
'''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 .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def      A (       A_							:					Dict    ,							A_							:					Union[str, Any]=0.999    ,							A_							:					Any="cosine"    ,							):
						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

						snake_case       :     str =    []
						for i in range(A_       ):
												snake_case       :     int =    i / num_diffusion_timesteps
												snake_case       :     Dict =    (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   (				__magic_name__				,__magic_name__    ):
				_snake_case		    = [e.name for e in KarrasDiffusionSchedulers]
				_snake_case		    = 2




				@register_to_config
				def __init__(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				int = 10_00,    SCREAMING_SNAKE_CASE_	:				float = 0.00085,    SCREAMING_SNAKE_CASE_	:				float = 0.012,    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:
																snake_case       :     List[str] =    torch.tensor(SCREAMING_SNAKE_CASE_,    dtype=torch.floataa      )
										elif beta_schedule == "linear":
																snake_case       :     int =    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.
																snake_case       :     int =    (
																    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
																snake_case       :     List[Any] =    betas_for_alpha_bar(SCREAMING_SNAKE_CASE_,    alpha_transform_type='''cosine'''      )
										elif beta_schedule == "exp":
																snake_case       :     List[str] =    betas_for_alpha_bar(SCREAMING_SNAKE_CASE_,    alpha_transform_type='''exp'''      )
										else:
																raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""      )

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

										#  set all values
										self.set_timesteps(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										snake_case       :     str =    use_karras_sigmas




				def 	__snake_case							(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				int=None      ):
										if schedule_timesteps is None:
																snake_case       :     Any =    self.timesteps

										snake_case       :     Tuple =    (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:
																snake_case       :     Optional[int] =    1 if len(SCREAMING_SNAKE_CASE_      ) > 1 else 0
										else:
																snake_case       :     Dict =    timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_      ) else timestep
																snake_case       :     int =    self._index_counter[timestep_int]

										return indices[pos].item()




				@property
				def 	__snake_case							(							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 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				torch.FloatTensor,    SCREAMING_SNAKE_CASE_	:				Union[float, torch.FloatTensor],    ):
										snake_case       :     str =    self.index_for_timestep(SCREAMING_SNAKE_CASE_      )

										snake_case       :     Any =    self.sigmas[step_index]
										snake_case       :     int =    sample / ((sigma**2 + 1) ** 0.5)
										return sample




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Union[str, torch.device] = None,    SCREAMING_SNAKE_CASE_	:				Optional[int] = None,    ):
										snake_case       :     List[str] =    num_inference_steps

										snake_case       :     str =    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":
																snake_case       :     Tuple =    np.linspace(0,    num_train_timesteps - 1,    SCREAMING_SNAKE_CASE_,    dtype=SCREAMING_SNAKE_CASE_      )[::-1].copy()
										elif self.config.timestep_spacing == "leading":
																snake_case       :     Dict =    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
																snake_case       :     int =    (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":
																snake_case       :     Dict =    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
																snake_case       :     Tuple =    (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'."""      )

										snake_case       :     Dict =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
										snake_case       :     Optional[Any] =    np.log(SCREAMING_SNAKE_CASE_      )
										snake_case       :     List[str] =    np.interp(SCREAMING_SNAKE_CASE_,    np.arange(0,    len(SCREAMING_SNAKE_CASE_      )      ),    SCREAMING_SNAKE_CASE_      )

										if self.config.use_karras_sigmas:
																snake_case       :     Optional[int] =    self._convert_to_karras(in_sigmas=SCREAMING_SNAKE_CASE_,    num_inference_steps=self.num_inference_steps      )
																snake_case       :     Dict =    np.array([self._sigma_to_t(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) for sigma in sigmas]      )

										snake_case       :     Optional[Any] =    np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
										snake_case       :     str =    torch.from_numpy(SCREAMING_SNAKE_CASE_      ).to(device=SCREAMING_SNAKE_CASE_      )
										snake_case       :     List[str] =    torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2      ), sigmas[-1:]]      )

										snake_case       :     Union[str, Any] =    torch.from_numpy(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Tuple =    torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2      )]      )

										if str(SCREAMING_SNAKE_CASE_      ).startswith('''mps'''      ):
																# mps does not support float64
																snake_case       :     Optional[int] =    timesteps.to(SCREAMING_SNAKE_CASE_,    dtype=torch.floataa      )
										else:
																snake_case       :     Tuple =    timesteps.to(device=SCREAMING_SNAKE_CASE_      )

