import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 		a__						(					A__    ,		unittest.TestCase					):
					A	       =							LEDTokenizer
					A	       =							LEDTokenizerFast
					A	       =							True

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							super().setUp()
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    [
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							]
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    dict(zip(_A,range(len(_A    )    )    )    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    {"unk_token": "<unk>"}

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["vocab_file"]    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["merges_file"]    )
							with open(self.vocab_file,"w",encoding="utf-8"    ) as fp:
									fp.write(json.dumps(_A    ) + "\n"    )
							with open(self.merges_file,"w",encoding="utf-8"    ) as fp:
									fp.write("\n".join(_A    )    )

					def 				__UpperCamelCase   (    self      :       Tuple,**_A      :       List[str]    ):







							"""simple docstring"""






							kwargs.update(self.special_tokens_map    )
							return self.tokenizer_class.from_pretrained(self.tmpdirname,**_A    )

					def 				__UpperCamelCase   (    self      :       str,**_A      :       Union[str, Any]    ):







							"""simple docstring"""






							kwargs.update(self.special_tokens_map    )
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,**_A    )

					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       Any    ):







							"""simple docstring"""






							return "lower newer", "lower newer"

					@cached_property
					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							return LEDTokenizer.from_pretrained("allenai/led-base-16384"    )

					@cached_property
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    ["A long paragraph for summarization.", "Another paragraph for summarization."]
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    tokenizer(_A,max_length=len(_A    ),padding=_A,return_tensors="pt"    )
									self.assertIsInstance(_A,_A    )

									self.assertEqual((2, 9),batch.input_ids.shape    )
									self.assertEqual((2, 9),batch.attention_mask.shape    )
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    batch.input_ids.tolist()[0]
									self.assertListEqual(_A,_A    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    ["A long paragraph for summarization.", "Another paragraph for summarization."]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									SCREAMING_SNAKE_CASE_  :		int				      =    tokenizer(_A,padding=_A,return_tensors="pt"    )
									self.assertIn("input_ids",_A    )
									self.assertIn("attention_mask",_A    )
									self.assertNotIn("labels",_A    )
									self.assertNotIn("decoder_attention_mask",_A    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    [
							    "Summary of the text.",
							    "Another summary.",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tokenizer(text_target=_A,max_length=32,padding="max_length",return_tensors="pt"    )
									self.assertEqual(32,targets["input_ids"].shape[1]    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    tokenizer(
									    ["I am a small frog" * 1024, "I am a small frog"],padding=_A,truncation=_A,return_tensors="pt"    )
									self.assertIsInstance(_A,_A    )
									self.assertEqual(batch.input_ids.shape,(2, 5122)    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    ["A long paragraph for summarization."]
							SCREAMING_SNAKE_CASE_  :		str				      =    [
							    "Summary of the text.",
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    tokenizer(_A,return_tensors="pt"    )
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    tokenizer(text_target=_A,return_tensors="pt"    )
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    inputs["input_ids"]
									SCREAMING_SNAKE_CASE_  :		Any				      =    targets["input_ids"]
									self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()    )
									self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()    )
									self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()    )
									self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()    )

					@require_torch
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									SCREAMING_SNAKE_CASE_  :		str				      =    ["Summary of the text.", "Another summary."]
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    tokenizer(_A,padding=_A    )
									SCREAMING_SNAKE_CASE_  :		List[Any]				      =    [[0] * len(_A    ) for x in encoded_output["input_ids"]]
									SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    tokenizer.pad(_A    )
									self.assertSequenceEqual(outputs["global_attention_mask"],_A    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							pass






