




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


a_      						=   logging.get_logger(__name__)

class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       Union[str, Any]			=    ["pixel_values"]







      def __init__(   self    :       Union[str, Any]       ,				snake_case    :       bool = True       ,				snake_case    :       Dict[str, int] = None       ,				snake_case    :       bool = True       ,				snake_case    :       Dict[str, int] = None       ,				snake_case    :       PILImageResampling = PILImageResampling.BICUBIC       ,				snake_case    :       bool = True       ,				snake_case    :       Union[int, float] = 1 / 255       ,				snake_case    :       bool = True       ,				snake_case    :       Optional[Union[float, List[float]]] = None       ,				snake_case    :       Optional[Union[float, List[float]]] = None       ,				**snake_case    :       Union[str, Any]       ,				):
          super().__init__(**snake_case )
          __UpperCamelCase     						=  crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
          __UpperCamelCase     						=  get_size_dict(snake_case       ,				param_name='''crop_size''' )
          __UpperCamelCase     						=  size if size is not None else {'''height''': 224, '''width''': 224}
          __UpperCamelCase     						=  get_size_dict(snake_case )

          __UpperCamelCase     						=  do_center_crop
          __UpperCamelCase     						=  crop_size
          __UpperCamelCase     						=  do_resize
          __UpperCamelCase     						=  size
          __UpperCamelCase     						=  resample
          __UpperCamelCase     						=  do_rescale
          __UpperCamelCase     						=  rescale_factor
          __UpperCamelCase     						=  do_normalize
          __UpperCamelCase     						=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
          __UpperCamelCase     						=  image_std if image_std is not None else IMAGENET_DEFAULT_STD







      def 					snake_case		(   self    :       Optional[Any]       ,				snake_case    :       np.ndarray       ,				snake_case    :       Dict[str, int]       ,				snake_case    :       Optional[int] = None       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       List[Any]       ,				):
          __UpperCamelCase     						=  self.size if size is None else size
          __UpperCamelCase     						=  get_size_dict(snake_case )
          __UpperCamelCase     						=  get_size_dict(snake_case       ,				param_name='''crop_size''' )

          __UpperCamelCase						,					__UpperCamelCase     						=  get_image_size(snake_case )
          __UpperCamelCase     						=  min(snake_case       ,				snake_case )
          __UpperCamelCase     						=  (size['''height'''] / crop_size['''height''']) * min_dim
          __UpperCamelCase     						=  (size['''width'''] / crop_size['''width''']) * min_dim
          return center_crop(snake_case       ,				size=(cropped_height, cropped_width)       ,				data_format=snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       Union[str, Any]       ,				snake_case    :       np.ndarray       ,				snake_case    :       Dict[str, int]       ,				snake_case    :       PILImageResampling = PIL.Image.BICUBIC       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       List[str]       ,				):
          __UpperCamelCase     						=  get_size_dict(snake_case )
          if "height" not in size or "width" not in size:
              raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}" )
          return resize(
              snake_case       ,				size=(size['''height'''], size['''width'''])       ,				resample=snake_case       ,				data_format=snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       Union[str, Any]       ,				snake_case    :       np.ndarray       ,				snake_case    :       Union[int, float]       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       Optional[int]       ,				):
          return rescale(snake_case       ,				scale=snake_case       ,				data_format=snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       Optional[int]       ,				snake_case    :       np.ndarray       ,				snake_case    :       Union[float, List[float]]       ,				snake_case    :       Union[float, List[float]]       ,				snake_case    :       Optional[Union[str, ChannelDimension]] = None       ,				**snake_case    :       Any       ,				):
          return normalize(snake_case       ,				mean=snake_case       ,				std=snake_case       ,				data_format=snake_case       ,				**snake_case )







