





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A   : Tuple             =			{'configuration_encoder_decoder': ['EncoderDecoderConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : List[Any]             =			['EncoderDecoderModel']

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : str             =			['TFEncoderDecoderModel']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A   : Union[str, Any]             =			['FlaxEncoderDecoderModel']

if TYPE_CHECKING:
		from .configuration_encoder_decoder import EncoderDecoderConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_encoder_decoder import EncoderDecoderModel

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
		import sys

		A   : Any             =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











'''simple docstring'''






import inspect
import unittest

from transformers import ViTConfig
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 ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
		from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor







class 			lowerCamelCase					:


					def __init__(					self	:  Union[str, Any]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  str=13       ,     __snake_case	:  Union[str, Any]=30       ,     __snake_case	:  Union[str, Any]=2       ,     __snake_case	:  Dict=3       ,     __snake_case	:  Optional[Any]=True       ,     __snake_case	:  Optional[int]=True       ,     __snake_case	:  Optional[int]=32       ,     __snake_case	:  Optional[int]=5       ,     __snake_case	:  Any=4       ,     __snake_case	:  int=37       ,     __snake_case	:  int="gelu"       ,     __snake_case	:  Union[str, Any]=0.1       ,     __snake_case	:  Optional[int]=0.1       ,     __snake_case	:  int=10       ,     __snake_case	:  Any=0.02       ,     __snake_case	:  List[str]=None       ,     __snake_case	:  Tuple=2       ,     ):





											'''simple docstring'''





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

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





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

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

											_snake_case:   Union[str, Any]  					=		self.get_config()

											return config, pixel_values, labels


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





											return ViTConfig(
											    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=__snake_case       ,     initializer_range=self.initializer_range       ,     encoder_stride=self.encoder_stride       ,     )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ,     __snake_case	:  int       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		ViTModel(config=__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   Tuple  					=		model(__snake_case       )
											self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  int       ):





											'''simple docstring'''





											_snake_case:   int  					=		ViTForMaskedImageModeling(config=__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   Dict  					=		model(__snake_case       )
											self.parent.assertEqual(
											    result.reconstruction.shape       ,     (self.batch_size, self.num_channels, self.image_size, self.image_size)       )

											# test greyscale images
											_snake_case:   List[str]  					=		1
											_snake_case:   Tuple  					=		ViTForMaskedImageModeling(__snake_case       )
											model.to(__snake_case       )
											model.eval()

											_snake_case:   List[str]  					=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
											_snake_case:   Union[str, Any]  					=		model(__snake_case       )
											self.parent.assertEqual(result.reconstruction.shape       ,     (self.batch_size, 1, self.image_size, self.image_size)       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  Dict       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		self.type_sequence_label_size
											_snake_case:   Union[str, Any]  					=		ViTForImageClassification(__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   List[Any]  					=		model(__snake_case       ,     labels=__snake_case       )
											self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.type_sequence_label_size)       )

											# test greyscale images
											_snake_case:   Tuple  					=		1
											_snake_case:   Optional[int]  					=		ViTForImageClassification(__snake_case       )
											model.to(__snake_case       )
											model.eval()

											_snake_case:   List[Any]  					=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
											_snake_case:   Any  					=		model(__snake_case       )
											self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.type_sequence_label_size)       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Any  					=		self.prepare_config_and_inputs()
											(
											    (
											    _snake_case
											)					,						(
											    _snake_case
											)					,						(
											    _snake_case
											)					,						
											):   int  					=		config_and_inputs
											_snake_case:   Dict  					=		{'pixel_values': pixel_values}
											return config, inputs_dict







@require_torch
class 			lowerCamelCase					(						__UpperCAmelCase     ,							__UpperCAmelCase     ,							unittest.TestCase  ):
					_SCREAMING_SNAKE_CASE							   =    (
					    (
					        ViTModel,
					        ViTForImageClassification,
					        ViTForMaskedImageModeling,
					    )
					    if is_torch_available()
					    else ()
					)
					_SCREAMING_SNAKE_CASE							   =    (
					    {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
					    if is_torch_available()
					    else {}
					)
					_SCREAMING_SNAKE_CASE							   =    True

