import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput



def   __A		(			_A							,				_A=0.999							,				_A="cosine"							,				):



 """simple docstring"""


 if alpha_transform_type == "cosine":

  def alpha_bar_fn(_A					):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2					) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(_A					):
   return math.exp(t * -12.0					)

 else:
  raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""					)

 __a       =  []
 for i in range(_A					):
  __a       =  i / num_diffusion_timesteps
  __a       =  (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(_A					) / alpha_bar_fn(_A					)							,				_A					)					)
 return torch.tensor(_A							,				dtype=torch.floataa					)






class    A_ (       a_			,       a_ ):
  _SCREAMING_SNAKE_CASE    				=			[e.name for e in KarrasDiffusionSchedulers]
  _SCREAMING_SNAKE_CASE    				=			2

  @register_to_config
  def __init__(		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      int = 10_00     ,							__SCREAMING_SNAKE_CASE :      float = 0.0_00_85     ,							__SCREAMING_SNAKE_CASE :      float = 0.0_12     ,							__SCREAMING_SNAKE_CASE :      str = "linear"     ,							__SCREAMING_SNAKE_CASE :      Optional[Union[np.ndarray, List[float]]] = None     ,							__SCREAMING_SNAKE_CASE :      str = "epsilon"     ,							__SCREAMING_SNAKE_CASE :      Optional[bool] = False     ,							__SCREAMING_SNAKE_CASE :      Optional[bool] = False     ,							__SCREAMING_SNAKE_CASE :      float = 1.0     ,							__SCREAMING_SNAKE_CASE :      str = "linspace"     ,							__SCREAMING_SNAKE_CASE :      int = 0     ,							):
   if trained_betas is not None:
    __a       =  torch.tensor(__SCREAMING_SNAKE_CASE     ,							dtype=torch.floataa       )
   elif beta_schedule == "linear":
    __a       =  torch.linspace(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							dtype=torch.floataa       )
   elif beta_schedule == "scaled_linear":
    # this schedule is very specific to the latent diffusion model.
    __a       =  (
        torch.linspace(beta_start**0.5     ,							beta_end**0.5     ,							__SCREAMING_SNAKE_CASE     ,							dtype=torch.floataa       ) ** 2
    )
   elif beta_schedule == "squaredcos_cap_v2":
    # Glide cosine schedule
    __a       =  betas_for_alpha_bar(__SCREAMING_SNAKE_CASE     ,							alpha_transform_type="cosine"       )
   elif beta_schedule == "exp":
    __a       =  betas_for_alpha_bar(__SCREAMING_SNAKE_CASE     ,							alpha_transform_type="exp"       )
   else:
    raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""       )

   __a       =  1.0 - self.betas
   __a       =  torch.cumprod(self.alphas     ,							dim=0       )

   #  set all values
   self.set_timesteps(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  use_karras_sigmas

  def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple=None       ):
   if schedule_timesteps is None:
    __a       =  self.timesteps

   __a       =  (schedule_timesteps == timestep).nonzero()

   # The sigma index that is taken for the **very** first `step`
   # is always the second index (or the last index if there is only 1)
   # This way we can ensure we don't accidentally skip a sigma in
   # case we start in the middle of the denoising schedule (e.g. for image-to-image)
   if len(self._index_counter       ) == 0:
    __a       =  1 if len(__SCREAMING_SNAKE_CASE       ) > 1 else 0
   else:
    __a       =  timestep.cpu().item() if torch.is_tensor(__SCREAMING_SNAKE_CASE       ) else timestep
    __a       =  self._index_counter[timestep_int]

   return indices[pos].item()

  @property
  def 		_UpperCAmelCase   (		self :      Any       ):
   # standard deviation of the initial noise distribution
   if self.config.timestep_spacing in ["linspace", "trailing"]:
    return self.sigmas.max()

   return (self.sigmas.max() ** 2 + 1) ** 0.5

  def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      torch.FloatTensor     ,							__SCREAMING_SNAKE_CASE :      Union[float, torch.FloatTensor]     ,							):
   __a       =  self.index_for_timestep(__SCREAMING_SNAKE_CASE       )