      def 					snake_case		(   self    :       List[Any]       ,				snake_case    :       ImageInput       ,				snake_case    :       Optional[bool] = None       ,				snake_case    :       Optional[Dict[str, int]] = None       ,				snake_case    :       Optional[bool] = None       ,				snake_case    :       Optional[Dict[str, int]] = None       ,				snake_case    :       PILImageResampling = None       ,				snake_case    :       Optional[bool] = None       ,				snake_case    :       Optional[float] = None       ,				snake_case    :       Optional[bool] = None       ,				snake_case    :       Optional[Union[float, List[float]]] = None       ,				snake_case    :       Optional[Union[float, List[float]]] = None       ,				snake_case    :       Optional[Union[str, TensorType]] = None       ,				snake_case    :       ChannelDimension = ChannelDimension.FIRST       ,				**snake_case    :       Tuple       ,				):
          __UpperCamelCase     						=  do_center_crop if do_center_crop is not None else self.do_center_crop
          __UpperCamelCase     						=  crop_size if crop_size is not None else self.crop_size
          __UpperCamelCase     						=  get_size_dict(snake_case       ,				param_name='''crop_size''' )
          __UpperCamelCase     						=  do_resize if do_resize is not None else self.do_resize
          __UpperCamelCase     						=  size if size is not None else self.size
          __UpperCamelCase     						=  get_size_dict(snake_case )
          __UpperCamelCase     						=  resample if resample is not None else self.resample
          __UpperCamelCase     						=  do_rescale if do_rescale is not None else self.do_rescale
          __UpperCamelCase     						=  rescale_factor if rescale_factor is not None else self.rescale_factor
          __UpperCamelCase     						=  do_normalize if do_normalize is not None else self.do_normalize
          __UpperCamelCase     						=  image_mean if image_mean is not None else self.image_mean
          __UpperCamelCase     						=  image_std if image_std is not None else self.image_std

          __UpperCamelCase     						=  make_list_of_images(snake_case )

          if not valid_images(snake_case ):
              raise ValueError(
                  '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                  '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

          if do_center_crop and crop_size is None:
              raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.''' )

          if do_resize and size is None:
              raise ValueError('''Size must be specified if do_resize is True.''' )

          if do_rescale and rescale_factor is None:
              raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

          if do_normalize and (image_mean is None or image_std is None):
              raise ValueError('''Image mean and image standard deviation must be specified if do_normalize is True.''' )

          # All transformations expect numpy arrays.
          __UpperCamelCase     						=  [to_numpy_array(snake_case ) for image in images]

          if do_center_crop:
              __UpperCamelCase     						=  [self.center_crop(snake_case       ,				snake_case       ,				size=snake_case ) for image in images]

          if do_resize:
              __UpperCamelCase     						=  [self.resize(image=snake_case       ,				size=snake_case       ,				resample=snake_case ) for image in images]

          if do_rescale:
              __UpperCamelCase     						=  [self.rescale(image=snake_case       ,				scale=snake_case ) for image in images]

          if do_normalize:
              __UpperCamelCase     						=  [self.normalize(image=snake_case       ,				mean=snake_case       ,				std=snake_case ) for image in images]

          __UpperCamelCase     						=  [to_channel_dimension_format(snake_case       ,				snake_case ) for image in images]

          __UpperCamelCase     						=  {'''pixel_values''': images}
          return BatchFeature(data=snake_case       ,				tensor_type=snake_case )











import collections
import inspect
import unittest

from transformers import SwinConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
	from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor






class 		lowercase						:






	"""simple docstring"""


