
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 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''





	def 	__snake_case					(      self		):
					A__	:						List[str]								=				self.config_class(**self.inputs_dict		)
					self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,							'''hidden_sizes'''		)		)
					self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,							'''num_attention_heads'''		)		)
					self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,							'''num_encoder_blocks'''		)		)







class 			UpperCamelCase__		:







	'''simple docstring'''





	def __init__(      self					,							UpperCamelCase__					,							UpperCamelCase__=13					,							UpperCamelCase__=64					,							UpperCamelCase__=3					,							UpperCamelCase__=4					,							UpperCamelCase__=[2, 2, 2, 2]					,							UpperCamelCase__=[8, 4, 2, 1]					,							UpperCamelCase__=[16, 32, 64, 128]					,							UpperCamelCase__=[1, 4, 8, 16]					,							UpperCamelCase__=[1, 2, 4, 8]					,							UpperCamelCase__=True					,							UpperCamelCase__=True					,							UpperCamelCase__="gelu"					,							UpperCamelCase__=0.1					,							UpperCamelCase__=0.1					,							UpperCamelCase__=0.0_2					,							UpperCamelCase__=16					,							UpperCamelCase__=3					,							UpperCamelCase__=None					,							):
					A__	:						Dict								=				parent
					A__	:						Tuple								=				batch_size
					A__	:						List[Any]								=				image_size
					A__	:						Any								=				num_channels
					A__	:						List[Any]								=				num_encoder_blocks
					A__	:						str								=				sr_ratios
					A__	:						int								=				depths
					A__	:						Tuple								=				hidden_sizes
					A__	:						Dict								=				downsampling_rates
					A__	:						Any								=				num_attention_heads
					A__	:						Union[str, Any]								=				is_training
					A__	:						List[Any]								=				use_labels
					A__	:						List[str]								=				hidden_act
					A__	:						Union[str, Any]								=				hidden_dropout_prob
					A__	:						List[Any]								=				attention_probs_dropout_prob
					A__	:						Optional[Any]								=				initializer_range
					A__	:						Any								=				decoder_hidden_size
					A__	:						Union[str, Any]								=				num_labels
					A__	:						Any								=				scope





	def 	__snake_case					(      self		):
					A__	:						int								=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

					A__	:						Optional[int]								=				None
					if self.use_labels:
									A__	:						List[Any]								=				ids_tensor([self.batch_size, self.image_size, self.image_size]					,							self.num_labels		)

					A__	:						Union[str, Any]								=				self.get_config()
					return config, pixel_values, labels





	def 	__snake_case					(      self		):
					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 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__		):
					A__	:						str								=				GLPNModel(config=_SCREAMING_SNAKE_CASE		)
					model.to(_SCREAMING_SNAKE_CASE		)
					model.eval()
					A__	:						Optional[Any]								=				model(_SCREAMING_SNAKE_CASE		)
					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 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__		):
					A__	:						Any								=				self.num_labels
					A__	:						Dict								=				GLPNForDepthEstimation(_SCREAMING_SNAKE_CASE		)
					model.to(_SCREAMING_SNAKE_CASE		)
					model.eval()
					A__	:						Any								=				model(_SCREAMING_SNAKE_CASE		)
					self.parent.assertEqual(result.predicted_depth.shape					,							(self.batch_size, self.image_size, self.image_size)		)
					A__	:						Tuple								=				model(_SCREAMING_SNAKE_CASE					,							labels=_SCREAMING_SNAKE_CASE		)
					self.parent.assertEqual(result.predicted_depth.shape					,							(self.batch_size, self.image_size, self.image_size)		)





	def 	__snake_case					(      self		):
					A__	:						str								=				self.prepare_config_and_inputs()
					A__							,				A__							,				A__	:						str								=				config_and_inputs
					A__	:						str								=				{'''pixel_values''': pixel_values}
					return config, inputs_dict







@require_torch
class 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      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 	__snake_case					(      self		):
					A__	:						Dict								=				GLPNModelTester(self		)
					A__	:						Any								=				GLPNConfigTester(self					,							config_class=_SCREAMING_SNAKE_CASE		)





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





	def 	__snake_case					(      self		):
					A__	:						List[Any]								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)





	def 	__snake_case					(      self		):
					A__	:						Tuple								=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_depth_estimation(*_SCREAMING_SNAKE_CASE		)





