





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_       (					lowerCAmelCase_ :     Optional[int],      lowerCAmelCase_ :     List[Any]=0.999,      lowerCAmelCase_ :     List[str]="cosine",      ):
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

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




class 							_UpperCAmelCase       (       _UpperCamelCase						,       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					[e.name for e in KarrasDiffusionSchedulers]
  a_              =					2






  @register_to_config
  def __init__(							self					:						int   ,    lowerCAmelCase_					:						int = 1_0_0_0   ,    lowerCAmelCase_					:						float = 0.0_00_85   ,    lowerCAmelCase_					:						float = 0.0_12   ,    lowerCAmelCase_					:						str = "linear"   ,    lowerCAmelCase_					:						Optional[Union[np.ndarray, List[float]]] = None   ,    lowerCAmelCase_					:						str = "epsilon"   ,    lowerCAmelCase_					:						Optional[bool] = False   ,    lowerCAmelCase_					:						Optional[bool] = False   ,    lowerCAmelCase_					:						float = 1.0   ,    lowerCAmelCase_					:						str = "linspace"   ,    lowerCAmelCase_					:						int = 0   ,    )				->		List[Any]:
     if trained_betas is not None:
        __lowerCAmelCase				      =      torch.tensor(lowerCAmelCase_   ,    dtype=torch.floataa	)
     elif beta_schedule == "linear":
        __lowerCAmelCase				      =      torch.linspace(lowerCAmelCase_   ,    lowerCAmelCase_   ,    lowerCAmelCase_   ,    dtype=torch.floataa	)
     elif beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        __lowerCAmelCase				      =      (
            torch.linspace(beta_start**0.5   ,    beta_end**0.5   ,    lowerCAmelCase_   ,    dtype=torch.floataa	) ** 2
        )
     elif beta_schedule == "squaredcos_cap_v2":
        # Glide cosine schedule
        __lowerCAmelCase				      =      betas_for_alpha_bar(lowerCAmelCase_   ,    alpha_transform_type='cosine'	)
     elif beta_schedule == "exp":
        __lowerCAmelCase				      =      betas_for_alpha_bar(lowerCAmelCase_   ,    alpha_transform_type='exp'	)
     else:
        raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""	)

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

     #  set all values
     self.set_timesteps(lowerCAmelCase_   ,    lowerCAmelCase_   ,    lowerCAmelCase_	)
     __lowerCAmelCase				      =      use_karras_sigmas






  def 							lowercase						(							self					:						Optional[int]   ,    lowerCAmelCase_					:						Optional[int]   ,    lowerCAmelCase_					:						Optional[Any]=None	)				->		str:
     if schedule_timesteps is None:
        __lowerCAmelCase				      =      self.timesteps

     __lowerCAmelCase				      =      (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:
        __lowerCAmelCase				      =      1 if len(lowerCAmelCase_	) > 1 else 0
     else:
        __lowerCAmelCase				      =      timestep.cpu().item() if torch.is_tensor(lowerCAmelCase_	) else timestep
        __lowerCAmelCase				      =      self._index_counter[timestep_int]

     return indices[pos].item()






  @property
  def 							lowercase						(							self					:						Any	)				->		List[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 							lowercase						(							self					:						Optional[Any]   ,    lowerCAmelCase_					:						torch.FloatTensor   ,    lowerCAmelCase_					:						Union[float, torch.FloatTensor]   ,    )				->		torch.FloatTensor:
     __lowerCAmelCase				      =      self.index_for_timestep(lowerCAmelCase_	)

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






  def 							lowercase						(							self					:						List[Any]   ,    lowerCAmelCase_					:						int   ,    lowerCAmelCase_					:						Union[str, torch.device] = None   ,    lowerCAmelCase_					:						Optional[int] = None   ,    )				->		Union[str, Any]:
     __lowerCAmelCase				      =      num_inference_steps

