


'''simple docstring'''
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   lowercase      :

		"""simple docstring"""

		def __init__(						self       ,  UpperCamelCase_       ,  UpperCamelCase_=13       ,  UpperCamelCase_=3       ,  UpperCamelCase_=True       ,  UpperCamelCase_=True       ,  UpperCamelCase_=0.1       ,  UpperCamelCase_=0.1       ,  UpperCamelCase_=224       ,  UpperCamelCase_=1000       ,  UpperCamelCase_=[3, 3, 6, 4]       ,  UpperCamelCase_=[48, 56, 112, 220]       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					parent
							UpperCamelCase__  :Tuple				   =					batch_size
							UpperCamelCase__  :List[Any]				   =					num_channels
							UpperCamelCase__  :Optional[Any]				   =					is_training
							UpperCamelCase__  :str				   =					use_labels
							UpperCamelCase__  :Optional[Any]				   =					hidden_dropout_prob
							UpperCamelCase__  :str				   =					attention_probs_dropout_prob
							UpperCamelCase__  :Any				   =					num_labels
							UpperCamelCase__  :int				   =					image_size
							UpperCamelCase__  :Dict				   =					layer_depths
							UpperCamelCase__  :str				   =					embed_dims




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[str]				   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

							UpperCamelCase__  :Optional[int]				   =					self.get_config()

							return config, pixel_values, labels




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							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        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					SwiftFormerModel(config=UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()
							UpperCamelCase__  :Optional[Any]				   =					model(UpperCamelCase_				)
							self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.embed_dims[-1], 7, 7)				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :List[str]				   =					self.num_labels
							UpperCamelCase__  :Union[str, Any]				   =					SwiftFormerForImageClassification(UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()
							UpperCamelCase__  :Optional[Any]				   =					model(UpperCamelCase_       ,  labels=UpperCamelCase_				)
							self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)				)

							UpperCamelCase__  :Tuple				   =					SwiftFormerForImageClassification(UpperCamelCase_				)
							model.to(UpperCamelCase_				)
							model.eval()

							UpperCamelCase__  :Optional[int]				   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
							UpperCamelCase__  :List[Any]				   =					model(UpperCamelCase_				)
							self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							((UpperCamelCase__)   ,							(UpperCamelCase__)   ,							(UpperCamelCase__))  :Tuple				   =					self.prepare_config_and_inputs()
							UpperCamelCase__  :Dict				   =					{'''pixel_values''': pixel_values}
							return config, inputs_dict



@require_torch
class   lowercase      (				A__      ,						A__      ,						unittest.TestCase   ):

		"""simple docstring"""

		_a       					=							(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
		_a       					=							(
		    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
		    if is_torch_available()
		    else {}
		)

		_a       					=							False
		_a       					=							False
		_a       					=							False
		_a       					=							False
		_a       					=							False




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					SwiftFormerModelTester(self				)
							UpperCamelCase__  :List[Any]				   =					ConfigTester(
							    self       ,  config_class=UpperCamelCase_       ,  has_text_modality=UpperCamelCase_       ,  hidden_size=37       ,  num_attention_heads=12       ,  num_hidden_layers=12       ,  )




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							self.config_tester.run_common_tests()




		@unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''				)
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							pass




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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

							for model_class in self.all_model_classes:
												UpperCamelCase__  :Tuple				   =					model_class(UpperCamelCase_				)
												UpperCamelCase__  :Optional[int]				   =					model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(UpperCamelCase_       ,  nn.Linear				)				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__   ,							UpperCamelCase__  :Dict				   =					self.model_tester.prepare_config_and_inputs_for_common()

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

												UpperCamelCase__  :Optional[int]				   =					['''pixel_values''']
												self.assertListEqual(arg_names[:1]       ,  UpperCamelCase_				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :str				   =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCamelCase_				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, Any]				   =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_				)




		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCamelCase__  :int				   =					SwiftFormerModel.from_pretrained(UpperCamelCase_				)
												self.assertIsNotNone(UpperCamelCase_				)




		@unittest.skip(reason='''SwiftFormer does not output attentions'''				)
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							pass




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							def check_hidden_states_output(UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				):
												UpperCamelCase__  :Any				   =					model_class(UpperCamelCase_				)
												model.to(UpperCamelCase_				)
												model.eval()