										# empty dt and derivative
										snake_case       :     str =    None
										snake_case       :     Tuple =    None

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




				def 	__snake_case							(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
										# get log sigma
										snake_case       :     str =    np.log(SCREAMING_SNAKE_CASE_      )

										# get distribution
										snake_case       :     List[Any] =    log_sigma - log_sigmas[:, np.newaxis]

										# get sigmas range
										snake_case       :     Optional[int] =    np.cumsum((dists >= 0),    axis=0      ).argmax(axis=0      ).clip(max=log_sigmas.shape[0] - 2      )
										snake_case       :     List[Any] =    low_idx + 1

										snake_case       :     Optional[Any] =    log_sigmas[low_idx]
										snake_case       :     Optional[int] =    log_sigmas[high_idx]

										# interpolate sigmas
										snake_case       :     Optional[int] =    (low - log_sigma) / (low - high)
										snake_case       :     str =    np.clip(SCREAMING_SNAKE_CASE_,    0,    1      )

										# transform interpolation to time range
										snake_case       :     List[Any] =    (1 - w) * low_idx + w * high_idx
										snake_case       :     Optional[Any] =    t.reshape(sigma.shape      )
										return t




				def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				torch.FloatTensor,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     float =    in_sigmas[-1].item()
										snake_case       :     float =    in_sigmas[0].item()

										snake_case       :     Union[str, Any] =    7.0  # 7.0 is the value used in the paper
										snake_case       :     Optional[Any] =    np.linspace(0,    1,    SCREAMING_SNAKE_CASE_      )
										snake_case       :     Tuple =    sigma_min ** (1 / rho)
										snake_case       :     Tuple =    sigma_max ** (1 / rho)
										snake_case       :     List[Any] =    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas




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




				def 	__snake_case							(							self	:				List[str],    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,    ):
										snake_case       :     List[Any] =    self.index_for_timestep(SCREAMING_SNAKE_CASE_      )

										# advance index counter by 1
										snake_case       :     int =    timestep.cpu().item() if torch.is_tensor(SCREAMING_SNAKE_CASE_      ) else timestep
										self._index_counter[timestep_int] += 1

										if self.state_in_first_order:
																snake_case       :     Tuple =    self.sigmas[step_index]
																snake_case       :     Dict =    self.sigmas[step_index + 1]
										else:
																# 2nd order / Heun's method
																snake_case       :     Dict =    self.sigmas[step_index - 1]
																snake_case       :     Union[str, Any] =    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
										snake_case       :     Any =    0
										snake_case       :     List[Any] =    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":
																snake_case       :     Dict =    sigma_hat if self.state_in_first_order else sigma_next
																snake_case       :     str =    sample - sigma_input * model_output
										elif self.config.prediction_type == "v_prediction":
																snake_case       :     Optional[Any] =    sigma_hat if self.state_in_first_order else sigma_next
																snake_case       :     Tuple =    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																    sample / (sigma_input**2 + 1)
																)
										elif self.config.prediction_type == "sample":
																snake_case       :     int =    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:
																snake_case       :     List[Any] =    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
																snake_case       :     Optional[int] =    (sample - pred_original_sample) / sigma_hat
																# 3. delta timestep
																snake_case       :     List[Any] =    sigma_next - sigma_hat

																# store for 2nd order step
																snake_case       :     Dict =    derivative
																snake_case       :     str =    dt
																snake_case       :     int =    sample
										else:
																# 2. 2nd order / Heun's method
																snake_case       :     Union[str, Any] =    (sample - pred_original_sample) / sigma_next
																snake_case       :     List[str] =    (self.prev_derivative + derivative) / 2

