



import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MarkupLMForQuestionAnswering,
      MarkupLMForSequenceClassification,
      MarkupLMForTokenClassification,
      MarkupLMModel,
  )

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer

class      snake_case__       :







      """simple docstring"""




      def __init__(     self      ,     __lowercase      ,     __lowercase=1_3      ,     __lowercase=7      ,     __lowercase=True      ,     __lowercase=True      ,     __lowercase=True      ,     __lowercase=True      ,     __lowercase=9_9      ,     __lowercase=3_2      ,     __lowercase=5      ,     __lowercase=4      ,     __lowercase=3_7      ,     __lowercase="gelu"      ,     __lowercase=0.1      ,     __lowercase=0.1      ,     __lowercase=5_1_2      ,     __lowercase=1_6      ,     __lowercase=2      ,     __lowercase=0.0_2      ,     __lowercase=3      ,     __lowercase=None      ,     __lowercase=2_0      ,     __lowercase=3_0      ,     __lowercase=2      ,     __lowercase=2      ,     __lowercase=1_0      ,     )      ->   int:
         """simple docstring"""




         a__     :		Optional[Any]		 =						parent
         a__     :		str		 =						batch_size
         a__     :		List[str]		 =						seq_length
         a__     :		Optional[int]		 =						is_training
         a__     :		Optional[Any]		 =						use_input_mask
         a__     :		List[str]		 =						use_token_type_ids
         a__     :		Optional[int]		 =						use_labels
         a__     :		Dict		 =						vocab_size
         a__     :		Optional[Any]		 =						hidden_size
         a__     :		str		 =						num_hidden_layers
         a__     :		List[str]		 =						num_attention_heads
         a__     :		str		 =						intermediate_size
         a__     :		int		 =						hidden_act
         a__     :		Any		 =						hidden_dropout_prob
         a__     :		Union[str, Any]		 =						attention_probs_dropout_prob
         a__     :		Union[str, Any]		 =						max_position_embeddings
         a__     :		Optional[int]		 =						type_vocab_size
         a__     :		List[str]		 =						type_sequence_label_size
         a__     :		List[str]		 =						initializer_range
         a__     :		Any		 =						num_labels
         a__     :		List[Any]		 =						scope
         a__     :		Optional[Any]		 =						max_xpath_tag_unit_embeddings
         a__     :		Union[str, Any]		 =						max_xpath_subs_unit_embeddings
         a__     :		Optional[int]		 =						tag_pad_id
         a__     :		Optional[Any]		 =						subs_pad_id
         a__     :		Union[str, Any]		 =						max_depth

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[str]:
         """simple docstring"""




         a__     :		List[Any]		 =						ids_tensor([self.batch_size, self.seq_length]      ,     self.vocab_size						)

         a__     :		Optional[int]		 =						ids_tensor(
             [self.batch_size, self.seq_length, self.max_depth]      ,     self.max_xpath_tag_unit_embeddings						)

         a__     :		List[Any]		 =						ids_tensor(
             [self.batch_size, self.seq_length, self.max_depth]      ,     self.max_xpath_subs_unit_embeddings						)

         a__     :		int		 =						None
         if self.use_input_mask:
            a__     :		Union[str, Any]		 =						ids_tensor([self.batch_size, self.seq_length]      ,     vocab_size=2						)

         a__     :		str		 =						None
         if self.use_token_type_ids:
            a__     :		Optional[int]		 =						ids_tensor([self.batch_size, self.seq_length]      ,     self.type_vocab_size						)

         a__     :		Optional[int]		 =						None
         a__     :		Dict		 =						None
         if self.use_labels:
            a__     :		Any		 =						ids_tensor([self.batch_size]      ,     self.type_sequence_label_size						)
            a__     :		Optional[int]		 =						ids_tensor([self.batch_size, self.seq_length]      ,     self.num_labels						)

         a__     :		Tuple		 =						self.get_config()

         return (
             config,
             input_ids,
             xpath_tags_seq,
             xpath_subs_seq,
             token_type_ids,
             input_mask,
             sequence_labels,
             token_labels,
         )

