

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 :     Tuple						,__a :     Any						,__a :     Optional[Any]      )     ->	int:

					"""simple docstring"""

					_a      :       Optional[Any]   =						SavedModel()
					_a      :       List[str]   =						[]

					with open(os.path.join(__a						,'''utils'''						,'''tf_ops'''						,'''onnx.json'''      )      ) as f:
										_a      :       int   =						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[int]   =						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      :       Union[str, Any]   =						sorted(__a      )
					_a      :       List[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 json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402

class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       int   =						0


							def 							__lowercase							(     self			)       ->       List[str]:
												_a      :       Dict   =						AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''			)
												self.assertIsInstance(_a			,				_a			)


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												with tempfile.TemporaryDirectory() as tmpdirname:
																	_a      :       Tuple   =						Path(_a			) / '''preprocessor_config.json'''
																	_a      :       Optional[Any]   =						Path(_a			) / '''config.json'''
																	json.dump(
																	    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			,				open(_a			,				'''w'''			)			,				)
																	json.dump({'''model_type''': '''clip'''}			,				open(_a			,				'''w'''			)			)

																	_a      :       List[str]   =						AutoImageProcessor.from_pretrained(_a			)
																	self.assertIsInstance(_a			,				_a			)


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												# Ensure we can load the image processor from the feature extractor config
												with tempfile.TemporaryDirectory() as tmpdirname:
																	_a      :       Optional[int]   =						Path(_a			) / '''preprocessor_config.json'''
																	_a      :       Any   =						Path(_a			) / '''config.json'''
																	json.dump(
																	    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}			,				open(_a			,				'''w'''			)			,				)
																	json.dump({'''model_type''': '''clip'''}			,				open(_a			,				'''w'''			)			)

																	_a      :       Optional[Any]   =						AutoImageProcessor.from_pretrained(_a			)
																	self.assertIsInstance(_a			,				_a			)


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												with tempfile.TemporaryDirectory() as tmpdirname:
																	_a      :       Dict   =						CLIPConfig()

																	# Create a dummy config file with image_proceesor_type
																	_a      :       Tuple   =						Path(_a			) / '''preprocessor_config.json'''
																	_a      :       List[str]   =						Path(_a			) / '''config.json'''
																	json.dump(
																	    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			,				open(_a			,				'''w'''			)			,				)
																	json.dump({'''model_type''': '''clip'''}			,				open(_a			,				'''w'''			)			)

																	# remove image_processor_type to make sure config.json alone is enough to load image processor locally
																	_a      :       Tuple   =						AutoImageProcessor.from_pretrained(_a			).to_dict()

																	config_dict.pop('''image_processor_type'''			)
																	_a      :       Tuple   =						CLIPImageProcessor(**_a			)

																	# save in new folder
																	model_config.save_pretrained(_a			)
																	config.save_pretrained(_a			)

																	_a      :       List[str]   =						AutoImageProcessor.from_pretrained(_a			)

																	# make sure private variable is not incorrectly saved
																	_a      :       Optional[int]   =						json.loads(config.to_json_string()			)
																	self.assertTrue('''_processor_class''' not in dict_as_saved			)

												self.assertIsInstance(_a			,				_a			)


							def 							__lowercase							(     self			)       ->       Dict:
												with tempfile.TemporaryDirectory() as tmpdirname:
																	_a      :       Optional[int]   =						Path(_a			) / '''preprocessor_config.json'''
																	json.dump(
																	    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			,				open(_a			,				'''w'''			)			,				)

																	_a      :       List[str]   =						AutoImageProcessor.from_pretrained(_a			)
																	self.assertIsInstance(_a			,				_a			)


							def 							__lowercase							(     self			)       ->       Any:
												with self.assertRaisesRegex(
												    _a			,				'''clip-base is not a local folder and is not a valid model identifier'''			):
																	_a      :       Dict   =						AutoImageProcessor.from_pretrained('''clip-base'''			)


							def 							__lowercase							(     self			)       ->       List[Any]:
												with self.assertRaisesRegex(
												    _a			,				R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''			):
																	_a      :       List[str]   =						AutoImageProcessor.from_pretrained(_a			,				revision='''aaaaaa'''			)


							def 							__lowercase							(     self			)       ->       Dict:
												with self.assertRaisesRegex(
												    _a			,				'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''			,				):
																	_a      :       Optional[int]   =						AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''			)


