'''simple docstring'''


import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


snake_case__	:     Tuple      =    logging.get_logger(__name__)


snake_case__	:     Optional[Any]      =    {
    '''suno/bark-small''': '''https://huggingface.co/suno/bark-small/resolve/main/config.json''',
    '''suno/bark''': '''https://huggingface.co/suno/bark/resolve/main/config.json''',
}

snake_case__	:     Union[str, Any]      =    '''
    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
    architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        block_size (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        input_vocab_size (`int`, *optional*, defaults to 10_048):
            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
            regards to the chosen sub-model.
        output_vocab_size (`int`, *optional*, defaults to 10_048):
            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
            with regards to the chosen sub-model.
        num_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the given sub-model.
        num_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer architecture.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use bias in the linear layers and layer norm layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
'''






class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:Optional[Any]		       =						'''bark_module'''
  lowerCamelCase_						:Optional[int]		       =						['''past_key_values''']

  lowerCamelCase_						:Any		       =						{
      '''num_attention_heads''': '''num_heads''',
      '''num_hidden_layers''': '''num_layers''',
      '''vocab_size''': '''input_vocab_size''',
      '''window_size''': '''block_size''',
  }


  def __init__(					self ,						snake_case_=1_0_2_4 ,						snake_case_=1_0_0_4_8 ,						snake_case_=1_0_0_4_8 ,						snake_case_=1_2 ,						snake_case_=1_2 ,						snake_case_=7_6_8 ,						snake_case_=0.0 ,						snake_case_=True ,						snake_case_=0.02 ,						snake_case_=True ,						**snake_case_ ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = block_size
       UpperCAmelCase_				:  Dict  = input_vocab_size
       UpperCAmelCase_				:  List[str]  = output_vocab_size
       UpperCAmelCase_				:  Any  = num_layers
       UpperCAmelCase_				:  Tuple  = num_heads
       UpperCAmelCase_				:  Optional[int]  = hidden_size
       UpperCAmelCase_				:  Optional[Any]  = dropout
       UpperCAmelCase_				:  Optional[Any]  = bias
       UpperCAmelCase_				:  int  = use_cache
       UpperCAmelCase_				:  Any  = initializer_range

       super().__init__(**snake_case_		)






  @classmethod
  def _UpperCamelCase	(					cls ,						snake_case_ ,						snake_case_ = None ,						snake_case_ = False ,						snake_case_ = False ,						snake_case_ = None ,						snake_case_ = "main" ,						**snake_case_ ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = cache_dir
       UpperCAmelCase_				:  Dict  = force_download
       UpperCAmelCase_				:  Optional[Any]  = local_files_only
       UpperCAmelCase_				:  List[Any]  = revision

       cls._set_token_in_kwargs(snake_case_ ,						snake_case_		)

       UpperCAmelCase_  ,     UpperCAmelCase_				:  List[Any]  = cls.get_config_dict(snake_case_ ,						**snake_case_		)

       # get the config dict if we are loading from Bark
       if config_dict.get('model_type'		) == "bark":
            UpperCAmelCase_				:  Dict  = config_dict[F'''{cls.model_type}_config''']

       if "model_type" in config_dict and hasattr(cls ,						'model_type'		) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
                F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''		)

       return cls.from_dict(snake_case_ ,						**snake_case_		)







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='''BarkSemanticConfig'''		,      model='''BarkSemanticModel'''			)		,      '''
    Example:

    ```python
    >>> from transformers import BarkSemanticConfig, BarkSemanticModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkSemanticConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkSemanticModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```'''		,      )
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:str		       =						'''semantic'''







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='''BarkCoarseConfig'''		,      model='''BarkCoarseModel'''			)		,      '''
    Example:

    ```python
    >>> from transformers import BarkCoarseConfig, BarkCoarseModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkCoarseConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkCoarseModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```'''		,      )
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:Optional[Any]		       =						'''coarse_acoustics'''







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config='''BarkFineConfig'''		,      model='''BarkFineModel'''			)		,      '''
        n_codes_total (`int`, *optional*, defaults to 8):
            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.
        n_codes_given (`int`, *optional*, defaults to 1):
            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics
            sub-models.
    Example:

    ```python
    >>> from transformers import BarkFineConfig, BarkFineModel

    >>> # Initializing a Bark sub-module style configuration
    >>> configuration = BarkFineConfig()

    >>> # Initializing a model (with random weights) from the suno/bark style configuration
    >>> model = BarkFineModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```'''		,      )
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:Any		       =						'''fine_acoustics'''


  def __init__(					self ,						snake_case_=True ,						snake_case_=8 ,						snake_case_=1 ,						**snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = n_codes_total
       UpperCAmelCase_				:  List[str]  = n_codes_given

       super().__init__(tie_word_embeddings=snake_case_ ,						**snake_case_		)







class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:Tuple		       =						'''bark'''
  lowerCamelCase_						:Optional[Any]		       =						True


  def __init__(					self ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_ = None ,						snake_case_=0.02 ,						**snake_case_ ,						):