																# 3. take prev timestep & sample
																snake_case       :     Optional[Any] =    self.dt
																snake_case       :     int =    self.sample

																# free dt and derivative
																# Note, this puts the scheduler in "first order mode"
																snake_case       :     Optional[int] =    None
																snake_case       :     Tuple =    None
																snake_case       :     int =    None

										snake_case       :     Optional[Any] =    sample + derivative * dt

										if not return_dict:
																return (prev_sample,)

										return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[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
										snake_case       :     Tuple =    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
																snake_case       :     List[Any] =    self.timesteps.to(original_samples.device,    dtype=torch.floataa      )
																snake_case       :     Optional[int] =    timesteps.to(original_samples.device,    dtype=torch.floataa      )
										else:
																snake_case       :     str =    self.timesteps.to(original_samples.device      )
																snake_case       :     List[str] =    timesteps.to(original_samples.device      )

										snake_case       :     Dict =    [self.index_for_timestep(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ) for t in timesteps]

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

										snake_case       :     List[str] =    original_samples + noise * sigma
										return noisy_samples




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



'''simple docstring'''





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 						a   :




				def __init__(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				List[Any]=2,    SCREAMING_SNAKE_CASE_	:				str=32,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=16,    SCREAMING_SNAKE_CASE_	:				str=3,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=True,    SCREAMING_SNAKE_CASE_	:				int=True,    SCREAMING_SNAKE_CASE_	:				Optional[Any]=32,    SCREAMING_SNAKE_CASE_	:				Any=4,    SCREAMING_SNAKE_CASE_	:				Dict=[0, 1, 2, 3],    SCREAMING_SNAKE_CASE_	:				Dict=4,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=37,    SCREAMING_SNAKE_CASE_	:				str="gelu",    SCREAMING_SNAKE_CASE_	:				Optional[int]=0.1,    SCREAMING_SNAKE_CASE_	:				List[Any]=0.1,    SCREAMING_SNAKE_CASE_	:				Dict=0.02,    SCREAMING_SNAKE_CASE_	:				int=3,    SCREAMING_SNAKE_CASE_	:				List[str]=[1, 3_84, 24, 24],    SCREAMING_SNAKE_CASE_	:				Optional[int]=True,    SCREAMING_SNAKE_CASE_	:				Optional[int]=None,    ):
										snake_case       :     int =    parent
										snake_case       :     Tuple =    batch_size
										snake_case       :     List[str] =    image_size
										snake_case       :     Union[str, Any] =    patch_size
										snake_case       :     Dict =    num_channels
										snake_case       :     int =    is_training
										snake_case       :     Any =    use_labels
										snake_case       :     Any =    hidden_size
										snake_case       :     Union[str, Any] =    num_hidden_layers
										snake_case       :     Tuple =    backbone_out_indices
										snake_case       :     Dict =    num_attention_heads
										snake_case       :     Optional[Any] =    intermediate_size
										snake_case       :     Any =    hidden_act
										snake_case       :     List[str] =    hidden_dropout_prob
										snake_case       :     List[str] =    attention_probs_dropout_prob
										snake_case       :     Tuple =    initializer_range
										snake_case       :     Any =    num_labels
										snake_case       :     Union[str, Any] =    backbone_featmap_shape
										snake_case       :     Optional[int] =    scope
										snake_case       :     Tuple =    is_hybrid
										# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
										snake_case       :     Dict =    (image_size // patch_size) ** 2
										snake_case       :     Any =    num_patches + 1




				def 	__snake_case							(							self	:				List[Any]      ):
										snake_case       :     Optional[Any] =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

										snake_case       :     Union[str, Any] =    None
										if self.use_labels:
																snake_case       :     List[Any] =    ids_tensor([self.batch_size, self.image_size, self.image_size],    self.num_labels      )

										snake_case       :     Optional[int] =    self.get_config()

										return config, pixel_values, labels




				def 	__snake_case							(							self	:				List[str]      ):
										snake_case       :     Optional[int] =    {
										    '''global_padding''': '''same''',
										    '''layer_type''': '''bottleneck''',
										    '''depths''': [3, 4, 9],
										    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
										    '''embedding_dynamic_padding''': True,
										    '''hidden_sizes''': [96, 1_92, 3_84, 7_68],
										    '''num_groups''': 2,
										}

