

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__				=		'''.'''

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



def    __UpperCAmelCase (	__a      :			Any		,__a      :			Optional[Any]		,__a      :			Union[str, Any]	)			->     List[str]:

					"""simple docstring"""

					_a	:    List[Any]         =		SavedModel()
					_a	:    int         =		[]

					with open(os.path.join(__a		,'''utils'''		,'''tf_ops'''		,'''onnx.json'''	)	) as f:
										_a	:    List[str]         =		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()	)

					_a	:    Optional[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
					_a	:    Optional[Any]         =		sorted(__a	)
					_a	:    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__				=		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__				=		parser.parse_args()

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






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Union[str, Any]	    =    ["image_processor", "tokenizer"]
							UpperCAmelCase__       :						Dict	    =    "ChineseCLIPImageProcessor"
							UpperCAmelCase__       :						List[str]	    =    ("BertTokenizer", "BertTokenizerFast")


							def __init__(			self       ,							_a=None       ,							_a=None       ,							**_a       )    ->    Any:
												_a	:    Optional[Any]         =		None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''       ,							_a       ,							)
																	_a	:    Tuple         =		kwargs.pop('''feature_extractor'''       )

												_a	:    Tuple         =		image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.'''       )
												if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.'''       )

												super().__init__(_a       ,							_a       )
												_a	:    List[str]         =		self.image_processor


							def __call__(			self       ,							_a=None       ,							_a=None       ,							_a=None       ,							**_a       )    ->    int:

												if text is None and images is None:
																	raise ValueError('''You have to specify either text or images. Both cannot be none.'''       )

												if text is not None:
																	_a	:    List[str]         =		self.tokenizer(_a       ,							return_tensors=_a       ,							**_a       )

												if images is not None:
																	_a	:    Optional[Any]         =		self.image_processor(_a       ,							return_tensors=_a       ,							**_a       )

												if text is not None and images is not None:
																	_a	:    Union[str, Any]         =		image_features.pixel_values
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_a       )       ,							tensor_type=_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Union[str, Any]:
												return self.tokenizer.batch_decode(*_a       ,							**_a       )


							def 				__lowercase				(			self       ,							*_a       ,							**_a       )    ->    Any:
												return self.tokenizer.decode(*_a       ,							**_a       )


							@property
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Any         =		self.tokenizer.model_input_names
												_a	:    Dict         =		self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )




							@property
							def 				__lowercase				(			self       )    ->    Dict:
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,							_a       ,							)
												return self.image_processor_class