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[Any]:
         """simple docstring"""




         return MarkupLMConfig(
             vocab_size=self.vocab_size      ,     hidden_size=self.hidden_size      ,     num_hidden_layers=self.num_hidden_layers      ,     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      ,     max_position_embeddings=self.max_position_embeddings      ,     type_vocab_size=self.type_vocab_size      ,     initializer_range=self.initializer_range      ,     max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings      ,     max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings      ,     tag_pad_id=self.tag_pad_id      ,     subs_pad_id=self.subs_pad_id      ,     max_depth=self.max_depth      ,     )

      def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     )      ->   Any:
         """simple docstring"""




         a__     :		int		 =						MarkupLMModel(config=__lowercase						)
         model.to(__lowercase						)
         model.eval()
         print("""Configs:"""      ,     model.config.tag_pad_id      ,     model.config.subs_pad_id						)
         a__     :		int		 =						model(__lowercase      ,     attention_mask=__lowercase      ,     token_type_ids=__lowercase						)
         a__     :		Union[str, Any]		 =						model(__lowercase      ,     token_type_ids=__lowercase						)
         a__     :		Dict		 =						model(__lowercase						)
         self.parent.assertEqual(result.last_hidden_state.shape      ,     (self.batch_size, self.seq_length, self.hidden_size)						)
         self.parent.assertEqual(result.pooler_output.shape      ,     (self.batch_size, self.hidden_size)						)

      def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     )      ->   Any:
         """simple docstring"""




         a__     :		Optional[int]		 =						self.num_labels
         a__     :		List[str]		 =						MarkupLMForSequenceClassification(__lowercase						)
         model.to(__lowercase						)
         model.eval()
         a__     :		Union[str, Any]		 =						model(
             __lowercase      ,     xpath_tags_seq=__lowercase      ,     xpath_subs_seq=__lowercase      ,     attention_mask=__lowercase      ,     token_type_ids=__lowercase      ,     labels=__lowercase      ,     )
         self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.num_labels)						)

      def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     )      ->   Any:
         """simple docstring"""




         a__     :		List[Any]		 =						self.num_labels
         a__     :		List[Any]		 =						MarkupLMForTokenClassification(config=__lowercase						)
         model.to(__lowercase						)
         model.eval()
         a__     :		Tuple		 =						model(
             __lowercase      ,     xpath_tags_seq=__lowercase      ,     xpath_subs_seq=__lowercase      ,     attention_mask=__lowercase      ,     token_type_ids=__lowercase      ,     labels=__lowercase      ,     )
         self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.seq_length, self.num_labels)						)

      def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     )      ->   List[str]:
         """simple docstring"""




         a__     :		Optional[Any]		 =						MarkupLMForQuestionAnswering(config=__lowercase						)
         model.to(__lowercase						)
         model.eval()
         a__     :		Any		 =						model(
             __lowercase      ,     xpath_tags_seq=__lowercase      ,     xpath_subs_seq=__lowercase      ,     attention_mask=__lowercase      ,     token_type_ids=__lowercase      ,     start_positions=__lowercase      ,     end_positions=__lowercase      ,     )
         self.parent.assertEqual(result.start_logits.shape      ,     (self.batch_size, self.seq_length)						)
         self.parent.assertEqual(result.end_logits.shape      ,     (self.batch_size, self.seq_length)						)

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[str]:
         """simple docstring"""




         a__     :		int		 =						self.prepare_config_and_inputs()
         (
             a__		
         )     :		List[str]		 =						config_and_inputs
         a__     :		int		 =						{
             """input_ids""": input_ids,
             """xpath_tags_seq""": xpath_tags_seq,
             """xpath_subs_seq""": xpath_subs_seq,
             """token_type_ids""": token_type_ids,
             """attention_mask""": input_mask,
         }
         return config, inputs_dict




@require_torch
class      snake_case__       (A__      ,			A__      ,			unittest.TestCase	):







      """simple docstring"""