   __a       =  self.sigmas[step_index]
   __a       =  sample / ((sigma**2 + 1) ** 0.5)
   return sample

  def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      Union[str, torch.device] = None     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							):
   __a       =  num_inference_steps

   __a       =  num_train_timesteps or self.config.num_train_timesteps

   # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
   if self.config.timestep_spacing == "linspace":
    __a       =  np.linspace(0     ,							num_train_timesteps - 1     ,							__SCREAMING_SNAKE_CASE     ,							dtype=__SCREAMING_SNAKE_CASE       )[::-1].copy()
   elif self.config.timestep_spacing == "leading":
    __a       =  num_train_timesteps // self.num_inference_steps
    # creates integer timesteps by multiplying by ratio
    # casting to int to avoid issues when num_inference_step is power of 3
    __a       =  (np.arange(0     ,							__SCREAMING_SNAKE_CASE       ) * step_ratio).round()[::-1].copy().astype(__SCREAMING_SNAKE_CASE       )
    timesteps += self.config.steps_offset
   elif self.config.timestep_spacing == "trailing":
    __a       =  num_train_timesteps / self.num_inference_steps
    # creates integer timesteps by multiplying by ratio
    # casting to int to avoid issues when num_inference_step is power of 3
    __a       =  (np.arange(__SCREAMING_SNAKE_CASE     ,							0     ,							-step_ratio       )).round().copy().astype(__SCREAMING_SNAKE_CASE       )
    timesteps -= 1
   else:
    raise ValueError(
        f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""       )

   __a       =  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
   __a       =  np.log(__SCREAMING_SNAKE_CASE       )
   __a       =  np.interp(__SCREAMING_SNAKE_CASE     ,							np.arange(0     ,							len(__SCREAMING_SNAKE_CASE       )       )     ,							__SCREAMING_SNAKE_CASE       )

   if self.config.use_karras_sigmas:
    __a       =  self._convert_to_karras(in_sigmas=__SCREAMING_SNAKE_CASE     ,							num_inference_steps=self.num_inference_steps       )
    __a       =  np.array([self._sigma_to_t(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ) for sigma in sigmas]       )

   __a       =  np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
   __a       =  torch.from_numpy(__SCREAMING_SNAKE_CASE       ).to(device=__SCREAMING_SNAKE_CASE       )
   __a       =  torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2       ), sigmas[-1:]]       )

   __a       =  torch.from_numpy(__SCREAMING_SNAKE_CASE       )
   __a       =  torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2       )]       )

   if str(__SCREAMING_SNAKE_CASE       ).startswith("mps"       ):
    # mps does not support float64
    __a       =  timesteps.to(__SCREAMING_SNAKE_CASE     ,							dtype=torch.floataa       )
   else:
    __a       =  timesteps.to(device=__SCREAMING_SNAKE_CASE       )

   # empty dt and derivative
   __a       =  None
   __a       =  None

   # for exp beta schedules, such as the one for `pipeline_shap_e.py`
   # we need an index counter
   __a       =  defaultdict(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      Optional[int]       ):
   # get log sigma
   __a       =  np.log(__SCREAMING_SNAKE_CASE       )

   # get distribution
   __a       =  log_sigma - log_sigmas[:, np.newaxis]

   # get sigmas range
   __a       =  np.cumsum((dists >= 0)     ,							axis=0       ).argmax(axis=0       ).clip(max=log_sigmas.shape[0] - 2       )
   __a       =  low_idx + 1

   __a       =  log_sigmas[low_idx]
   __a       =  log_sigmas[high_idx]

   # interpolate sigmas
   __a       =  (low - log_sigma) / (low - high)
   __a       =  np.clip(__SCREAMING_SNAKE_CASE     ,							0     ,							1       )

   # transform interpolation to time range
   __a       =  (1 - w) * low_idx + w * high_idx
   __a       =  t.reshape(sigma.shape       )
   return t

  def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      torch.FloatTensor     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]       ):
   __a       =  in_sigmas[-1].item()
   __a       =  in_sigmas[0].item()

