'''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
__lowerCAmelCase	:       Tuple						       =	"."

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





def       lowerCAmelCase					(		UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   Optional[int]    ,					UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			SavedModel()
	__UpperCAmelCase							=			[]

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

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

	with open(UpperCamelCase__    ,					'''rb'''       ) as f:
		saved_model.ParseFromString(f.read()       )

	__UpperCAmelCase							=			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
	__UpperCAmelCase							=			sorted(UpperCamelCase__       )
	__UpperCAmelCase							=			[]

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

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


if __name__ == "__main__":
					__lowerCAmelCase	:       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)"
					)
					__lowerCAmelCase	:       str						       =	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_speech_available, is_torch_available


__lowerCAmelCase	:       List[Any]						       =	{
    "configuration_audio_spectrogram_transformer": [
        "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ASTConfig",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       str						       =	[
					    "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ASTForAudioClassification",
					    "ASTModel",
					    "ASTPreTrainedModel",
					]

try:
					if not is_speech_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       Union[str, Any]						       =	["ASTFeatureExtractor"]

if TYPE_CHECKING:
					from .configuration_audio_spectrogram_transformer import (
					    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ASTConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_audio_spectrogram_transformer import (
										    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ASTForAudioClassification,
										    ASTModel,
										    ASTPreTrainedModel,
										)

					try:
										if not is_speech_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
					import sys

					__lowerCAmelCase	:       Optional[Any]						       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