       '''simple docstring'''

       if semantic_config is None:
            UpperCAmelCase_				:  str  = {}
            logger.info('semantic_config is None. initializing the semantic model with default values.'		)

       if coarse_acoustics_config is None:
            UpperCAmelCase_				:  List[str]  = {}
            logger.info('coarse_acoustics_config is None. initializing the coarse model with default values.'		)

       if fine_acoustics_config is None:
            UpperCAmelCase_				:  Dict  = {}
            logger.info('fine_acoustics_config is None. initializing the fine model with default values.'		)

       if codec_config is None:
            UpperCAmelCase_				:  Optional[Any]  = {}
            logger.info('codec_config is None. initializing the codec model with default values.'		)

       UpperCAmelCase_				:  Union[str, Any]  = BarkSemanticConfig(**snake_case_		)
       UpperCAmelCase_				:  Any  = BarkCoarseConfig(**snake_case_		)
       UpperCAmelCase_				:  Dict  = BarkFineConfig(**snake_case_		)
       UpperCAmelCase_				:  Dict  = AutoConfig.for_model(**snake_case_		)

       UpperCAmelCase_				:  Any  = initializer_range

       super().__init__(**snake_case_		)


  @classmethod
  def _UpperCamelCase	(					cls ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						**snake_case_ ,						):




       '''simple docstring'''

       return cls(
           semantic_config=semantic_config.to_dict() ,						coarse_acoustics_config=coarse_acoustics_config.to_dict() ,						fine_acoustics_config=fine_acoustics_config.to_dict() ,						codec_config=codec_config.to_dict() ,						**snake_case_ ,						)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = copy.deepcopy(self.__dict__		)

       UpperCAmelCase_				:  Optional[int]  = self.semantic_config.to_dict()
       UpperCAmelCase_				:  Union[str, Any]  = self.coarse_acoustics_config.to_dict()
       UpperCAmelCase_				:  str  = self.fine_acoustics_config.to_dict()
       UpperCAmelCase_				:  List[str]  = self.codec_config.to_dict()

       UpperCAmelCase_				:  List[Any]  = self.__class__.model_type
       return output







'''simple docstring'''


import inspect
import unittest

from transformers import ResNetConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
 from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_=3 ,						snake_case_=3_2 ,						snake_case_=3 ,						snake_case_=1_0 ,						snake_case_=[1_0, 2_0, 3_0, 4_0] ,						snake_case_=[1, 1, 2, 1] ,						snake_case_=True ,						snake_case_=True ,						snake_case_="relu" ,						snake_case_=3 ,						snake_case_=None ,						snake_case_=["stage2", "stage3", "stage4"] ,						snake_case_=[2, 3, 4] ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = parent
       UpperCAmelCase_				:  int  = batch_size
       UpperCAmelCase_				:  Optional[int]  = image_size
       UpperCAmelCase_				:  List[str]  = num_channels
       UpperCAmelCase_				:  Dict  = embeddings_size
       UpperCAmelCase_				:  List[str]  = hidden_sizes
       UpperCAmelCase_				:  int  = depths
       UpperCAmelCase_				:  Dict  = is_training
       UpperCAmelCase_				:  Optional[int]  = use_labels
       UpperCAmelCase_				:  Union[str, Any]  = hidden_act
       UpperCAmelCase_				:  Optional[Any]  = num_labels
       UpperCAmelCase_				:  List[Any]  = scope
       UpperCAmelCase_				:  Tuple  = len(snake_case_		)
       UpperCAmelCase_				:  Dict  = out_features
       UpperCAmelCase_				:  Dict  = out_indices


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

       UpperCAmelCase_				:  List[str]  = None
       if self.use_labels:
            UpperCAmelCase_				:  str  = ids_tensor([self.batch_size] ,						self.num_labels		)

       UpperCAmelCase_				:  List[str]  = self.get_config()

       return config, pixel_values, labels


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return ResNetConfig(
           num_channels=self.num_channels ,						embeddings_size=self.embeddings_size ,						hidden_sizes=self.hidden_sizes ,						depths=self.depths ,						hidden_act=self.hidden_act ,						num_labels=self.num_labels ,						out_features=self.out_features ,						out_indices=self.out_indices ,						)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = ResNetModel(config=snake_case_		)
       model.to(snake_case_		)
       model.eval()
       UpperCAmelCase_				:  List[Any]  = model(snake_case_		)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape ,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) ,						)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = self.num_labels
       UpperCAmelCase_				:  Optional[Any]  = ResNetForImageClassification(snake_case_		)
       model.to(snake_case_		)
       model.eval()
       UpperCAmelCase_				:  Optional[int]  = model(snake_case_ ,						labels=snake_case_		)
       self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)		)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = ResNetBackbone(config=snake_case_		)
       model.to(snake_case_		)
       model.eval()
       UpperCAmelCase_				:  Dict  = model(snake_case_		)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps		) ,						len(config.out_features		)		)
       self.parent.assertListEqual(list(result.feature_maps[0].shape		) ,						[self.batch_size, self.hidden_sizes[1], 4, 4]		)

