'''simple docstring'''

import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
    from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class  _UpperCAmelCase :

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_=13							,		lowerCAmelCase_=32							,		lowerCAmelCase_=2							,		lowerCAmelCase_=3							,		lowerCAmelCase_=16							,		lowerCAmelCase_=[1, 2, 1]							,		lowerCAmelCase_=[2, 2, 4]							,		lowerCAmelCase_=2							,		lowerCAmelCase_=2.0							,		lowerCAmelCase_=True							,		lowerCAmelCase_=0.0							,		lowerCAmelCase_=0.0							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_="gelu"							,		lowerCAmelCase_=False							,		lowerCAmelCase_=True							,		lowerCAmelCase_=0.02							,		lowerCAmelCase_=1E-5							,		lowerCAmelCase_=True							,		lowerCAmelCase_=None							,		lowerCAmelCase_=True							,		lowerCAmelCase_=10							,		lowerCAmelCase_=8							,		):

       '''simple docstring'''

       a_      :	int      =							parent
       a_      :	Union[str, Any]      =							batch_size
       a_      :	List[Any]      =							image_size
       a_      :	int      =							patch_size
       a_      :	List[str]      =							num_channels
       a_      :	Dict      =							embed_dim
       a_      :	Union[str, Any]      =							depths
       a_      :	Any      =							num_heads
       a_      :	Optional[int]      =							window_size
       a_      :	Union[str, Any]      =							mlp_ratio
       a_      :	Any      =							qkv_bias
       a_      :	Tuple      =							hidden_dropout_prob
       a_      :	Optional[Any]      =							attention_probs_dropout_prob
       a_      :	Optional[int]      =							drop_path_rate
       a_      :	Tuple      =							hidden_act
       a_      :	Optional[Any]      =							use_absolute_embeddings
       a_      :	List[Any]      =							patch_norm
       a_      :	Optional[int]      =							layer_norm_eps
       a_      :	Optional[Any]      =							initializer_range
       a_      :	str      =							is_training
       a_      :	Optional[int]      =							scope
       a_      :	Union[str, Any]      =							use_labels
       a_      :	int      =							type_sequence_label_size
       a_      :	str      =							encoder_stride


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

       a_      :	Tuple      =							None
       if self.use_labels:
         a_      :	str      =							ids_tensor([self.batch_size]							,		self.type_sequence_label_size )

       a_      :	int      =							self.get_config()

       return config, pixel_values, labels


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return SwinvaConfig(
           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 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Tuple      =							SwinvaModel(config=lowercase__ )
       model.to(lowercase__ )
       model.eval()
       a_      :	Union[str, Any]      =							model(lowercase__ )

       a_      :	Dict      =							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
       a_      :	Dict      =							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 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							SwinvaForMaskedImageModeling(config=lowercase__ )
       model.to(lowercase__ )
       model.eval()
       a_      :	str      =							model(lowercase__ )
       self.parent.assertEqual(
           result.logits.shape							,		(self.batch_size, self.num_channels, self.image_size, self.image_size) )

       # test greyscale images
       a_      :	str      =							1
       a_      :	List[str]      =							SwinvaForMaskedImageModeling(lowercase__ )
       model.to(lowercase__ )
       model.eval()

       a_      :	List[Any]      =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
       a_      :	Optional[Any]      =							model(lowercase__ )
       self.parent.assertEqual(result.logits.shape							,		(self.batch_size, 1, self.image_size, self.image_size) )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	List[Any]      =							self.type_sequence_label_size
       a_      :	Optional[Any]      =							SwinvaForImageClassification(lowercase__ )
       model.to(lowercase__ )
       model.eval()
       a_      :	Any      =							model(lowercase__							,		labels=lowercase__ )
       self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.type_sequence_label_size) )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							self.prepare_config_and_inputs()
       a_      :	Dict      =							config_and_inputs
       a_      :	Dict      =							{"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_torch
class  _UpperCAmelCase ( _UpperCAmelCase	,_UpperCAmelCase	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					(
         (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
     )
     a_    	=					(
         {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
         if is_torch_available()
         else {}
     )

     a_    	=					False
     a_    	=					False
     a_    	=					False
     a_    	=					False


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							SwinvaModelTester(self )
       a_      :	Union[str, Any]      =							ConfigTester(self							,		config_class=lowercase__							,		embed_dim=37 )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase__ )


     @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0.""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass


     @unittest.skip(reason="""Swinv2 does not use inputs_embeds""" )
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         a_      :	List[str]      =							model_class(lowercase__ )
         self.assertIsInstance(model.get_input_embeddings()							,		(nn.Module) )
         a_      :	Any      =							model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(lowercase__							,		nn.Linear ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         a_      :	Optional[int]      =							model_class(lowercase__ )
         a_      :	Union[str, Any]      =							inspect.signature(model.forward )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         a_      :	int      =							[*signature.parameters.keys()]

         a_      :	Dict      =							["""pixel_values"""]
         self.assertListEqual(arg_names[:1]							,		lowercase__ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							self.model_tester.prepare_config_and_inputs_for_common()
       a_      :	Any      =							True

       for model_class in self.all_model_classes:
         a_      :	Union[str, Any]      =							True
         a_      :	str      =							False
         a_      :	Any      =							True
         a_      :	Tuple      =							model_class(lowercase__ )
         model.to(lowercase__ )
         model.eval()
         with torch.no_grad():
           a_      :	Optional[int]      =							model(**self._prepare_for_class(lowercase__							,		lowercase__ ) )
         a_      :	List[str]      =							outputs.attentions
         a_      :	List[str]      =							len(self.model_tester.depths )
         self.assertEqual(len(lowercase__ )							,		lowercase__ )

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

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

         # Check attention is always last and order is fine
         a_      :	Tuple      =							True
         a_      :	Any      =							True
         a_      :	List[Any]      =							model_class(lowercase__ )
         model.to(lowercase__ )
         model.eval()
         with torch.no_grad():
           a_      :	str      =							model(**self._prepare_for_class(lowercase__							,		lowercase__ ) )

         if hasattr(self.model_tester							,		"""num_hidden_states_types""" ):
           a_      :	str      =							self.model_tester.num_hidden_states_types
         else:
           # also another +1 for reshaped_hidden_states
           a_      :	List[Any]      =							2
         self.assertEqual(out_len + added_hidden_states							,		len(lowercase__ ) )

         a_      :	Any      =							outputs.attentions

         self.assertEqual(len(lowercase__ )							,		lowercase__ )

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


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Any      =							model_class(lowercase__ )
       model.to(lowercase__ )
       model.eval()

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

       a_      :	List[str]      =							outputs.hidden_states

       a_      :	List[Any]      =							getattr(
           self.model_tester							,		"""expected_num_hidden_layers"""							,		len(self.model_tester.depths ) + 1 )
       self.assertEqual(len(lowercase__ )							,		lowercase__ )

       # Swinv2 has a different seq_length
       a_      :	List[Any]      =							(
           config.patch_size
           if isinstance(config.patch_size							,		collections.abc.Iterable )
           else (config.patch_size, config.patch_size)
       )

       a_      :	Tuple      =							(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]							,		)

       a_      :	Union[str, Any]      =							outputs.reshaped_hidden_states
       self.assertEqual(len(lowercase__ )							,		lowercase__ )

       a_      :	Union[str, Any]      =							reshaped_hidden_states[0].shape
       a_      :	List[str]      =							(
           reshaped_hidden_states[0].view(lowercase__							,		lowercase__							,		height * width ).permute(0							,		2							,		1 )
       )
       self.assertListEqual(
           list(reshaped_hidden_states.shape[-2:] )							,		[num_patches, self.model_tester.embed_dim]							,		)


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							self.model_tester.prepare_config_and_inputs_for_common()

       a_      :	Dict      =							(
           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:
         a_      :	Optional[int]      =							True
         self.check_hidden_states_output(lowercase__							,		lowercase__							,		lowercase__							,		lowercase__ )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         a_      :	str      =							True

         self.check_hidden_states_output(lowercase__							,		lowercase__							,		lowercase__							,		lowercase__ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							self.model_tester.prepare_config_and_inputs_for_common()
       a_      :	Optional[Any]      =							3

       a_      :	Optional[int]      =							(
           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)
       )
       a_      :	List[Any]      =							(
           config.patch_size
           if isinstance(config.patch_size							,		collections.abc.Iterable )
           else (config.patch_size, config.patch_size)
       )

       a_      :	List[Any]      =							image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       a_      :	int      =							image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
         a_      :	Optional[int]      =							True
         self.check_hidden_states_output(lowercase__							,		lowercase__							,		lowercase__							,		(padded_height, padded_width) )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         a_      :	List[str]      =							True
         self.check_hidden_states_output(lowercase__							,		lowercase__							,		lowercase__							,		(padded_height, padded_width) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*lowercase__ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowercase__ )


     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a_      :	Dict      =							SwinvaModel.from_pretrained(lowercase__ )
         self.assertIsNotNone(lowercase__ )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							self.model_tester.prepare_config_and_inputs_for_common()

       a_      :	Optional[Any]      =							_config_zero_init(lowercase__ )
       for model_class in self.all_model_classes:
         a_      :	Union[str, Any]      =							model_class(config=lowercase__ )
         for name, param in model.named_parameters():
           if "embeddings" not in name and "logit_scale" 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  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     @cached_property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return (
           AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256""" )
           if is_vision_available()
           else None
       )