     __lowerCAmelCase				      =      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":
        __lowerCAmelCase				      =      np.linspace(0   ,    num_train_timesteps - 1   ,    lowerCAmelCase_   ,    dtype=lowerCAmelCase_	)[::-1].copy()
     elif self.config.timestep_spacing == "leading":
        __lowerCAmelCase				      =      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
        __lowerCAmelCase				      =      (np.arange(0   ,    lowerCAmelCase_	) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase_	)
        timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
        __lowerCAmelCase				      =      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
        __lowerCAmelCase				      =      (np.arange(lowerCAmelCase_   ,    0   ,    -step_ratio	)).round().copy().astype(lowerCAmelCase_	)
        timesteps -= 1
     else:
        raise ValueError(
            f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""	)

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

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

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

     __lowerCAmelCase				      =      torch.from_numpy(lowerCAmelCase_	)
     __lowerCAmelCase				      =      torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2	)]	)

     if str(lowerCAmelCase_	).startswith('mps'	):
        # mps does not support float64
        __lowerCAmelCase				      =      timesteps.to(lowerCAmelCase_   ,    dtype=torch.floataa	)
     else:
        __lowerCAmelCase				      =      timesteps.to(device=lowerCAmelCase_	)

     # empty dt and derivative
     __lowerCAmelCase				      =      None
     __lowerCAmelCase				      =      None

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






  def 							lowercase						(							self					:						Tuple   ,    lowerCAmelCase_					:						Optional[int]   ,    lowerCAmelCase_					:						List[Any]	)				->		int:
     # get log sigma
     __lowerCAmelCase				      =      np.log(lowerCAmelCase_	)

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

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

     __lowerCAmelCase				      =      log_sigmas[low_idx]
     __lowerCAmelCase				      =      log_sigmas[high_idx]

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

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






  def 							lowercase						(							self					:						Tuple   ,    lowerCAmelCase_					:						torch.FloatTensor   ,    lowerCAmelCase_					:						Optional[Any]	)				->		torch.FloatTensor:
     __lowerCAmelCase				      =      in_sigmas[-1].item()
     __lowerCAmelCase				      =      in_sigmas[0].item()

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






  @property
  def 							lowercase						(							self					:						List[str]	)				->		Any:
     return self.dt is None






  def 							lowercase						(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						Union[torch.FloatTensor, np.ndarray]   ,    lowerCAmelCase_					:						Union[float, torch.FloatTensor]   ,    lowerCAmelCase_					:						Union[torch.FloatTensor, np.ndarray]   ,    lowerCAmelCase_					:						bool = True   ,    )				->		Union[SchedulerOutput, Tuple]:
     __lowerCAmelCase				      =      self.index_for_timestep(lowerCAmelCase_	)

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

     if self.state_in_first_order:
        __lowerCAmelCase				      =      self.sigmas[step_index]
        __lowerCAmelCase				      =      self.sigmas[step_index + 1]
     else:
        # 2nd order / Heun's method
        __lowerCAmelCase				      =      self.sigmas[step_index - 1]
        __lowerCAmelCase				      =      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
     __lowerCAmelCase				      =      0
     __lowerCAmelCase				      =      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":
        __lowerCAmelCase				      =      sigma_hat if self.state_in_first_order else sigma_next
        __lowerCAmelCase				      =      sample - sigma_input * model_output
     elif self.config.prediction_type == "v_prediction":
        __lowerCAmelCase				      =      sigma_hat if self.state_in_first_order else sigma_next
        __lowerCAmelCase				      =      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
            sample / (sigma_input**2 + 1)
        )
     elif self.config.prediction_type == "sample":
        __lowerCAmelCase				      =      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:
        __lowerCAmelCase				      =      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
        __lowerCAmelCase				      =      (sample - pred_original_sample) / sigma_hat
        # 3. delta timestep
        __lowerCAmelCase				      =      sigma_next - sigma_hat

