




'''simple docstring'''





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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase_		:    Optional[int]									=		logging.get_logger(__name__)

class      _a       (		__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:					Optional[int]					       =		["""pixel_values"""]






     def __init__(				self      ,_SCREAMING_SNAKE_CASE = True      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR      ,_SCREAMING_SNAKE_CASE = True      ,_SCREAMING_SNAKE_CASE = 1 / 255      ,_SCREAMING_SNAKE_CASE = True      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,**_SCREAMING_SNAKE_CASE      ,)							->    None:
          super().__init__(**_SCREAMING_SNAKE_CASE					)
          _snake_case							=   size if size is not None else {"height": 224, "width": 224}
          _snake_case							=   get_size_dict(_SCREAMING_SNAKE_CASE					)
          _snake_case							=   do_resize
          _snake_case							=   do_rescale
          _snake_case							=   do_normalize
          _snake_case							=   size
          _snake_case							=   resample
          _snake_case							=   rescale_factor
          _snake_case							=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          _snake_case							=   image_std if image_std is not None else IMAGENET_STANDARD_STD






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR      ,_SCREAMING_SNAKE_CASE = None      ,**_SCREAMING_SNAKE_CASE      ,)							->    np.ndarray:
          _snake_case							=   get_size_dict(_SCREAMING_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(
              _SCREAMING_SNAKE_CASE      ,size=(size["height"], size["width"])      ,resample=_SCREAMING_SNAKE_CASE      ,data_format=_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None      ,**_SCREAMING_SNAKE_CASE					)							->    np.ndarray:
          return rescale(_SCREAMING_SNAKE_CASE      ,scale=_SCREAMING_SNAKE_CASE      ,data_format=_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None      ,**_SCREAMING_SNAKE_CASE      ,)							->    np.ndarray:
          return normalize(_SCREAMING_SNAKE_CASE      ,mean=_SCREAMING_SNAKE_CASE      ,std=_SCREAMING_SNAKE_CASE      ,data_format=_SCREAMING_SNAKE_CASE      ,**_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = None      ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST      ,**_SCREAMING_SNAKE_CASE      ,)							->    int:
          _snake_case							=   do_resize if do_resize is not None else self.do_resize
          _snake_case							=   do_rescale if do_rescale is not None else self.do_rescale
          _snake_case							=   do_normalize if do_normalize is not None else self.do_normalize
          _snake_case							=   resample if resample is not None else self.resample
          _snake_case							=   rescale_factor if rescale_factor is not None else self.rescale_factor
          _snake_case							=   image_mean if image_mean is not None else self.image_mean
          _snake_case							=   image_std if image_std is not None else self.image_std

          _snake_case							=   size if size is not None else self.size
          _snake_case							=   get_size_dict(_SCREAMING_SNAKE_CASE					)

          _snake_case							=   make_list_of_images(_SCREAMING_SNAKE_CASE					)

          if not valid_images(_SCREAMING_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_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."					)

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

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

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

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

          _snake_case							=   [to_channel_dimension_format(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					) for image in images]

          _snake_case							=   {"pixel_values": images}
          return BatchFeature(data=_SCREAMING_SNAKE_CASE      ,tensor_type=_SCREAMING_SNAKE_CASE					)








'''simple docstring'''





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers.models.swin.modeling_tf_swin import (
       TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFSwinForImageClassification,
       TFSwinForMaskedImageModeling,
       TFSwinModel,
   )


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor

class      _a       :






     def __init__(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE=13      ,_SCREAMING_SNAKE_CASE=32      ,_SCREAMING_SNAKE_CASE=2      ,_SCREAMING_SNAKE_CASE=3      ,_SCREAMING_SNAKE_CASE=16      ,_SCREAMING_SNAKE_CASE=[1, 2, 1]      ,_SCREAMING_SNAKE_CASE=[2, 2, 4]      ,_SCREAMING_SNAKE_CASE=2      ,_SCREAMING_SNAKE_CASE=2.0      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=0.0      ,_SCREAMING_SNAKE_CASE=0.0      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE="gelu"      ,_SCREAMING_SNAKE_CASE=False      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=0.0_2      ,_SCREAMING_SNAKE_CASE=1e-5      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=None      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=10      ,_SCREAMING_SNAKE_CASE=8      ,)							->    None:
          _snake_case							=   parent
          _snake_case							=   batch_size
          _snake_case							=   image_size
          _snake_case							=   patch_size
          _snake_case							=   num_channels
          _snake_case							=   embed_dim
          _snake_case							=   depths
          _snake_case							=   num_heads
          _snake_case							=   window_size
          _snake_case							=   mlp_ratio
          _snake_case							=   qkv_bias
          _snake_case							=   hidden_dropout_prob
          _snake_case							=   attention_probs_dropout_prob
          _snake_case							=   drop_path_rate
          _snake_case							=   hidden_act
          _snake_case							=   use_absolute_embeddings
          _snake_case							=   patch_norm
          _snake_case							=   layer_norm_eps
          _snake_case							=   initializer_range
          _snake_case							=   is_training
          _snake_case							=   scope
          _snake_case							=   use_labels
          _snake_case							=   type_sequence_label_size
          _snake_case							=   encoder_stride