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												# If remote code is not set, we will time out when asking whether to load the model.
												with self.assertRaises(_a			):
																	_a      :       str   =						AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''			)
												# If remote code is disabled, we can't load this config.
												with self.assertRaises(_a			):
																	_a      :       Optional[Any]   =						AutoImageProcessor.from_pretrained(
																	    '''hf-internal-testing/test_dynamic_image_processor'''			,				trust_remote_code=_a			)

												_a      :       Union[str, Any]   =						AutoImageProcessor.from_pretrained(
												    '''hf-internal-testing/test_dynamic_image_processor'''			,				trust_remote_code=_a			)
												self.assertEqual(image_processor.__class__.__name__			,				'''NewImageProcessor'''			)

												# Test image processor can be reloaded.
												with tempfile.TemporaryDirectory() as tmp_dir:
																	image_processor.save_pretrained(_a			)
																	_a      :       Optional[Any]   =						AutoImageProcessor.from_pretrained(_a			,				trust_remote_code=_a			)
												self.assertEqual(reloaded_image_processor.__class__.__name__			,				'''NewImageProcessor'''			)


							def 							__lowercase							(     self			)       ->       Dict:
												try:
																	AutoConfig.register('''custom'''			,				_a			)
																	AutoImageProcessor.register(_a			,				_a			)
																	# Trying to register something existing in the Transformers library will raise an error
																	with self.assertRaises(_a			):
																						AutoImageProcessor.register(_a			,				_a			)

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						_a      :       int   =						Path(_a			) / '''preprocessor_config.json'''
																						_a      :       int   =						Path(_a			) / '''config.json'''
																						json.dump(
																						    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}			,				open(_a			,				'''w'''			)			,				)
																						json.dump({'''model_type''': '''clip'''}			,				open(_a			,				'''w'''			)			)

																						_a      :       int   =						CustomImageProcessor.from_pretrained(_a			)

																	# Now that the config is registered, it can be used as any other config with the auto-API
																	with tempfile.TemporaryDirectory() as tmp_dir:
																						image_processor.save_pretrained(_a			)
																						_a      :       Optional[Any]   =						AutoImageProcessor.from_pretrained(_a			)
																						self.assertIsInstance(_a			,				_a			)

												finally:
																	if "custom" in CONFIG_MAPPING._extra_content:
																						del CONFIG_MAPPING._extra_content["custom"]
																	if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
																						del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]




							def 							__lowercase							(     self			)       ->       Union[str, Any]:

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







																			UpperCAmelCase__			:		Tuple             =       True

												try:
																	AutoConfig.register('''custom'''			,				_a			)
																	AutoImageProcessor.register(_a			,				_a			)
																	# If remote code is not set, the default is to use local
																	_a      :       str   =						AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''			)
																	self.assertEqual(image_processor.__class__.__name__			,				'''NewImageProcessor'''			)
																	self.assertTrue(image_processor.is_local			)

																	# If remote code is disabled, we load the local one.
																	_a      :       int   =						AutoImageProcessor.from_pretrained(
																	    '''hf-internal-testing/test_dynamic_image_processor'''			,				trust_remote_code=_a			)
																	self.assertEqual(image_processor.__class__.__name__			,				'''NewImageProcessor'''			)
																	self.assertTrue(image_processor.is_local			)

																	# If remote is enabled, we load from the Hub
																	_a      :       Dict   =						AutoImageProcessor.from_pretrained(
																	    '''hf-internal-testing/test_dynamic_image_processor'''			,				trust_remote_code=_a			)
																	self.assertEqual(image_processor.__class__.__name__			,				'''NewImageProcessor'''			)
																	self.assertTrue(not hasattr(_a			,				'''is_local'''			)			)

												finally:
																	if "custom" in CONFIG_MAPPING._extra_content:
																						del CONFIG_MAPPING._extra_content["custom"]
																	if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
																						del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]



