





"""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 			snake_case		:

      def __init__(			self   :Dict						,							_lowerCamelCase   :List[Any]						,							_lowerCamelCase   :Optional[int]=1_3						,							_lowerCamelCase   :List[str]=3_2						,							_lowerCamelCase   :List[Any]=2						,							_lowerCamelCase   :List[Any]=3						,							_lowerCamelCase   :List[str]=1_6						,							_lowerCamelCase   :List[str]=[1, 2, 1]						,							_lowerCamelCase   :Union[str, Any]=[2, 2, 4]						,							_lowerCamelCase   :Dict=2						,							_lowerCamelCase   :List[str]=2.0						,							_lowerCamelCase   :Optional[Any]=True						,							_lowerCamelCase   :Dict=0.0						,							_lowerCamelCase   :Optional[int]=0.0						,							_lowerCamelCase   :List[Any]=0.1						,							_lowerCamelCase   :List[Any]="gelu"						,							_lowerCamelCase   :Any=False						,							_lowerCamelCase   :Tuple=True						,							_lowerCamelCase   :str=0.0_2						,							_lowerCamelCase   :List[Any]=1e-5						,							_lowerCamelCase   :List[str]=True						,							_lowerCamelCase   :List[str]=None						,							_lowerCamelCase   :str=True						,							_lowerCamelCase   :Optional[Any]=1_0						,							_lowerCamelCase   :str=8						,							):
       __SCREAMING_SNAKE_CASE :			str												=       parent
       __SCREAMING_SNAKE_CASE :			List[Any]												=       batch_size
       __SCREAMING_SNAKE_CASE :			Tuple												=       image_size
       __SCREAMING_SNAKE_CASE :			List[Any]												=       patch_size
       __SCREAMING_SNAKE_CASE :			str												=       num_channels
       __SCREAMING_SNAKE_CASE :			Tuple												=       embed_dim
       __SCREAMING_SNAKE_CASE :			List[Any]												=       depths
       __SCREAMING_SNAKE_CASE :			Tuple												=       num_heads
       __SCREAMING_SNAKE_CASE :			List[str]												=       window_size
       __SCREAMING_SNAKE_CASE :			Dict												=       mlp_ratio
       __SCREAMING_SNAKE_CASE :			Dict												=       qkv_bias
       __SCREAMING_SNAKE_CASE :			List[Any]												=       hidden_dropout_prob
       __SCREAMING_SNAKE_CASE :			Any												=       attention_probs_dropout_prob
       __SCREAMING_SNAKE_CASE :			Any												=       drop_path_rate
       __SCREAMING_SNAKE_CASE :			Dict												=       hidden_act
       __SCREAMING_SNAKE_CASE :			Tuple												=       use_absolute_embeddings
       __SCREAMING_SNAKE_CASE :			Any												=       patch_norm
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       layer_norm_eps
       __SCREAMING_SNAKE_CASE :			List[str]												=       initializer_range
       __SCREAMING_SNAKE_CASE :			int												=       is_training
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       scope
       __SCREAMING_SNAKE_CASE :			int												=       use_labels
       __SCREAMING_SNAKE_CASE :			List[str]												=       type_sequence_label_size
       __SCREAMING_SNAKE_CASE :			int												=       encoder_stride

      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       self.get_config()

       return config, pixel_values, labels

      def 						SCREAMING_SNAKE_CASE_			(			self   :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						,							)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						,							_lowerCamelCase   :Dict						,							_lowerCamelCase   :int						,							_lowerCamelCase   :List[Any]						):
       __SCREAMING_SNAKE_CASE :			Tuple												=       TFSwinModel(config=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       model(_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			int												=       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
       __SCREAMING_SNAKE_CASE :			str												=       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   :Optional[int]						,							_lowerCamelCase   :int						,							_lowerCamelCase   :str						,							_lowerCamelCase   :List[Any]						):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       TFSwinForMaskedImageModeling(config=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			str												=       model(_lowerCamelCase						)
       self.parent.assertEqual(
           result.logits.shape						,							(self.batch_size, self.num_channels, self.image_size, self.image_size)						)

       # test greyscale images
       __SCREAMING_SNAKE_CASE :			Dict												=       1
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       TFSwinForMaskedImageModeling(_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			List[str]												=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       model(_lowerCamelCase						)
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, 1, self.image_size, self.image_size)						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :List[str]						,							_lowerCamelCase   :Tuple						,							_lowerCamelCase   :Tuple						):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.type_sequence_label_size
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       TFSwinForImageClassification(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       model(_lowerCamelCase						,							labels=_lowerCamelCase						)
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.type_sequence_label_size)						)

       # test greyscale images
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       1
       __SCREAMING_SNAKE_CASE :			Any												=       TFSwinForImageClassification(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			str												=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
       __SCREAMING_SNAKE_CASE :			Any												=       model(_lowerCamelCase						)
       self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.type_sequence_label_size)						)