										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=SCREAMING_SNAKE_CASE_,    initializer_range=self.initializer_range,    is_hybrid=self.is_hybrid,    backbone_config=SCREAMING_SNAKE_CASE_,    backbone_featmap_shape=self.backbone_featmap_shape,    )




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Any      ):
										snake_case       :     Dict =    DPTModel(config=SCREAMING_SNAKE_CASE_      )
										model.to(SCREAMING_SNAKE_CASE_      )
										model.eval()
										snake_case       :     List[Any] =    model(SCREAMING_SNAKE_CASE_      )
										self.parent.assertEqual(result.last_hidden_state.shape,    (self.batch_size, self.seq_length, self.hidden_size)      )




				def 	__snake_case							(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				Optional[Any]      ):
										snake_case       :     int =    self.num_labels
										snake_case       :     Any =    DPTForDepthEstimation(SCREAMING_SNAKE_CASE_      )
										model.to(SCREAMING_SNAKE_CASE_      )
										model.eval()
										snake_case       :     Union[str, Any] =    model(SCREAMING_SNAKE_CASE_      )
										self.parent.assertEqual(result.predicted_depth.shape,    (self.batch_size, self.image_size, self.image_size)      )




				def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										snake_case       :     List[str] =    self.num_labels
										snake_case       :     List[Any] =    DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE_      )
										model.to(SCREAMING_SNAKE_CASE_      )
										model.eval()
										snake_case       :     Union[str, Any] =    model(SCREAMING_SNAKE_CASE_,    labels=SCREAMING_SNAKE_CASE_      )
										self.parent.assertEqual(
										    result.logits.shape,    (self.batch_size, self.num_labels, self.image_size, self.image_size)      )




				def 	__snake_case							(							self	:				Union[str, Any]      ):
										snake_case       :     Optional[int] =    self.prepare_config_and_inputs()
										snake_case,					snake_case,					snake_case       :     Tuple =    config_and_inputs
										snake_case       :     List[str] =    {'''pixel_values''': pixel_values}
										return config, inputs_dict




@require_torch
class 						a   (				__magic_name__				,__magic_name__				,unittest.TestCase    ):
				_snake_case		    = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
				_snake_case		    = (
				    {
				        '''depth-estimation''': DPTForDepthEstimation,
				        '''feature-extraction''': DPTModel,
				        '''image-segmentation''': DPTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				_snake_case		    = False
				_snake_case		    = False
				_snake_case		    = False




				def 	__snake_case							(							self	:				Any      ):
										snake_case       :     Optional[Any] =    DPTModelTester(self      )
										snake_case       :     Any =    ConfigTester(self,    config_class=SCREAMING_SNAKE_CASE_,    has_text_modality=SCREAMING_SNAKE_CASE_,    hidden_size=37      )




				def 	__snake_case							(							self	:				Dict      ):
										self.config_tester.run_common_tests()




				@unittest.skip(reason='''DPT does not use inputs_embeds'''      )
				def 	__snake_case							(							self	:				int      ):
										pass




				def 	__snake_case							(							self	:				List[str]      ):
										snake_case,					snake_case       :     str =    self.model_tester.prepare_config_and_inputs_for_common()

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




				def 	__snake_case							(							self	:				Tuple      ):
										snake_case,					snake_case       :     Optional[int] =    self.model_tester.prepare_config_and_inputs_for_common()

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

																snake_case       :     Union[str, Any] =    ['''pixel_values''']
																self.assertListEqual(arg_names[:1],    SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[str]      ):
										snake_case       :     Tuple =    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Tuple      ):
										snake_case       :     List[Any] =    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_depth_estimation(*SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Dict      ):
										snake_case       :     Tuple =    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*SCREAMING_SNAKE_CASE_      )




