




'''simple docstring'''






import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


lowercase	:      Dict				=       logging.get_logger(__name__)  # pylint: disable=invalid-name




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






       '''simple docstring'''




       lowercase  :		List[Any]						=      image.to(device=lowerCamelCase      )
       lowercase  :		Any						=      image  # take image as image_embeddings
       lowercase  :		Optional[Any]						=      image_embeddings.unsqueeze(1      )

       # duplicate image embeddings for each generation per prompt, using mps friendly method
       lowercase,     lowercase,     lowercase  :		List[str]						=      image_embeddings.shape
       lowercase  :		Optional[int]						=      image_embeddings.repeat(1   ,				lowerCamelCase   ,				1      )
       lowercase  :		Any						=      image_embeddings.view(bs_embed * num_images_per_prompt   ,				lowerCamelCase   ,				-1      )

       if do_classifier_free_guidance:
              lowercase  :		List[Any]						=      torch.zeros_like(lowerCamelCase      )

              # For classifier free guidance, we need to do two forward passes.
              # Here we concatenate the unconditional and text embeddings into a single batch
              # to avoid doing two forward passes
              lowercase  :		Union[str, Any]						=      torch.cat([uncond_embeddings, image_embeddings]      )

       return image_embeddings





class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):



 def __init__(  self   ,		A   ,		A   ,		A   ,		A   ,		A = None   ,		):






        '''simple docstring'''




        super().__init__()

        lowercase  :		Tuple						=      {'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

        lowercase  :		Optional[int]						=      decoder_pipe_kwargs.get('''torch_dtype'''   ,		A ) or prior.dtype

        lowercase  :		Union[str, Any]						=      StableDiffusionImageVariationPipeline.from_pretrained(
            '''lambdalabs/sd-image-variations-diffusers'''   ,		**A )

        # replace `_encode_image` method
        lowercase  :		int						=      types.MethodType(_encode_image   ,		self.decoder_pipe )

        self.register_modules(
            prior=A   ,		tokenizer=A   ,		text_encoder=A   ,		prior_scheduler=A   ,		)



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A   ,		A = None   ,		A = None   ,		):






        '''simple docstring'''




        if text_model_output is None:
               lowercase  :		List[Any]						=      len(A ) if isinstance(A   ,		A ) else 1
               # get prompt text embeddings
               lowercase  :		Optional[Any]						=      self.tokenizer(
                   A   ,		padding='''max_length'''   ,		max_length=self.tokenizer.model_max_length   ,		return_tensors='''pt'''   ,		)
               lowercase  :		Optional[int]						=      text_inputs.input_ids
               lowercase  :		int						=      text_inputs.attention_mask.bool().to(A )

               if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                      lowercase  :		Dict						=      self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
                      logger.warning(
                          '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                          f""" {self.tokenizer.model_max_length} tokens: {removed_text}""" )
                      lowercase  :		List[Any]						=      text_input_ids[:, : self.tokenizer.model_max_length]

               lowercase  :		Union[str, Any]						=      self.text_encoder(text_input_ids.to(A ) )

               lowercase  :		Tuple						=      text_encoder_output.text_embeds
               lowercase  :		Optional[Any]						=      text_encoder_output.last_hidden_state

        else:
               lowercase  :		Optional[int]						=      text_model_output[0].shape[0]
               lowercase,     lowercase  :		Optional[int]						=      text_model_output[0], text_model_output[1]
               lowercase  :		List[Any]						=      text_attention_mask

        lowercase  :		Optional[int]						=      text_embeddings.repeat_interleave(A   ,		dim=0 )
        lowercase  :		Optional[int]						=      text_encoder_hidden_states.repeat_interleave(A   ,		dim=0 )
        lowercase  :		Dict						=      text_mask.repeat_interleave(A   ,		dim=0 )

        if do_classifier_free_guidance:
               lowercase  :		List[Any]						=      [''''''] * batch_size

               lowercase  :		List[str]						=      self.tokenizer(
                   A   ,		padding='''max_length'''   ,		max_length=self.tokenizer.model_max_length   ,		truncation=A   ,		return_tensors='''pt'''   ,		)
               lowercase  :		Optional[int]						=      uncond_input.attention_mask.bool().to(A )
               lowercase  :		List[str]						=      self.text_encoder(uncond_input.input_ids.to(A ) )

               lowercase  :		List[Any]						=      uncond_embeddings_text_encoder_output.text_embeds
               lowercase  :		Dict						=      uncond_embeddings_text_encoder_output.last_hidden_state

               # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

               lowercase  :		int						=      uncond_embeddings.shape[1]
               lowercase  :		Optional[int]						=      uncond_embeddings.repeat(1   ,		A )
               lowercase  :		Optional[int]						=      uncond_embeddings.view(batch_size * num_images_per_prompt   ,		A )

               lowercase  :		str						=      uncond_text_encoder_hidden_states.shape[1]
               lowercase  :		Optional[Any]						=      uncond_text_encoder_hidden_states.repeat(1   ,		A   ,		1 )
               lowercase  :		List[str]						=      uncond_text_encoder_hidden_states.view(
                   batch_size * num_images_per_prompt   ,		A   ,		-1 )
               lowercase  :		Any						=      uncond_text_mask.repeat_interleave(A   ,		dim=0 )

               # done duplicates

               # For classifier free guidance, we need to do two forward passes.
               # Here we concatenate the unconditional and text embeddings into a single batch
               # to avoid doing two forward passes
               lowercase  :		Dict						=      torch.cat([uncond_embeddings, text_embeddings] )
               lowercase  :		List[Any]						=      torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )

               lowercase  :		Optional[Any]						=      torch.cat([uncond_text_mask, text_mask] )

        return text_embeddings, text_encoder_hidden_states, text_mask



 @property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        if self.device != torch.device('''meta''' ) or not hasattr(self.prior   ,		'''_hf_hook''' ):
               return self.device
        for module in self.prior.modules():
               if (
                   hasattr(A   ,		'''_hf_hook''' )
                   and hasattr(module._hf_hook   ,		'''execution_device''' )
                   and module._hf_hook.execution_device is not None
               ):
                      return torch.device(module._hf_hook.execution_device )
        return self.device



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        if latents is None:
               lowercase  :		Union[str, Any]						=      randn_tensor(A   ,		generator=A   ,		device=A   ,		dtype=A )
        else:
               if latents.shape != shape:
                      raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
               lowercase  :		List[str]						=      latents.to(A )

        lowercase  :		Optional[int]						=      latents * scheduler.init_noise_sigma
        return latents



 def    SCREAMING_SNAKE_CASE(  self   ,		A = None ):






        '''simple docstring'''




        self.decoder_pipe.to(A )
        super().to(A )





 @torch.no_grad()
 def __call__(  self   ,		A = None   ,		A = None   ,		A = None   ,		A = 1   ,		A = 25   ,		A = None   ,		A = None   ,		A = None   ,		A = None   ,		A = 4.0   ,		A = 8.0   ,		A = 50   ,		A = 1   ,		A = 0.0   ,		A = "pil"   ,		A = True   ,		):






        '''simple docstring'''




        if prompt is not None:
               if isinstance(A   ,		A ):
                      lowercase  :		Any						=      1
               elif isinstance(A   ,		A ):
                      lowercase  :		str						=      len(A )
               else:
                      raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(A )}""" )
        else:
               lowercase  :		List[str]						=      text_model_output[0].shape[0]

        lowercase  :		int						=      self._execution_device

        lowercase  :		str						=      batch_size * num_images_per_prompt

        lowercase  :		Dict						=      prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

        lowercase,     lowercase,     lowercase  :		Dict						=      self._encode_prompt(
            A   ,		A   ,		A   ,		A   ,		A   ,		A )

        # prior

        self.prior_scheduler.set_timesteps(A   ,		device=A )
        lowercase  :		Optional[int]						=      self.prior_scheduler.timesteps

        lowercase  :		List[Any]						=      self.prior.config.embedding_dim

        lowercase  :		Any						=      self.prepare_latents(
            (batch_size, embedding_dim)   ,		text_embeddings.dtype   ,		A   ,		A   ,		A   ,		self.prior_scheduler   ,		)

        for i, t in enumerate(self.progress_bar(A ) ):
               # expand the latents if we are doing classifier free guidance
               lowercase  :		Tuple						=      torch.cat([prior_latents] * 2 ) if do_classifier_free_guidance else prior_latents

               lowercase  :		Optional[int]						=      self.prior(
                   A   ,		timestep=A   ,		proj_embedding=A   ,		encoder_hidden_states=A   ,		attention_mask=A   ,		).predicted_image_embedding

               if do_classifier_free_guidance:
                      lowercase,     lowercase  :		Optional[Any]						=      predicted_image_embedding.chunk(2 )
                      lowercase  :		Optional[int]						=      predicted_image_embedding_uncond + prior_guidance_scale * (
                          predicted_image_embedding_text - predicted_image_embedding_uncond
                      )

               if i + 1 == prior_timesteps_tensor.shape[0]:
                      lowercase  :		str						=      None
               else:
                      lowercase  :		List[Any]						=      prior_timesteps_tensor[i + 1]

               lowercase  :		int						=      self.prior_scheduler.step(
                   A   ,		timestep=A   ,		sample=A   ,		generator=A   ,		prev_timestep=A   ,		).prev_sample

        lowercase  :		Union[str, Any]						=      self.prior.post_process_latents(A )

        lowercase  :		List[Any]						=      prior_latents

        lowercase  :		int						=      self.decoder_pipe(
            image=A   ,		height=A   ,		width=A   ,		num_inference_steps=A   ,		guidance_scale=A   ,		generator=A   ,		output_type=A   ,		return_dict=A   ,		num_images_per_prompt=A   ,		eta=A   ,		)
        return output










