
"""simple docstring"""




import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import (
					    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
					    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    MODEL_MAPPING,
					    DeiTForImageClassification,
					    DeiTForImageClassificationWithTeacher,
					    DeiTForMaskedImageModeling,
					    DeiTModel,
					)
					from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DeiTImageProcessor



class 	lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=13   ,       SCREAMING_SNAKE_CASE__=30   ,       SCREAMING_SNAKE_CASE__=2   ,       SCREAMING_SNAKE_CASE__=3   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=True   ,       SCREAMING_SNAKE_CASE__=32   ,       SCREAMING_SNAKE_CASE__=5   ,       SCREAMING_SNAKE_CASE__=4   ,       SCREAMING_SNAKE_CASE__=37   ,       SCREAMING_SNAKE_CASE__="gelu"   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=0.1   ,       SCREAMING_SNAKE_CASE__=10   ,       SCREAMING_SNAKE_CASE__=0.02   ,       SCREAMING_SNAKE_CASE__=3   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=2   ,       )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   parent
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   batch_size
											SCREAMING_SNAKE_CASE__						:       Any           =   image_size
											SCREAMING_SNAKE_CASE__						:       Tuple           =   patch_size
											SCREAMING_SNAKE_CASE__						:       Tuple           =   num_channels
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   is_training
											SCREAMING_SNAKE_CASE__						:       List[str]           =   use_labels
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   hidden_size
											SCREAMING_SNAKE_CASE__						:       Any           =   num_hidden_layers
											SCREAMING_SNAKE_CASE__						:       int           =   num_attention_heads
											SCREAMING_SNAKE_CASE__						:       Dict           =   intermediate_size
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   hidden_act
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   hidden_dropout_prob
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE__						:       int           =   type_sequence_label_size
											SCREAMING_SNAKE_CASE__						:       List[str]           =   initializer_range
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   scope
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   encoder_stride

											# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   (image_size // patch_size) ** 2
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   num_patches + 2



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

											SCREAMING_SNAKE_CASE__						:       List[str]           =   None
											if self.use_labels:
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   ids_tensor([self.batch_size]   ,       self.type_sequence_label_size    )

											SCREAMING_SNAKE_CASE__						:       int           =   self.get_config()

											return config, pixel_values, labels



				def 	__magic_name__    (self    )					->		str:



											"""simple docstring"""




											return DeiTConfig(
											    image_size=self.image_size   ,       patch_size=self.patch_size   ,       num_channels=self.num_channels   ,       hidden_size=self.hidden_size   ,       num_hidden_layers=self.num_hidden_layers   ,       num_attention_heads=self.num_attention_heads   ,       intermediate_size=self.intermediate_size   ,       hidden_act=self.hidden_act   ,       hidden_dropout_prob=self.hidden_dropout_prob   ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,       is_decoder=SCREAMING_SNAKE_CASE__   ,       initializer_range=self.initializer_range   ,       encoder_stride=self.encoder_stride   ,       )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   DeiTModel(config=SCREAMING_SNAKE_CASE__    )
											model.to(SCREAMING_SNAKE_CASE__    )
											model.eval()
											SCREAMING_SNAKE_CASE__						:       int           =   model(SCREAMING_SNAKE_CASE__    )
											self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, self.seq_length, self.hidden_size)    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   DeiTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE__    )
											model.to(SCREAMING_SNAKE_CASE__    )
											model.eval()
											SCREAMING_SNAKE_CASE__						:       List[str]           =   model(SCREAMING_SNAKE_CASE__    )
											self.parent.assertEqual(
											    result.reconstruction.shape   ,       (self.batch_size, self.num_channels, self.image_size, self.image_size)    )

											# test greyscale images
											SCREAMING_SNAKE_CASE__						:       int           =   1
											SCREAMING_SNAKE_CASE__						:       List[str]           =   DeiTForMaskedImageModeling(SCREAMING_SNAKE_CASE__    )
											model.to(SCREAMING_SNAKE_CASE__    )
											model.eval()

											SCREAMING_SNAKE_CASE__						:       Tuple           =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   model(SCREAMING_SNAKE_CASE__    )
											self.parent.assertEqual(result.reconstruction.shape   ,       (self.batch_size, 1, self.image_size, self.image_size)    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   self.type_sequence_label_size
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   DeiTForImageClassification(SCREAMING_SNAKE_CASE__    )
											model.to(SCREAMING_SNAKE_CASE__    )
											model.eval()
											SCREAMING_SNAKE_CASE__						:       int           =   model(SCREAMING_SNAKE_CASE__   ,       labels=SCREAMING_SNAKE_CASE__    )
											self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.type_sequence_label_size)    )

