from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


SCREAMING_SNAKE_CASE :	List[Any]							     =		logging.get_logger(__name__)  # pylint: disable=invalid-name

SCREAMING_SNAKE_CASE :	int							     =		"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to(\"cuda\")

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""



def   __A		(			_A							,				_A							,				_A=8					):



 """simple docstring"""


 __a       =  height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 __a       =  width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor



def   __A		(			_A							,				_A=512							,				_A=512					):



 """simple docstring"""


 __a       =  pil_image.resize((w, h)							,				resample=Image.BICUBIC							,				reducing_gap=1					)
 __a       =  np.array(pil_image.convert("RGB"					)					)
 __a       =  arr.astype(np.floataa					) / 127.5 - 1
 __a       =  np.transpose(_A							,				[2, 0, 1]					)
 __a       =  torch.from_numpy(_A					).unsqueeze(0					)
 return image






class    A_ (       a_ ):

  def __init__(		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      UNetaDConditionModel     ,							__SCREAMING_SNAKE_CASE :      DDPMScheduler     ,							__SCREAMING_SNAKE_CASE :      VQModel     ,							):
   super().__init__()

   self.register_modules(
       unet=__SCREAMING_SNAKE_CASE     ,							scheduler=__SCREAMING_SNAKE_CASE     ,							movq=__SCREAMING_SNAKE_CASE     ,							)
   __a       =  2 ** (len(self.movq.config.block_out_channels       ) - 1)

  def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple       ):
   # get the original timestep using init_timestep
   __a       =  min(int(num_inference_steps * strength       )     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  max(num_inference_steps - init_timestep     ,							0       )
   __a       =  self.scheduler.timesteps[t_start:]

   return timesteps, num_inference_steps - t_start

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=None       ):
   if not isinstance(__SCREAMING_SNAKE_CASE     ,							(torch.Tensor, PIL.Image.Image, list)       ):
    raise ValueError(
        f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__SCREAMING_SNAKE_CASE       )}"""       )

   __a       =  image.to(device=__SCREAMING_SNAKE_CASE     ,							dtype=__SCREAMING_SNAKE_CASE       )

   __a       =  batch_size * num_images_per_prompt

   if image.shape[1] == 4:
    __a       =  image

   else:
    if isinstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ) and len(__SCREAMING_SNAKE_CASE       ) != batch_size:
     raise ValueError(
         f"""You have passed a list of generators of length {len(__SCREAMING_SNAKE_CASE       )}, but requested an effective batch"""
         f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""       )

    elif isinstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
     __a       =  [
         self.movq.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(__SCREAMING_SNAKE_CASE       )
     ]
     __a       =  torch.cat(__SCREAMING_SNAKE_CASE     ,							dim=0       )
    else:
     __a       =  self.movq.encode(__SCREAMING_SNAKE_CASE       ).latent_dist.sample(__SCREAMING_SNAKE_CASE       )

    __a       =  self.movq.config.scaling_factor * init_latents

   __a       =  torch.cat([init_latents]     ,							dim=0       )

   __a       =  init_latents.shape
   __a       =  randn_tensor(__SCREAMING_SNAKE_CASE     ,							generator=__SCREAMING_SNAKE_CASE     ,							device=__SCREAMING_SNAKE_CASE     ,							dtype=__SCREAMING_SNAKE_CASE       )

   # get latents
   __a       =  self.scheduler.add_noise(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  init_latents

   return latents

  def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      List[Any]=0       ):
   if is_accelerate_available():
    from accelerate import cpu_offload
   else:
    raise ImportError("Please install accelerate via `pip install accelerate`"       )

   __a       =  torch.device(f"""cuda:{gpu_id}"""       )

   __a       =  [
       self.unet,
       self.movq,
   ]
   for cpu_offloaded_model in models:
    if cpu_offloaded_model is not None:
     cpu_offload(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=0       ):
   if is_accelerate_available() and is_accelerate_version(">="     ,							"0.17.0.dev0"       ):
    from accelerate import cpu_offload_with_hook
   else:
    raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."       )

