






"""simple docstring"""



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 	_UpperCamelCase :



    '''simple docstring'''


    def __init__(      self   ,			__lowercase   ,			__lowercase=13   ,			__lowercase=32   ,			__lowercase=2   ,			__lowercase=3   ,			__lowercase=16   ,			__lowercase=[1, 2, 1]   ,			__lowercase=[2, 2, 4]   ,			__lowercase=2   ,			__lowercase=2.0   ,			__lowercase=True   ,			__lowercase=0.0   ,			__lowercase=0.0   ,			__lowercase=0.1   ,			__lowercase="gelu"   ,			__lowercase=False   ,			__lowercase=True   ,			__lowercase=0.02   ,			__lowercase=1e-5   ,			__lowercase=True   ,			__lowercase=None   ,			__lowercase=True   ,			__lowercase=10   ,			__lowercase=8   ,			__lowercase=["stage1", "stage2"]   ,			__lowercase=[1, 2]   ,			):
     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 		A__      (      self			):
     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 		A__      (      self			):
     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 		A__      (      self   ,			__lowercase   ,			__lowercase   ,			__lowercase			):
     UpperCAmelCase__ 						=				SwinModel(config=SCREAMING_SNAKE_CASE_			)
     model.to(SCREAMING_SNAKE_CASE_			)
     model.eval()
     UpperCAmelCase__ 						=				model(SCREAMING_SNAKE_CASE_			)

     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 		A__      (      self   ,			__lowercase   ,			__lowercase   ,			__lowercase			):
     UpperCAmelCase__ 						=				SwinBackbone(config=SCREAMING_SNAKE_CASE_			)
     model.to(SCREAMING_SNAKE_CASE_			)
     model.eval()
     UpperCAmelCase__ 						=				model(SCREAMING_SNAKE_CASE_			)

     # 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=SCREAMING_SNAKE_CASE_			)
     model.to(SCREAMING_SNAKE_CASE_			)
     model.eval()
     UpperCAmelCase__ 						=				model(SCREAMING_SNAKE_CASE_			)

     # 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 		A__      (      self   ,			__lowercase   ,			__lowercase   ,			__lowercase			):
     UpperCAmelCase__ 						=				SwinForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_			)
     model.to(SCREAMING_SNAKE_CASE_			)
     model.eval()
     UpperCAmelCase__ 						=				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
     UpperCAmelCase__ 						=				1
     UpperCAmelCase__ 						=				SwinForMaskedImageModeling(SCREAMING_SNAKE_CASE_			)
     model.to(SCREAMING_SNAKE_CASE_			)
     model.eval()

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


    def 		A__      (      self   ,			__lowercase   ,			__lowercase   ,			__lowercase			):
     UpperCAmelCase__ 						=				self.type_sequence_label_size
     UpperCAmelCase__ 						=				SwinForImageClassification(SCREAMING_SNAKE_CASE_			)
     model.to(SCREAMING_SNAKE_CASE_			)
     model.eval()
     UpperCAmelCase__ 						=				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
     UpperCAmelCase__ 						=				1
     UpperCAmelCase__ 						=				SwinForImageClassification(SCREAMING_SNAKE_CASE_			)
     model.to(SCREAMING_SNAKE_CASE_			)
     model.eval()

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






    def 		A__      (      self			):
     UpperCAmelCase__ 						=				self.prepare_config_and_inputs()
     (
         UpperCAmelCase__
     ) 						=				config_and_inputs
     UpperCAmelCase__ 						=				{'pixel_values': pixel_values}
     return config, inputs_dict



@require_torch
class 	_UpperCamelCase ( lowercase_		,     lowercase_		,     unittest.TestCase							):



    '''simple docstring'''


    __lowercase    :   Union[str, Any]											=						(
        (
            SwinModel,
            SwinBackbone,
            SwinForImageClassification,
            SwinForMaskedImageModeling,
        )
        if is_torch_available()
        else ()
    )
    __lowercase    :   int											=						(
        {'feature-extraction': SwinModel, 'image-classification': SwinForImageClassification}
        if is_torch_available()
        else {}
    )
    __lowercase    :   int											=						True