     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256""" ).to(
           lowercase__ )
       a_      :	Any      =							self.default_image_processor

       a_      :	Union[str, Any]      =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
       a_      :	int      =							image_processor(images=lowercase__							,		return_tensors="""pt""" ).to(lowercase__ )

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

       # verify the logits
       a_      :	Any      =							torch.Size((1, 10_00) )
       self.assertEqual(outputs.logits.shape							,		lowercase__ )
       a_      :	str      =							torch.tensor([-0.3947, -0.4306, 0.0026] ).to(lowercase__ )
       self.assertTrue(torch.allclose(outputs.logits[0, :3]							,		lowercase__							,		atol=1E-4 ) )




'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DonutImageProcessor


class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_=7							,		lowerCAmelCase_=3							,		lowerCAmelCase_=18							,		lowerCAmelCase_=30							,		lowerCAmelCase_=4_00							,		lowerCAmelCase_=True							,		lowerCAmelCase_=None							,		lowerCAmelCase_=True							,		lowerCAmelCase_=False							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=[0.5, 0.5, 0.5]							,		lowerCAmelCase_=[0.5, 0.5, 0.5]							,		):

       '''simple docstring'''

       a_      :	Optional[int]      =							parent
       a_      :	Union[str, Any]      =							batch_size
       a_      :	Any      =							num_channels
       a_      :	Tuple      =							image_size
       a_      :	Any      =							min_resolution
       a_      :	Dict      =							max_resolution
       a_      :	Optional[Any]      =							do_resize
       a_      :	Tuple      =							size if size is not None else {"""height""": 18, """width""": 20}
       a_      :	int      =							do_thumbnail
       a_      :	Optional[int]      =							do_align_axis
       a_      :	Tuple      =							do_pad
       a_      :	int      =							do_normalize
       a_      :	Tuple      =							image_mean
       a_      :	Optional[Any]      =							image_std






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_thumbnail": self.do_thumbnail,
           "do_align_long_axis": self.do_align_axis,
           "do_pad": self.do_pad,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }







@require_torch
@require_vision
class  _UpperCAmelCase ( lowerCAmelCase__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					DonutImageProcessor if is_vision_available() else None


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							DonutImageProcessingTester(self )


     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return self.image_processor_tester.prepare_image_processor_dict()


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_resize""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""size""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_thumbnail""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_align_long_axis""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_pad""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""do_normalize""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""image_mean""" ) )
       self.assertTrue(hasattr(lowerCAmelCase_							,		"""image_std""" ) )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.image_processing_class.from_dict(self.image_processor_dict )
       self.assertEqual(image_processor.size							,		{"""height""": 18, """width""": 20} )

       a_      :	str      =							self.image_processing_class.from_dict(self.image_processor_dict							,		size=42 )
       self.assertEqual(image_processor.size							,		{"""height""": 42, """width""": 42} )

       # Previous config had dimensions in (width, height) order
       a_      :	Any      =							self.image_processing_class.from_dict(self.image_processor_dict							,		size=(42, 84) )
       self.assertEqual(image_processor.size							,		{"""height""": 84, """width""": 42} )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       pass


     @is_flaky()
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							self.image_processing_class(**self.image_processor_dict )
       # create random PIL images
       a_      :	str      =							prepare_image_inputs(self.image_processor_tester							,		equal_resolution=lowerCAmelCase_ )
       for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_							,		Image.Image )

       # Test not batched input
       a_      :	Dict      =							image_processing(image_inputs[0]							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)

       # Test batched
       a_      :	int      =							image_processing(lowerCAmelCase_							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)


     @is_flaky()
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       a_      :	int      =							prepare_image_inputs(self.image_processor_tester							,		equal_resolution=lowerCAmelCase_							,		numpify=lowerCAmelCase_ )
       for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_							,		np.ndarray )

       # Test not batched input
       a_      :	Optional[Any]      =							image_processing(image_inputs[0]							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)

       # Test batched
       a_      :	Dict      =							image_processing(lowerCAmelCase_							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)






     @is_flaky()
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       a_      :	Optional[Any]      =							prepare_image_inputs(self.image_processor_tester							,		equal_resolution=lowerCAmelCase_							,		torchify=lowerCAmelCase_ )
       for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_							,		torch.Tensor )

       # Test not batched input
       a_      :	Optional[int]      =							image_processing(image_inputs[0]							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)

       # Test batched
       a_      :	Any      =							image_processing(lowerCAmelCase_							,		return_tensors="""pt""" ).pixel_values
       self.assertEqual(
           encoded_images.shape							,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )							,		)