     def        _lowercase			(				self					)							->    Dict:
          _snake_case							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

          _snake_case							=   self.get_config()

          return config, pixel_values, labels






     def        _lowercase			(				self					)							->    List[str]:
          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      ,)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    int:
          _snake_case							=   TFSwinModel(config=_SCREAMING_SNAKE_CASE					)
          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)

          _snake_case							=   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths					) - 1))
          _snake_case							=   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        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    Tuple:
          _snake_case							=   TFSwinForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE					)
          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)
          self.parent.assertEqual(
              result.logits.shape      ,(self.batch_size, self.num_channels, self.image_size, self.image_size)					)

          # test greyscale images
          _snake_case							=   1
          _snake_case							=   TFSwinForMaskedImageModeling(_SCREAMING_SNAKE_CASE					)

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






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    Union[str, Any]:
          _snake_case							=   self.type_sequence_label_size
          _snake_case							=   TFSwinForImageClassification(_SCREAMING_SNAKE_CASE					)
          _snake_case							=   model(_SCREAMING_SNAKE_CASE      ,labels=_SCREAMING_SNAKE_CASE					)
          self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.type_sequence_label_size)					)

          # test greyscale images
          _snake_case							=   1
          _snake_case							=   TFSwinForImageClassification(_SCREAMING_SNAKE_CASE					)
          _snake_case							=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)
          self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.type_sequence_label_size)					)






     def        _lowercase			(				self					)							->    int:
          _snake_case							=   self.prepare_config_and_inputs()
          _snake_case							,      _snake_case							,      _snake_case							=   config_and_inputs
          _snake_case							=   {"pixel_values": pixel_values}
          return config, inputs_dict







@require_tf
class      _a       (		__lowerCAmelCase      ,      __lowerCAmelCase      ,      unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:					int					       =		(
         (
             TFSwinModel,
             TFSwinForImageClassification,
             TFSwinForMaskedImageModeling,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE_							:					Optional[int]					       =		(
         {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
         if is_tf_available()
         else {}
     )

     SCREAMING_SNAKE_CASE_							:					str					       =		False
     SCREAMING_SNAKE_CASE_							:					Optional[int]					       =		False
     SCREAMING_SNAKE_CASE_							:					List[str]					       =		False
     SCREAMING_SNAKE_CASE_							:					str					       =		False






     def        _lowercase			(				self					)							->    Dict:
          _snake_case							=   TFSwinModelTester(self					)
          _snake_case							=   ConfigTester(self      ,config_class=_SCREAMING_SNAKE_CASE      ,embed_dim=37					)






     def        _lowercase			(				self					)							->    Optional[Any]:
          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        _lowercase			(				self					)							->    Tuple:
          return






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    Optional[int]:
          _snake_case							=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    Optional[Any]:
          _snake_case							=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE					)






     @unittest.skip(reason="Swin does not use inputs_embeds"					)
     def        _lowercase			(				self					)							->    Optional[Any]:
          pass






     def        _lowercase			(				self					)							->    List[Any]:
          _snake_case							,      _snake_case							=   self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               _snake_case							=   model_class(_SCREAMING_SNAKE_CASE					)
               self.assertIsInstance(model.get_input_embeddings()      ,tf.keras.layers.Layer					)
               _snake_case							=   model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE      ,tf.keras.layers.Dense					)					)






     def        _lowercase			(				self					)							->    Tuple:
          _snake_case							,      _snake_case							=   self.model_tester.prepare_config_and_inputs_for_common()