    __lowercase    :   Union[str, Any]											=						False
    __lowercase    :   str											=						False
    __lowercase    :   int											=						False


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				SwinModelTester(self			)
     UpperCAmelCase__ 						=				ConfigTester(self   ,			config_class=SCREAMING_SNAKE_CASE_   ,			embed_dim=37			)


    def 		A__      (      self			):
     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 		A__      (      self			):
     return


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)


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


    def 		A__      (      self			):
     super().test_training_gradient_checkpointing()


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE_			)


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE_			)


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				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 		A__      (      self			):
     pass


    @unittest.skip(reason="""Swin Transformer does not use feedforward chunking"""			)
    def 		A__      (      self			):
     pass


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				self.model_tester.prepare_config_and_inputs_for_common()

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


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      UpperCAmelCase__ 						=				model_class(SCREAMING_SNAKE_CASE_			)
      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]   ,			SCREAMING_SNAKE_CASE_			)


    def 		A__      (      self			):
     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(SCREAMING_SNAKE_CASE_			)
      model.to(SCREAMING_SNAKE_CASE_			)
      model.eval()
      with torch.no_grad():
       UpperCAmelCase__ 						=				model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_			)			)
      UpperCAmelCase__ 						=				outputs.attentions
      UpperCAmelCase__ 						=				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"]
      UpperCAmelCase__ 						=				True
      UpperCAmelCase__ 						=				config.window_size**2
      UpperCAmelCase__ 						=				model_class(SCREAMING_SNAKE_CASE_			)
      model.to(SCREAMING_SNAKE_CASE_			)
      model.eval()
      with torch.no_grad():
       UpperCAmelCase__ 						=				model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_			)			)
      UpperCAmelCase__ 						=				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]   ,			)
      UpperCAmelCase__ 						=				len(SCREAMING_SNAKE_CASE_			)

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

      # also another +1 for reshaped_hidden_states
      UpperCAmelCase__ 						=				1 if model_class.__name__ == 'SwinBackbone' else 2
      self.assertEqual(out_len + added_hidden_states   ,			len(SCREAMING_SNAKE_CASE_			)			)

      UpperCAmelCase__ 						=				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 		A__      (      self   ,			__lowercase   ,			__lowercase   ,			__lowercase   ,			__lowercase			):
     UpperCAmelCase__ 						=				model_class(SCREAMING_SNAKE_CASE_			)
     model.to(SCREAMING_SNAKE_CASE_			)
     model.eval()

     with torch.no_grad():
      UpperCAmelCase__ 						=				model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_			)			)

     UpperCAmelCase__ 						=				outputs.hidden_states

     UpperCAmelCase__ 						=				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
     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(SCREAMING_SNAKE_CASE_			)   ,			SCREAMING_SNAKE_CASE_			)

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


    def 		A__      (      self			):
     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(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"]
      UpperCAmelCase__ 						=				True

      self.check_hidden_states_output(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_			)


    def 		A__      (      self			):
     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(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"]
      UpperCAmelCase__ 						=				True
      self.check_hidden_states_output(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			(padded_height, padded_width)			)


    @slow
    def 		A__      (      self			):
     for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      UpperCAmelCase__ 						=				SwinModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
      self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)






    def 		A__      (      self			):
     UpperCAmelCase__ 						=				self.model_tester.prepare_config_and_inputs_for_common()

     UpperCAmelCase__ 						=				_config_zero_init(SCREAMING_SNAKE_CASE_			)
     for model_class in self.all_model_classes:
      UpperCAmelCase__ 						=				model_class(config=SCREAMING_SNAKE_CASE_			)
      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 	_UpperCamelCase ( unittest.TestCase							):



    '''simple docstring'''