      __lowerCAmelCase  :int			      =  (
          (
              MarkupLMModel,
              MarkupLMForSequenceClassification,
              MarkupLMForTokenClassification,
              MarkupLMForQuestionAnswering,
          )
          if is_torch_available()
          else None
      )
      __lowerCAmelCase  :List[str]			      =  (
          {
              "feature-extraction": MarkupLMModel,
              "question-answering": MarkupLMForQuestionAnswering,
              "text-classification": MarkupLMForSequenceClassification,
              "token-classification": MarkupLMForTokenClassification,
              "zero-shot": MarkupLMForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )

      def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase						)      ->   Optional[int]:
         """simple docstring"""




         return True

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[Any]:
         """simple docstring"""




         a__     :		Optional[Any]		 =						MarkupLMModelTester(self						)
         a__     :		List[str]		 =						ConfigTester(self      ,     config_class=__lowercase      ,     hidden_size=3_7						)

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Tuple:
         """simple docstring"""




         self.config_tester.run_common_tests()

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Union[str, Any]:
         """simple docstring"""




         a__     :		Any		 =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__lowercase						)

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Tuple:
         """simple docstring"""




         a__     :		List[Any]		 =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*__lowercase						)

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Tuple:
         """simple docstring"""




         a__     :		Optional[Any]		 =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*__lowercase						)

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Any:
         """simple docstring"""




         a__     :		Tuple		 =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*__lowercase						)




def 			lowerCAmelCase_  (						)							-> Optional[Any]:







   """simple docstring"""
   a__     :		Union[str, Any]		 =						"""
    <!DOCTYPE html>
    <html>
    <head>
    <title>Page Title</title>
    </head>
    <body>

    <h1>This is a Heading</h1>
    <p>This is a paragraph.</p>

    </body>
    </html>
    """

   return html_string





@require_torch
class      snake_case__       (unittest.TestCase	):







      """simple docstring"""




      @cached_property
      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Any:
         """simple docstring"""




         a__     :		Any		 =						MarkupLMFeatureExtractor()
         a__     :		Any		 =						MarkupLMTokenizer.from_pretrained("""microsoft/markuplm-base"""						)

         return MarkupLMProcessor(__lowercase      ,     __lowercase						)

      @slow
      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Any:
         """simple docstring"""




         a__     :		int		 =						MarkupLMModel.from_pretrained("""microsoft/markuplm-base"""						).to(__lowercase						)

         a__     :		List[Any]		 =						self.default_processor

         a__     :		Union[str, Any]		 =						processor(prepare_html_string()      ,     return_tensors="""pt"""						)
         a__     :		str		 =						inputs.to(__lowercase						)

         # forward pass
         with torch.no_grad():
            a__     :		Union[str, Any]		 =						model(**__lowercase						)

         # verify the last hidden states
         a__     :		Tuple		 =						torch.Size([1, 1_4, 7_6_8]						)
         self.assertEqual(outputs.last_hidden_state.shape      ,     __lowercase						)

         a__     :		Union[str, Any]		 =						torch.tensor(
             [[0.0_6_7_5, -0.0_0_5_2, 0.5_0_0_1], [-0.2_2_8_1, 0.0_8_0_2, 0.2_1_9_2], [-0.0_5_8_3, -0.3_3_1_1, 0.1_1_8_5]]						).to(__lowercase						)

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









from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


_lowercase	:   Tuple    	=logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
class      snake_case__       (A__	):







						"""simple docstring"""




						__lowerCAmelCase  :torch.FloatTensor




class      snake_case__       (A__      ,			A__	):







						"""simple docstring"""




						__lowerCAmelCase  :Optional[int]			      =  1

						@register_to_config
						def __init__(     self      ,     __lowercase = 4_0      ,     __lowercase = 0.0_0_2      ,     __lowercase = 8_0.0      ,     __lowercase = 0.5      ,     __lowercase = 1.0      ,     __lowercase = 7.0      ,     __lowercase = True      ,     )      ->   str:
									"""simple docstring"""




									a__     :		Dict		 =						sigma_max

									a__     :		int		 =						np.linspace(0      ,     1      ,     __lowercase						)
									a__     :		int		 =						self._convert_to_karras(__lowercase						)
									a__     :		Any		 =						self.sigma_to_t(__lowercase						)

									# setable values
									a__     :		Optional[int]		 =						None
									a__     :		Union[str, Any]		 =						torch.from_numpy(__lowercase						)
									a__     :		Tuple		 =						torch.from_numpy(__lowercase						)
									a__     :		Optional[Any]		 =						False
									a__     :		List[str]		 =						False