					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'    ):
											SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.rust_tokenizer_class.from_pretrained(_A,**_A    )
											SCREAMING_SNAKE_CASE_  :		Dict				      =    self.tokenizer_class.from_pretrained(_A,**_A    )
											SCREAMING_SNAKE_CASE_  :		Any				      =    "A, <mask> AllenNLP sentence."
											SCREAMING_SNAKE_CASE_  :		Tuple				      =    tokenizer_r.encode_plus(_A,add_special_tokens=_A,return_token_type_ids=_A    )
											SCREAMING_SNAKE_CASE_  :		Any				      =    tokenizer_p.encode_plus(_A,add_special_tokens=_A,return_token_type_ids=_A    )
											self.assertEqual(sum(tokens_r["token_type_ids"]    ),sum(tokens_p["token_type_ids"]    )    )
											self.assertEqual(
											    sum(tokens_r["attention_mask"]    ) / len(tokens_r["attention_mask"]    ),sum(tokens_p["attention_mask"]    ) / len(tokens_p["attention_mask"]    ),)

											SCREAMING_SNAKE_CASE_  :		Dict				      =    tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]    )
											SCREAMING_SNAKE_CASE_  :		str				      =    tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]    )
											self.assertSequenceEqual(tokens_p["input_ids"],[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]    )
											self.assertSequenceEqual(tokens_r["input_ids"],[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]    )

											self.assertSequenceEqual(
											    _A,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]    )
											self.assertSequenceEqual(
											    _A,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]    )


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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=0.999					,	SCREAMING_SNAKE_CASE__="cosine"					,	) ->    List[Any]:
       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

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

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

       lowercase							:     int       	=      []
       for i in range(SCREAMING_SNAKE_CASE__				):
              lowercase							:     Dict       	=      i / num_diffusion_timesteps
              lowercase							:     Optional[int]       	=      (i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE__				) / alpha_bar_fn(SCREAMING_SNAKE_CASE__				)					,	SCREAMING_SNAKE_CASE__				)				)
       return torch.tensor(SCREAMING_SNAKE_CASE__					,	dtype=torch.floataa				)




class __snake_case		(      lowerCAmelCase					,	lowerCAmelCase     ):
    _a		:						Tuple=				[e.name for e in KarrasDiffusionSchedulers]
    _a		:						int=				2
    @register_to_config
    def __init__(  self		,snake_case = 1000		,snake_case = 0.00_085		,snake_case = 0.012		,snake_case = "linear"		,snake_case = None		,snake_case = "epsilon"		,snake_case = False		,snake_case = False		,snake_case = 1.0		,snake_case = "linspace"		,snake_case = 0		,):



           '''simple docstring'''







           if trained_betas is not None:
                  lowercase							:     List[str]       	=      torch.tensor(snake_case		,dtype=torch.floataa      )
           elif beta_schedule == "linear":
                  lowercase							:     Optional[Any]       	=      torch.linspace(snake_case		,snake_case		,snake_case		,dtype=torch.floataa      )
           elif beta_schedule == "scaled_linear":
                  # this schedule is very specific to the latent diffusion model.
                  lowercase							:     int       	=      (
                      torch.linspace(beta_start**0.5		,beta_end**0.5		,snake_case		,dtype=torch.floataa      ) ** 2
                  )
           elif beta_schedule == "squaredcos_cap_v2":
                  # Glide cosine schedule
                  lowercase							:     Union[str, Any]       	=      betas_for_alpha_bar(snake_case		,alpha_transform_type="""cosine"""      )
           elif beta_schedule == "exp":
                  lowercase							:     int       	=      betas_for_alpha_bar(snake_case		,alpha_transform_type="""exp"""      )
           else:
                  raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"      )

           lowercase							:     Any       	=      1.0 - self.betas
           lowercase							:     Dict       	=      torch.cumprod(self.alphas		,dim=0      )

           #  set all values
           self.set_timesteps(snake_case		,snake_case		,snake_case      )
           lowercase							:     Tuple       	=      use_karras_sigmas
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           if schedule_timesteps is None:
                  lowercase							:     Union[str, Any]       	=      self.timesteps

           lowercase							:     Dict       	=      (schedule_timesteps == timestep).nonzero()