   __a       =  7.0  # 7.0 is the value used in the paper
   __a       =  np.linspace(0     ,							1     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  sigma_min ** (1 / rho)
   __a       =  sigma_max ** (1 / rho)
   __a       =  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
   return sigmas

  @property
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   return self.dt is None

  def 		_UpperCAmelCase   (		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Union[torch.FloatTensor, np.ndarray]     ,							__SCREAMING_SNAKE_CASE :      Union[float, torch.FloatTensor]     ,							__SCREAMING_SNAKE_CASE :      Union[torch.FloatTensor, np.ndarray]     ,							__SCREAMING_SNAKE_CASE :      bool = True     ,							):
   __a       =  self.index_for_timestep(__SCREAMING_SNAKE_CASE       )

   # advance index counter by 1
   __a       =  timestep.cpu().item() if torch.is_tensor(__SCREAMING_SNAKE_CASE       ) else timestep
   self._index_counter[timestep_int] += 1

   if self.state_in_first_order:
    __a       =  self.sigmas[step_index]
    __a       =  self.sigmas[step_index + 1]
   else:
    # 2nd order / Heun's method
    __a       =  self.sigmas[step_index - 1]
    __a       =  self.sigmas[step_index]

   # currently only gamma=0 is supported. This usually works best anyways.
   # We can support gamma in the future but then need to scale the timestep before
   # passing it to the model which requires a change in API
   __a       =  0
   __a       =  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

   # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
   if self.config.prediction_type == "epsilon":
    __a       =  sigma_hat if self.state_in_first_order else sigma_next
    __a       =  sample - sigma_input * model_output
   elif self.config.prediction_type == "v_prediction":
    __a       =  sigma_hat if self.state_in_first_order else sigma_next
    __a       =  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
        sample / (sigma_input**2 + 1)
    )
   elif self.config.prediction_type == "sample":
    __a       =  model_output
   else:
    raise ValueError(
        f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""       )

   if self.config.clip_sample:
    __a       =  pred_original_sample.clamp(
        -self.config.clip_sample_range     ,							self.config.clip_sample_range       )

   if self.state_in_first_order:
    # 2. Convert to an ODE derivative for 1st order
    __a       =  (sample - pred_original_sample) / sigma_hat
    # 3. delta timestep
    __a       =  sigma_next - sigma_hat

    # store for 2nd order step
    __a       =  derivative
    __a       =  dt
    __a       =  sample
   else:
    # 2. 2nd order / Heun's method
    __a       =  (sample - pred_original_sample) / sigma_next
    __a       =  (self.prev_derivative + derivative) / 2

    # 3. take prev timestep & sample
    __a       =  self.dt
    __a       =  self.sample

    # free dt and derivative
    # Note, this puts the scheduler in "first order mode"
    __a       =  None
    __a       =  None
    __a       =  None

   __a       =  sample + derivative * dt

   if not return_dict:
    return (prev_sample,)

   return SchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      torch.FloatTensor     ,							__SCREAMING_SNAKE_CASE :      torch.FloatTensor     ,							__SCREAMING_SNAKE_CASE :      torch.FloatTensor     ,							):
   # Make sure sigmas and timesteps have the same device and dtype as original_samples
   __a       =  self.sigmas.to(device=original_samples.device     ,							dtype=original_samples.dtype       )
   if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE       ):
    # mps does not support float64
    __a       =  self.timesteps.to(original_samples.device     ,							dtype=torch.floataa       )
    __a       =  timesteps.to(original_samples.device     ,							dtype=torch.floataa       )
   else:
    __a       =  self.timesteps.to(original_samples.device       )
    __a       =  timesteps.to(original_samples.device       )

   __a       =  [self.index_for_timestep(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ) for t in timesteps]

   __a       =  sigmas[step_indices].flatten()
   while len(sigma.shape       ) < len(original_samples.shape       ):
    __a       =  sigma.unsqueeze(-1       )

   __a       =  original_samples + noise * sigma
   return noisy_samples

  def __len__(		self :      List[Any]       ):
   return self.config.num_train_timesteps



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       )