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

               _snake_case							=   ["pixel_values"]
               self.assertListEqual(arg_names[:1]      ,_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							,      _snake_case							=   self.model_tester.prepare_config_and_inputs_for_common()
          _snake_case							=   True

          for model_class in self.all_model_classes:
               _snake_case							=   True
               _snake_case							=   False
               _snake_case							=   True
               _snake_case							=   model_class(_SCREAMING_SNAKE_CASE					)
               _snake_case							=   model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)					)
               _snake_case							=   outputs.attentions
               _snake_case							=   len(self.model_tester.depths					)
               self.assertEqual(len(_SCREAMING_SNAKE_CASE					)      ,_SCREAMING_SNAKE_CASE					)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               _snake_case							=   True
               _snake_case							=   config.window_size**2
               _snake_case							=   model_class(_SCREAMING_SNAKE_CASE					)
               _snake_case							=   model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)					)
               _snake_case							=   outputs.attentions
               self.assertEqual(len(_SCREAMING_SNAKE_CASE					)      ,_SCREAMING_SNAKE_CASE					)

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

               # Check attention is always last and order is fine
               _snake_case							=   True
               _snake_case							=   True
               _snake_case							=   model_class(_SCREAMING_SNAKE_CASE					)
               _snake_case							=   model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)					)

               if hasattr(self.model_tester      ,"num_hidden_states_types"					):
                    _snake_case							=   self.model_tester.num_hidden_states_types
               else:
                    # also another +1 for reshaped_hidden_states
                    _snake_case							=   2
               self.assertEqual(out_len + added_hidden_states      ,len(_SCREAMING_SNAKE_CASE					)					)

               _snake_case							=   outputs.attentions

               self.assertEqual(len(_SCREAMING_SNAKE_CASE					)      ,_SCREAMING_SNAKE_CASE					)

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






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)							->    Optional[int]:
          _snake_case							=   model_class(_SCREAMING_SNAKE_CASE					)
          _snake_case							=   model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)					)
          _snake_case							=   outputs.hidden_states

          _snake_case							=   getattr(
              self.model_tester      ,"expected_num_hidden_layers"      ,len(self.model_tester.depths					) + 1					)
          self.assertEqual(len(_SCREAMING_SNAKE_CASE					)      ,_SCREAMING_SNAKE_CASE					)

          # Swin has a different seq_length
          _snake_case							=   to_atuple(config.patch_size					)

          _snake_case							=   (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]      ,)

          _snake_case							=   outputs.reshaped_hidden_states
          self.assertEqual(len(_SCREAMING_SNAKE_CASE					)      ,_SCREAMING_SNAKE_CASE					)

          _snake_case							,      _snake_case							,      _snake_case							,      _snake_case							=   reshaped_hidden_states[0].shape

          _snake_case							=   tf.reshape(reshaped_hidden_states[0]      ,(batch_size, num_channels, height * width)					)
          _snake_case							=   tf.transpose(_SCREAMING_SNAKE_CASE      ,(0, 2, 1)					)

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






     def        _lowercase			(				self					)							->    Tuple:
          _snake_case							,      _snake_case							=   self.model_tester.prepare_config_and_inputs_for_common()

          _snake_case							=   to_atuple(self.model_tester.image_size					)

          for model_class in self.all_model_classes:
               _snake_case							=   True
               self.check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)

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

               self.check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE					)






     def        _lowercase			(				self					)							->    Tuple:
          _snake_case							,      _snake_case							=   self.model_tester.prepare_config_and_inputs_for_common()
          _snake_case							=   3

          _snake_case							=   to_atuple(self.model_tester.image_size					)
          _snake_case							=   to_atuple(config.patch_size					)

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

          for model_class in self.all_model_classes:
               _snake_case							=   True
               self.check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,(padded_height, padded_width)					)

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






     @slow
     def        _lowercase			(				self					)							->    Any:
          for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _snake_case							=   TFSwinModel.from_pretrained(_SCREAMING_SNAKE_CASE					)
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)







@require_vision
@require_tf
class      _a       (		unittest.TestCase							):






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






     @slow
     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"					)
          _snake_case							=   self.default_image_processor

          _snake_case							=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
          _snake_case							=   image_processor(images=_SCREAMING_SNAKE_CASE      ,return_tensors="tf"					)

          # forward pass
          _snake_case							=   model(_SCREAMING_SNAKE_CASE					)

          # verify the logits
          _snake_case							=   tf.TensorShape((1, 1_000)					)
          self.assertEqual(outputs.logits.shape      ,_SCREAMING_SNAKE_CASE					)
          _snake_case							=   tf.constant([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]					)
          self.assertTrue(np.allclose(outputs.logits[0, :3]      ,_SCREAMING_SNAKE_CASE      ,atol=1e-4					)					)


