


'''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 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__=0.9_9_9   ,UpperCAmelCase__="cosine"   ,):

				"""simple docstring"""






				if alpha_transform_type == "cosine":

								def alpha_bar_fn(UpperCAmelCase__							):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2							) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(UpperCAmelCase__							):
												return math.exp(t * -1_2.0							)

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

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



class    __lowercase  (       UpperCamelCase      ,     UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						[e.name for e in KarrasDiffusionSchedulers]
							SCREAMING_SNAKE_CASE   :					int        =						2





							@register_to_config
							def __init__(    self				, A_ = 1000				, A_ = 0.0_0085				, A_ = 0.012				, A_ = "linear"				, A_ = None				, A_ = "epsilon"				, A_ = "linspace"				, A_ = 0				, )->	Dict:







											if trained_betas is not None:
															_SCREAMING_SNAKE_CASE      = torch.tensor(A_				, dtype=torch.floataa	)
											elif beta_schedule == "linear":
															_SCREAMING_SNAKE_CASE      = torch.linspace(A_				, A_				, A_				, dtype=torch.floataa	)
											elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															_SCREAMING_SNAKE_CASE      = (
															    torch.linspace(beta_start**0.5				, beta_end**0.5				, A_				, dtype=torch.floataa	) ** 2
															)
											elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															_SCREAMING_SNAKE_CASE      = betas_for_alpha_bar(A_	)
											else:
															raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''	)

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

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





							def 		__magic_name__			(    self				, A_				, A_=None	)->	Optional[Any]:







											if schedule_timesteps is None:
															_SCREAMING_SNAKE_CASE      = self.timesteps

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

											return indices[pos].item()





							@property
							def 		__magic_name__			(    self	)->	List[str]:







											if self.config.timestep_spacing in ["linspace", "trailing"]:
															return self.sigmas.max()

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





							def 		__magic_name__			(    self				, A_				, A_				, )->	torch.FloatTensor:
											_SCREAMING_SNAKE_CASE      = self.index_for_timestep(A_	)

											if self.state_in_first_order:
															_SCREAMING_SNAKE_CASE      = self.sigmas[step_index]
											else:
															_SCREAMING_SNAKE_CASE      = self.sigmas_interpol[step_index - 1]

											_SCREAMING_SNAKE_CASE      = sample / ((sigma**2 + 1) ** 0.5)
											return sample





							def 		__magic_name__			(    self				, A_				, A_ = None				, A_ = None				, )->	Tuple:
											_SCREAMING_SNAKE_CASE      = num_inference_steps

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

											_SCREAMING_SNAKE_CASE      = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
											_SCREAMING_SNAKE_CASE      = torch.from_numpy(np.log(A_	)	).to(A_	)

											_SCREAMING_SNAKE_CASE      = np.interp(A_				, np.arange(0				, len(A_	)	)				, A_	)
											_SCREAMING_SNAKE_CASE      = np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
											_SCREAMING_SNAKE_CASE      = torch.from_numpy(A_	).to(device=A_	)

											# compute up and down sigmas
											_SCREAMING_SNAKE_CASE      = sigmas.roll(-1	)
											_SCREAMING_SNAKE_CASE      = 0.0
											_SCREAMING_SNAKE_CASE      = (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
											_SCREAMING_SNAKE_CASE      = (sigmas_next**2 - sigmas_up**2) ** 0.5
											_SCREAMING_SNAKE_CASE      = 0.0

											# compute interpolated sigmas
											_SCREAMING_SNAKE_CASE      = sigmas.log().lerp(sigmas_down.log()				, 0.5	).exp()
											_SCREAMING_SNAKE_CASE      = 0.0

											# set sigmas
											_SCREAMING_SNAKE_CASE      = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2	), sigmas[-1:]]	)
											_SCREAMING_SNAKE_CASE      = torch.cat(
											    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2	), sigmas_interpol[-1:]]	)
											_SCREAMING_SNAKE_CASE      = torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2	), sigmas_up[-1:]]	)
											_SCREAMING_SNAKE_CASE      = torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2	), sigmas_down[-1:]]	)

											if str(A_	).startswith('mps'	):
															# mps does not support float64
															_SCREAMING_SNAKE_CASE      = torch.from_numpy(A_	).to(A_				, dtype=torch.floataa	)
											else:
															_SCREAMING_SNAKE_CASE      = torch.from_numpy(A_	).to(A_	)

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

											_SCREAMING_SNAKE_CASE      = torch.cat([timesteps[:1], interleaved_timesteps]	)

											_SCREAMING_SNAKE_CASE      = None

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





							def 		__magic_name__			(    self				, A_	)->	Any:
											_SCREAMING_SNAKE_CASE      = sigma.log()

