



import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCAmelCase          =       get_tests_dir('''fixtures/test_sentencepiece.model''')



@require_sentencepiece
@require_tokenizers
class 				lowerCAmelCase_		(  a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					int        =  ReformerTokenizer
       UpperCAmelCase__				:					Optional[Any]        =  ReformerTokenizerFast
       UpperCAmelCase__				:					Tuple        =  True
       UpperCAmelCase__				:					Any        =  False
       UpperCAmelCase__				:					List[Any]        =  True
       def 					snake_case_       (			self       )     ->		List[str]:
          super().setUp()

          UpperCamelCase    :  int							=	ReformerTokenizer(SCREAMING_SNAKE_CASE_,			keep_accents=SCREAMING_SNAKE_CASE_       )
          tokenizer.save_pretrained(self.tmpdirname       )
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  Optional[int]							=	'<s>'
          UpperCamelCase    :  Any							=	1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE_       ),			SCREAMING_SNAKE_CASE_       )
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE_       ),			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  Any							=	list(self.get_tokenizer().get_vocab().keys()       )

          self.assertEqual(vocab_keys[0],			'<unk>'       )
          self.assertEqual(vocab_keys[1],			'<s>'       )
          self.assertEqual(vocab_keys[-1],			'j'       )
          self.assertEqual(len(SCREAMING_SNAKE_CASE_       ),			1000       )
       def 					snake_case_       (			self       )     ->		int:
          self.assertEqual(self.get_tokenizer().vocab_size,			1000       )
       def 					snake_case_       (			self       )     ->		Dict:
          if not self.test_rust_tokenizer:
             return

          UpperCamelCase    :  Any							=	self.get_tokenizer()
          UpperCamelCase    :  Optional[Any]							=	self.get_rust_tokenizer()

          UpperCamelCase    :  Tuple							=	'I was born in 92000, and this is falsé.'

          UpperCamelCase    :  str							=	tokenizer.tokenize(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[int]							=	rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_       )
          self.assertListEqual(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Optional[int]							=	tokenizer.encode(SCREAMING_SNAKE_CASE_,			add_special_tokens=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Dict							=	rust_tokenizer.encode(SCREAMING_SNAKE_CASE_,			add_special_tokens=SCREAMING_SNAKE_CASE_       )
          self.assertListEqual(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  List[Any]							=	self.get_rust_tokenizer()
          UpperCamelCase    :  List[Any]							=	tokenizer.encode(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[str]							=	rust_tokenizer.encode(SCREAMING_SNAKE_CASE_       )
          self.assertListEqual(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_=15       )     ->		Dict:
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
                UpperCamelCase    :  List[Any]							=	self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )

                # Simple input
                UpperCamelCase    :  Optional[Any]							=	'This is a simple input'
                UpperCamelCase    :  Optional[int]							=	['This is a simple input 1', 'This is a simple input 2']
                UpperCamelCase    :  List[str]							=	('This is a simple input', 'This is a pair')
                UpperCamelCase    :  List[str]							=	[
                    ('This is a simple input 1', 'This is a simple input 2'),
                    ('This is a simple pair 1', 'This is a simple pair 2'),
                ]

                # Simple input tests
                self.assertRaises(SCREAMING_SNAKE_CASE_,			tokenizer_r.encode,			SCREAMING_SNAKE_CASE_,			max_length=SCREAMING_SNAKE_CASE_,			padding='max_length'       )

                # Simple input
                self.assertRaises(SCREAMING_SNAKE_CASE_,			tokenizer_r.encode_plus,			SCREAMING_SNAKE_CASE_,			max_length=SCREAMING_SNAKE_CASE_,			padding='max_length'       )

                # Simple input
                self.assertRaises(
                    SCREAMING_SNAKE_CASE_,			tokenizer_r.batch_encode_plus,			SCREAMING_SNAKE_CASE_,			max_length=SCREAMING_SNAKE_CASE_,			padding='max_length',			)

                # Pair input
                self.assertRaises(SCREAMING_SNAKE_CASE_,			tokenizer_r.encode,			SCREAMING_SNAKE_CASE_,			max_length=SCREAMING_SNAKE_CASE_,			padding='max_length'       )

                # Pair input
                self.assertRaises(SCREAMING_SNAKE_CASE_,			tokenizer_r.encode_plus,			SCREAMING_SNAKE_CASE_,			max_length=SCREAMING_SNAKE_CASE_,			padding='max_length'       )

