



'''simple docstring'''





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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput



def     UpperCAmelCase     (    a_      ,		a_=0.999      ,		a_="cosine"      ,		)   ->       Tuple:



   """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_  :		str						=					[]
   for i in range(a_		):
      A_  :		Optional[Any]						=					i / num_diffusion_timesteps
      A_  :		List[Any]						=					(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 							_lowerCAmelCase	(  __A,							__A							):


 """simple docstring"""

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







 @register_to_config
 def __init__(      self					,    _lowerCamelCase = 1000					,    _lowerCamelCase = 0.0_0085					,    _lowerCamelCase = 0.012					,    _lowerCamelCase = "linear"					,    _lowerCamelCase = None					,    _lowerCamelCase = "epsilon"					,    _lowerCamelCase = "linspace"					,    _lowerCamelCase = 0					,    )    ->    str:
    if trained_betas is not None:
       A_  :		List[str]						=					torch.tensor(_lowerCamelCase					,    dtype=torch.floataa  )
    elif beta_schedule == "linear":
       A_  :		Optional[Any]						=					torch.linspace(_lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    dtype=torch.floataa  )
    elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       A_  :		List[str]						=					(
           torch.linspace(beta_start**0.5					,    beta_end**0.5					,    _lowerCamelCase					,    dtype=torch.floataa  ) ** 2
       )
    elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       A_  :		int						=					betas_for_alpha_bar(_lowerCamelCase  )
    else:
       raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"  )

    A_  :		str						=					1.0 - self.betas
    A_  :		Optional[Any]						=					torch.cumprod(self.alphas					,    dim=0  )

    #  set all values
    self.set_timesteps(_lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase=None  )    ->    Union[str, Any]:
    if schedule_timesteps is None:
       A_  :		Optional[Any]						=					self.timesteps

    A_  :		Dict						=					(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_  :		Any						=					1 if len(_lowerCamelCase  ) > 1 else 0
    else:
       A_  :		Dict						=					timestep.cpu().item() if torch.is_tensor(_lowerCamelCase  ) else timestep
       A_  :		List[Any]						=					self._index_counter[timestep_int]

    return indices[pos].item()







 @property
 def       UpperCAmelCase_       (      self  )    ->    str:
    # 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					,    _lowerCamelCase					,    _lowerCamelCase					,    )    ->    torch.FloatTensor:
    A_  :		Tuple						=					self.index_for_timestep(_lowerCamelCase  )

    if self.state_in_first_order:
       A_  :		str						=					self.sigmas[step_index]
    else:
       A_  :		List[str]						=					self.sigmas_interpol[step_index - 1]

    A_  :		List[str]						=					sample / ((sigma**2 + 1) ** 0.5)
    return sample







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase = None					,    _lowerCamelCase = None					,    )    ->    Tuple:
    A_  :		List[str]						=					num_inference_steps

    A_  :		int						=					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_  :		List[Any]						=					np.linspace(0					,    num_train_timesteps - 1					,    _lowerCamelCase					,    dtype=_lowerCamelCase  )[::-1].copy()
    elif self.config.timestep_spacing == "leading":
       A_  :		Optional[int]						=					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_  :		Any						=					(np.arange(0					,    _lowerCamelCase  ) * step_ratio).round()[::-1].copy().astype(_lowerCamelCase  )
       timesteps += self.config.steps_offset
    elif self.config.timestep_spacing == "trailing":
       A_  :		List[Any]						=					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_  :		Optional[int]						=					(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'."  )

    A_  :		List[str]						=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
    A_  :		List[Any]						=					torch.from_numpy(np.log(_lowerCamelCase  )  ).to(_lowerCamelCase  )

    A_  :		Optional[Any]						=					np.interp(_lowerCamelCase					,    np.arange(0					,    len(_lowerCamelCase  )  )					,    _lowerCamelCase  )
    A_  :		int						=					np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
    A_  :		str						=					torch.from_numpy(_lowerCamelCase  ).to(device=_lowerCamelCase  )

    # compute up and down sigmas
    A_  :		Dict						=					sigmas.roll(-1  )
    A_  :		Optional[int]						=					0.0
    A_  :		Tuple						=					(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
    A_  :		int						=					(sigmas_next**2 - sigmas_up**2) ** 0.5
    A_  :		Any						=					0.0