											# get distribution
											_SCREAMING_SNAKE_CASE      = log_sigma - self.log_sigmas[:, None]

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

											_SCREAMING_SNAKE_CASE      = self.log_sigmas[low_idx]
											_SCREAMING_SNAKE_CASE      = self.log_sigmas[high_idx]

											# interpolate sigmas
											_SCREAMING_SNAKE_CASE      = (low - log_sigma) / (low - high)
											_SCREAMING_SNAKE_CASE      = w.clamp(0				, 1	)

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





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





							def 		__magic_name__			(    self				, A_				, A_				, A_				, A_ = None				, A_ = True				, )->	Union[SchedulerOutput, Tuple]:
											_SCREAMING_SNAKE_CASE      = self.index_for_timestep(A_	)

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

											if self.state_in_first_order:
															_SCREAMING_SNAKE_CASE      = self.sigmas[step_index]
															_SCREAMING_SNAKE_CASE      = self.sigmas_interpol[step_index]
															_SCREAMING_SNAKE_CASE      = self.sigmas_up[step_index]
															_SCREAMING_SNAKE_CASE      = self.sigmas_down[step_index - 1]
											else:
															# 2nd order / KPDM2's method
															_SCREAMING_SNAKE_CASE      = self.sigmas[step_index - 1]
															_SCREAMING_SNAKE_CASE      = self.sigmas_interpol[step_index - 1]
															_SCREAMING_SNAKE_CASE      = self.sigmas_up[step_index - 1]
															_SCREAMING_SNAKE_CASE      = 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
											_SCREAMING_SNAKE_CASE      = 0
											_SCREAMING_SNAKE_CASE      = sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

											_SCREAMING_SNAKE_CASE      = model_output.device
											_SCREAMING_SNAKE_CASE      = randn_tensor(model_output.shape				, dtype=model_output.dtype				, device=A_				, generator=A_	)

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											if self.config.prediction_type == "epsilon":
															_SCREAMING_SNAKE_CASE      = sigma_hat if self.state_in_first_order else sigma_interpol
															_SCREAMING_SNAKE_CASE      = sample - sigma_input * model_output
											elif self.config.prediction_type == "v_prediction":
															_SCREAMING_SNAKE_CASE      = sigma_hat if self.state_in_first_order else sigma_interpol
															_SCREAMING_SNAKE_CASE      = 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
															_SCREAMING_SNAKE_CASE      = (sample - pred_original_sample) / sigma_hat
															# 3. delta timestep
															_SCREAMING_SNAKE_CASE      = sigma_interpol - sigma_hat

															# store for 2nd order step
															_SCREAMING_SNAKE_CASE      = sample
															_SCREAMING_SNAKE_CASE      = dt
															_SCREAMING_SNAKE_CASE      = sample + derivative * dt
											else:
															# DPM-Solver-2
															# 2. Convert to an ODE derivative for 2nd order
															_SCREAMING_SNAKE_CASE      = (sample - pred_original_sample) / sigma_interpol
															# 3. delta timestep
															_SCREAMING_SNAKE_CASE      = sigma_down - sigma_hat

															_SCREAMING_SNAKE_CASE      = self.sample
															_SCREAMING_SNAKE_CASE      = None

															_SCREAMING_SNAKE_CASE      = sample + derivative * dt
															_SCREAMING_SNAKE_CASE      = prev_sample + noise * sigma_up

											if not return_dict:
															return (prev_sample,)

											return SchedulerOutput(prev_sample=A_	)





							def 		__magic_name__			(    self				, A_				, A_				, A_				, )->	torch.FloatTensor:
											_SCREAMING_SNAKE_CASE      = self.sigmas.to(device=original_samples.device				, dtype=original_samples.dtype	)
											if original_samples.device.type == "mps" and torch.is_floating_point(A_	):
															# mps does not support float64
															_SCREAMING_SNAKE_CASE      = self.timesteps.to(original_samples.device				, dtype=torch.floataa	)
															_SCREAMING_SNAKE_CASE      = timesteps.to(original_samples.device				, dtype=torch.floataa	)
											else:
															_SCREAMING_SNAKE_CASE      = self.timesteps.to(original_samples.device	)
															_SCREAMING_SNAKE_CASE      = timesteps.to(original_samples.device	)

											_SCREAMING_SNAKE_CASE      = [self.index_for_timestep(A_				, A_	) for t in timesteps]

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

											_SCREAMING_SNAKE_CASE      = original_samples + noise * sigma
											return noisy_samples





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




import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
							from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import DPTImageProcessor



class    __lowercase  :