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

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

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

     __lowerCAmelCase				      =      sample + derivative * dt

     if not return_dict:
        return (prev_sample,)

     return SchedulerOutput(prev_sample=lowerCAmelCase_	)






  def 							lowercase						(							self					:						List[Any]   ,    lowerCAmelCase_					:						torch.FloatTensor   ,    lowerCAmelCase_					:						torch.FloatTensor   ,    lowerCAmelCase_					:						torch.FloatTensor   ,    )				->		torch.FloatTensor:
     # Make sure sigmas and timesteps have the same device and dtype as original_samples
     __lowerCAmelCase				      =      self.sigmas.to(device=original_samples.device   ,    dtype=original_samples.dtype	)
     if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase_	):
        # mps does not support float64
        __lowerCAmelCase				      =      self.timesteps.to(original_samples.device   ,    dtype=torch.floataa	)
        __lowerCAmelCase				      =      timesteps.to(original_samples.device   ,    dtype=torch.floataa	)
     else:
        __lowerCAmelCase				      =      self.timesteps.to(original_samples.device	)
        __lowerCAmelCase				      =      timesteps.to(original_samples.device	)

     __lowerCAmelCase				      =      [self.index_for_timestep(lowerCAmelCase_   ,    lowerCAmelCase_	) for t in timesteps]

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

     __lowerCAmelCase				      =      original_samples + noise * sigma
     return noisy_samples






  def __len__(							self					:						Union[str, Any]	)				->		Union[str, Any]:
     return self.config.num_train_timesteps






"""simple docstring"""






import inspect
import unittest

from transformers import MobileNetVaConfig
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_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 transformers import MobileNetVaForImageClassification, MobileNetVaModel
     from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor



class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      self.config_class(**self.inputs_dict				)
     self.parent.assertTrue(hasattr(__lowerCamelCase							,       '''tf_padding'''				)				)
     self.parent.assertTrue(hasattr(__lowerCamelCase							,       '''depth_multiplier'''				)				)



class     UpperCAmelCase    :



   def __init__(       self    :	List[str]							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Tuple=1_3							,       __lowerCamelCase    :	List[Any]=3							,       __lowerCamelCase    :	List[str]=3_2							,       __lowerCamelCase    :	Dict=0.2_5							,       __lowerCamelCase    :	Optional[Any]=8							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	List[Any]=1_0_2_4							,       __lowerCamelCase    :	Union[str, Any]=3_2							,       __lowerCamelCase    :	int="relu6"							,       __lowerCamelCase    :	Optional[int]=0.1							,       __lowerCamelCase    :	Tuple=0.0_2							,       __lowerCamelCase    :	Tuple=True							,       __lowerCamelCase    :	Dict=True							,       __lowerCamelCase    :	Union[str, Any]=1_0							,       __lowerCamelCase    :	Optional[Any]=None							,       ):



     """simple docstring"""
     _snake_case   	=      parent
     _snake_case   	=      batch_size
     _snake_case   	=      num_channels
     _snake_case   	=      image_size
     _snake_case   	=      depth_multiplier
     _snake_case   	=      min_depth
     _snake_case   	=      tf_padding
     _snake_case   	=      int(last_hidden_size * depth_multiplier				)
     _snake_case   	=      output_stride
     _snake_case   	=      hidden_act
     _snake_case   	=      classifier_dropout_prob
     _snake_case   	=      use_labels
     _snake_case   	=      is_training
     _snake_case   	=      num_labels
     _snake_case   	=      initializer_range
     _snake_case   	=      scope



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

     _snake_case   	=      None
     _snake_case   	=      None
     if self.use_labels:
       _snake_case   	=      ids_tensor([self.batch_size]							,       self.num_labels				)
       _snake_case   	=      ids_tensor([self.batch_size, self.image_size, self.image_size]							,       self.num_labels				)

     _snake_case   	=      self.get_config()

     return config, pixel_values, labels, pixel_labels



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     return MobileNetVaConfig(
         num_channels=self.num_channels							,       image_size=self.image_size							,       depth_multiplier=self.depth_multiplier							,       min_depth=self.min_depth							,       tf_padding=self.tf_padding							,       hidden_act=self.hidden_act							,       classifier_dropout_prob=self.classifier_dropout_prob							,       initializer_range=self.initializer_range							,       )