   __a       =  torch.device(f"""cuda:{gpu_id}"""       )

   if self.device.type != "cpu":
    self.to("cpu"     ,							silence_dtype_warnings=__SCREAMING_SNAKE_CASE       )
    torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

   __a       =  None
   for cpu_offloaded_model in [self.unet, self.movq]:
    __a       ,     __a       =  cpu_offload_with_hook(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							prev_module_hook=__SCREAMING_SNAKE_CASE       )

   # We'll offload the last model manually.
   __a       =  hook

  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 		_UpperCAmelCase   (		self :      int       ):
   if not hasattr(self.unet     ,							"_hf_hook"       ):
    return self.device
   for module in self.unet.modules():
    if (
        hasattr(__SCREAMING_SNAKE_CASE     ,							"_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

  @torch.no_grad()
  @replace_example_docstring(__SCREAMING_SNAKE_CASE       )
  def __call__(		self :      str     ,							__SCREAMING_SNAKE_CASE :      Union[torch.FloatTensor, List[torch.FloatTensor]]     ,							__SCREAMING_SNAKE_CASE :      Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]     ,							__SCREAMING_SNAKE_CASE :      Union[torch.FloatTensor, List[torch.FloatTensor]]     ,							__SCREAMING_SNAKE_CASE :      int = 5_12     ,							__SCREAMING_SNAKE_CASE :      int = 5_12     ,							__SCREAMING_SNAKE_CASE :      int = 1_00     ,							__SCREAMING_SNAKE_CASE :      float = 4.0     ,							__SCREAMING_SNAKE_CASE :      float = 0.3     ,							__SCREAMING_SNAKE_CASE :      int = 1     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[str] = "pil"     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							):
   __a       =  self._execution_device

   __a       =  guidance_scale > 1.0

   if isinstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
    __a       =  torch.cat(__SCREAMING_SNAKE_CASE     ,							dim=0       )
   __a       =  image_embeds.shape[0]
   if isinstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
    __a       =  torch.cat(__SCREAMING_SNAKE_CASE     ,							dim=0       )

   if do_classifier_free_guidance:
    __a       =  image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE     ,							dim=0       )
    __a       =  negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE     ,							dim=0       )

   __a       =  torch.cat([negative_image_embeds, image_embeds]     ,							dim=0       ).to(dtype=self.unet.dtype     ,							device=__SCREAMING_SNAKE_CASE       )

   if not isinstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
    __a       =  [image]
   if not all(isinstance(__SCREAMING_SNAKE_CASE     ,							(PIL.Image.Image, torch.Tensor)       ) for i in image       ):
    raise ValueError(
        f"""Input is in incorrect format: {[type(__SCREAMING_SNAKE_CASE       ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""       )

   __a       =  torch.cat([prepare_image(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ) for i in image]     ,							dim=0       )
   __a       =  image.to(dtype=image_embeds.dtype     ,							device=__SCREAMING_SNAKE_CASE       )

   __a       =  self.movq.encode(__SCREAMING_SNAKE_CASE       )["latents"]
   __a       =  latents.repeat_interleave(__SCREAMING_SNAKE_CASE     ,							dim=0       )
   self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE     ,							device=__SCREAMING_SNAKE_CASE       )
   __a       ,     __a       =  self.get_timesteps(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  timesteps[:1].repeat(batch_size * num_images_per_prompt       )
   __a       ,     __a       =  downscale_height_and_width(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							self.movq_scale_factor       )
   __a       =  self.prepare_latents(
       __SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							image_embeds.dtype     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   for i, t in enumerate(self.progress_bar(__SCREAMING_SNAKE_CASE       )       ):
    # expand the latents if we are doing classifier free guidance
    __a       =  torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