    # compute interpolated sigmas
    A_  :		List[str]						=					sigmas.log().lerp(sigmas_down.log()					,    0.5  ).exp()
    A_  :		List[Any]						=					0.0

    # set sigmas
    A_  :		Optional[int]						=					torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2  ), sigmas[-1:]]  )
    A_  :		Dict						=					torch.cat(
        [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2  ), sigmas_interpol[-1:]]  )
    A_  :		Optional[int]						=					torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2  ), sigmas_up[-1:]]  )
    A_  :		List[Any]						=					torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2  ), sigmas_down[-1:]]  )

    if str(_lowerCamelCase  ).startswith("""mps"""  ):
       # mps does not support float64
       A_  :		Optional[Any]						=					torch.from_numpy(_lowerCamelCase  ).to(_lowerCamelCase					,    dtype=torch.floataa  )
    else:
       A_  :		Dict						=					torch.from_numpy(_lowerCamelCase  ).to(_lowerCamelCase  )

    A_  :		int						=					self.sigma_to_t(_lowerCamelCase  ).to(_lowerCamelCase					,    dtype=timesteps.dtype  )
    A_  :		List[str]						=					torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])					,    dim=-1  ).flatten()

    A_  :		List[str]						=					torch.cat([timesteps[:1], interleaved_timesteps]  )

    A_  :		str						=					None

    # for exp beta schedules, such as the one for `pipeline_shap_e.py`
    # we need an index counter
    A_  :		List[Any]						=					defaultdict(_lowerCamelCase  )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[str]:
    # get log sigma
    A_  :		Tuple						=					sigma.log()

    # get distribution
    A_  :		Optional[Any]						=					log_sigma - self.log_sigmas[:, None]

    # get sigmas range
    A_  :		Optional[Any]						=					dists.ge(0  ).cumsum(dim=0  ).argmax(dim=0  ).clamp(max=self.log_sigmas.shape[0] - 2  )
    A_  :		Tuple						=					low_idx + 1

    A_  :		Tuple						=					self.log_sigmas[low_idx]
    A_  :		str						=					self.log_sigmas[high_idx]

    # interpolate sigmas
    A_  :		str						=					(low - log_sigma) / (low - high)
    A_  :		List[str]						=					w.clamp(0					,    1  )

    # transform interpolation to time range
    A_  :		Tuple						=					(1 - w) * low_idx + w * high_idx
    A_  :		str						=					t.view(sigma.shape  )
    return t







 @property
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    return self.sample is None







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase = None					,    _lowerCamelCase = True					,    )    ->    Union[SchedulerOutput, Tuple]:
    A_  :		int						=					self.index_for_timestep(_lowerCamelCase  )

    # advance index counter by 1
    A_  :		List[str]						=					timestep.cpu().item() if torch.is_tensor(_lowerCamelCase  ) else timestep
    self._index_counter[timestep_int] += 1

    if self.state_in_first_order:
       A_  :		Tuple						=					self.sigmas[step_index]
       A_  :		str						=					self.sigmas_interpol[step_index]
       A_  :		List[str]						=					self.sigmas_up[step_index]
       A_  :		List[str]						=					self.sigmas_down[step_index - 1]
    else:
       # 2nd order / KPDM2's method
       A_  :		Dict						=					self.sigmas[step_index - 1]
       A_  :		List[Any]						=					self.sigmas_interpol[step_index - 1]
       A_  :		List[Any]						=					self.sigmas_up[step_index - 1]
       A_  :		int						=					self.sigmas_down[step_index - 1]

    # 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_  :		Optional[Any]						=					0
    A_  :		List[str]						=					sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

    A_  :		Optional[Any]						=					model_output.device
    A_  :		Union[str, Any]						=					randn_tensor(model_output.shape					,    dtype=model_output.dtype					,    device=_lowerCamelCase					,    generator=_lowerCamelCase  )

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    if self.config.prediction_type == "epsilon":
       A_  :		Optional[Any]						=					sigma_hat if self.state_in_first_order else sigma_interpol
       A_  :		str						=					sample - sigma_input * model_output
    elif self.config.prediction_type == "v_prediction":
       A_  :		Dict						=					sigma_hat if self.state_in_first_order else sigma_interpol
       A_  :		str						=					model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
           sample / (sigma_input**2 + 1)
       )
    elif self.config.prediction_type == "sample":
       raise NotImplementedError("""prediction_type not implemented yet: sample"""  )
    else:
       raise ValueError(
           F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"  )

    if self.state_in_first_order:
       # 2. Convert to an ODE derivative for 1st order
       A_  :		str						=					(sample - pred_original_sample) / sigma_hat
       # 3. delta timestep
       A_  :		Optional[Any]						=					sigma_interpol - sigma_hat