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	List[str]							,       __lowerCamelCase    :	Optional[int]							,       __lowerCamelCase    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      MobileNetVaModel(config=__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      model(__lowerCamelCase				)
     self.parent.assertEqual(
         result.last_hidden_state.shape							,       (
             self.batch_size,
             self.last_hidden_size,
             self.image_size // self.output_stride,
             self.image_size // self.output_stride,
         )							,       )



   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	str							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Union[str, Any]							,       __lowerCamelCase    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      self.num_labels
     _snake_case   	=      MobileNetVaForImageClassification(__lowerCamelCase				)
     model.to(__lowerCamelCase				)
     model.eval()
     _snake_case   	=      model(__lowerCamelCase							,       labels=__lowerCamelCase				)
     self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels)				)





   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      self.prepare_config_and_inputs()
     _snake_case	,     _snake_case	,     _snake_case	,     _snake_case   	=      config_and_inputs
     _snake_case   	=      {'''pixel_values''': pixel_values}
     return config, inputs_dict



@require_torch
class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	List[Any]           =  (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
   A__		:	Optional[int]           =  (
       {'''feature-extraction''': MobileNetVaModel, '''image-classification''': MobileNetVaForImageClassification}
       if is_torch_available()
       else {}
   )

   A__		:	List[str]           =  False
   A__		:	Tuple           =  False
   A__		:	List[Any]           =  False
   A__		:	List[str]           =  False



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      MobileNetVaModelTester(self				)
     _snake_case   	=      MobileNetVaConfigTester(self							,       config_class=__lowerCamelCase							,       has_text_modality=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     self.config_tester.run_common_tests()



   @unittest.skip(reason='''MobileNetV1 does not use inputs_embeds'''				)
   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     pass



   @unittest.skip(reason='''MobileNetV1 does not support input and output embeddings'''				)
   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     pass



   @unittest.skip(reason='''MobileNetV1 does not output attentions'''				)
   def    __UpperCAmelCase    (       self    :	Any				):



     """simple docstring"""
     pass



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

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

       _snake_case   	=      ['''pixel_values''']
       self.assertListEqual(arg_names[:1]							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     def check_hidden_states_output(__lowerCamelCase    :	Any							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	Union[str, Any]				):
       _snake_case   	=      model_class(__lowerCamelCase				)
       model.to(__lowerCamelCase				)
       model.eval()

       with torch.no_grad():
         _snake_case   	=      model(**self._prepare_for_class(__lowerCamelCase							,       __lowerCamelCase				)				)

       _snake_case   	=      outputs.hidden_states

       _snake_case   	=      2_6
       self.assertEqual(len(__lowerCamelCase				)							,       __lowerCamelCase				)

     _snake_case	,     _snake_case   	=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       _snake_case   	=      True
       check_hidden_states_output(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       _snake_case   	=      True

       check_hidden_states_output(__lowerCamelCase							,       __lowerCamelCase							,       __lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase				)





   @slow
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _snake_case   	=      MobileNetVaModel.from_pretrained(__lowerCamelCase				)
       self.assertIsNotNone(__lowerCamelCase				)




def 			snake_case		(		)						->				int:
  _snake_case   	=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
  return image







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



   @cached_property
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     return (
         MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v1_1.0_224'''				) if is_vision_available() else None
     )





   @slow
   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224'''				).to(__lowerCamelCase				)

     _snake_case   	=      self.default_image_processor
     _snake_case   	=      prepare_img()
     _snake_case   	=      image_processor(images=__lowerCamelCase							,       return_tensors='''pt'''				).to(__lowerCamelCase				)

     # forward pass
     with torch.no_grad():
       _snake_case   	=      model(**__lowerCamelCase				)

     # verify the logits
     _snake_case   	=      torch.Size((1, 1_0_0_1)				)
     self.assertEqual(outputs.logits.shape							,       __lowerCamelCase				)

     _snake_case   	=      torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]				).to(__lowerCamelCase				)

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





