





import inspect
import unittest

from transformers import ViTMSNConfig
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 ViTMSNForImageClassification, ViTMSNModel
				from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor





class 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A=13     ,					_A=30     ,					_A=2     ,					_A=3     ,					_A=True     ,					_A=True     ,					_A=32     ,					_A=5     ,					_A=4     ,					_A=37     ,					_A="gelu"     ,					_A=0.1     ,					_A=0.1     ,					_A=10     ,					_A=0.0_2     ,					_A=None     ,					):
									__A	:						str       =				parent
									__A	:						int       =				batch_size
									__A	:						Any       =				image_size
									__A	:						Any       =				patch_size
									__A	:						Optional[Any]       =				num_channels
									__A	:						Union[str, Any]       =				is_training
									__A	:						int       =				use_labels
									__A	:						Any       =				hidden_size
									__A	:						Tuple       =				num_hidden_layers
									__A	:						Any       =				num_attention_heads
									__A	:						List[Any]       =				intermediate_size
									__A	:						int       =				hidden_act
									__A	:						Any       =				hidden_dropout_prob
									__A	:						List[str]       =				attention_probs_dropout_prob
									__A	:						Optional[Any]       =				type_sequence_label_size
									__A	:						Optional[Any]       =				initializer_range
									__A	:						Optional[Any]       =				scope

									# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									__A	:						List[Any]       =				(image_size // patch_size) ** 2
									__A	:						Optional[Any]       =				num_patches + 1



				def 					UpperCAmelCase_		(			self     ):
									__A	:						str       =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

									__A	:						List[str]       =				self.get_config()

									return config, pixel_values, labels



				def 					UpperCAmelCase_		(			self     ):
									return ViTMSNConfig(
									    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     ,					initializer_range=self.initializer_range     ,					)



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									__A	:						Optional[int]       =				ViTMSNModel(config=_A     )
									model.to(_A     )
									model.eval()
									__A	:						List[Any]       =				model(_A     )
									self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, self.seq_length, self.hidden_size)     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									__A	:						str       =				self.type_sequence_label_size
									__A	:						Optional[Any]       =				ViTMSNForImageClassification(_A     )
									model.to(_A     )
									model.eval()
									__A	:						Dict       =				model(_A     ,					labels=_A     )
									print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}'     )
									print('Labels: {labels}'     )
									self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)     )

									# test greyscale images
									__A	:						str       =				1
									__A	:						Any       =				ViTMSNForImageClassification(_A     )
									model.to(_A     )
									model.eval()

									__A	:						Any       =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
									__A	:						int       =				model(_A     )
									self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)     )





				def 					UpperCAmelCase_		(			self     ):
									__A	:						int       =				self.prepare_config_and_inputs()
									__A    ,				__A    ,				__A	:						str       =				config_and_inputs
									__A	:						List[str]       =				{'pixel_values': pixel_values}
									return config, inputs_dict





@require_torch
class 						_A(					snake_case__				, snake_case__				, unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  Optional[Any]					    =	(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				UpperCamelCase    :  str					    =	(
				    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

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



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Dict       =				ViTMSNModelTester(self     )
									__A	:						Tuple       =				ConfigTester(self     ,					config_class=_A     ,					has_text_modality=_A     ,					hidden_size=37     )



				def 					UpperCAmelCase_		(			self     ):
									self.config_tester.run_common_tests()



				@unittest.skip(reason='ViTMSN does not use inputs_embeds'     )
				def 					UpperCAmelCase_		(			self     ):
									pass



				def 					UpperCAmelCase_		(			self     ):
									__A    ,				__A	:						Tuple       =				self.model_tester.prepare_config_and_inputs_for_common()

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



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

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

														__A	:						Union[str, Any]       =				['pixel_values']
														self.assertListEqual(arg_names[:1]     ,					_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						int       =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[Any]       =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_A     )





				@slow
				def 					UpperCAmelCase_		(			self     ):
									for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														__A	:						str       =				ViTMSNModel.from_pretrained(_A     )
														self.assertIsNotNone(_A     )







def  _SCREAMING_SNAKE_CASE				(   )      ->      Any:
					__A	:						str       =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
					return image





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


				"""simple docstring"""


				@cached_property
				def 					UpperCAmelCase_		(			self     ):
									return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'     ) if is_vision_available() else None





				@slow
				def 					UpperCAmelCase_		(			self     ):
									torch.manual_seed(2     )
									__A	:						Union[str, Any]       =				ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'     ).to(_A     )

									__A	:						Dict       =				self.default_image_processor
									__A	:						Tuple       =				prepare_img()
									__A	:						List[Any]       =				image_processor(images=_A     ,					return_tensors='pt'     ).to(_A     )

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

									# verify the logits
									__A	:						int       =				torch.Size((1, 1000)     )
									self.assertEqual(outputs.logits.shape     ,					_A     )

									__A	:						str       =				torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]     ).to(_A     )

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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase						:   Optional[int]			  =     {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   Union[str, Any]			  =     [
				    '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''XLMForMultipleChoice''',
				    '''XLMForQuestionAnswering''',
				    '''XLMForQuestionAnsweringSimple''',
				    '''XLMForSequenceClassification''',
				    '''XLMForTokenClassification''',
				    '''XLMModel''',
				    '''XLMPreTrainedModel''',
				    '''XLMWithLMHeadModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   Optional[Any]			  =     [
				    '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFXLMForMultipleChoice''',
				    '''TFXLMForQuestionAnsweringSimple''',
				    '''TFXLMForSequenceClassification''',
				    '''TFXLMForTokenClassification''',
				    '''TFXLMMainLayer''',
				    '''TFXLMModel''',
				    '''TFXLMPreTrainedModel''',
				    '''TFXLMWithLMHeadModel''',
				]


if TYPE_CHECKING:
				from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
				from .tokenization_xlm import XLMTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlm import (
								    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLMForMultipleChoice,
								    XLMForQuestionAnswering,
								    XLMForQuestionAnsweringSimple,
								    XLMForSequenceClassification,
								    XLMForTokenClassification,
								    XLMModel,
								    XLMPreTrainedModel,
								    XLMWithLMHeadModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xlm import (
								    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXLMForMultipleChoice,
								    TFXLMForQuestionAnsweringSimple,
								    TFXLMForSequenceClassification,
								    TFXLMForTokenClassification,
								    TFXLMMainLayer,
								    TFXLMModel,
								    TFXLMPreTrainedModel,
								    TFXLMWithLMHeadModel,
								)

else:
				import sys

				UpperCAmelCase						:   str			  =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