	def __init__(	self	:						str						,  __UpperCAmelCase	:						Any						,  __UpperCAmelCase	:						Dict=13						,  __UpperCAmelCase	:						Optional[int]=32						,  __UpperCAmelCase	:						Optional[Any]=2						,  __UpperCAmelCase	:						Union[str, Any]=3						,  __UpperCAmelCase	:						Dict=16						,  __UpperCAmelCase	:						List[str]=[1, 2, 1]						,  __UpperCAmelCase	:						str=[2, 2, 4]						,  __UpperCAmelCase	:						Optional[Any]=2						,  __UpperCAmelCase	:						Optional[Any]=2.0						,  __UpperCAmelCase	:						List[Any]=True						,  __UpperCAmelCase	:						Tuple=0.0						,  __UpperCAmelCase	:						Dict=0.0						,  __UpperCAmelCase	:						int=0.1						,  __UpperCAmelCase	:						List[str]="gelu"						,  __UpperCAmelCase	:						Dict=False						,  __UpperCAmelCase	:						Optional[Any]=True						,  __UpperCAmelCase	:						str=0.02						,  __UpperCAmelCase	:						List[str]=1E-5						,  __UpperCAmelCase	:						int=True						,  __UpperCAmelCase	:						List[str]=None						,  __UpperCAmelCase	:						Union[str, Any]=True						,  __UpperCAmelCase	:						List[str]=10						,  __UpperCAmelCase	:						Dict=8						,  __UpperCAmelCase	:						Optional[Any]=["stage1", "stage2"]						,  __UpperCAmelCase	:						Optional[int]=[1, 2]						,  )			-> Optional[int]:
							UpperCAmelCase_=   parent
							UpperCAmelCase_=   batch_size
							UpperCAmelCase_=   image_size
							UpperCAmelCase_=   patch_size
							UpperCAmelCase_=   num_channels
							UpperCAmelCase_=   embed_dim
							UpperCAmelCase_=   depths
							UpperCAmelCase_=   num_heads
							UpperCAmelCase_=   window_size
							UpperCAmelCase_=   mlp_ratio
							UpperCAmelCase_=   qkv_bias
							UpperCAmelCase_=   hidden_dropout_prob
							UpperCAmelCase_=   attention_probs_dropout_prob
							UpperCAmelCase_=   drop_path_rate
							UpperCAmelCase_=   hidden_act
							UpperCAmelCase_=   use_absolute_embeddings
							UpperCAmelCase_=   patch_norm
							UpperCAmelCase_=   layer_norm_eps
							UpperCAmelCase_=   initializer_range
							UpperCAmelCase_=   is_training
							UpperCAmelCase_=   scope
							UpperCAmelCase_=   use_labels
							UpperCAmelCase_=   type_sequence_label_size
							UpperCAmelCase_=   encoder_stride
							UpperCAmelCase_=   out_features
							UpperCAmelCase_=   out_indices




	def   _SCREAMING_SNAKE_CASE		(	self	:						Tuple     )			-> List[str]:
							UpperCAmelCase_=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

							UpperCAmelCase_=   None
							if self.use_labels:
													UpperCAmelCase_=   ids_tensor([self.batch_size]						,  self.type_sequence_label_size     )

							UpperCAmelCase_=   self.get_config()

							return config, pixel_values, labels




	def   _SCREAMING_SNAKE_CASE		(	self	:						str     )			-> Optional[int]:
							return SwinConfig(
							    image_size=self.image_size						,  patch_size=self.patch_size						,  num_channels=self.num_channels						,  embed_dim=self.embed_dim						,  depths=self.depths						,  num_heads=self.num_heads						,  window_size=self.window_size						,  mlp_ratio=self.mlp_ratio						,  qkv_bias=self.qkv_bias						,  hidden_dropout_prob=self.hidden_dropout_prob						,  attention_probs_dropout_prob=self.attention_probs_dropout_prob						,  drop_path_rate=self.drop_path_rate						,  hidden_act=self.hidden_act						,  use_absolute_embeddings=self.use_absolute_embeddings						,  path_norm=self.patch_norm						,  layer_norm_eps=self.layer_norm_eps						,  initializer_range=self.initializer_range						,  encoder_stride=self.encoder_stride						,  out_features=self.out_features						,  out_indices=self.out_indices						,  )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Any						,  __UpperCAmelCase	:						List[str]						,  __UpperCAmelCase	:						Tuple						,  __UpperCAmelCase	:						Dict     )			-> Optional[Any]:
							UpperCAmelCase_=   SwinModel(config=__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()
							UpperCAmelCase_=   model(__UpperCAmelCase     )

							UpperCAmelCase_=   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
							UpperCAmelCase_=   int(config.embed_dim * 2 ** (len(config.depths     ) - 1)     )

							self.parent.assertEqual(result.last_hidden_state.shape						,  (self.batch_size, expected_seq_len, expected_dim)     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]						,  __UpperCAmelCase	:						Any						,  __UpperCAmelCase	:						Optional[Any]						,  __UpperCAmelCase	:						Optional[Any]     )			-> Optional[int]:
							UpperCAmelCase_=   SwinBackbone(config=__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()
							UpperCAmelCase_=   model(__UpperCAmelCase     )