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

           return indices[pos].item()
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







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

           return (self.sigmas.max() ** 2 + 1) ** 0.5
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      self.index_for_timestep(snake_case      )

           lowercase							:     Dict       	=      self.sigmas[step_index]
           lowercase							:     List[str]       	=      sample / ((sigma**2 + 1) ** 0.5)
           return sample
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = None		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:     Any       	=      num_inference_steps

           lowercase							:     List[Any]       	=      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":
                  lowercase							:     Optional[int]       	=      np.linspace(0		,num_train_timesteps - 1		,snake_case		,dtype=snake_case      )[::-1].copy()
           elif self.config.timestep_spacing == "leading":
                  lowercase							:     int       	=      num_train_timesteps // self.num_inference_steps
                  # creates integer timesteps by multiplying by ratio
                  # casting to int to avoid issues when num_inference_step is power of 3
                  lowercase							:     List[str]       	=      (np.arange(0		,snake_case      ) * step_ratio).round()[::-1].copy().astype(snake_case      )
                  timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
                  lowercase							:     List[str]       	=      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
                  lowercase							:     Optional[int]       	=      (np.arange(snake_case		,0		,-step_ratio      )).round().copy().astype(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'."      )

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

           if self.config.use_karras_sigmas:
                  lowercase							:     List[Any]       	=      self._convert_to_karras(in_sigmas=snake_case		,num_inference_steps=self.num_inference_steps      )
                  lowercase							:     Tuple       	=      np.array([self._sigma_to_t(snake_case		,snake_case      ) for sigma in sigmas]      )

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

           lowercase							:     Dict       	=      torch.from_numpy(snake_case      )
           lowercase							:     List[Any]       	=      torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2      )]      )

           if str(snake_case      ).startswith("""mps"""      ):
                  # mps does not support float64
                  lowercase							:     Any       	=      timesteps.to(snake_case		,dtype=torch.floataa      )
           else:
                  lowercase							:     str       	=      timesteps.to(device=snake_case      )

           # empty dt and derivative
           lowercase							:     Union[str, Any]       	=      None
           lowercase							:     Any       	=      None

           # for exp beta schedules, such as the one for `pipeline_shap_e.py`
           # we need an index counter
           lowercase							:     str       	=      defaultdict(snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Optional[int]       	=      np.log(snake_case      )

           # get distribution
           lowercase							:     Union[str, Any]       	=      log_sigma - log_sigmas[:, np.newaxis]

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

           lowercase							:     str       	=      log_sigmas[low_idx]
           lowercase							:     Dict       	=      log_sigmas[high_idx]

           # interpolate sigmas
           lowercase							:     int       	=      (low - log_sigma) / (low - high)
           lowercase							:     Dict       	=      np.clip(snake_case		,0		,1      )

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



           '''simple docstring'''







           lowercase							:     float       	=      in_sigmas[-1].item()
           lowercase							:     float       	=      in_sigmas[0].item()

           lowercase							:     Dict       	=      7.0  # 7.0 is the value used in the paper
           lowercase							:     Optional[int]       	=      np.linspace(0		,1		,snake_case      )
           lowercase							:     int       	=      sigma_min ** (1 / rho)
           lowercase							:     Any       	=      sigma_max ** (1 / rho)
           lowercase							:     Tuple       	=      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self.dt is None
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case = True		,):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      self.index_for_timestep(snake_case      )

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

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

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

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

                  # free dt and derivative
                  # Note, this puts the scheduler in "first order mode"
                  lowercase							:     List[str]       	=      None
                  lowercase							:     Tuple       	=      None
                  lowercase							:     Dict       	=      None

           lowercase							:     List[Any]       	=      sample + derivative * dt

           if not return_dict:
                  return (prev_sample,)

           return SchedulerOutput(prev_sample=snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,):



           '''simple docstring'''







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

           lowercase							:     Any       	=      [self.index_for_timestep(snake_case		,snake_case      ) for t in timesteps]

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

           lowercase							:     Optional[int]       	=      original_samples + noise * sigma
           return noisy_samples
    def __len__(  self      ):



           '''simple docstring'''







           return self.config.num_train_timesteps