       # store for 2nd order step
       A_  :		Optional[Any]						=					sample
       A_  :		Any						=					dt
       A_  :		Union[str, Any]						=					sample + derivative * dt
    else:
       # DPM-Solver-2
       # 2. Convert to an ODE derivative for 2nd order
       A_  :		List[Any]						=					(sample - pred_original_sample) / sigma_interpol
       # 3. delta timestep
       A_  :		Dict						=					sigma_down - sigma_hat

       A_  :		Dict						=					self.sample
       A_  :		Any						=					None

       A_  :		List[Any]						=					sample + derivative * dt
       A_  :		Optional[Any]						=					prev_sample + noise * sigma_up

    if not return_dict:
       return (prev_sample,)

    return SchedulerOutput(prev_sample=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    )    ->    torch.FloatTensor:
    # Make sure sigmas and timesteps have the same device and dtype as original_samples
    A_  :		List[str]						=					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
       A_  :		Optional[int]						=					self.timesteps.to(original_samples.device					,    dtype=torch.floataa  )
       A_  :		List[Any]						=					timesteps.to(original_samples.device					,    dtype=torch.floataa  )
    else:
       A_  :		int						=					self.timesteps.to(original_samples.device  )
       A_  :		Optional[Any]						=					timesteps.to(original_samples.device  )

    A_  :		Dict						=					[self.index_for_timestep(_lowerCamelCase					,    _lowerCamelCase  ) for t in timesteps]

    A_  :		str						=					sigmas[step_indices].flatten()
    while len(sigma.shape  ) < len(original_samples.shape  ):
       A_  :		Union[str, Any]						=					sigma.unsqueeze(-1  )

    A_  :		Dict						=					original_samples + noise * sigma
    return noisy_samples







 def __len__(      self  )    ->    Optional[Any]:
    return self.config.num_train_timesteps





'''simple docstring'''





import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
  from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import GLPNImageProcessor


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 def       UpperCAmelCase_       (      self  )    ->    int:
    A_  :		Tuple						=					self.config_class(**self.inputs_dict  )
    self.parent.assertTrue(hasattr(_lowerCamelCase					,    """hidden_sizes"""  )  )
    self.parent.assertTrue(hasattr(_lowerCamelCase					,    """num_attention_heads"""  )  )
    self.parent.assertTrue(hasattr(_lowerCamelCase					,    """num_encoder_blocks"""  )  )


class 							_lowerCAmelCase	:


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase					,    _lowerCamelCase=13					,    _lowerCamelCase=64					,    _lowerCamelCase=3					,    _lowerCamelCase=4					,    _lowerCamelCase=[2, 2, 2, 2]					,    _lowerCamelCase=[8, 4, 2, 1]					,    _lowerCamelCase=[16, 32, 64, 128]					,    _lowerCamelCase=[1, 4, 8, 16]					,    _lowerCamelCase=[1, 2, 4, 8]					,    _lowerCamelCase=True					,    _lowerCamelCase=True					,    _lowerCamelCase="gelu"					,    _lowerCamelCase=0.1					,    _lowerCamelCase=0.1					,    _lowerCamelCase=0.02					,    _lowerCamelCase=16					,    _lowerCamelCase=3					,    _lowerCamelCase=None					,    )    ->    Optional[int]:
    A_  :		int						=					parent
    A_  :		int						=					batch_size
    A_  :		Optional[int]						=					image_size
    A_  :		str						=					num_channels
    A_  :		Optional[Any]						=					num_encoder_blocks
    A_  :		List[str]						=					sr_ratios
    A_  :		int						=					depths
    A_  :		str						=					hidden_sizes
    A_  :		Optional[int]						=					downsampling_rates
    A_  :		Any						=					num_attention_heads
    A_  :		List[str]						=					is_training
    A_  :		Optional[Any]						=					use_labels
    A_  :		List[str]						=					hidden_act
    A_  :		Tuple						=					hidden_dropout_prob
    A_  :		Optional[int]						=					attention_probs_dropout_prob
    A_  :		Optional[int]						=					initializer_range
    A_  :		str						=					decoder_hidden_size
    A_  :		Optional[Any]						=					num_labels
    A_  :		List[Any]						=					scope