											# test greyscale images
											SCREAMING_SNAKE_CASE__						:       Tuple           =   1
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   DeiTForImageClassification(SCREAMING_SNAKE_CASE__    )
											model.to(SCREAMING_SNAKE_CASE__    )
											model.eval()

											SCREAMING_SNAKE_CASE__						:       Dict           =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
											SCREAMING_SNAKE_CASE__						:       str           =   model(SCREAMING_SNAKE_CASE__   ,       labels=SCREAMING_SNAKE_CASE__    )
											self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.type_sequence_label_size)    )



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.prepare_config_and_inputs()
											(
											    (
											    SCREAMING_SNAKE_CASE__
											)      ,      (
											    SCREAMING_SNAKE_CASE__
											)      ,      (
											    SCREAMING_SNAKE_CASE__
											)      ,      
											)						:       List[Any]           =   config_and_inputs
											SCREAMING_SNAKE_CASE__						:       List[str]           =   {"""pixel_values""": pixel_values}
											return config, inputs_dict






@require_torch
class 	lowerCAmelCase_						(a__ ,					a__ ,					unittest.TestCase  ):


				"""simple docstring"""



				__UpperCamelCase      :						Any					       =   (
				    (
				        DeiTModel,
				        DeiTForImageClassification,
				        DeiTForImageClassificationWithTeacher,
				        DeiTForMaskedImageModeling,
				    )
				    if is_torch_available()
				    else ()
				)
				__UpperCamelCase      :						List[str]					       =   (
				    {
				        '''feature-extraction''': DeiTModel,
				        '''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
				    }
				    if is_torch_available()
				    else {}
				)

				__UpperCamelCase      :						List[Any]					       =   False
				__UpperCamelCase      :						Union[str, Any]					       =   False
				__UpperCamelCase      :						Tuple					       =   False



				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[Any]           =   DeiTModelTester(self    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   ConfigTester(self   ,       config_class=SCREAMING_SNAKE_CASE__   ,       has_text_modality=SCREAMING_SNAKE_CASE__   ,       hidden_size=37    )



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											self.config_tester.run_common_tests()



				@unittest.skip(reason="""DeiT does not use inputs_embeds"""    )
				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											pass



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE__						:       Any           =   model_class(SCREAMING_SNAKE_CASE__    )
																		self.assertIsInstance(model.get_input_embeddings()   ,       (nn.Module)    )
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   model.get_output_embeddings()
																		self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__   ,       nn.Linear    )    )



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[str]           =   self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE__						:       int           =   model_class(SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__						:       int           =   inspect.signature(model.forward    )
																		# signature.parameters is an OrderedDict => so arg_names order is deterministic
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   [*signature.parameters.keys()]

																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   ["""pixel_values"""]
																		self.assertListEqual(arg_names[:1]   ,       SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Dict           =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__=False    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   super()._prepare_for_class(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       return_labels=SCREAMING_SNAKE_CASE__    )

											if return_labels:
																		if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
																									del inputs_dict["labels"]

											return inputs_dict



				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											if not self.model_tester.is_training:
																		return

											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Any           =   self.model_tester.prepare_config_and_inputs_for_common()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   True

											for model_class in self.all_model_classes:
																		# DeiTForImageClassificationWithTeacher supports inference-only
																		if (
																		    model_class in get_values(SCREAMING_SNAKE_CASE__    )
																		    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
																		):
																									continue
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   model_class(SCREAMING_SNAKE_CASE__    )
																		model.to(SCREAMING_SNAKE_CASE__    )
																		model.train()
																		SCREAMING_SNAKE_CASE__						:       Any           =   self._prepare_for_class(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       return_labels=SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   model(**SCREAMING_SNAKE_CASE__    ).loss
																		loss.backward()



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[str]           =   self.model_tester.prepare_config_and_inputs_for_common()
											if not self.model_tester.is_training:
																		return

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   False
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   True

											for model_class in self.all_model_classes:
																		if model_class in get_values(SCREAMING_SNAKE_CASE__    ) or not model_class.supports_gradient_checkpointing:
																									continue
																		# DeiTForImageClassificationWithTeacher supports inference-only
																		if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
																									continue
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   model_class(SCREAMING_SNAKE_CASE__    )
																		model.gradient_checkpointing_enable()
																		model.to(SCREAMING_SNAKE_CASE__    )
																		model.train()
																		SCREAMING_SNAKE_CASE__						:       int           =   self._prepare_for_class(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       return_labels=SCREAMING_SNAKE_CASE__    )
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   model(**SCREAMING_SNAKE_CASE__    ).loss
																		loss.backward()



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.model_tester.prepare_config_and_inputs_for_common()