'''simple docstring'''






import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 (
          MODEL_MAPPING,
          DataaVecVisionForImageClassification,
          DataaVecVisionForSemanticSegmentation,
          DataaVecVisionModel,
      )
      from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import BeitImageProcessor

class   SCREAMING_SNAKE_CASE_		:



 def __init__(  self   ,		A   ,		A=100   ,		A=13   ,		A=30   ,		A=2   ,		A=3   ,		A=True   ,		A=True   ,		A=32   ,		A=4   ,		A=4   ,		A=37   ,		A="gelu"   ,		A=0.1   ,		A=0.1   ,		A=10   ,		A=0.0_2   ,		A=3   ,		A=None   ,		A=[0, 1, 2, 3]   ,		):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      parent
        lowercase  :		str						=      100
        lowercase  :		Dict						=      batch_size
        lowercase  :		Tuple						=      image_size
        lowercase  :		int						=      patch_size
        lowercase  :		Optional[int]						=      num_channels
        lowercase  :		Any						=      is_training
        lowercase  :		Dict						=      use_labels
        lowercase  :		Any						=      hidden_size
        lowercase  :		List[Any]						=      num_hidden_layers
        lowercase  :		int						=      num_attention_heads
        lowercase  :		Dict						=      intermediate_size
        lowercase  :		Optional[int]						=      hidden_act
        lowercase  :		Union[str, Any]						=      hidden_dropout_prob
        lowercase  :		str						=      attention_probs_dropout_prob
        lowercase  :		List[str]						=      type_sequence_label_size
        lowercase  :		Dict						=      initializer_range
        lowercase  :		str						=      scope
        lowercase  :		Tuple						=      out_indices
        lowercase  :		Union[str, Any]						=      num_labels

        # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowercase  :		Union[str, Any]						=      (image_size // patch_size) ** 2
        lowercase  :		List[Any]						=      num_patches + 1



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

        lowercase  :		Union[str, Any]						=      None
        lowercase  :		int						=      None
        if self.use_labels:
               lowercase  :		Dict						=      ids_tensor([self.batch_size]   ,		self.type_sequence_label_size )
               lowercase  :		Tuple						=      ids_tensor([self.batch_size, self.image_size, self.image_size]   ,		self.num_labels )

        lowercase  :		int						=      self.get_config()

        return config, pixel_values, labels, pixel_labels



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return DataaVecVisionConfig(
            vocab_size=self.vocab_size   ,		image_size=self.image_size   ,		patch_size=self.patch_size   ,		num_channels=self.num_channels   ,		hidden_size=self.hidden_size   ,		num_hidden_layers=self.num_hidden_layers   ,		num_attention_heads=self.num_attention_heads   ,		intermediate_size=self.intermediate_size   ,		hidden_act=self.hidden_act   ,		hidden_dropout_prob=self.hidden_dropout_prob   ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,		is_decoder=A   ,		initializer_range=self.initializer_range   ,		out_indices=self.out_indices   ,		)



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		List[str]						=      DataaVecVisionModel(config=A )
        model.to(A )
        model.eval()
        lowercase  :		Tuple						=      model(A )
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        lowercase  :		Union[str, Any]						=      (self.image_size // self.patch_size) ** 2
        self.parent.assertEqual(result.last_hidden_state.shape   ,		(self.batch_size, num_patches + 1, self.hidden_size) )



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		Any						=      self.type_sequence_label_size
        lowercase  :		Any						=      DataaVecVisionForImageClassification(A )
        model.to(A )
        model.eval()
        lowercase  :		Dict						=      model(A   ,		labels=A )
        self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.type_sequence_label_size) )



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		List[str]						=      self.num_labels
        lowercase  :		List[Any]						=      DataaVecVisionForSemanticSegmentation(A )
        model.to(A )
        model.eval()
        lowercase  :		int						=      model(A )
        self.parent.assertEqual(
            result.logits.shape   ,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
        lowercase  :		List[str]						=      model(A   ,		labels=A )
        self.parent.assertEqual(
            result.logits.shape   ,		(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )





 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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