	@unittest.skip('''GLPN does not use inputs_embeds'''		)
	def 	__snake_case					(      self		):
					pass





	@unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''		)
	def 	__snake_case					(      self		):
					pass





	def 	__snake_case					(      self		):
					A__							,				A__	:						List[Any]								=				self.model_tester.prepare_config_and_inputs_for_common()

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

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





	def 	__snake_case					(      self		):
					A__							,				A__	:						Optional[int]								=				self.model_tester.prepare_config_and_inputs_for_common()
					A__	:						List[Any]								=				True

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

									A__	:						Optional[int]								=				sum(self.model_tester.depths		)
									self.assertEqual(len(_SCREAMING_SNAKE_CASE		)					,							_SCREAMING_SNAKE_CASE		)

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

									self.assertEqual(len(_SCREAMING_SNAKE_CASE		)					,							_SCREAMING_SNAKE_CASE		)

									# verify the first attentions (first block, first layer)
									A__	:						Optional[int]								=				(self.model_tester.image_size // 4) ** 2
									A__	:						List[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__	:						Tuple								=				(self.model_tester.image_size // 32) ** 2
									A__	:						List[str]								=				(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__	:						Any								=				len(_SCREAMING_SNAKE_CASE		)

									# Check attention is always last and order is fine
									A__	:						int								=				True
									A__	:						Optional[int]								=				True
									A__	:						Tuple								=				model_class(_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()
									with torch.no_grad():
													A__	:						int								=				model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE		)		)

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

									A__	:						Optional[int]								=				outputs.attentions

									self.assertEqual(len(_SCREAMING_SNAKE_CASE		)					,							_SCREAMING_SNAKE_CASE		)
									# verify the first attentions (first block, first layer)
									A__	:						Optional[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(self_attentions[0].shape[-3:]		)					,							[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,							)





	def 	__snake_case					(      self		):

					def check_hidden_states_output(UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__		):
									A__	:						Optional[Any]								=				model_class(_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.eval()

									with torch.no_grad():
													A__	:						List[Any]								=				model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE		)		)

									A__	:						Optional[int]								=				outputs.hidden_states

									A__	:						int								=				self.model_tester.num_encoder_blocks
									self.assertEqual(len(_SCREAMING_SNAKE_CASE		)					,							_SCREAMING_SNAKE_CASE		)

									# 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__	:						str								=				self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									A__	:						Optional[Any]								=				True
									check_hidden_states_output(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE		)

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

									check_hidden_states_output(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE		)





	def 	__snake_case					(      self		):

					if not self.model_tester.is_training:
									return

					A__							,				A__	:						int								=				self.model_tester.prepare_config_and_inputs_for_common()
					A__	:						List[Any]								=				True

					for model_class in self.all_model_classes:
									if model_class in get_values(_SCREAMING_SNAKE_CASE		):
													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__	:						str								=				inputs_dict['''pixel_values'''].shape
													A__	:						str								=				torch.zeros(
													    [self.model_tester.batch_size, height, width]					,							device=_SCREAMING_SNAKE_CASE		).long()
									A__	:						Optional[Any]								=				model_class(_SCREAMING_SNAKE_CASE		)
									model.to(_SCREAMING_SNAKE_CASE		)
									model.train()
									A__	:						Tuple								=				self._prepare_for_class(_SCREAMING_SNAKE_CASE					,							_SCREAMING_SNAKE_CASE					,							return_labels=_SCREAMING_SNAKE_CASE		)
									A__	:						Union[str, Any]								=				model(**_SCREAMING_SNAKE_CASE		).loss
									loss.backward()





	@slow
	def 	__snake_case					(      self		):

					for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									A__	:						int								=				GLPNModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
									self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)




def 					SCREAMING_SNAKE_CASE  (   )			->							Union[str, Any]:

				"""simple docstring"""







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







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







	'''simple docstring'''





	@slow
	def 	__snake_case					(      self		):
					A__	:						List[str]								=				GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
					A__	:						List[str]								=				GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(_SCREAMING_SNAKE_CASE		)