 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    A_  :		List[Any]						=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

    A_  :		Dict						=					None
    if self.use_labels:
       A_  :		Optional[Any]						=					ids_tensor([self.batch_size, self.image_size, self.image_size]					,    self.num_labels  )

    A_  :		Any						=					self.get_config()
    return config, pixel_values, labels







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    return GLPNConfig(
        image_size=self.image_size					,    num_channels=self.num_channels					,    num_encoder_blocks=self.num_encoder_blocks					,    depths=self.depths					,    hidden_sizes=self.hidden_sizes					,    num_attention_heads=self.num_attention_heads					,    hidden_act=self.hidden_act					,    hidden_dropout_prob=self.hidden_dropout_prob					,    attention_probs_dropout_prob=self.attention_probs_dropout_prob					,    initializer_range=self.initializer_range					,    decoder_hidden_size=self.decoder_hidden_size					,    )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    str:
    A_  :		Tuple						=					GLPNModel(config=_lowerCamelCase  )
    model.to(_lowerCamelCase  )
    model.eval()
    A_  :		Optional[Any]						=					model(_lowerCamelCase  )
    A_  :		Optional[int]						=					self.image_size // (self.downsampling_rates[-1] * 2)
    self.parent.assertEqual(
        result.last_hidden_state.shape					,    (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)  )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    Optional[int]:
    A_  :		Tuple						=					self.num_labels
    A_  :		Any						=					GLPNForDepthEstimation(_lowerCamelCase  )
    model.to(_lowerCamelCase  )
    model.eval()
    A_  :		Dict						=					model(_lowerCamelCase  )
    self.parent.assertEqual(result.predicted_depth.shape					,    (self.batch_size, self.image_size, self.image_size)  )
    A_  :		Optional[int]						=					model(_lowerCamelCase					,    labels=_lowerCamelCase  )
    self.parent.assertEqual(result.predicted_depth.shape					,    (self.batch_size, self.image_size, self.image_size)  )







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    A_  :		Optional[Any]						=					self.prepare_config_and_inputs()
    A_						,  A_						,  A_  :		Tuple						=					config_and_inputs
    A_  :		Dict						=					{"""pixel_values""": pixel_values}
    return config, inputs_dict


@require_torch
class 							_lowerCAmelCase	(  __A,							__A,							unittest.TestCase							):


 """simple docstring"""

 lowerCamelCase									=     (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
 lowerCamelCase									=     (
     {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
 )

 lowerCamelCase									=     False
 lowerCamelCase									=     False
 lowerCamelCase									=     False







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    A_  :		Optional[Any]						=					GLPNModelTester(self  )
    A_  :		Dict						=					GLPNConfigTester(self					,    config_class=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    self.config_tester.run_common_tests()







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		int						=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		Dict						=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_depth_estimation(*_lowerCamelCase  )







 @unittest.skip("""GLPN does not use inputs_embeds"""  )
 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    pass







 @unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""  )
 def       UpperCAmelCase_       (      self  )    ->    Any:
    pass







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    A_						,  A_  :		Union[str, Any]						=					self.model_tester.prepare_config_and_inputs_for_common()

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

       A_  :		Dict						=					["""pixel_values"""]
       self.assertListEqual(arg_names[:1]					,    _lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    A_						,  A_  :		List[str]						=					self.model_tester.prepare_config_and_inputs_for_common()
    A_  :		Optional[Any]						=					True

    for model_class in self.all_model_classes:
       A_  :		int						=					True
       A_  :		str						=					False
       A_  :		Any						=					True
       A_  :		Dict						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       with torch.no_grad():
          A_  :		List[str]						=					model(**self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase  )  )
       A_  :		Union[str, Any]						=					outputs.attentions

       A_  :		List[str]						=					sum(self.model_tester.depths  )
       self.assertEqual(len(_lowerCamelCase  )					,    _lowerCamelCase  )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       A_  :		Dict						=					True
       A_  :		List[str]						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       with torch.no_grad():
          A_  :		Any						=					model(**self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase  )  )
       A_  :		str						=					outputs.attentions

       self.assertEqual(len(_lowerCamelCase  )					,    _lowerCamelCase  )