                # Pair input
                self.assertRaises(
                    SCREAMING_SNAKE_CASE_,			tokenizer_r.batch_encode_plus,			SCREAMING_SNAKE_CASE_,			max_length=SCREAMING_SNAKE_CASE_,			padding='max_length',			)
       def 					snake_case_       (			self       )     ->		Dict:
          pass
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  Any							=	ReformerTokenizer(SCREAMING_SNAKE_CASE_,			keep_accents=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  List[Any]							=	tokenizer.tokenize('This is a test'       )
          self.assertListEqual(SCREAMING_SNAKE_CASE_,			['▁This', '▁is', '▁a', '▁t', 'est']       )

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_       ),			[285, 46, 10, 170, 382],			)

          UpperCamelCase    :  List[str]							=	tokenizer.tokenize('I was born in 92000, and this is falsé.'       )
          self.assertListEqual(
              SCREAMING_SNAKE_CASE_,			[
                  SPIECE_UNDERLINE + 'I',
                  SPIECE_UNDERLINE + 'was',
                  SPIECE_UNDERLINE + 'b',
                  'or',
                  'n',
                  SPIECE_UNDERLINE + 'in',
                  SPIECE_UNDERLINE + '',
                  '9',
                  '2',
                  '0',
                  '0',
                  '0',
                  ',',
                  SPIECE_UNDERLINE + 'and',
                  SPIECE_UNDERLINE + 'this',
                  SPIECE_UNDERLINE + 'is',
                  SPIECE_UNDERLINE + 'f',
                  'al',
                  's',
                  'é',
                  '.',
              ],			)
          UpperCamelCase    :  Optional[Any]							=	tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_       )
          self.assertListEqual(
              SCREAMING_SNAKE_CASE_,			[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4],			)

          UpperCamelCase    :  Union[str, Any]							=	tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_       )
          self.assertListEqual(
              SCREAMING_SNAKE_CASE_,			[
                  SPIECE_UNDERLINE + 'I',
                  SPIECE_UNDERLINE + 'was',
                  SPIECE_UNDERLINE + 'b',
                  'or',
                  'n',
                  SPIECE_UNDERLINE + 'in',
                  SPIECE_UNDERLINE + '',
                  '<unk>',
                  '2',
                  '0',
                  '0',
                  '0',
                  ',',
                  SPIECE_UNDERLINE + 'and',
                  SPIECE_UNDERLINE + 'this',
                  SPIECE_UNDERLINE + 'is',
                  SPIECE_UNDERLINE + 'f',
                  'al',
                  's',
                  '<unk>',
                  '.',
              ],			)
       @cached_property
       def 					snake_case_       (			self       )     ->		Tuple:
          return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment'       )
       @slow
       def 					snake_case_       (			self       )     ->		List[str]:
          UpperCamelCase    :  Any							=	'Hello World!'
          UpperCamelCase    :  Any							=	[126, 32, 262, 152, 38, 72, 287]

          self.assertListEqual(SCREAMING_SNAKE_CASE_,			self.big_tokenizer.encode(SCREAMING_SNAKE_CASE_       )       )
       @slow
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  str							=	(
              'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
              ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
          )
          UpperCamelCase    :  List[Any]							=	[
              108,
              265,
              24,
              111,
              4,
              258,
              156,
              35,
              28,
              275,
              3,
              259,
              297,
              260,
              84,
              4,
              35,
              110,
              44,
              8,
              259,
              91,
              268,
              21,
              11,
              209,
              274,
              109,
              266,
              277,
              117,
              86,
              93,
              315,
              258,
              278,
              258,
              277,
              258,
              0,
              258,
              288,
              258,
              319,
              258,
              0,
              258,
              0,
              258,
              0,
              258,
              0,
              258,
              287,
              258,
              315,
              258,
              289,
              258,
              278,
              99,
              269,
              266,
              262,
              8,
              259,
              241,
              4,
              217,
              230,
              268,
              266,
              55,
              168,
              106,
              75,
              193,
              266,
              223,
              27,
              49,
              26,
              282,
              25,
              264,
              299,
              19,
              26,
              0,
              258,
              277,
              117,
              86,
              93,
              176,
              183,
              270,
              11,
              262,
              42,
              61,
              265,
          ]

          self.assertListEqual(SCREAMING_SNAKE_CASE_,			self.big_tokenizer.encode(SCREAMING_SNAKE_CASE_       )       )
       @require_torch
       @slow
       def 					snake_case_       (			self       )     ->		Dict:
          import torch

          from transformers import ReformerConfig, ReformerModel

          # Build sequence
          UpperCamelCase    :  Optional[int]							=	list(self.big_tokenizer.get_vocab().keys()       )[:10]
          UpperCamelCase    :  Optional[int]							=	' '.join(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[int]							=	self.big_tokenizer.encode_plus(SCREAMING_SNAKE_CASE_,			return_tensors='pt'       )
          UpperCamelCase    :  Any							=	self.big_tokenizer.batch_encode_plus([sequence, sequence],			return_tensors='pt'       )