					_SCREAMING_SNAKE_CASE							   =    False
					_SCREAMING_SNAKE_CASE							   =    False
					_SCREAMING_SNAKE_CASE							   =    False


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		ViTModelTester(self       )
											_snake_case:   Union[str, Any]  					=		ConfigTester(self       ,     config_class=__snake_case       ,     has_text_modality=__snake_case       ,     hidden_size=37       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											self.config_tester.run_common_tests()


					@unittest.skip(reason='ViT does not use inputs_embeds'       )
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											pass


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





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

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





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

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

																	_snake_case:   str  					=		['pixel_values']
																	self.assertListEqual(arg_names[:1]       ,     __snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   int  					=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__snake_case       )




					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_snake_case:   Any  					=		ViTModel.from_pretrained(__snake_case       )
																	self.assertIsNotNone(__snake_case       )





def       lowercase_      (		)   ->List[Any]:
						_snake_case:   Dict  					=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
						return image







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


					@cached_property
					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'       ) if is_vision_available() else None


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		ViTForImageClassification.from_pretrained('google/vit-base-patch16-224'       ).to(__snake_case       )

											_snake_case:   Dict  					=		self.default_image_processor
											_snake_case:   Optional[Any]  					=		prepare_img()
											_snake_case:   List[str]  					=		image_processor(images=__snake_case       ,     return_tensors='pt'       ).to(__snake_case       )

											# forward pass
											with torch.no_grad():
																	_snake_case:   Optional[int]  					=		model(**__snake_case       )

											# verify the logits
											_snake_case:   Union[str, Any]  					=		torch.Size((1, 10_00)       )
											self.assertEqual(outputs.logits.shape       ,     __snake_case       )

											_snake_case:   Dict  					=		torch.tensor([-0.2_744, 0.8_215, -0.0_836]       ).to(__snake_case       )

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


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		ViTModel.from_pretrained('facebook/dino-vits8'       ).to(__snake_case       )

											_snake_case:   Any  					=		ViTImageProcessor.from_pretrained('facebook/dino-vits8'       ,     size=4_80       )
											_snake_case:   Optional[int]  					=		prepare_img()
											_snake_case:   Dict  					=		image_processor(images=__snake_case       ,     return_tensors='pt'       )
											_snake_case:   Optional[Any]  					=		inputs.pixel_values.to(__snake_case       )

											# forward pass
											with torch.no_grad():
																	_snake_case:   str  					=		model(__snake_case       ,     interpolate_pos_encoding=__snake_case       )

											# verify the logits
											_snake_case:   List[str]  					=		torch.Size((1, 36_01, 3_84)       )
											self.assertEqual(outputs.last_hidden_state.shape       ,     __snake_case       )

											_snake_case:   Any  					=		torch.tensor(
											    [[4.2_340, 4.3_906, -6.6_692], [4.5_463, 1.8_928, -6.7_257], [4.4_429, 0.8_496, -5.8_585]]       ).to(__snake_case       )

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




					@slow
					@require_accelerate
					@require_torch_gpu
					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		ViTModel.from_pretrained('facebook/dino-vits8'       ,     torch_dtype=torch.floataa       ,     device_map='auto'       )
											_snake_case:   Dict  					=		self.default_image_processor

											_snake_case:   Any  					=		prepare_img()
											_snake_case:   str  					=		image_processor(images=__snake_case       ,     return_tensors='pt'       )
											_snake_case:   Any  					=		inputs.pixel_values.to(__snake_case       )

											# forward pass to make sure inference works in fp16
											with torch.no_grad():
																	_snake_case:   int  					=		model(__snake_case       )