    __a       =  {"image_embeds": image_embeds}
    __a       =  self.unet(
        sample=__SCREAMING_SNAKE_CASE     ,							timestep=__SCREAMING_SNAKE_CASE     ,							encoder_hidden_states=__SCREAMING_SNAKE_CASE     ,							added_cond_kwargs=__SCREAMING_SNAKE_CASE     ,							return_dict=__SCREAMING_SNAKE_CASE     ,							)[0]

    if do_classifier_free_guidance:
     __a       ,     __a       =  noise_pred.split(latents.shape[1]     ,							dim=1       )
     __a       ,     __a       =  noise_pred.chunk(2       )
     __a       ,     __a       =  variance_pred.chunk(2       )
     __a       =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
     __a       =  torch.cat([noise_pred, variance_pred_text]     ,							dim=1       )

    if not (
        hasattr(self.scheduler.config     ,							"variance_type"       )
        and self.scheduler.config.variance_type in ["learned", "learned_range"]
    ):
     __a       ,     __a       =  noise_pred.split(latents.shape[1]     ,							dim=1       )

    # compute the previous noisy sample x_t -> x_t-1
    __a       =  self.scheduler.step(
        __SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							generator=__SCREAMING_SNAKE_CASE     ,							)[0]

   # post-processing
   __a       =  self.movq.decode(__SCREAMING_SNAKE_CASE     ,							force_not_quantize=__SCREAMING_SNAKE_CASE       )["sample"]

   if output_type not in ["pt", "np", "pil"]:
    raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""       )

   if output_type in ["np", "pil"]:
    __a       =  image * 0.5 + 0.5
    __a       =  image.clamp(0     ,							1       )
    __a       =  image.cpu().permute(0     ,							2     ,							3     ,							1       ).float().numpy()

   if output_type == "pil":
    __a       =  self.numpy_to_pil(__SCREAMING_SNAKE_CASE       )

   if not return_dict:
    return (image,)

   return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE       )



import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
  from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import ViTImageProcessor
class    A_ :

  def __init__(		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      List[Any]=13     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=30     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=2     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=3     ,							__SCREAMING_SNAKE_CASE :      List[Any]=True     ,							__SCREAMING_SNAKE_CASE :      Dict=True     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=32     ,							__SCREAMING_SNAKE_CASE :      str=5     ,							__SCREAMING_SNAKE_CASE :      List[str]=4     ,							__SCREAMING_SNAKE_CASE :      List[Any]=37     ,							__SCREAMING_SNAKE_CASE :      int="gelu"     ,							__SCREAMING_SNAKE_CASE :      List[Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      List[Any]=10     ,							__SCREAMING_SNAKE_CASE :      int=0.02     ,							__SCREAMING_SNAKE_CASE :      Dict=None     ,							__SCREAMING_SNAKE_CASE :      str=2     ,							):
   __a       =  parent
   __a       =  batch_size
   __a       =  image_size
   __a       =  patch_size
   __a       =  num_channels
   __a       =  is_training
   __a       =  use_labels
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  intermediate_size
   __a       =  hidden_act
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  type_sequence_label_size
   __a       =  initializer_range
   __a       =  scope
   __a       =  encoder_stride

   # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
   __a       =  (image_size // patch_size) ** 2
   __a       =  num_patches + 1

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

   __a       =  self.get_config()

   return config, pixel_values, labels

  def 		_UpperCAmelCase   (		self :      str       ):
   return ViTConfig(
       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=__SCREAMING_SNAKE_CASE     ,							initializer_range=self.initializer_range     ,							encoder_stride=self.encoder_stride     ,							)

  def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   __a       =  ViTModel(config=__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Dict       ):
   __a       =  ViTForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(
       result.reconstruction.shape     ,							(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

   # test greyscale images
   __a       =  1
   __a       =  ViTForMaskedImageModeling(__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()

   __a       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.reconstruction.shape     ,							(self.batch_size, 1, self.image_size, self.image_size)       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      List[str]       ):
   __a       =  self.type_sequence_label_size
   __a       =  ViTForImageClassification(__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  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
   __a       =  1
   __a       =  ViTForImageClassification(__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()