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase=None						)      ->   Union[str, Any]:
									"""simple docstring"""




									if schedule_timesteps is None:
												a__     :		int		 =						self.timesteps

									a__     :		Optional[int]		 =						(schedule_timesteps == timestep).nonzero()
									return indices.item()

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase						)      ->   torch.FloatTensor:
									"""simple docstring"""




									if isinstance(__lowercase      ,     torch.Tensor						):
												a__     :		str		 =						timestep.to(self.timesteps.device						)
									a__     :		Optional[Any]		 =						self.index_for_timestep(__lowercase						)
									a__     :		Tuple		 =						self.sigmas[step_idx]

									a__     :		Optional[Any]		 =						sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

									a__     :		List[str]		 =						True
									return sample

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   Dict:
									"""simple docstring"""




									if not isinstance(__lowercase      ,     np.ndarray						):
												a__     :		Optional[Any]		 =						np.array(__lowercase      ,     dtype=np.floataa						)

									a__     :		str		 =						1_0_0_0 * 0.2_5 * np.log(sigmas + 1E-44						)

									return timesteps

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase = None      ,     __lowercase = None      ,     __lowercase = None      ,     )      ->   List[Any]:
									"""simple docstring"""




									if num_inference_steps is None and timesteps is None:
												raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""						)

									if num_inference_steps is not None and timesteps is not None:
												raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""						)

									# Follow DDPMScheduler custom timesteps logic
									if timesteps is not None:
												for i in range(1      ,     len(__lowercase						)						):
															if timesteps[i] >= timesteps[i - 1]:
																		raise ValueError("""`timesteps` must be in descending order."""						)

												if timesteps[0] >= self.config.num_train_timesteps:
															raise ValueError(
															    F'''`timesteps` must start before `self.config.train_timesteps`:'''
															    F''' {self.config.num_train_timesteps}.'''						)

												a__     :		List[str]		 =						np.array(__lowercase      ,     dtype=np.intaa						)
												a__     :		str		 =						True
									else:
												if num_inference_steps > self.config.num_train_timesteps:
															raise ValueError(
															    F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
															    F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
															    F''' maximal {self.config.num_train_timesteps} timesteps.'''						)

												a__     :		List[str]		 =						num_inference_steps

												a__     :		str		 =						self.config.num_train_timesteps // self.num_inference_steps
												a__     :		int		 =						(np.arange(0      ,     __lowercase						) * step_ratio).round()[::-1].copy().astype(np.intaa						)
												a__     :		Union[str, Any]		 =						False

									# Map timesteps to Karras sigmas directly for multistep sampling
									# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
									a__     :		int		 =						self.config.num_train_timesteps
									a__     :		Optional[Any]		 =						timesteps[::-1].copy()
									a__     :		Optional[Any]		 =						ramp / (num_train_timesteps - 1)
									a__     :		Union[str, Any]		 =						self._convert_to_karras(__lowercase						)
									a__     :		Any		 =						self.sigma_to_t(__lowercase						)

									a__     :		Dict		 =						np.concatenate([sigmas, [self.sigma_min]]						).astype(np.floataa						)
									a__     :		List[str]		 =						torch.from_numpy(__lowercase						).to(device=__lowercase						)

									if str(__lowercase						).startswith("""mps"""						):
												# mps does not support float64
												a__     :		int		 =						torch.from_numpy(__lowercase						).to(__lowercase      ,     dtype=torch.floataa						)
									else:
												a__     :		int		 =						torch.from_numpy(__lowercase						).to(device=__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   Optional[Any]:
									"""simple docstring"""




									a__     :		float		 =						self.config.sigma_min
									a__     :		float		 =						self.config.sigma_max

									a__     :		Optional[int]		 =						self.config.rho
									a__     :		List[str]		 =						sigma_min ** (1 / rho)
									a__     :		Any		 =						sigma_max ** (1 / rho)
									a__     :		Any		 =						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   Optional[int]:
									"""simple docstring"""




									a__     :		int		 =						self.config.sigma_data

									a__     :		List[Any]		 =						sigma_data**2 / (sigma**2 + sigma_data**2)
									a__     :		Optional[Any]		 =						sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   int:
									"""simple docstring"""




