassification''',
				    '''TFDebertaModel''',
				    '''TFDebertaPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
				from .tokenization_deberta import DebertaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_deberta_fast import DebertaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deberta import (
								    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DebertaForMaskedLM,
								    DebertaForQuestionAnswering,
								    DebertaForSequenceClassification,
								    DebertaForTokenClassification,
								    DebertaModel,
								    DebertaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_deberta import (
								    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFDebertaForMaskedLM,
								    TFDebertaForQuestionAnswering,
								    TFDebertaForSequenceClassification,
								    TFDebertaForTokenClassification,
								    TFDebertaModel,
								    TFDebertaPreTrainedModel,
								)


else:
				import sys

				lowerCAmelCase__					  =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