@require_torch
class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_		,     lowerCamelCase_		,     unittest.TestCase				):
 __snake_case	:     Tuple			   =      (
     (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 __snake_case	:     Optional[Any]			   =      (
     {
         "feature-extraction": DataaVecVisionModel,
         "image-classification": DataaVecVisionForImageClassification,
         "image-segmentation": DataaVecVisionForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 __snake_case	:     str			   =      False
 __snake_case	:     str			   =      False
 __snake_case	:     int			   =      False



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Dict						=      DataaVecVisionModelTester(self )
        lowercase  :		List[str]						=      ConfigTester(
            self   ,		config_class=A   ,		has_text_modality=A   ,		hidden_size=37 )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        self.config_tester.run_common_tests()



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        pass



 @require_torch_multi_gpu
 @unittest.skip(
     reason='''Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        pass



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Tuple						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowercase  :		int						=      model_class(A )
               self.assertIsInstance(model.get_input_embeddings()   ,		(nn.Module) )
               lowercase  :		Optional[int]						=      model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(A   ,		nn.Linear ) )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Dict						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowercase  :		Tuple						=      model_class(A )
               lowercase  :		List[str]						=      inspect.signature(model.forward )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               lowercase  :		Tuple						=      [*signature.parameters.keys()]

               lowercase  :		List[Any]						=      ['''pixel_values''']
               self.assertListEqual(arg_names[:1]   ,		A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_segmentation(*A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        if not self.model_tester.is_training:
               return

        lowercase,     lowercase  :		Tuple						=      self.model_tester.prepare_config_and_inputs_for_common()
        lowercase  :		Union[str, Any]						=      True

        for model_class in self.all_model_classes:
               if model_class in [*get_values(A )]:
                      continue

               lowercase  :		Optional[int]						=      model_class(A )
               model.to(A )
               model.train()
               lowercase  :		int						=      self._prepare_for_class(A   ,		A   ,		return_labels=A )
               lowercase  :		List[str]						=      model(**A ).loss
               loss.backward()



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Tuple						=      self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
               return

        lowercase  :		List[str]						=      False
        lowercase  :		str						=      True

        for model_class in self.all_model_classes:
               if model_class in [*get_values(A )] or not model_class.supports_gradient_checkpointing:
                      continue
               # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
               # this can then be incorporated into _prepare_for_class in test_modeling_common.py
               elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
                      lowercase,     lowercase,     lowercase,     lowercase  :		Tuple						=      inputs_dict['''pixel_values'''].shape
                      lowercase  :		int						=      torch.zeros(
                          [self.model_tester.batch_size, height, width]   ,		device=A ).long()
               lowercase  :		Optional[Any]						=      model_class(A )
               model.gradient_checkpointing_enable()
               model.to(A )
               model.train()
               lowercase  :		Any						=      self._prepare_for_class(A   ,		A   ,		return_labels=A )
               lowercase  :		Optional[int]						=      model(**A ).loss
               loss.backward()



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

        lowercase  :		Union[str, Any]						=      _config_zero_init(A )
        for model_class in self.all_model_classes:
               lowercase  :		Dict						=      model_class(config=A )
               for name, param in model.named_parameters():
                      # we skip lambda parameters as these require special initial values
                      # determined by config.layer_scale_init_value
                      if "lambda" in name:
                             continue
                      if 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"""   ,		)



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A=2E-4   ,		A="outputs"   ,		A=None ):






        '''simple docstring'''




        super().check_pt_tf_outputs(A   ,		A   ,		A   ,		A   ,		A   ,		A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Any						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*A )





 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase  :		List[Any]						=      DataaVecVisionModel.from_pretrained(A )
               self.assertIsNotNone(A )






def 		a_( )				->Any:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
       return image





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



 @cached_property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return (
            BeitImageProcessor.from_pretrained('''facebook/data2vec-vision-base-ft1k''' ) if is_vision_available() else None
        )





 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      DataaVecVisionForImageClassification.from_pretrained('''facebook/data2vec-vision-base-ft1k''' ).to(
            A )

        lowercase  :		List[str]						=      self.default_image_processor
        lowercase  :		List[Any]						=      prepare_img()
        lowercase  :		List[Any]						=      image_processor(images=A   ,		return_tensors='''pt''' ).to(A )

        # forward pass
        with torch.no_grad():
               lowercase  :		List[Any]						=      model(**A )
        lowercase  :		Any						=      outputs.logits

        # verify the logits
        lowercase  :		List[Any]						=      torch.Size((1, 1000) )
        self.assertEqual(logits.shape   ,		A )

        lowercase  :		str						=      torch.tensor([0.3_2_7_7, -0.1_3_9_5, 0.0_9_1_1] ).to(A )

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

        lowercase  :		Dict						=      [model.config.labelaid[i] for i in ['''remote control, remote''', '''tabby, tabby cat''']]
        self.assertEqual(logits[0].topk(2 ).indices.cpu().tolist()   ,		A )