    @cached_property
    def 		A__      (      self			):
     return (
         AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""			)
         if is_vision_available()
         else None
     )






    @slow
    def 		A__      (      self			):
     UpperCAmelCase__ 						=				SwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""			).to(SCREAMING_SNAKE_CASE_			)
     UpperCAmelCase__ 						=				self.default_image_processor

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

     # forward pass
     with torch.no_grad():
      UpperCAmelCase__ 						=				model(**SCREAMING_SNAKE_CASE_			)

     # verify the logits
     UpperCAmelCase__ 						=				torch.Size((1, 1000)			)
     self.assertEqual(outputs.logits.shape   ,			SCREAMING_SNAKE_CASE_			)
     UpperCAmelCase__ 						=				torch.tensor([-0.0_948, -0.6_454, -0.0_921]			).to(SCREAMING_SNAKE_CASE_			)
     self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,			SCREAMING_SNAKE_CASE_   ,			atol=1e-4			)			)



@require_torch
class 	_UpperCamelCase ( unittest.TestCase		,     lowercase_							):



    '''simple docstring'''


    __lowercase    :   Optional[int]											=						(SwinBackbone,) if is_torch_available() else ()
    __lowercase    :   Optional[Any]											=						SwinConfig


    def 		A__      (      self			):
     UpperCAmelCase__ 						=				SwinModelTester(self			)








"""simple docstring"""







import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax







@require_flax
class  UpperCamelCase__  :




      """simple docstring"""




      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]			):
        lowerCAmelCase_		,lowerCAmelCase_  :							Any								=					self.prepare_init_args_and_inputs_for_common()

        lowerCAmelCase_  :							List[str]								=					self.model_class(**SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Any								=					model.init(inputs_dict['prng_key']       ,     inputs_dict['sample']			)
        jax.lax.stop_gradient(SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Tuple								=					model.apply(SCREAMING_SNAKE_CASE_       ,     inputs_dict['sample']			)

        if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			):
          lowerCAmelCase_  :							str								=					output.sample

        self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Any								=					inputs_dict['sample'].shape
        self.assertEqual(output.shape       ,     SCREAMING_SNAKE_CASE_       ,     'Input and output shapes do not match'			)



      def 	SCREAMING_SNAKE_CASE__					( self							: List[str]			):
        lowerCAmelCase_		,lowerCAmelCase_  :							Union[str, Any]								=					self.prepare_init_args_and_inputs_for_common()

        lowerCAmelCase_  :							Tuple								=					1_6
        lowerCAmelCase_  :							Dict								=					(1_6, 3_2)

        lowerCAmelCase_  :							Any								=					self.model_class(**SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Optional[Any]								=					model.init(inputs_dict['prng_key']       ,     inputs_dict['sample']			)
        jax.lax.stop_gradient(SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Optional[Any]								=					model.apply(SCREAMING_SNAKE_CASE_       ,     inputs_dict['sample']			)

        if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			):
          lowerCAmelCase_  :							Dict								=					output.sample

        self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Optional[Any]								=					inputs_dict['sample'].shape
        self.assertEqual(output.shape       ,     SCREAMING_SNAKE_CASE_       ,     'Input and output shapes do not match'			)





      def 	SCREAMING_SNAKE_CASE__					( self							: List[Any]			):
        lowerCAmelCase_  :							List[str]								=					'kwargs' in inspect.signature(self.model_class.__init__			).parameters
        lowerCAmelCase_  :							Optional[Any]								=					len(self.model_class._deprecated_kwargs			) > 0

        if has_kwarg_in_model_class and not has_deprecated_kwarg:
          raise ValueError(
              F"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
              ' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'
              ' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='
              ' [<deprecated_argument>]`'			)

        if not has_kwarg_in_model_class and has_deprecated_kwarg:
          raise ValueError(
              F"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
              ' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'
              F" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
              ' from `_deprecated_kwargs = [<deprecated_argument>]`'			)