          UpperCamelCase    :  List[str]							=	ReformerConfig()
          # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
          UpperCamelCase    :  Any							=	encoded_sequence['input_ids'].shape
          UpperCamelCase    :  Any							=	ReformerModel(SCREAMING_SNAKE_CASE_       )

          # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
          assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

          with torch.no_grad():
             model(**SCREAMING_SNAKE_CASE_       )
             model(**SCREAMING_SNAKE_CASE_       )






       @slow
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          # fmt: off
          UpperCamelCase    :  List[Any]							=	{'input_ids': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
          # fmt: on

          # This tokenizer does not know some characters like ")".
          # That is the reason why we use very simple texts here.
          # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
          UpperCamelCase    :  Any							=	[
              'This is a very simple sentence.',
              'The quick brown fox jumps over the lazy dog.',
          ]

          self.tokenizer_integration_test_util(
              expected_encoding=SCREAMING_SNAKE_CASE_,			model_name='google/reformer-crime-and-punishment',			revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a',			padding=SCREAMING_SNAKE_CASE_,			sequences=SCREAMING_SNAKE_CASE_,			)








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 		UpperCamelCase	(   snake_case__       :    Dict       ,     snake_case__       :    Any=0.999       ,     snake_case__       :    List[Any]="cosine"       ,     )	->     Optional[int]:
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

   UpperCamelCase    :  List[Any]							=	[]
   for i in range(snake_case__	):
      UpperCamelCase    :  Optional[Any]							=	i / num_diffusion_timesteps
      UpperCamelCase    :  Union[str, Any]							=	(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(snake_case__	) / alpha_bar_fn(snake_case__	)       ,     snake_case__	)	)
   return torch.tensor(snake_case__       ,     dtype=torch.floataa	)



class 				lowerCAmelCase_		(  a__       ,    a__							):
       UpperCAmelCase__				:					List[Any]        =  [e.name for e in KarrasDiffusionSchedulers]
       UpperCAmelCase__				:					List[str]        =  2
       @register_to_config
       def __init__(			self,			SCREAMING_SNAKE_CASE_ = 1000,			SCREAMING_SNAKE_CASE_ = 0.0_00_85,			SCREAMING_SNAKE_CASE_ = 0.0_12,			SCREAMING_SNAKE_CASE_ = "linear",			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = "epsilon",			SCREAMING_SNAKE_CASE_ = "linspace",			SCREAMING_SNAKE_CASE_ = 0,			)     ->		List[str]:
          if trained_betas is not None:
             UpperCamelCase    :  Union[str, Any]							=	torch.tensor(SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       )
          elif beta_schedule == "linear":
             UpperCamelCase    :  List[Any]							=	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.
             UpperCamelCase    :  int							=	(
                 torch.linspace(beta_start**0.5,			beta_end**0.5,			SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       ) ** 2
             )
          elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             UpperCamelCase    :  Optional[int]							=	betas_for_alpha_bar(SCREAMING_SNAKE_CASE_       )
          else:
             raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""       )

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

          #  set all values
          self.set_timesteps(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=None       )     ->		str:
          if schedule_timesteps is None:
             UpperCamelCase    :  Union[str, Any]							=	self.timesteps

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

          return indices[pos].item()
       @property
       def 					snake_case_       (			self       )     ->		List[Any]:
          # standard deviation of the initial noise distribution
          if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()

          return (self.sigmas.max() ** 2 + 1) ** 0.5
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			)     ->		torch.FloatTensor:
          UpperCamelCase    :  Optional[int]							=	self.index_for_timestep(SCREAMING_SNAKE_CASE_       )

          if self.state_in_first_order:
             UpperCamelCase    :  Dict							=	self.sigmas[step_index]
          else:
             UpperCamelCase    :  int							=	self.sigmas_interpol[step_index]

          UpperCamelCase    :  Any							=	sample / ((sigma**2 + 1) ** 0.5)
          return sample
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			)     ->		Optional[int]:
          UpperCamelCase    :  Dict							=	num_inference_steps