									a__     :		str		 =						self.config.sigma_min
									a__     :		Dict		 =						self.config.sigma_data

									a__     :		Dict		 =						sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
									a__     :		List[Any]		 =						(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase = None      ,     __lowercase = True      ,     )      ->   Union[CMStochasticIterativeSchedulerOutput, Tuple]:
									"""simple docstring"""




									if (
									    isinstance(__lowercase      ,     __lowercase						)
									    or isinstance(__lowercase      ,     torch.IntTensor						)
									    or isinstance(__lowercase      ,     torch.LongTensor						)
									):
												raise ValueError(
												    (
												        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
												        F''' `{self.__class__}.step()` is not supported. Make sure to pass'''
												        """ one of the `scheduler.timesteps` as a timestep."""
												    )      ,     )

									if not self.is_scale_input_called:
												logger.warning(
												    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
												    """See `StableDiffusionPipeline` for a usage example."""						)

									if isinstance(__lowercase      ,     torch.Tensor						):
												a__     :		Optional[int]		 =						timestep.to(self.timesteps.device						)

									a__     :		Tuple		 =						self.config.sigma_min
									a__     :		Tuple		 =						self.config.sigma_max

									a__     :		Union[str, Any]		 =						self.index_for_timestep(__lowercase						)

									# sigma_next corresponds to next_t in original implementation
									a__     :		List[Any]		 =						self.sigmas[step_index]
									if step_index + 1 < self.config.num_train_timesteps:
												a__     :		int		 =						self.sigmas[step_index + 1]
									else:
												# Set sigma_next to sigma_min
												a__     :		Dict		 =						self.sigmas[-1]

									# Get scalings for boundary conditions
									a__ ,		a__     :		Any		 =						self.get_scalings_for_boundary_condition(__lowercase						)

									# 1. Denoise model output using boundary conditions
									a__     :		Optional[int]		 =						c_out * model_output + c_skip * sample
									if self.config.clip_denoised:
												a__     :		Dict		 =						denoised.clamp(-1      ,     1						)

									# 2. Sample z ~ N(0, s_noise^2 * I)
									# Noise is not used for onestep sampling.
									if len(self.timesteps						) > 1:
												a__     :		Optional[Any]		 =						randn_tensor(
												    model_output.shape      ,     dtype=model_output.dtype      ,     device=model_output.device      ,     generator=__lowercase						)
									else:
												a__     :		Any		 =						torch.zeros_like(__lowercase						)
									a__     :		Union[str, Any]		 =						noise * self.config.s_noise

									a__     :		Union[str, Any]		 =						sigma_next.clamp(min=__lowercase      ,     max=__lowercase						)

									# 3. Return noisy sample
									# tau = sigma_hat, eps = sigma_min
									a__     :		Union[str, Any]		 =						denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

									if not return_dict:
												return (prev_sample,)

									return CMStochasticIterativeSchedulerOutput(prev_sample=__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     )      ->   torch.FloatTensor:
									"""simple docstring"""




									a__     :		Union[str, Any]		 =						self.sigmas.to(device=original_samples.device      ,     dtype=original_samples.dtype						)
									if original_samples.device.type == "mps" and torch.is_floating_point(__lowercase						):
												# mps does not support float64
												a__     :		List[str]		 =						self.timesteps.to(original_samples.device      ,     dtype=torch.floataa						)
												a__     :		Any		 =						timesteps.to(original_samples.device      ,     dtype=torch.floataa						)
									else:
												a__     :		str		 =						self.timesteps.to(original_samples.device						)
												a__     :		List[str]		 =						timesteps.to(original_samples.device						)

									a__     :		Optional[Any]		 =						[(schedule_timesteps == t).nonzero().item() for t in timesteps]

									a__     :		Tuple		 =						sigmas[step_indices].flatten()
									while len(sigma.shape						) < len(original_samples.shape						):
												a__     :		List[Any]		 =						sigma.unsqueeze(-1						)

									a__     :		Optional[int]		 =						original_samples + noise * sigma
									return noisy_samples

						def __len__(     self						)      ->   int:
									"""simple docstring"""




									return self.config.num_train_timesteps