							"""simple docstring"""


							def __init__(    self				, A_				, A_=2				, A_=32				, A_=16				, A_=3				, A_=True				, A_=True				, A_=32				, A_=4				, A_=[0, 1, 2, 3]				, A_=4				, A_=37				, A_="gelu"				, A_=0.1				, A_=0.1				, A_=0.02				, A_=3				, A_=False				, A_=None				, )->	int:
											_SCREAMING_SNAKE_CASE      = parent
											_SCREAMING_SNAKE_CASE      = batch_size
											_SCREAMING_SNAKE_CASE      = image_size
											_SCREAMING_SNAKE_CASE      = patch_size
											_SCREAMING_SNAKE_CASE      = num_channels
											_SCREAMING_SNAKE_CASE      = is_training
											_SCREAMING_SNAKE_CASE      = use_labels
											_SCREAMING_SNAKE_CASE      = hidden_size
											_SCREAMING_SNAKE_CASE      = num_hidden_layers
											_SCREAMING_SNAKE_CASE      = backbone_out_indices
											_SCREAMING_SNAKE_CASE      = num_attention_heads
											_SCREAMING_SNAKE_CASE      = intermediate_size
											_SCREAMING_SNAKE_CASE      = hidden_act
											_SCREAMING_SNAKE_CASE      = hidden_dropout_prob
											_SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE      = initializer_range
											_SCREAMING_SNAKE_CASE      = num_labels
											_SCREAMING_SNAKE_CASE      = scope
											_SCREAMING_SNAKE_CASE      = is_hybrid
											# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
											_SCREAMING_SNAKE_CASE      = (image_size // patch_size) ** 2
											_SCREAMING_SNAKE_CASE      = num_patches + 1





							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

											_SCREAMING_SNAKE_CASE      = self.get_config()

											return config, pixel_values, labels





							def 		__magic_name__			(    self	)->	Optional[int]:
											return DPTConfig(
											    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				, backbone_out_indices=self.backbone_out_indices				, num_attention_heads=self.num_attention_heads				, intermediate_size=self.intermediate_size				, hidden_act=self.hidden_act				, hidden_dropout_prob=self.hidden_dropout_prob				, attention_probs_dropout_prob=self.attention_probs_dropout_prob				, is_decoder=A_				, initializer_range=self.initializer_range				, is_hybrid=self.is_hybrid				, )





							def 		__magic_name__			(    self				, A_				, A_				, A_	)->	List[Any]:
											_SCREAMING_SNAKE_CASE      = DPTModel(config=A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_	)
											self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)	)





							def 		__magic_name__			(    self				, A_				, A_				, A_	)->	Tuple:
											_SCREAMING_SNAKE_CASE      = self.num_labels
											_SCREAMING_SNAKE_CASE      = DPTForDepthEstimation(A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_	)
											self.parent.assertEqual(result.predicted_depth.shape				, (self.batch_size, self.image_size, self.image_size)	)





							def 		__magic_name__			(    self				, A_				, A_				, A_	)->	str:
											_SCREAMING_SNAKE_CASE      = self.num_labels
											_SCREAMING_SNAKE_CASE      = DPTForSemanticSegmentation(A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_				, labels=A_	)
											self.parent.assertEqual(
											    result.logits.shape				, (self.batch_size, self.num_labels, self.image_size, self.image_size)	)





							def 		__magic_name__			(    self	)->	str:
											_SCREAMING_SNAKE_CASE      = self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = config_and_inputs
											_SCREAMING_SNAKE_CASE      = {'pixel_values': pixel_values}
											return config, inputs_dict







@require_torch
class    __lowercase  (       UpperCamelCase      ,     UpperCamelCase      ,     unittest.TestCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Optional[int]        =						(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
							SCREAMING_SNAKE_CASE   :					Any        =						(
							    {
							        "depth-estimation": DPTForDepthEstimation,
							        "feature-extraction": DPTModel,
							        "image-segmentation": DPTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							SCREAMING_SNAKE_CASE   :					Tuple        =						False
							SCREAMING_SNAKE_CASE   :					str        =						False
							SCREAMING_SNAKE_CASE   :					Dict        =						False





							def 		__magic_name__			(    self	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = DPTModelTester(self	)
											_SCREAMING_SNAKE_CASE      = ConfigTester(self				, config_class=A_				, has_text_modality=A_				, hidden_size=37	)





							def 		__magic_name__			(    self	)->	str:
											self.config_tester.run_common_tests()





							@unittest.skip(reason='DPT does not use inputs_embeds'	)
							def 		__magic_name__			(    self	)->	Optional[Any]:
											pass





							def 		__magic_name__			(    self	)->	int:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

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





							def 		__magic_name__			(    self	)->	Any:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

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

															_SCREAMING_SNAKE_CASE      = ['pixel_values']
															self.assertListEqual(arg_names[:1]				, A_	)





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*A_	)





							def 		__magic_name__			(    self	)->	str:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_depth_estimation(*A_	)





							def 		__magic_name__			(    self	)->	Any:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*A_	)





							def 		__magic_name__			(    self	)->	List[Any]:
											for model_class in self.all_model_classes:
															if model_class.__name__ == "DPTForDepthEstimation":
																			continue