							# verify hidden states
							self.parent.assertEqual(len(result.feature_maps     )						,  len(config.out_features     )     )
							self.parent.assertListEqual(list(result.feature_maps[0].shape     )						,  [self.batch_size, model.channels[0], 16, 16]     )

							# verify channels
							self.parent.assertEqual(len(model.channels     )						,  len(config.out_features     )     )

							# verify backbone works with out_features=None
							UpperCAmelCase_=   None
							UpperCAmelCase_=   SwinBackbone(config=__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()
							UpperCAmelCase_=   model(__UpperCAmelCase     )

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps     )						,  1     )
							self.parent.assertListEqual(list(result.feature_maps[0].shape     )						,  [self.batch_size, model.channels[-1], 4, 4]     )

							# verify channels
							self.parent.assertEqual(len(model.channels     )						,  1     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]						,  __UpperCAmelCase	:						Tuple						,  __UpperCAmelCase	:						Optional[Any]						,  __UpperCAmelCase	:						List[str]     )			-> int:
							UpperCAmelCase_=   SwinForMaskedImageModeling(config=__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()
							UpperCAmelCase_=   model(__UpperCAmelCase     )
							self.parent.assertEqual(
							    result.logits.shape						,  (self.batch_size, self.num_channels, self.image_size, self.image_size)     )

							# test greyscale images
							UpperCAmelCase_=   1
							UpperCAmelCase_=   SwinForMaskedImageModeling(__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()

							UpperCAmelCase_=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
							UpperCAmelCase_=   model(__UpperCAmelCase     )
							self.parent.assertEqual(result.logits.shape						,  (self.batch_size, 1, self.image_size, self.image_size)     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]						,  __UpperCAmelCase	:						str						,  __UpperCAmelCase	:						List[str]						,  __UpperCAmelCase	:						int     )			-> List[Any]:
							UpperCAmelCase_=   self.type_sequence_label_size
							UpperCAmelCase_=   SwinForImageClassification(__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()
							UpperCAmelCase_=   model(__UpperCAmelCase						,  labels=__UpperCAmelCase     )
							self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.type_sequence_label_size)     )

							# test greyscale images
							UpperCAmelCase_=   1
							UpperCAmelCase_=   SwinForImageClassification(__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()

							UpperCAmelCase_=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
							UpperCAmelCase_=   model(__UpperCAmelCase     )
							self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.type_sequence_label_size)     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]     )			-> Any:
							UpperCAmelCase_=   self.prepare_config_and_inputs()
							(
							    (
							    UpperCAmelCase_
							),   (
							    UpperCAmelCase_
							),   (
							    UpperCAmelCase_
							),   
							)=   config_and_inputs
							UpperCAmelCase_=   {"""pixel_values""": pixel_values}
							return config, inputs_dict






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






	"""simple docstring"""


	a__      :					int		   =							(
	    (
	        SwinModel,
	        SwinBackbone,
	        SwinForImageClassification,
	        SwinForMaskedImageModeling,
	    )
	    if is_torch_available()
	    else ()
	)
	a__      :					str		   =							(
	    {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
	    if is_torch_available()
	    else {}
	)
	a__      :					int		   =							True

	a__      :					Tuple		   =							False
	a__      :					Union[str, Any]		   =							False
	a__      :					Optional[int]		   =							False




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]     )			-> Optional[int]:
							UpperCAmelCase_=   SwinModelTester(self     )
							UpperCAmelCase_=   ConfigTester(self						,  config_class=__UpperCAmelCase						,  embed_dim=37     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]     )			-> Dict:
							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()




	def   _SCREAMING_SNAKE_CASE		(	self	:						Dict     )			-> List[Any]:
							return




	def   _SCREAMING_SNAKE_CASE		(	self	:						Any     )			-> List[Any]:
							UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCAmelCase     )




	@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""     )
	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]     )			-> Tuple:
							pass