											SCREAMING_SNAKE_CASE__						:       int           =   [
											    {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
											    {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
											    {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
											]

											for model_class in self.all_model_classes:
																		if (
																		    model_class
																		    not in [
																		        *get_values(SCREAMING_SNAKE_CASE__    ),
																		        *get_values(SCREAMING_SNAKE_CASE__    ),
																		    ]
																		    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
																		):
																									continue

																		for problem_type in problem_types:
																									with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}'''    ):
																																SCREAMING_SNAKE_CASE__						:       Dict           =   problem_type["""title"""]
																																SCREAMING_SNAKE_CASE__						:       List[Any]           =   problem_type["""num_labels"""]

																																SCREAMING_SNAKE_CASE__						:       Dict           =   model_class(SCREAMING_SNAKE_CASE__    )
																																model.to(SCREAMING_SNAKE_CASE__    )
																																model.train()

																																SCREAMING_SNAKE_CASE__						:       List[Any]           =   self._prepare_for_class(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       return_labels=SCREAMING_SNAKE_CASE__    )

																																if problem_type["num_labels"] > 1:
																																							SCREAMING_SNAKE_CASE__						:       List[str]           =   inputs["""labels"""].unsqueeze(1    ).repeat(1   ,       problem_type["""num_labels"""]    )

																																SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   inputs["""labels"""].to(problem_type["""dtype"""]    )

																																# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																																# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																																# they have the same size." which is a symptom something in wrong for the regression problem.
																																# See https://github.com/huggingface/transformers/issues/11780
																																with warnings.catch_warnings(record=SCREAMING_SNAKE_CASE__    ) as warning_list:
																																							SCREAMING_SNAKE_CASE__						:       List[Any]           =   model(**SCREAMING_SNAKE_CASE__    ).loss
																																for w in warning_list:
																																							if "Using a target size that is different to the input size" in str(w.message    ):
																																														raise ValueError(
																																														    F'''Something is going wrong in the regression problem: intercepted {w.message}'''    )

																																loss.backward()



				@slow
				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   DeiTModel.from_pretrained(SCREAMING_SNAKE_CASE__    )
																		self.assertIsNotNone(SCREAMING_SNAKE_CASE__    )






def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       int           =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
							return image



@require_torch
@require_vision
class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				@cached_property
				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




											return (
											    DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""    )
											    if is_vision_available()
											    else None
											)



				@slow
				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       int           =   DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""    ).to(
											    SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.default_image_processor
											SCREAMING_SNAKE_CASE__						:       str           =   prepare_img()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   image_processor(images=SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    ).to(SCREAMING_SNAKE_CASE__    )

											# forward pass
											with torch.no_grad():
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   model(**SCREAMING_SNAKE_CASE__    )

											# verify the logits
											SCREAMING_SNAKE_CASE__						:       Tuple           =   torch.Size((1, 10_00)    )
											self.assertEqual(outputs.logits.shape   ,       SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       List[str]           =   torch.tensor([-1.0266, 0.1912, -1.2861]    ).to(SCREAMING_SNAKE_CASE__    )

											self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,       SCREAMING_SNAKE_CASE__   ,       atol=1E-4    )    )



				@slow
				@require_accelerate
				@require_torch_gpu
				def 	__magic_name__    (self    )					->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   DeiTModel.from_pretrained(
											    """facebook/deit-base-distilled-patch16-224"""   ,       torch_dtype=torch.floataa   ,       device_map="""auto"""    )
											SCREAMING_SNAKE_CASE__						:       Any           =   self.default_image_processor

											SCREAMING_SNAKE_CASE__						:       List[Any]           =   prepare_img()
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   image_processor(images=SCREAMING_SNAKE_CASE__   ,       return_tensors="""pt"""    )
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   inputs.pixel_values.to(SCREAMING_SNAKE_CASE__    )

											# forward pass to make sure inference works in fp16
											with torch.no_grad():
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   model(SCREAMING_SNAKE_CASE__    )






"""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
UpperCAmelCase__      :    List[str]    							= '.'

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

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       List[str]           =   SavedModel()
							SCREAMING_SNAKE_CASE__						:       Dict           =   []

							with open(os.path.join(_snake_case   ,"""utils"""   ,"""tf_ops"""   ,"""onnx.json"""						)						) as f:
														SCREAMING_SNAKE_CASE__						:       Any           =   json.load(_snake_case						)["""opsets"""]

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

							with open(_snake_case   ,"""rb"""						) as f:
														saved_model.ParseFromString(f.read()						)

							SCREAMING_SNAKE_CASE__						:       List[str]           =   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
							SCREAMING_SNAKE_CASE__						:       int           =   sorted(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   []

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

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


if __name__ == "__main__":
					UpperCAmelCase__      :    Optional[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=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)'
					)
					UpperCAmelCase__      :    Dict    							= parser.parse_args()

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