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

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   __a       =  self.prepare_config_and_inputs()
   (
       (
       __a
   )       ,     (
       __a
   )       ,     (
       __a
   )       ,     
   )       =  config_and_inputs
   __a       =  {"pixel_values": pixel_values}
   return config, inputs_dict
@require_torch
class    A_ (       a_			,       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			(
      (
          ViTModel,
          ViTForImageClassification,
          ViTForMaskedImageModeling,
      )
      if is_torch_available()
      else ()
  )
  _SCREAMING_SNAKE_CASE    				=			(
      {"""feature-extraction""": ViTModel, """image-classification""": ViTForImageClassification}
      if is_torch_available()
      else {}
  )
  _SCREAMING_SNAKE_CASE    				=			True

  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  ViTModelTester(self       )
   __a       =  ConfigTester(self     ,							config_class=__SCREAMING_SNAKE_CASE     ,							has_text_modality=__SCREAMING_SNAKE_CASE     ,							hidden_size=37       )

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   self.config_tester.run_common_tests()

  @unittest.skip(reason="ViT does not use inputs_embeds"       )
  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   pass

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

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

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

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

    __a       =  ["pixel_values"]
    self.assertListEqual(arg_names[:1]     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE       )

  @slow
  def 		_UpperCAmelCase   (		self :      Dict       ):
   for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __a       =  ViTModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )



def   __A		(			):



 """simple docstring"""


 __a       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
 return image






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

  @cached_property
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"       ) if is_vision_available() else None

  @slow
  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  ViTForImageClassification.from_pretrained("google/vit-base-patch16-224"       ).to(__SCREAMING_SNAKE_CASE       )

   __a       =  self.default_image_processor
   __a       =  prepare_img()
   __a       =  image_processor(images=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       ).to(__SCREAMING_SNAKE_CASE       )

   # forward pass
   with torch.no_grad():
    __a       =  model(**__SCREAMING_SNAKE_CASE       )

   # verify the logits
   __a       =  torch.Size((1, 10_00)       )
   self.assertEqual(outputs.logits.shape     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  torch.tensor([-0.27_44, 0.82_15, -0.08_36]       ).to(__SCREAMING_SNAKE_CASE       )

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

  @slow
  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   # ViT models have an `interpolate_pos_encoding` argument in their forward method,
   # allowing to interpolate the pre-trained position embeddings in order to use
   # the model on higher resolutions. The DINO model by Facebook AI leverages this
   # to visualize self-attention on higher resolution images.
   __a       =  ViTModel.from_pretrained("facebook/dino-vits8"       ).to(__SCREAMING_SNAKE_CASE       )

   __a       =  ViTImageProcessor.from_pretrained("facebook/dino-vits8"     ,							size=4_80       )
   __a       =  prepare_img()
   __a       =  image_processor(images=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       )
   __a       =  inputs.pixel_values.to(__SCREAMING_SNAKE_CASE       )

   # forward pass
   with torch.no_grad():
    __a       =  model(__SCREAMING_SNAKE_CASE     ,							interpolate_pos_encoding=__SCREAMING_SNAKE_CASE       )

   # verify the logits
   __a       =  torch.Size((1, 36_01, 3_84)       )
   self.assertEqual(outputs.last_hidden_state.shape     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  torch.tensor(
       [[4.23_40, 4.39_06, -6.66_92], [4.54_63, 1.89_28, -6.72_57], [4.44_29, 0.84_96, -5.85_85]]       ).to(__SCREAMING_SNAKE_CASE       )

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

  @slow
  @require_accelerate
  @require_torch_gpu
  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  ViTModel.from_pretrained("facebook/dino-vits8"     ,							torch_dtype=torch.floataa     ,							device_map="auto"       )
   __a       =  self.default_image_processor

   __a       =  prepare_img()
   __a       =  image_processor(images=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       )
   __a       =  inputs.pixel_values.to(__SCREAMING_SNAKE_CASE       )

   # forward pass to make sure inference works in fp16
   with torch.no_grad():
    __a       =  model(__SCREAMING_SNAKE_CASE       )