       # verify the first attentions (first block, first layer)
       A_  :		List[Any]						=					(self.model_tester.image_size // 4) ** 2
       A_  :		str						=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
       self.assertListEqual(
           list(attentions[0].shape[-3:]  )					,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,    )

       # verify the last attentions (last block, last layer)
       A_  :		Optional[Any]						=					(self.model_tester.image_size // 32) ** 2
       A_  :		Optional[Any]						=					(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
       self.assertListEqual(
           list(attentions[-1].shape[-3:]  )					,    [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]					,    )
       A_  :		str						=					len(_lowerCamelCase  )

       # Check attention is always last and order is fine
       A_  :		Union[str, Any]						=					True
       A_  :		str						=					True
       A_  :		Tuple						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       with torch.no_grad():
          A_  :		Dict						=					model(**self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase  )  )

       self.assertEqual(out_len + 1					,    len(_lowerCamelCase  )  )

       A_  :		Any						=					outputs.attentions

       self.assertEqual(len(_lowerCamelCase  )					,    _lowerCamelCase  )
       # verify the first attentions (first block, first layer)
       A_  :		List[Any]						=					(self.model_tester.image_size // 4) ** 2
       A_  :		Optional[int]						=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
       self.assertListEqual(
           list(self_attentions[0].shape[-3:]  )					,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,    )







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    def check_hidden_states_output(_lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  ):
       A_  :		List[Any]						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()

       with torch.no_grad():
          A_  :		Tuple						=					model(**self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase  )  )

       A_  :		List[str]						=					outputs.hidden_states

       A_  :		Dict						=					self.model_tester.num_encoder_blocks
       self.assertEqual(len(_lowerCamelCase  )					,    _lowerCamelCase  )

       # verify the first hidden states (first block)
       self.assertListEqual(
           list(hidden_states[0].shape[-3:]  )					,    [
               self.model_tester.hidden_sizes[0],
               self.model_tester.image_size // 4,
               self.model_tester.image_size // 4,
           ]					,    )

    A_						,  A_  :		Optional[int]						=					self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       A_  :		Any						=					True
       check_hidden_states_output(_lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )

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

       check_hidden_states_output(_lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    if not self.model_tester.is_training:
       return

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

    for model_class in self.all_model_classes:
       if model_class in get_values(_lowerCamelCase  ):
          continue
       # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
       # this can then be incorporated into _prepare_for_class in test_modeling_common.py
       if model_class.__name__ == "GLPNForDepthEstimation":
          A_						,  A_						,  A_						,  A_  :		Any						=					inputs_dict["""pixel_values"""].shape
          A_  :		List[Any]						=					torch.zeros(
              [self.model_tester.batch_size, height, width]					,    device=_lowerCamelCase  ).long()
       A_  :		Union[str, Any]						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.train()
       A_  :		List[str]						=					self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase					,    return_labels=_lowerCamelCase  )
       A_  :		Dict						=					model(**_lowerCamelCase  ).loss
       loss.backward()







 @slow
 def       UpperCAmelCase_       (      self  )    ->    Dict:
    for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       A_  :		Union[str, Any]						=					GLPNModel.from_pretrained(_lowerCamelCase  )
       self.assertIsNotNone(_lowerCamelCase  )



def     UpperCAmelCase     (    )   ->       int:



   """simple docstring"""






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







@require_torch
@require_vision
@slow
class 							_lowerCAmelCase	(  unittest.TestCase							):


 """simple docstring"""

 @slow
 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    A_  :		Tuple						=					GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
    A_  :		List[Any]						=					GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(_lowerCamelCase  )

    A_  :		str						=					prepare_img()
    A_  :		int						=					image_processor(images=_lowerCamelCase					,    return_tensors="""pt"""  ).to(_lowerCamelCase  )

    # forward pass
    with torch.no_grad():
       A_  :		Optional[int]						=					model(**_lowerCamelCase  )

    # verify the predicted depth
    A_  :		List[Any]						=					torch.Size([1, 480, 640]  )
    self.assertEqual(outputs.predicted_depth.shape					,    _lowerCamelCase  )

    A_  :		Union[str, Any]						=					torch.tensor(
        [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]  ).to(_lowerCamelCase  )

    self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]					,    _lowerCamelCase					,    atol=1e-4  )  )
