





import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import BertGenerationDecoder, BertGenerationEncoder






class     lowercase__       :

   '''simple docstring'''


   def __init__(    self,		__magic_name__,		__magic_name__=13,		__magic_name__=7,		__magic_name__=True,		__magic_name__=True,		__magic_name__=99,		__magic_name__=32,		__magic_name__=5,		__magic_name__=4,		__magic_name__=37,		__magic_name__="gelu",		__magic_name__=0.1,		__magic_name__=0.1,		__magic_name__=50,		__magic_name__=0.02,		__magic_name__=True,		__magic_name__=None,		) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				str          =      parent
       UpperCamelCase__							:				Tuple          =      batch_size
       UpperCamelCase__							:				Tuple          =      seq_length
       UpperCamelCase__							:				Optional[Any]          =      is_training
       UpperCamelCase__							:				Optional[int]          =      use_input_mask
       UpperCamelCase__							:				Any          =      vocab_size
       UpperCamelCase__							:				int          =      hidden_size
       UpperCamelCase__							:				List[str]          =      num_hidden_layers
       UpperCamelCase__							:				Dict          =      num_attention_heads
       UpperCamelCase__							:				List[str]          =      intermediate_size
       UpperCamelCase__							:				Optional[int]          =      hidden_act
       UpperCamelCase__							:				Union[str, Any]          =      hidden_dropout_prob
       UpperCamelCase__							:				Dict          =      attention_probs_dropout_prob
       UpperCamelCase__							:				int          =      max_position_embeddings
       UpperCamelCase__							:				List[Any]          =      initializer_range
       UpperCamelCase__							:				Optional[Any]          =      use_labels
       UpperCamelCase__							:				Tuple          =      scope




   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				str          =      ids_tensor([self.batch_size, self.seq_length],		self.vocab_size							)

       UpperCamelCase__							:				Tuple          =      None
       if self.use_input_mask:
           UpperCamelCase__							:				Union[str, Any]          =      random_attention_mask([self.batch_size, self.seq_length]							)

       if self.use_labels:
           UpperCamelCase__							:				Optional[int]          =      ids_tensor([self.batch_size, self.seq_length],		self.vocab_size							)

       UpperCamelCase__							:				Dict          =      self.get_config()

       return config, input_ids, input_mask, token_labels




   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       return BertGenerationConfig(
           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,		is_decoder=__magic_name__,		initializer_range=self.initializer_range,		)




   def       UpperCamelCase__  (    self							) ->      Any:






       """simple docstring"""
       (
           (
           UpperCamelCase__
       )   ,(
           UpperCamelCase__
       )   ,(
           UpperCamelCase__
       )   ,(
           UpperCamelCase__
       )   ,
       )							:				Optional[int]          =      self.prepare_config_and_inputs()

       UpperCamelCase__							:				str          =      True
       UpperCamelCase__							:				Tuple          =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
       UpperCamelCase__							:				Dict          =      ids_tensor([self.batch_size, self.seq_length],		vocab_size=2							)

       return (
           config,
           input_ids,
           input_mask,
           token_labels,
           encoder_hidden_states,
           encoder_attention_mask,
       )




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		**__magic_name__,		) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      BertGenerationEncoder(config=__magic_name__							)
       model.to(__magic_name__							)
       model.eval()
       UpperCamelCase__							:				Optional[int]          =      model(__magic_name__,		attention_mask=__magic_name__							)
       UpperCamelCase__							:				int          =      model(__magic_name__							)
       self.parent.assertEqual(result.last_hidden_state.shape,		(self.batch_size, self.seq_length, self.hidden_size)							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		**__magic_name__,		) ->      Tuple:






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      True
       UpperCamelCase__							:				List[str]          =      BertGenerationEncoder(config=__magic_name__							)
       model.to(__magic_name__							)
       model.eval()
       UpperCamelCase__							:				Any          =      model(
           __magic_name__,		attention_mask=__magic_name__,		encoder_hidden_states=__magic_name__,		encoder_attention_mask=__magic_name__,		)
       UpperCamelCase__							:				int          =      model(
           __magic_name__,		attention_mask=__magic_name__,		encoder_hidden_states=__magic_name__,		)
       self.parent.assertEqual(result.last_hidden_state.shape,		(self.batch_size, self.seq_length, self.hidden_size)							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		**__magic_name__,		) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      True
       UpperCamelCase__							:				Any          =      True
       UpperCamelCase__							:				Optional[Any]          =      BertGenerationDecoder(config=__magic_name__							).to(__magic_name__							).eval()

       # first forward pass
       UpperCamelCase__							:				Tuple          =      model(
           __magic_name__,		attention_mask=__magic_name__,		encoder_hidden_states=__magic_name__,		encoder_attention_mask=__magic_name__,		use_cache=__magic_name__,		)
       UpperCamelCase__							:				Optional[Any]          =      outputs.past_key_values

       # create hypothetical multiple next token and extent to next_input_ids
       UpperCamelCase__							:				Any          =      ids_tensor((self.batch_size, 3),		config.vocab_size							)
       UpperCamelCase__							:				Tuple          =      ids_tensor((self.batch_size, 3),		vocab_size=2							)

       # append to next input_ids and
       UpperCamelCase__							:				List[Any]          =      torch.cat([input_ids, next_tokens],		dim=-1							)
       UpperCamelCase__							:				Any          =      torch.cat([input_mask, next_mask],		dim=-1							)

       UpperCamelCase__							:				Optional[int]          =      model(
           __magic_name__,		attention_mask=__magic_name__,		encoder_hidden_states=__magic_name__,		encoder_attention_mask=__magic_name__,		output_hidden_states=__magic_name__,		)['''hidden_states'''][0]
       UpperCamelCase__							:				Optional[Any]          =      model(
           __magic_name__,		attention_mask=__magic_name__,		encoder_hidden_states=__magic_name__,		encoder_attention_mask=__magic_name__,		past_key_values=__magic_name__,		output_hidden_states=__magic_name__,		)['''hidden_states'''][0]

       # select random slice
       UpperCamelCase__							:				List[str]          =      ids_tensor((1,),		output_from_past.shape[-1]							).item()
       UpperCamelCase__							:				Optional[Any]          =      output_from_no_past[:, -3:, random_slice_idx].detach()
       UpperCamelCase__							:				Tuple          =      output_from_past[:, :, random_slice_idx].detach()

       self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(__magic_name__,		__magic_name__,		atol=1E-3							)							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		*__magic_name__,		) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      BertGenerationDecoder(__magic_name__							)
       model.to(__magic_name__							)
       model.eval()
       UpperCamelCase__							:				Union[str, Any]          =      model(__magic_name__,		attention_mask=__magic_name__,		labels=__magic_name__							)
       self.parent.assertEqual(result.logits.shape,		(self.batch_size, self.seq_length, self.vocab_size)							)




   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Optional[int]          =      self.prepare_config_and_inputs()
       UpperCamelCase__							:				int          =      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict






@require_torch
class     lowercase__       (	__lowerCamelCase			,				__lowerCamelCase			,				__lowerCamelCase			,				unittest.TestCase				):

   '''simple docstring'''


   a						:							Optional[Any]   		=		(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
   a						:							Optional[int]   		=		(BertGenerationDecoder,) if is_torch_available() else ()
   a						:							str   		=		(
       {"feature-extraction": BertGenerationEncoder, "text-generation": BertGenerationDecoder}
       if is_torch_available()
       else {}
   )




   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      BertGenerationEncoderTester(self							)
       UpperCamelCase__							:				List[str]          =      ConfigTester(self,		config_class=__magic_name__,		hidden_size=37							)




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






       """simple docstring"""
       self.config_tester.run_common_tests()




   def       UpperCamelCase__  (    self							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				str          =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Optional[int]          =      self.model_tester.prepare_config_and_inputs()
       UpperCamelCase__							:				Any          =      '''bert'''
       self.model_tester.create_and_check_model(__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_model_as_decoder(*__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(*__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       # This regression test was failing with PyTorch < 1.3
       (
           (
           UpperCamelCase__
       )   ,(
           UpperCamelCase__
       )   ,(
           UpperCamelCase__
       )   ,(
           UpperCamelCase__
       )   ,(
           UpperCamelCase__
       )   ,(
           UpperCamelCase__
       )   ,
       )							:				Tuple          =      self.model_tester.prepare_config_and_inputs_for_decoder()

       UpperCamelCase__							:				Union[str, Any]          =      None

       self.model_tester.create_and_check_model_as_decoder(
           __magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		)




   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_for_causal_lm(*__magic_name__							)




   @slow
   def       UpperCamelCase__  (    self							) ->      int:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''							)
       self.assertIsNotNone(__magic_name__							)






@require_torch
class     lowercase__       (	unittest.TestCase				):

   '''simple docstring'''


   @slow
   def       UpperCamelCase__  (    self							) ->      Tuple:






       """simple docstring"""
       UpperCamelCase__							:				str          =      BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''							)
       UpperCamelCase__							:				str          =      torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]							)
       with torch.no_grad():
           UpperCamelCase__							:				int          =      model(__magic_name__							)[0]
       UpperCamelCase__							:				Optional[int]          =      torch.Size([1, 8, 1024]							)
       self.assertEqual(output.shape,		__magic_name__							)
       UpperCamelCase__							:				int          =      torch.tensor(
           [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]							)
       self.assertTrue(torch.allclose(output[:, :3, :3],		__magic_name__,		atol=1E-4							)							)






@require_torch
class     lowercase__       (	unittest.TestCase				):

   '''simple docstring'''


   @slow
   def       UpperCamelCase__  (    self							) ->      Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''							)
       UpperCamelCase__							:				Dict          =      torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]							)
       with torch.no_grad():
           UpperCamelCase__							:				List[Any]          =      model(__magic_name__							)[0]
       UpperCamelCase__							:				int          =      torch.Size([1, 8, 50358]							)
       self.assertEqual(output.shape,		__magic_name__							)
       UpperCamelCase__							:				List[str]          =      torch.tensor(
           [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]							)
       self.assertTrue(torch.allclose(output[:, :3, :3],		__magic_name__,		atol=1E-4							)							)








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 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			List[Any]=0.999		,   __UpperCAmelCase:			Tuple="cosine"		,   )      ->	Optional[Any]:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__UpperCAmelCase:			List[Any]      ):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2      ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__UpperCAmelCase:			List[Any]      ):
            return math.exp(t * -12.0      )

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

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






class     lowercase__       (	__lowerCamelCase			,				__lowerCamelCase				):

   '''simple docstring'''


   a						:							Optional[Any]   		=		[e.name for e in KarrasDiffusionSchedulers]
   a						:							Union[str, Any]   		=		2




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






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

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

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




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__=None							) ->      str:






       """simple docstring"""
       if schedule_timesteps is None:
           UpperCamelCase__							:				Dict          =      self.timesteps

       UpperCamelCase__							:				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:
           UpperCamelCase__							:				List[str]          =      1 if len(__magic_name__							) > 1 else 0
       else:
           UpperCamelCase__							:				List[Any]          =      timestep.cpu().item() if torch.is_tensor(__magic_name__							) else timestep
           UpperCamelCase__							:				int          =      self._index_counter[timestep_int]

       return indices[pos].item()




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






       """simple docstring"""
       # 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       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		) ->      torch.FloatTensor:






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      self.index_for_timestep(__magic_name__							)

       if self.state_in_first_order:
           UpperCamelCase__							:				str          =      self.sigmas[step_index]
       else:
           UpperCamelCase__							:				Optional[int]          =      self.sigmas_interpol[step_index]

       UpperCamelCase__							:				Optional[int]          =      sample / ((sigma**2 + 1) ** 0.5)
       return sample




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = None,		) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      num_inference_steps

       UpperCamelCase__							:				Tuple          =      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":
           UpperCamelCase__							:				Union[str, Any]          =      np.linspace(0,		num_train_timesteps - 1,		__magic_name__,		dtype=__magic_name__							)[::-1].copy()
       elif self.config.timestep_spacing == "leading":
           UpperCamelCase__							:				Union[str, Any]          =      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
           UpperCamelCase__							:				List[str]          =      (np.arange(0,		__magic_name__							) * step_ratio).round()[::-1].copy().astype(__magic_name__							)
           timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
           UpperCamelCase__							:				Optional[Any]          =      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
           UpperCamelCase__							:				List[str]          =      (np.arange(__magic_name__,		0,		-step_ratio							)).round().copy().astype(__magic_name__							)
           timesteps -= 1
       else:
           raise ValueError(
               f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."							)

       UpperCamelCase__							:				int          =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
       UpperCamelCase__							:				Optional[Any]          =      torch.from_numpy(np.log(__magic_name__							)							).to(__magic_name__							)

       UpperCamelCase__							:				Any          =      np.interp(__magic_name__,		np.arange(0,		len(__magic_name__							)							),		__magic_name__							)
       UpperCamelCase__							:				Union[str, Any]          =      np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
       UpperCamelCase__							:				Any          =      torch.from_numpy(__magic_name__							).to(device=__magic_name__							)

       # interpolate sigmas
       UpperCamelCase__							:				int          =      sigmas.log().lerp(sigmas.roll(1							).log(),		0.5							).exp()

       UpperCamelCase__							:				List[str]          =      torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2							), sigmas[-1:]]							)
       UpperCamelCase__							:				str          =      torch.cat(
           [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2							), sigmas_interpol[-1:]]							)

       if str(__magic_name__							).startswith('''mps'''							):
           # mps does not support float64
           UpperCamelCase__							:				Optional[Any]          =      torch.from_numpy(__magic_name__							).to(__magic_name__,		dtype=torch.floataa							)
       else:
           UpperCamelCase__							:				List[Any]          =      torch.from_numpy(__magic_name__							).to(__magic_name__							)

       # interpolate timesteps
       UpperCamelCase__							:				str          =      self.sigma_to_t(__magic_name__							).to(__magic_name__,		dtype=timesteps.dtype							)
       UpperCamelCase__							:				Dict          =      torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]),		dim=-1							).flatten()

       UpperCamelCase__							:				Optional[int]          =      torch.cat([timesteps[:1], interleaved_timesteps]							)

       UpperCamelCase__							:				List[str]          =      None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       UpperCamelCase__							:				Dict          =      defaultdict(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Optional[Any]:






       """simple docstring"""
       # get log sigma
       UpperCamelCase__							:				Any          =      sigma.log()

       # get distribution
       UpperCamelCase__							:				List[str]          =      log_sigma - self.log_sigmas[:, None]

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

       UpperCamelCase__							:				str          =      self.log_sigmas[low_idx]
       UpperCamelCase__							:				int          =      self.log_sigmas[high_idx]

       # interpolate sigmas
       UpperCamelCase__							:				List[Any]          =      (low - log_sigma) / (low - high)
       UpperCamelCase__							:				str          =      w.clamp(0,		1							)

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




   @property
   def       UpperCamelCase__  (    self							) ->      Union[str, Any]:






       """simple docstring"""
       return self.sample is None




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__ = True,		) ->      Union[SchedulerOutput, Tuple]:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      self.index_for_timestep(__magic_name__							)

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

       if self.state_in_first_order:
           UpperCamelCase__							:				Optional[Any]          =      self.sigmas[step_index]
           UpperCamelCase__							:				Union[str, Any]          =      self.sigmas_interpol[step_index + 1]
           UpperCamelCase__							:				List[Any]          =      self.sigmas[step_index + 1]
       else:
           # 2nd order / KDPM2's method
           UpperCamelCase__							:				Tuple          =      self.sigmas[step_index - 1]
           UpperCamelCase__							:				Tuple          =      self.sigmas_interpol[step_index]
           UpperCamelCase__							:				Dict          =      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
       UpperCamelCase__							:				Optional[int]          =      0
       UpperCamelCase__							:				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":
           UpperCamelCase__							:				Union[str, Any]          =      sigma_hat if self.state_in_first_order else sigma_interpol
           UpperCamelCase__							:				List[str]          =      sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
           UpperCamelCase__							:				List[Any]          =      sigma_hat if self.state_in_first_order else sigma_interpol
           UpperCamelCase__							:				Optional[Any]          =      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
           UpperCamelCase__							:				List[Any]          =      (sample - pred_original_sample) / sigma_hat
           # 3. delta timestep
           UpperCamelCase__							:				List[str]          =      sigma_interpol - sigma_hat

           # store for 2nd order step
           UpperCamelCase__							:				Dict          =      sample
       else:
           # DPM-Solver-2
           # 2. Convert to an ODE derivative for 2nd order
           UpperCamelCase__							:				List[str]          =      (sample - pred_original_sample) / sigma_interpol

           # 3. delta timestep
           UpperCamelCase__							:				Union[str, Any]          =      sigma_next - sigma_hat

           UpperCamelCase__							:				Union[str, Any]          =      self.sample
           UpperCamelCase__							:				Dict          =      None

       UpperCamelCase__							:				Optional[int]          =      sample + derivative * dt

       if not return_dict:
           return (prev_sample,)

       return SchedulerOutput(prev_sample=__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		) ->      torch.FloatTensor:






       """simple docstring"""
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       UpperCamelCase__							:				List[str]          =      self.sigmas.to(device=original_samples.device,		dtype=original_samples.dtype							)
       if original_samples.device.type == "mps" and torch.is_floating_point(__magic_name__							):
           # mps does not support float64
           UpperCamelCase__							:				Union[str, Any]          =      self.timesteps.to(original_samples.device,		dtype=torch.floataa							)
           UpperCamelCase__							:				Tuple          =      timesteps.to(original_samples.device,		dtype=torch.floataa							)
       else:
           UpperCamelCase__							:				str          =      self.timesteps.to(original_samples.device							)
           UpperCamelCase__							:				int          =      timesteps.to(original_samples.device							)

       UpperCamelCase__							:				Any          =      [self.index_for_timestep(__magic_name__,		__magic_name__							) for t in timesteps]

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

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




   def __len__(    self							) ->      Any:






       """simple docstring"""
       return self.config.num_train_timesteps