       # verify channels
       self.parent.assertEqual(len(model.channels		) ,						len(config.out_features		)		)
       self.parent.assertListEqual(model.channels ,						config.hidden_sizes[1:]		)

       # verify backbone works with out_features=None
       UpperCAmelCase_				:  Tuple  = None
       UpperCAmelCase_				:  Tuple  = ResNetBackbone(config=snake_case_		)
       model.to(snake_case_		)
       model.eval()
       UpperCAmelCase_				:  List[Any]  = model(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, self.hidden_sizes[-1], 1, 1]		)

       # verify channels
       self.parent.assertEqual(len(model.channels		) ,						1		)
       self.parent.assertListEqual(model.channels ,						[config.hidden_sizes[-1]]		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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







@require_torch
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:str		       =						(
      (
          ResNetModel,
          ResNetForImageClassification,
          ResNetBackbone,
      )
      if is_torch_available()
      else ()
  )
  lowerCamelCase_						:List[str]		       =						(
      {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
      if is_torch_available()
      else {}
  )

  lowerCamelCase_						:int		       =						True
  lowerCamelCase_						:Optional[Any]		       =						False
  lowerCamelCase_						:Union[str, Any]		       =						False
  lowerCamelCase_						:int		       =						False
  lowerCamelCase_						:str		       =						False


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = ResNetModelTester(self		)
       UpperCAmelCase_				:  int  = ConfigTester(self ,						config_class=snake_case_ ,						has_text_modality=snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       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 _UpperCamelCase	(					self		):




       '''simple docstring'''

       return


  @unittest.skip(reason='ResNet does not use inputs_embeds'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass


  @unittest.skip(reason='ResNet does not support input and output embeddings'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  List[Any]  = self.model_tester.prepare_config_and_inputs_for_common()

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

            UpperCAmelCase_				:  List[str]  = ['pixel_values']
            self.assertListEqual(arg_names[:1] ,						snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  List[str]  = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCAmelCase_				:  List[Any]  = model_class(config=snake_case_		)
            for name, module in model.named_modules():
                 if isinstance(snake_case_ ,						(nn.BatchNormad, nn.GroupNorm)		):
                      self.assertTrue(
                          torch.all(module.weight == 1		) ,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' ,						)
                      self.assertTrue(
                          torch.all(module.bias == 0		) ,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' ,						)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       def check_hidden_states_output(snake_case_ ,						snake_case_ ,						snake_case_		):
            UpperCAmelCase_				:  int  = model_class(snake_case_		)
            model.to(snake_case_		)
            model.eval()

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

            UpperCAmelCase_				:  Union[str, Any]  = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            UpperCAmelCase_				:  str  = self.model_tester.num_stages
            self.assertEqual(len(snake_case_		) ,						expected_num_stages + 1		)

            # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]		) ,						[self.model_tester.image_size // 4, self.model_tester.image_size // 4] ,						)

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Any  = self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_				:  Optional[Any]  = ['basic', 'bottleneck']
       for model_class in self.all_model_classes:
            for layer_type in layers_type:
                 UpperCAmelCase_				:  Any  = layer_type
                 UpperCAmelCase_				:  str  = True
                 check_hidden_states_output(snake_case_ ,						snake_case_ ,						snake_case_		)

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

                 check_hidden_states_output(snake_case_ ,						snake_case_ ,						snake_case_		)


  @unittest.skip(reason='ResNet does not use feedforward chunking'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case_		)






  @slow
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase_				:  str  = ResNetModel.from_pretrained(snake_case_		)
            self.assertIsNotNone(snake_case_		)







def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Union[str, Any]  = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
     return image






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







  '''simple docstring'''






  @cached_property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return (
           AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
           if is_vision_available()
           else None
       )






  @slow
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(snake_case_		)

       UpperCAmelCase_				:  str  = self.default_image_processor
       UpperCAmelCase_				:  Union[str, Any]  = prepare_img()
       UpperCAmelCase_				:  Union[str, Any]  = image_processor(images=snake_case_ ,						return_tensors='pt'		).to(snake_case_		)

       # forward pass
       with torch.no_grad():
            UpperCAmelCase_				:  Union[str, Any]  = model(**snake_case_		)

       # verify the logits
       UpperCAmelCase_				:  Union[str, Any]  = torch.Size((1, 1_0_0_0)		)
       self.assertEqual(outputs.logits.shape ,						snake_case_		)

       UpperCAmelCase_				:  Optional[int]  = torch.tensor([-11.10_69, -9.78_77, -8.37_77]		).to(snake_case_		)

       self.assertTrue(torch.allclose(outputs.logits[0, :3] ,						snake_case_ ,						atol=1E-4		)		)







@require_torch
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:Any		       =						(ResNetBackbone,) if is_torch_available() else ()
  lowerCamelCase_						:List[str]		       =						False
  lowerCamelCase_						:Optional[Any]		       =						ResNetConfig


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = ResNetModelTester(self		)