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

												UpperCamelCase__  :str				   =					outputs.hidden_states

												UpperCamelCase__  :Optional[Any]				   =					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[int]				   =					True

												check_hidden_states_output(UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							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__  :Union[str, Any]				   =					_config_zero_init(getattr(UpperCamelCase_       ,  UpperCamelCase_				)				)
																						setattr(UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_				)
												return configs_no_init

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

							UpperCamelCase__  :Optional[Any]				   =					_config_zero_init(UpperCamelCase_				)
							for model_class in self.all_model_classes:
												UpperCamelCase__  :List[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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							pass



def      a (     )    ->      Any:



					'''simple docstring'''






					UpperCamelCase__  :Optional[int]				   =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
					return image







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

		"""simple docstring"""

		@cached_property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''				) if is_vision_available() else None




		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''				).to(UpperCamelCase_				)

							UpperCamelCase__  :List[str]				   =					self.default_image_processor
							UpperCamelCase__  :Optional[int]				   =					prepare_img()
							UpperCamelCase__  :Any				   =					image_processor(images=UpperCamelCase_       ,  return_tensors='''pt'''				).to(UpperCamelCase_				)

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

							# verify the logits
							UpperCamelCase__  :str				   =					torch.Size((1, 1000)				)
							self.assertEqual(outputs.logits.shape       ,  UpperCamelCase_				)

							UpperCamelCase__  :Any				   =					torch.tensor([[-2.17_03e00, 2.11_07e00, -2.08_11e00]]				).to(UpperCamelCase_				)
							self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,  UpperCamelCase_       ,  atol=1e-4				)				)



'''simple docstring'''
import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class   lowercase      (				A__      ,						unittest.TestCase   ):

		"""simple docstring"""

		_a       					=							LxmertTokenizer
		_a       					=							LxmertTokenizerFast
		_a       					=							True
		_a       					=							True




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							super().setUp()

							UpperCamelCase__  :Any				   =					[
							    '''[UNK]''',
							    '''[CLS]''',
							    '''[SEP]''',
							    '''want''',
							    '''##want''',
							    '''##ed''',
							    '''wa''',
							    '''un''',
							    '''runn''',
							    '''##ing''',
							    ''',''',
							    '''low''',
							    '''lowest''',
							]
							UpperCamelCase__  :str				   =					os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES['''vocab_file''']				)
							with open(self.vocab_file       ,  '''w'''       ,  encoding='''utf-8'''				) as vocab_writer:
												vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]				)				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					'''UNwant\u00E9d,running'''
							UpperCamelCase__  :Union[str, Any]				   =					'''unwanted, running'''
							return input_text, output_text




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					self.tokenizer_class(self.vocab_file				)

							UpperCamelCase__  :List[Any]				   =					tokenizer.tokenize('''UNwant\u00E9d,running'''				)
							self.assertListEqual(UpperCamelCase_       ,  ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']				)
							self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_				)       ,  [7, 4, 5, 10, 8, 9]				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							if not self.test_rust_tokenizer:
												return

							UpperCamelCase__  :str				   =					self.get_tokenizer()
							UpperCamelCase__  :Union[str, Any]				   =					self.get_rust_tokenizer()

							UpperCamelCase__  :int				   =					'''I was born in 92000, and this is falsé.'''

							UpperCamelCase__  :Optional[Any]				   =					tokenizer.tokenize(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					rust_tokenizer.tokenize(UpperCamelCase_				)
							self.assertListEqual(UpperCamelCase_       ,  UpperCamelCase_				)

							UpperCamelCase__  :Optional[Any]				   =					tokenizer.encode(UpperCamelCase_       ,  add_special_tokens=UpperCamelCase_				)
							UpperCamelCase__  :int				   =					rust_tokenizer.encode(UpperCamelCase_       ,  add_special_tokens=UpperCamelCase_				)
							self.assertListEqual(UpperCamelCase_       ,  UpperCamelCase_				)

							UpperCamelCase__  :Optional[int]				   =					self.get_rust_tokenizer()
							UpperCamelCase__  :Any				   =					tokenizer.encode(UpperCamelCase_				)
							UpperCamelCase__  :Optional[int]				   =					rust_tokenizer.encode(UpperCamelCase_				)
							self.assertListEqual(UpperCamelCase_       ,  UpperCamelCase_				)