          UpperCamelCase    :  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":
             UpperCamelCase    :  int							=	np.linspace(0,			num_train_timesteps - 1,			SCREAMING_SNAKE_CASE_,			dtype=SCREAMING_SNAKE_CASE_       )[::-1].copy()
          elif self.config.timestep_spacing == "leading":
             UpperCamelCase    :  List[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    :  Optional[Any]							=	(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":
             UpperCamelCase    :  Optional[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
             UpperCamelCase    :  Any							=	(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'."""       )

          UpperCamelCase    :  Tuple							=	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
          UpperCamelCase    :  Optional[int]							=	torch.from_numpy(np.log(SCREAMING_SNAKE_CASE_       )       ).to(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Optional[int]							=	np.interp(SCREAMING_SNAKE_CASE_,			np.arange(0,			len(SCREAMING_SNAKE_CASE_       )       ),			SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Dict							=	np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )
          UpperCamelCase    :  Optional[int]							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(device=SCREAMING_SNAKE_CASE_       )

          # interpolate sigmas
          UpperCamelCase    :  Union[str, Any]							=	sigmas.log().lerp(sigmas.roll(1       ).log(),			0.5       ).exp()

          UpperCamelCase    :  Any							=	torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2       ), sigmas[-1:]]       )
          UpperCamelCase    :  Optional[Any]							=	torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2       ), sigmas_interpol[-1:]]       )

          if str(SCREAMING_SNAKE_CASE_       ).startswith('mps'       ):
             # mps does not support float64
             UpperCamelCase    :  Any							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_,			dtype=torch.floataa       )
          else:
             UpperCamelCase    :  Dict							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )

          # interpolate timesteps
          UpperCamelCase    :  int							=	self.sigma_to_t(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_,			dtype=timesteps.dtype       )
          UpperCamelCase    :  List[str]							=	torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]),			dim=-1       ).flatten()

          UpperCamelCase    :  Optional[Any]							=	torch.cat([timesteps[:1], interleaved_timesteps]       )

          UpperCamelCase    :  Optional[Any]							=	None

          # for exp beta schedules, such as the one for `pipeline_shap_e.py`
          # we need an index counter
          UpperCamelCase    :  Dict							=	defaultdict(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		Union[str, Any]:
          # get log sigma
          UpperCamelCase    :  List[Any]							=	sigma.log()

          # get distribution
          UpperCamelCase    :  Optional[int]							=	log_sigma - self.log_sigmas[:, None]

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

          UpperCamelCase    :  List[str]							=	self.log_sigmas[low_idx]
          UpperCamelCase    :  Optional[Any]							=	self.log_sigmas[high_idx]

          # interpolate sigmas
          UpperCamelCase    :  int							=	(low - log_sigma) / (low - high)
          UpperCamelCase    :  Tuple							=	w.clamp(0,			1       )

          # transform interpolation to time range
          UpperCamelCase    :  List[str]							=	(1 - w) * low_idx + w * high_idx
          UpperCamelCase    :  Dict							=	t.view(sigma.shape       )
          return t
       @property
       def 					snake_case_       (			self       )     ->		Optional[int]:
          return self.sample is None
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = True,			)     ->		Union[SchedulerOutput, Tuple]:
          UpperCamelCase    :  str							=	self.index_for_timestep(SCREAMING_SNAKE_CASE_       )

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

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

             # store for 2nd order step
             UpperCamelCase    :  Tuple							=	sample
          else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             UpperCamelCase    :  Union[str, Any]							=	(sample - pred_original_sample) / sigma_interpol

             # 3. delta timestep
             UpperCamelCase    :  Dict							=	sigma_next - sigma_hat

             UpperCamelCase    :  Any							=	self.sample
             UpperCamelCase    :  str							=	None

          UpperCamelCase    :  Any							=	sample + derivative * dt

          if not return_dict:
             return (prev_sample,)

          return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			)     ->		torch.FloatTensor:
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          UpperCamelCase    :  Optional[Any]							=	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
             UpperCamelCase    :  List[str]							=	self.timesteps.to(original_samples.device,			dtype=torch.floataa       )
             UpperCamelCase    :  str							=	timesteps.to(original_samples.device,			dtype=torch.floataa       )
          else:
             UpperCamelCase    :  Dict							=	self.timesteps.to(original_samples.device       )
             UpperCamelCase    :  int							=	timesteps.to(original_samples.device       )

          UpperCamelCase    :  str							=	[self.index_for_timestep(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ) for t in timesteps]

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

          UpperCamelCase    :  Any							=	original_samples + noise * sigma
          return noisy_samples






       def __len__(			self       )     ->		Optional[int]:
          return self.config.num_train_timesteps