					A__	:						Optional[int]								=				prepare_img()
					A__	:						Optional[int]								=				image_processor(images=_SCREAMING_SNAKE_CASE					,							return_tensors='''pt'''		).to(_SCREAMING_SNAKE_CASE		)

					# forward pass
					with torch.no_grad():
									A__	:						List[Any]								=				model(**_SCREAMING_SNAKE_CASE		)

					# verify the predicted depth
					A__	:						str								=				torch.Size([1, 480, 640]		)
					self.assertEqual(outputs.predicted_depth.shape					,							_SCREAMING_SNAKE_CASE		)

					A__	:						Optional[int]								=				torch.tensor(
					    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]		).to(_SCREAMING_SNAKE_CASE		)

					self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]					,							_SCREAMING_SNAKE_CASE					,							atol=1e-4		)		)
"""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       A__    (	lowercase_     :	Any      , lowercase_     :	int=0.9_9_9      , lowercase_     :	Dict="cosine"      , )  ->							Optional[int]:
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(lowercase_     :	List[str]						):
         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(lowercase_     :	Union[str, Any]						):
         return math.exp(t * -1_2.0						)

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

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






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							int    =    [e.name for e in KarrasDiffusionSchedulers]
   __UpperCAmelCase	:							Tuple    =    2







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


      '''simple docstring'''

      if trained_betas is not None:
         __lowerCamelCase    		=					torch.tensor(_SCREAMING_SNAKE_CASE ,				dtype=torch.floataa	)
      elif beta_schedule == "linear":
         __lowerCamelCase    		=					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.
         __lowerCamelCase    		=					(
             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
         __lowerCamelCase    		=					betas_for_alpha_bar(_SCREAMING_SNAKE_CASE	)
      else:
         raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''	)

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

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=None	):


      '''simple docstring'''

      if schedule_timesteps is None:
         __lowerCamelCase    		=					self.timesteps

      __lowerCamelCase    		=					(schedule_timesteps == timestep).nonzero()

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

      return indices[pos].item()







   @property
   def    _A				(	self	):


      '''simple docstring'''

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

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					self.index_for_timestep(_SCREAMING_SNAKE_CASE	)

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

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = None ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					num_inference_steps

      __lowerCamelCase    		=					num_train_timesteps or self.config.num_train_timesteps

      # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
      if self.config.timestep_spacing == "linspace":
         __lowerCamelCase    		=					np.linspace(0 ,				num_train_timesteps - 1 ,				_SCREAMING_SNAKE_CASE ,				dtype=_SCREAMING_SNAKE_CASE	)[::-1].copy()
      elif self.config.timestep_spacing == "leading":
         __lowerCamelCase    		=					num_train_timesteps // self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         __lowerCamelCase    		=					(np.arange(0 ,				_SCREAMING_SNAKE_CASE	) * step_ratio).round()[::-1].copy().astype(_SCREAMING_SNAKE_CASE	)
         timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
         __lowerCamelCase    		=					num_train_timesteps / self.num_inference_steps
         # creates integer timesteps by multiplying by ratio
         # casting to int to avoid issues when num_inference_step is power of 3
         __lowerCamelCase    		=					(np.arange(_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\'.'''	)

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

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

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

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

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

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

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

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

      __lowerCamelCase    		=					None

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







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					sigma.log()

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

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

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

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

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







   @property
   def    _A				(	self	):


      '''simple docstring'''

      return self.sample is None







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE = None ,				_SCREAMING_SNAKE_CASE = True ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					self.index_for_timestep(_SCREAMING_SNAKE_CASE	)

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

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

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

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

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

         __lowerCamelCase    		=					self.sample
         __lowerCamelCase    		=					None

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

      if not return_dict:
         return (prev_sample,)

      return SchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					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
         __lowerCamelCase    		=					self.timesteps.to(original_samples.device ,				dtype=torch.floataa	)
         __lowerCamelCase    		=					timesteps.to(original_samples.device ,				dtype=torch.floataa	)
      else:
         __lowerCamelCase    		=					self.timesteps.to(original_samples.device	)
         __lowerCamelCase    		=					timesteps.to(original_samples.device	)

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

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

      __lowerCamelCase    		=					original_samples + noise * sigma
      return noisy_samples







   def __len__(	self	):


      '''simple docstring'''

      return self.config.num_train_timesteps