      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       __SCREAMING_SNAKE_CASE :			str												=       self.prepare_config_and_inputs()
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			List[Any]												=       config_and_inputs
       __SCREAMING_SNAKE_CASE :			Tuple												=       {'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_tf
class 			snake_case		(   __UpperCAmelCase ,     __UpperCAmelCase ,     unittest.TestCase   ):
      lowerCamelCase__						= (
          (
              TFSwinModel,
              TFSwinForImageClassification,
              TFSwinForMaskedImageModeling,
          )
          if is_tf_available()
          else ()
      )
      lowerCamelCase__						= (
          {'''feature-extraction''': TFSwinModel, '''image-classification''': TFSwinForImageClassification}
          if is_tf_available()
          else {}
      )

      lowerCamelCase__						= False
      lowerCamelCase__						= False
      lowerCamelCase__						= False
      lowerCamelCase__						= False

      def 						SCREAMING_SNAKE_CASE_			(			self   :Union[str, Any]						):
       __SCREAMING_SNAKE_CASE :			int												=       TFSwinModelTester(self						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       ConfigTester(self						,							config_class=_lowerCamelCase						,							embed_dim=3_7						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       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   :int						):
       return

      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       __SCREAMING_SNAKE_CASE :			Any												=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						):
       __SCREAMING_SNAKE_CASE :			List[Any]												=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       __SCREAMING_SNAKE_CASE :			List[str]												=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase						)

      @unittest.skip(reason='''Swin does not use inputs_embeds'''						)
      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						):
       pass

      def 						SCREAMING_SNAKE_CASE_			(			self   :str						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       model_class(_lowerCamelCase						)
        self.assertIsInstance(model.get_input_embeddings()						,							tf.keras.layers.Layer						)
        __SCREAMING_SNAKE_CASE :			Any												=       model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(_lowerCamelCase						,							tf.keras.layers.Dense						)						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			int												=       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __SCREAMING_SNAKE_CASE :			str												=       model_class(_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE :			Dict												=       inspect.signature(model.call						)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       [*signature.parameters.keys()]

        __SCREAMING_SNAKE_CASE :			Tuple												=       ['''pixel_values''']
        self.assertListEqual(arg_names[:1]						,							_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.model_tester.prepare_config_and_inputs_for_common()
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       True

       for model_class in self.all_model_classes:
        __SCREAMING_SNAKE_CASE :			List[Any]												=       True
        __SCREAMING_SNAKE_CASE :			str												=       False
        __SCREAMING_SNAKE_CASE :			Tuple												=       True
        __SCREAMING_SNAKE_CASE :			List[str]												=       model_class(_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       model(**self._prepare_for_class(_lowerCamelCase						,							_lowerCamelCase						)						)
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       outputs.attentions
        __SCREAMING_SNAKE_CASE :			int												=       len(self.model_tester.depths						)
        self.assertEqual(len(_lowerCamelCase						)						,							_lowerCamelCase						)

        # check that output_attentions also work using config
        del inputs_dict["output_attentions"]
        __SCREAMING_SNAKE_CASE :			List[str]												=       True
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       config.window_size**2
        __SCREAMING_SNAKE_CASE :			List[str]												=       model_class(_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE :			int												=       model(**self._prepare_for_class(_lowerCamelCase						,							_lowerCamelCase						)						)
        __SCREAMING_SNAKE_CASE :			Tuple												=       outputs.attentions
        self.assertEqual(len(_lowerCamelCase						)						,							_lowerCamelCase						)

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

        # Check attention is always last and order is fine
        __SCREAMING_SNAKE_CASE :			Dict												=       True
        __SCREAMING_SNAKE_CASE :			Any												=       True
        __SCREAMING_SNAKE_CASE :			Any												=       model_class(_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE :			Any												=       model(**self._prepare_for_class(_lowerCamelCase						,							_lowerCamelCase						)						)

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

        __SCREAMING_SNAKE_CASE :			Optional[int]												=       outputs.attentions

        self.assertEqual(len(_lowerCamelCase						)						,							_lowerCamelCase						)

        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]						,							_lowerCamelCase   :Any						,							_lowerCamelCase   :Optional[Any]						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       model_class(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Any												=       model(**self._prepare_for_class(_lowerCamelCase						,							_lowerCamelCase						)						)
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       outputs.hidden_states

       __SCREAMING_SNAKE_CASE :			int												=       getattr(
           self.model_tester						,							'''expected_num_hidden_layers'''						,							len(self.model_tester.depths						) + 1						)
       self.assertEqual(len(_lowerCamelCase						)						,							_lowerCamelCase						)

       # Swin has a different seq_length
       __SCREAMING_SNAKE_CASE :			int												=       to_atuple(config.patch_size						)

       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       (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]						,							)

       __SCREAMING_SNAKE_CASE :			Tuple												=       outputs.reshaped_hidden_states
       self.assertEqual(len(_lowerCamelCase						)						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Any												=       reshaped_hidden_states[0].shape

       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       tf.reshape(reshaped_hidden_states[0]						,							(batch_size, num_channels, height * width)						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       tf.transpose(_lowerCamelCase						,							(0, 2, 1)						)

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

      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Tuple												=       self.model_tester.prepare_config_and_inputs_for_common()

