






import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    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 SwiftFormerForImageClassification, SwiftFormerModel
		from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor



class   lowerCamelCase				:







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase=1_3    ,		_UpperCamelCase=3    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=2_2_4    ,		_UpperCamelCase=1_0_0_0    ,		_UpperCamelCase=[3, 3, 6, 4]    ,		_UpperCamelCase=[4_8, 5_6, 1_1_2, 2_2_0]    ,		)     ->							List[Any]:
							UpperCAmelCase_   :				str						=						parent
							UpperCAmelCase_   :				Union[str, Any]						=						batch_size
							UpperCAmelCase_   :				List[str]						=						num_channels
							UpperCAmelCase_   :				Dict						=						is_training
							UpperCAmelCase_   :				Union[str, Any]						=						use_labels
							UpperCAmelCase_   :				List[Any]						=						hidden_dropout_prob
							UpperCAmelCase_   :				Tuple						=						attention_probs_dropout_prob
							UpperCAmelCase_   :				Any						=						num_labels
							UpperCAmelCase_   :				str						=						image_size
							UpperCAmelCase_   :				Tuple						=						layer_depths
							UpperCAmelCase_   :				List[Any]						=						embed_dims



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				str						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

							UpperCAmelCase_   :				Any						=						None
							if self.use_labels:
												UpperCAmelCase_   :				Dict						=						ids_tensor([self.batch_size]    ,		self.num_labels       )

							UpperCAmelCase_   :				str						=						self.get_config()

							return config, pixel_values, labels



		def  __UpperCAmelCase      (							self       )     ->							str:
							return SwiftFormerConfig(
							    depths=self.layer_depths    ,		embed_dims=self.embed_dims    ,		mlp_ratio=4    ,		downsamples=[True, True, True, True]    ,		hidden_act='gelu'    ,		num_labels=self.num_labels    ,		down_patch_size=3    ,		down_stride=2    ,		down_pad=1    ,		drop_rate=0.0    ,		drop_path_rate=0.0    ,		use_layer_scale=_UpperCamelCase    ,		layer_scale_init_value=1E-5    ,		)



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Optional[int]:
							UpperCAmelCase_   :				Optional[Any]						=						SwiftFormerModel(config=_UpperCamelCase       )
							model.to(_UpperCamelCase       )
							model.eval()
							UpperCAmelCase_   :				Any						=						model(_UpperCamelCase       )
							self.parent.assertEqual(result.last_hidden_state.shape    ,		(self.batch_size, self.embed_dims[-1], 7, 7)       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							List[Any]:
							UpperCAmelCase_   :				List[Any]						=						self.num_labels
							UpperCAmelCase_   :				Dict						=						SwiftFormerForImageClassification(_UpperCamelCase       )
							model.to(_UpperCamelCase       )
							model.eval()
							UpperCAmelCase_   :				Tuple						=						model(_UpperCamelCase    ,		labels=_UpperCamelCase       )
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)       )

							UpperCAmelCase_   :				Union[str, Any]						=						SwiftFormerForImageClassification(_UpperCamelCase       )
							model.to(_UpperCamelCase       )
							model.eval()

							UpperCAmelCase_   :				int						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
							UpperCAmelCase_   :				Any						=						model(_UpperCamelCase       )
							self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)       )



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							((UpperCAmelCase_)	,  (UpperCAmelCase_)	,  (UpperCAmelCase_))   :				Union[str, Any]						=						self.prepare_config_and_inputs()
							UpperCAmelCase_   :				Tuple						=						{'pixel_values': pixel_values}
							return config, inputs_dict





@require_torch
class   lowerCamelCase				(_snake_case			,  _snake_case			,  unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   Union[str, Any]							=  (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
		_snake_case   :   List[str]							=  (
		    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
		    if is_torch_available()
		    else {}
		)

		_snake_case   :   str							=  False
		_snake_case   :   Optional[Any]							=  False
		_snake_case   :   Dict							=  False
		_snake_case   :   Optional[int]							=  False
		_snake_case   :   Optional[int]							=  False



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_   :				Optional[Any]						=						SwiftFormerModelTester(self       )
							UpperCAmelCase_   :				List[Any]						=						ConfigTester(
							    self    ,		config_class=_UpperCamelCase    ,		has_text_modality=_UpperCamelCase    ,		hidden_size=3_7    ,		num_attention_heads=1_2    ,		num_hidden_layers=1_2    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Any:
							self.config_tester.run_common_tests()



		@unittest.skip(reason='SwiftFormer does not use inputs_embeds'       )
		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							pass



		def  __UpperCAmelCase      (							self       )     ->							str:
							UpperCAmelCase_	,  UpperCAmelCase_   :				Any						=						self.model_tester.prepare_config_and_inputs_for_common()

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



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_	,  UpperCAmelCase_   :				Optional[Any]						=						self.model_tester.prepare_config_and_inputs_for_common()

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

												UpperCAmelCase_   :				Any						=						['pixel_values']
												self.assertListEqual(arg_names[:1]    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				int						=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				Dict						=						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase       )



		@slow
		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCAmelCase_   :				Optional[Any]						=						SwiftFormerModel.from_pretrained(_UpperCamelCase       )
												self.assertIsNotNone(_UpperCamelCase       )



		@unittest.skip(reason='SwiftFormer does not output attentions'       )
		def  __UpperCAmelCase      (							self       )     ->							Any:
							pass



		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							def check_hidden_states_output(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       ):
												UpperCAmelCase_   :				int						=						model_class(_UpperCamelCase       )
												model.to(_UpperCamelCase       )
												model.eval()

												with torch.no_grad():
																	UpperCAmelCase_   :				Union[str, Any]						=						model(**self._prepare_for_class(_UpperCamelCase    ,		_UpperCamelCase       )       )

												UpperCAmelCase_   :				Dict						=						outputs.hidden_states

												UpperCAmelCase_   :				int						=						8
												self.assertEqual(len(_UpperCamelCase       )    ,		_UpperCamelCase       )  # TODO

												# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
												# with the width and height being successively divided by 2, after every 2 blocks
												for i in range(len(_UpperCamelCase       )       ):
																	self.assertEqual(
																	    hidden_states[i].shape    ,		torch.Size(
																	        [
																	            self.model_tester.batch_size,
																	            self.model_tester.embed_dims[i // 2],
																	            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																	            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																	        ]       )    ,		)

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

							for model_class in self.all_model_classes:
												UpperCAmelCase_   :				int						=						True
												check_hidden_states_output(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												UpperCAmelCase_   :				Optional[Any]						=						True

												check_hidden_states_output(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							def _config_zero_init(_UpperCamelCase       ):
												UpperCAmelCase_   :				Dict						=						copy.deepcopy(_UpperCamelCase       )
												for key in configs_no_init.__dict__.keys():
																	if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																						setattr(_UpperCamelCase    ,		_UpperCamelCase    ,		1E-10       )
																	if isinstance(getattr(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )    ,		_UpperCamelCase       ):
																						UpperCAmelCase_   :				List[str]						=						_config_zero_init(getattr(_UpperCamelCase    ,		_UpperCamelCase       )       )
																						setattr(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )
												return configs_no_init

							UpperCAmelCase_	,  UpperCAmelCase_   :				int						=						self.model_tester.prepare_config_and_inputs_for_common()

							UpperCAmelCase_   :				int						=						_config_zero_init(_UpperCamelCase       )
							for model_class in self.all_model_classes:
												UpperCAmelCase_   :				Optional[Any]						=						model_class(config=_UpperCamelCase       )
												for name, param in model.named_parameters():
																	if param.requires_grad:
																						self.assertIn(
																						    ((param.data.mean() * 1E9) / 1E9).round().item()    ,		[0.0, 1.0]    ,		msg=f"Parameter {name} of model {model_class} seems not properly initialized"    ,		)



		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'       )
		def  __UpperCAmelCase      (							self       )     ->							Optional[Any]:
							pass





def 					lowercase__				(					):


					'''simple docstring'''


					UpperCAmelCase_   :				Dict						=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
					return image






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







		'''simple docstring'''



		@cached_property
		def  __UpperCAmelCase      (							self       )     ->							Dict:
							return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'       ) if is_vision_available() else None



		@slow
		def  __UpperCAmelCase      (							self       )     ->							Dict:
							UpperCAmelCase_   :				List[Any]						=						SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'       ).to(_UpperCamelCase       )

							UpperCAmelCase_   :				Dict						=						self.default_image_processor
							UpperCAmelCase_   :				Tuple						=						prepare_img()
							UpperCAmelCase_   :				Dict						=						image_processor(images=_UpperCamelCase    ,		return_tensors='pt'       ).to(_UpperCamelCase       )

							# forward pass
							with torch.no_grad():
												UpperCAmelCase_   :				List[str]						=						model(**_UpperCamelCase       )

							# verify the logits
							UpperCAmelCase_   :				Optional[Any]						=						torch.Size((1, 1_0_0_0)       )
							self.assertEqual(outputs.logits.shape    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				Dict						=						torch.tensor([[-2.1_703E00, 2.1_107E00, -2.0_811E00]]       ).to(_UpperCamelCase       )
							self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,		_UpperCamelCase    ,		atol=1E-4       )       )














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     							=						'.'

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




def 					lowercase__				(					__snake_case   :							Union[str, Any]					,	__snake_case   :							Optional[int]					,	__snake_case   :							Union[str, Any]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[Any]						=						SavedModel()
					UpperCAmelCase_   :				Optional[Any]						=						[]

					with open(os.path.join(__snake_case					,	'utils'					,	'tf_ops'					,	'onnx.json'  )  ) as f:
										UpperCAmelCase_   :				Optional[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()  )

					UpperCAmelCase_   :				List[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
					UpperCAmelCase_   :				Optional[int]						=						sorted(__snake_case  )
					UpperCAmelCase_   :				int						=						[]

					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     							=						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)'
		)
		__UpperCAmelCase     							=						parser.parse_args()

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