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

																snake_case,					snake_case       :     Optional[Any] =    self.model_tester.prepare_config_and_inputs_for_common()
																snake_case       :     Dict =    True

																if model_class in get_values(SCREAMING_SNAKE_CASE_      ):
																						continue

																snake_case       :     Tuple =    model_class(SCREAMING_SNAKE_CASE_      )
																model.to(SCREAMING_SNAKE_CASE_      )
																model.train()
																snake_case       :     Any =    self._prepare_for_class(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    return_labels=SCREAMING_SNAKE_CASE_      )
																snake_case       :     Dict =    model(**SCREAMING_SNAKE_CASE_      ).loss
																loss.backward()




				def 	__snake_case							(							self	:				int      ):
										for model_class in self.all_model_classes:
																if model_class.__name__ == "DPTForDepthEstimation":
																						continue

																snake_case,					snake_case       :     Any =    self.model_tester.prepare_config_and_inputs_for_common()
																snake_case       :     Dict =    False
																snake_case       :     Dict =    True

																if model_class in get_values(SCREAMING_SNAKE_CASE_      ) or not model_class.supports_gradient_checkpointing:
																						continue
																snake_case       :     Optional[Any] =    model_class(SCREAMING_SNAKE_CASE_      )
																model.to(SCREAMING_SNAKE_CASE_      )
																model.gradient_checkpointing_enable()
																model.train()
																snake_case       :     Union[str, Any] =    self._prepare_for_class(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    return_labels=SCREAMING_SNAKE_CASE_      )
																snake_case       :     List[str] =    model(**SCREAMING_SNAKE_CASE_      ).loss
																loss.backward()




				def 	__snake_case							(							self	:				Tuple      ):
										snake_case,					snake_case       :     List[str] =    self.model_tester.prepare_config_and_inputs_for_common()

										snake_case       :     Union[str, Any] =    _config_zero_init(SCREAMING_SNAKE_CASE_      )
										for model_class in self.all_model_classes:
																snake_case       :     List[str] =    model_class(config=SCREAMING_SNAKE_CASE_      )
																# Skip the check for the backbone
																snake_case       :     Optional[int] =    []
																for name, module in model.named_modules():
																						if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																												snake_case       :     List[Any] =    [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 	__snake_case							(							self	:				Union[str, Any]      ):
										pass




				@slow
				def 	__snake_case							(							self	:				List[Any]      ):
										for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
																snake_case       :     int =    DPTModel.from_pretrained(SCREAMING_SNAKE_CASE_      )
																self.assertIsNotNone(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Dict      ):
										# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
										snake_case,					snake_case       :     str =    self.model_tester.prepare_config_and_inputs_for_common()
										snake_case       :     int =    '''add'''
										with self.assertRaises(SCREAMING_SNAKE_CASE_      ):
																snake_case       :     Any =    DPTForDepthEstimation(SCREAMING_SNAKE_CASE_      )




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





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




				def 	__snake_case							(							self	:				Dict      ):
										snake_case       :     Dict =    DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''      )
										snake_case       :     int =    DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''      ).to(SCREAMING_SNAKE_CASE_      )

										snake_case       :     str =    prepare_img()
										snake_case       :     List[str] =    image_processor(images=SCREAMING_SNAKE_CASE_,    return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE_      )

										# forward pass
										with torch.no_grad():
																snake_case       :     List[Any] =    model(**SCREAMING_SNAKE_CASE_      )
																snake_case       :     Optional[Any] =    outputs.predicted_depth

										# verify the predicted depth
										snake_case       :     List[str] =    torch.Size((1, 3_84, 3_84)      )
										self.assertEqual(predicted_depth.shape,    SCREAMING_SNAKE_CASE_      )

										snake_case       :     Dict =    torch.tensor(
										    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]      ).to(SCREAMING_SNAKE_CASE_      )

										self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00,    SCREAMING_SNAKE_CASE_,    atol=1e-4      )      )