       __SCREAMING_SNAKE_CASE :			Any												=       to_atuple(self.model_tester.image_size						)

       for model_class in self.all_model_classes:
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       True
        self.check_hidden_states_output(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       True

        self.check_hidden_states_output(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						):
       __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Dict												=       self.model_tester.prepare_config_and_inputs_for_common()
       __SCREAMING_SNAKE_CASE :			Any												=       3

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       to_atuple(self.model_tester.image_size						)
       __SCREAMING_SNAKE_CASE :			Any												=       to_atuple(config.patch_size						)

       __SCREAMING_SNAKE_CASE :			Tuple												=       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
        __SCREAMING_SNAKE_CASE :			int												=       True
        self.check_hidden_states_output(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							(padded_height, padded_width)						)

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



      @slow
      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       TFSwinModel.from_pretrained(_lowerCamelCase						)
        self.assertIsNotNone(_lowerCamelCase						)







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

      @cached_property
      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       return (
           AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''						)
           if is_vision_available()
           else None
       )



      @slow
      def 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       TFSwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       self.default_image_processor

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
       __SCREAMING_SNAKE_CASE :			str												=       image_processor(images=_lowerCamelCase						,							return_tensors='''tf'''						)

       # forward pass
       __SCREAMING_SNAKE_CASE :			List[Any]												=       model(_lowerCamelCase						)

       # verify the logits
       __SCREAMING_SNAKE_CASE :			int												=       tf.TensorShape((1, 1_0_0_0)						)
       self.assertEqual(outputs.logits.shape						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			str												=       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]						,							_lowerCamelCase						,							atol=1e-4						)						)









import unittest

import numpy as np

from transformers.testing_utils import 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 ConvNextImageProcessor


class        SCREAMING_SNAKE_CASE	(	unittest.TestCase    ):
     '''simple docstring'''


     def __init__( self				:		Optional[Any]    ,       UpperCAmelCase_				:		Optional[int]    ,       UpperCAmelCase_				:		Any=7    ,       UpperCAmelCase_				:		Union[str, Any]=3    ,       UpperCAmelCase_				:		List[str]=18    ,       UpperCAmelCase_				:		Optional[int]=30    ,       UpperCAmelCase_				:		int=400    ,       UpperCAmelCase_				:		int=True    ,       UpperCAmelCase_				:		List[Any]=None    ,       UpperCAmelCase_				:		Tuple=0.875    ,       UpperCAmelCase_				:		str=True    ,       UpperCAmelCase_				:		Optional[Any]=[0.5, 0.5, 0.5]    ,       UpperCAmelCase_				:		List[Any]=[0.5, 0.5, 0.5]    ,       ):
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			size if size is not None else {"shortest_edge": 20}
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			parent
       SCREAMING_SNAKE_CASE				:					int		  =			batch_size
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			num_channels
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			image_size
       SCREAMING_SNAKE_CASE				:					Any		  =			min_resolution
       SCREAMING_SNAKE_CASE				:					List[str]		  =			max_resolution
       SCREAMING_SNAKE_CASE				:					int		  =			do_resize
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			size
       SCREAMING_SNAKE_CASE				:					Any		  =			crop_pct
       SCREAMING_SNAKE_CASE				:					Dict		  =			do_normalize
       SCREAMING_SNAKE_CASE				:					List[str]		  =			image_mean
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			image_std






     def  _A				( self				:		str					):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_pct": self.crop_pct,
       }





@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase					,						unittest.TestCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						str								=		ConvNextImageProcessor if is_vision_available() else None


     def  _A				( self				:		Optional[int]					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			ConvNextImageProcessingTester(self					)


     @property
     def  _A				( self				:		str					):
       return self.image_processor_tester.prepare_image_processor_dict()


     def  _A				( self				:		str					):
       SCREAMING_SNAKE_CASE				:					str		  =			self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "do_resize"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "size"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "crop_pct"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "do_normalize"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "image_mean"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "image_std"					)					)


     def  _A				( self				:		Union[str, Any]					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.image_processing_class.from_dict(self.image_processor_dict					)
       self.assertEqual(image_processor.size    ,       {"shortest_edge": 20}					)

       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42					)
       self.assertEqual(image_processor.size    ,       {"shortest_edge": 42}					)


     def  _A				( self				:		List[str]					):
       pass


     def  _A				( self				:		Optional[Any]					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCAmelCase_					)
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_    ,       Image.Image					)

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					str		  =			image_processing(UpperCAmelCase_    ,       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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )


     def  _A				( self				:		Optional[Any]					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					List[str]		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCAmelCase_    ,       numpify=UpperCAmelCase_					)
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_    ,       np.ndarray					)

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					str		  =			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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					Dict		  =			image_processing(UpperCAmelCase_    ,       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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )






     def  _A				( self				:		Tuple					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					str		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE				:					Tuple		  =			prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCAmelCase_    ,       torchify=UpperCAmelCase_					)
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_    ,       torch.Tensor					)

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					Any		  =			image_processing(UpperCAmelCase_    ,       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["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )