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]     )			-> List[str]:
							super().test_training_gradient_checkpointing()




	def   _SCREAMING_SNAKE_CASE		(	self	:						Union[str, Any]     )			-> int:
							UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*__UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Dict     )			-> Tuple:
							UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]     )			-> Optional[Any]:
							UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase     )




	@unittest.skip(reason="""Swin does not use inputs_embeds"""     )
	def   _SCREAMING_SNAKE_CASE		(	self	:						Union[str, Any]     )			-> Dict:
							pass




	@unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""     )
	def   _SCREAMING_SNAKE_CASE		(	self	:						Union[str, Any]     )			-> Union[str, Any]:
							pass




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[str]     )			-> Any:
							UpperCAmelCase_,   UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													UpperCAmelCase_=   model_class(__UpperCAmelCase     )
													self.assertIsInstance(model.get_input_embeddings()						,  (nn.Module)     )
													UpperCAmelCase_=   model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(__UpperCAmelCase						,  nn.Linear     )     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						List[str]     )			-> List[Any]:
							UpperCAmelCase_,   UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs_for_common()

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

													UpperCAmelCase_=   ["""pixel_values"""]
													self.assertListEqual(arg_names[:1]						,  __UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]     )			-> Tuple:
							UpperCAmelCase_,   UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs_for_common()
							UpperCAmelCase_=   True

							for model_class in self.all_model_classes:
													UpperCAmelCase_=   True
													UpperCAmelCase_=   False
													UpperCAmelCase_=   True
													UpperCAmelCase_=   model_class(__UpperCAmelCase     )
													model.to(__UpperCAmelCase     )
													model.eval()
													with torch.no_grad():
																			UpperCAmelCase_=   model(**self._prepare_for_class(__UpperCAmelCase						,  __UpperCAmelCase     )     )
													UpperCAmelCase_=   outputs.attentions
													UpperCAmelCase_=   len(self.model_tester.depths     )
													self.assertEqual(len(__UpperCAmelCase     )						,  __UpperCAmelCase     )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													UpperCAmelCase_=   True
													UpperCAmelCase_=   config.window_size**2
													UpperCAmelCase_=   model_class(__UpperCAmelCase     )
													model.to(__UpperCAmelCase     )
													model.eval()
													with torch.no_grad():
																			UpperCAmelCase_=   model(**self._prepare_for_class(__UpperCAmelCase						,  __UpperCAmelCase     )     )
													UpperCAmelCase_=   outputs.attentions
													self.assertEqual(len(__UpperCAmelCase     )						,  __UpperCAmelCase     )

													self.assertListEqual(
													    list(attentions[0].shape[-3:]     )						,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,  )
													UpperCAmelCase_=   len(__UpperCAmelCase     )

													# Check attention is always last and order is fine
													UpperCAmelCase_=   True
													UpperCAmelCase_=   True
													UpperCAmelCase_=   model_class(__UpperCAmelCase     )
													model.to(__UpperCAmelCase     )
													model.eval()
													with torch.no_grad():
																			UpperCAmelCase_=   model(**self._prepare_for_class(__UpperCAmelCase						,  __UpperCAmelCase     )     )

													# also another +1 for reshaped_hidden_states
													UpperCAmelCase_=   1 if model_class.__name__ == """SwinBackbone""" else 2
													self.assertEqual(out_len + added_hidden_states						,  len(__UpperCAmelCase     )     )

													UpperCAmelCase_=   outputs.attentions

													self.assertEqual(len(__UpperCAmelCase     )						,  __UpperCAmelCase     )

													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]     )						,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]						,  )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]						,  __UpperCAmelCase	:						str						,  __UpperCAmelCase	:						Any						,  __UpperCAmelCase	:						Tuple						,  __UpperCAmelCase	:						Dict     )			-> str:
							UpperCAmelCase_=   model_class(__UpperCAmelCase     )
							model.to(__UpperCAmelCase     )
							model.eval()

							with torch.no_grad():
													UpperCAmelCase_=   model(**self._prepare_for_class(__UpperCAmelCase						,  __UpperCAmelCase     )     )

							UpperCAmelCase_=   outputs.hidden_states

							UpperCAmelCase_=   getattr(
							    self.model_tester						,  """expected_num_hidden_layers"""						,  len(self.model_tester.depths     ) + 1     )
							self.assertEqual(len(__UpperCAmelCase     )						,  __UpperCAmelCase     )

							# Swin has a different seq_length
							UpperCAmelCase_=   (
							    config.patch_size
							    if isinstance(config.patch_size						,  collections.abc.Iterable     )
							    else (config.patch_size, config.patch_size)
							)

							UpperCAmelCase_=   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]     )						,  [num_patches, self.model_tester.embed_dim]						,  )

							if not model_class.__name__ == "SwinBackbone":
													UpperCAmelCase_=   outputs.reshaped_hidden_states
													self.assertEqual(len(__UpperCAmelCase     )						,  __UpperCAmelCase     )

													UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_=   reshaped_hidden_states[0].shape
													UpperCAmelCase_=   (
													    reshaped_hidden_states[0].view(__UpperCAmelCase						,  __UpperCAmelCase						,  height * width     ).permute(0						,  2						,  1     )
													)
													self.assertListEqual(
													    list(reshaped_hidden_states.shape[-2:]     )						,  [num_patches, self.model_tester.embed_dim]						,  )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Any     )			-> Tuple:
							UpperCAmelCase_,   UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs_for_common()

							UpperCAmelCase_=   (
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size						,  collections.abc.Iterable     )
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)

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

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

													self.check_hidden_states_output(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]     )			-> List[Any]:
							UpperCAmelCase_,   UpperCAmelCase_=   self.model_tester.prepare_config_and_inputs_for_common()
							UpperCAmelCase_=   3

							UpperCAmelCase_=   (
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size						,  collections.abc.Iterable     )
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)
							UpperCAmelCase_=   (
							    config.patch_size
							    if isinstance(config.patch_size						,  collections.abc.Iterable     )
							    else (config.patch_size, config.patch_size)
							)

							UpperCAmelCase_=   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							UpperCAmelCase_=   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

							for model_class in self.all_model_classes:
													UpperCAmelCase_=   True
													self.check_hidden_states_output(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  (padded_height, padded_width)     )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													UpperCAmelCase_=   True
													self.check_hidden_states_output(__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  (padded_height, padded_width)     )




	@slow
	def   _SCREAMING_SNAKE_CASE		(	self	:						List[Any]     )			-> List[Any]:
							for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													UpperCAmelCase_=   SwinModel.from_pretrained(__UpperCAmelCase     )
													self.assertIsNotNone(__UpperCAmelCase     )




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

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






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






	"""simple docstring"""


	@cached_property
	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[int]     )			-> str:
							return (
							    AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""     )
							    if is_vision_available()
							    else None
							)




	@slow
	def   _SCREAMING_SNAKE_CASE		(	self	:						Tuple     )			-> int:
							UpperCAmelCase_=   SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""     ).to(__UpperCAmelCase     )
							UpperCAmelCase_=   self.default_image_processor

							UpperCAmelCase_=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
							UpperCAmelCase_=   image_processor(images=__UpperCAmelCase						,  return_tensors="""pt"""     ).to(__UpperCAmelCase     )

							# forward pass
							with torch.no_grad():
													UpperCAmelCase_=   model(**__UpperCAmelCase     )

							# verify the logits
							UpperCAmelCase_=   torch.Size((1, 1_000)     )
							self.assertEqual(outputs.logits.shape						,  __UpperCAmelCase     )
							UpperCAmelCase_=   torch.tensor([-0.0_948, -0.6_454, -0.0_921]     ).to(__UpperCAmelCase     )
							self.assertTrue(torch.allclose(outputs.logits[0, :3]						,  __UpperCAmelCase						,  atol=1E-4     )     )






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






	"""simple docstring"""


	a__      :					Any		   =							(SwinBackbone,) if is_torch_available() else ()
	a__      :					int		   =							SwinConfig




	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]     )			-> Optional[Any]:
							UpperCAmelCase_=   SwinModelTester(self     )