															_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()
															_SCREAMING_SNAKE_CASE      = True

															if model_class in get_values(A_	):
																			continue

															_SCREAMING_SNAKE_CASE      = model_class(A_	)
															model.to(A_	)
															model.train()
															_SCREAMING_SNAKE_CASE      = self._prepare_for_class(A_				, A_				, return_labels=A_	)
															_SCREAMING_SNAKE_CASE      = model(**A_	).loss
															loss.backward()





							def 		__magic_name__			(    self	)->	Any:
											for model_class in self.all_model_classes:
															if model_class.__name__ == "DPTForDepthEstimation":
																			continue

															_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()
															_SCREAMING_SNAKE_CASE      = False
															_SCREAMING_SNAKE_CASE      = True

															if model_class in get_values(A_	) or not model_class.supports_gradient_checkpointing:
																			continue
															_SCREAMING_SNAKE_CASE      = model_class(A_	)
															model.to(A_	)
															model.gradient_checkpointing_enable()
															model.train()
															_SCREAMING_SNAKE_CASE      = self._prepare_for_class(A_				, A_				, return_labels=A_	)
															_SCREAMING_SNAKE_CASE      = model(**A_	).loss
															loss.backward()





							def 		__magic_name__			(    self	)->	str:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

											_SCREAMING_SNAKE_CASE      = _config_zero_init(A_	)
											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE      = model_class(config=A_	)
															# Skip the check for the backbone
															_SCREAMING_SNAKE_CASE      = []
															for name, module in model.named_modules():
																			if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																							_SCREAMING_SNAKE_CASE      = [F'''{name}.{key}''' for key in module.state_dict().keys()]
																							break

															for name, param in model.named_parameters():
																			if param.requires_grad:
																							if name in backbone_params:
																											continue
																							self.assertIn(
																							    ((param.data.mean() * 1e9).round() / 1e9).item()				, [0.0, 1.0]				, msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''				, )





							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
							def 		__magic_name__			(    self	)->	List[Any]:
											pass





							@slow
							def 		__magic_name__			(    self	)->	Optional[int]:
											for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE      = DPTModel.from_pretrained(A_	)
															self.assertIsNotNone(A_	)







def 				SCREAMING_SNAKE_CASE (     ):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
				return image



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

							"""simple docstring"""


							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = DPTImageProcessor.from_pretrained('Intel/dpt-large'	)
											_SCREAMING_SNAKE_CASE      = DPTForDepthEstimation.from_pretrained('Intel/dpt-large'	).to(A_	)

											_SCREAMING_SNAKE_CASE      = prepare_img()
											_SCREAMING_SNAKE_CASE      = image_processor(images=A_				, return_tensors='pt'	).to(A_	)

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE      = model(**A_	)
															_SCREAMING_SNAKE_CASE      = outputs.predicted_depth

											# verify the predicted depth
											_SCREAMING_SNAKE_CASE      = torch.Size((1, 384, 384)	)
											self.assertEqual(predicted_depth.shape				, A_	)

											_SCREAMING_SNAKE_CASE      = torch.tensor(
											    [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]	).to(A_	)

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





							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'	)
											_SCREAMING_SNAKE_CASE      = DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'	).to(A_	)

											_SCREAMING_SNAKE_CASE      = prepare_img()
											_SCREAMING_SNAKE_CASE      = image_processor(images=A_				, return_tensors='pt'	).to(A_	)

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE      = model(**A_	)

											# verify the logits
											_SCREAMING_SNAKE_CASE      = torch.Size((1, 150, 480, 480)	)
											self.assertEqual(outputs.logits.shape				, A_	)

											_SCREAMING_SNAKE_CASE      = torch.tensor(
											    [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]	).to(A_	)

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





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'	)
											_SCREAMING_SNAKE_CASE      = DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'	).to(A_	)

											_SCREAMING_SNAKE_CASE      = prepare_img()
											_SCREAMING_SNAKE_CASE      = image_processor(images=A_				, return_tensors='pt'	).to(A_	)

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE      = model(**A_	)

											_SCREAMING_SNAKE_CASE      = outputs.logits.detach().cpu()

											_SCREAMING_SNAKE_CASE      = image_processor.post_process_semantic_segmentation(outputs=A_				, target_sizes=[(500, 300)]	)
											_SCREAMING_SNAKE_CASE      = torch.Size((500, 300)	)
											self.assertEqual(segmentation[0].shape				, A_	)

											_SCREAMING_SNAKE_CASE      = image_processor.post_process_semantic_segmentation(outputs=A_	)
											_SCREAMING_SNAKE_CASE      = torch.Size((480, 480)	)
											self.assertEqual(segmentation[0].shape				, A_	)
