



'''simple docstring'''





import os
import unittest

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    BertTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english






@require_tokenizers
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  a_												=BertTokenizer
  a_												=BertTokenizerFast
  a_												=True
  a_												=True
  a_												=filter_non_english




  def 						_lowercase     ( self      :	Any   )					->					Union[str, Any]:
    super().setUp()

    __lowerCamelCase       :       Union[str, Any]           = [
        '[UNK]',
        '[CLS]',
        '[SEP]',
        '[PAD]',
        '[MASK]',
        'want',
        '##want',
        '##ed',
        'wa',
        'un',
        'runn',
        '##ing',
        ',',
        'low',
        'lowest',
    ]
    __lowerCamelCase       :       int           = os.path.join(self.tmpdirname			,	VOCAB_FILES_NAMES['vocab_file']   )
    with open(self.vocab_file			,	'w'			,	encoding='utf-8'   ) as vocab_writer:
      vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]   )   )




  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	Any   )					->					Tuple:
    __lowerCamelCase       :       int           = 'UNwant\u00E9d,running'
    __lowerCamelCase       :       Optional[Any]           = 'unwanted, running'
    return input_text, output_text




  def 						_lowercase     ( self      :	str   )					->					Any:
    __lowerCamelCase       :       Dict           = self.tokenizer_class(self.vocab_file   )

    __lowerCamelCase       :       Union[str, Any]           = tokenizer.tokenize('UNwant\u00E9d,running'   )
    self.assertListEqual(_a			,	['un', '##want', '##ed', ',', 'runn', '##ing']   )
    self.assertListEqual(tokenizer.convert_tokens_to_ids(_a   )			,	[9, 6, 7, 12, 10, 11]   )




  def 						_lowercase     ( self      :	Optional[Any]   )					->					str:
    if not self.test_rust_tokenizer:
      return

    __lowerCamelCase       :       int           = self.get_tokenizer()
    __lowerCamelCase       :       int           = self.get_rust_tokenizer()

    __lowerCamelCase       :       Union[str, Any]           = 'UNwant\u00E9d,running'

    __lowerCamelCase       :       Tuple           = tokenizer.tokenize(_a   )
    __lowerCamelCase       :       Optional[int]           = rust_tokenizer.tokenize(_a   )
    self.assertListEqual(_a			,	_a   )

    __lowerCamelCase       :       List[str]           = tokenizer.encode(_a			,	add_special_tokens=_a   )
    __lowerCamelCase       :       Union[str, Any]           = rust_tokenizer.encode(_a			,	add_special_tokens=_a   )
    self.assertListEqual(_a			,	_a   )

    __lowerCamelCase       :       str           = self.get_rust_tokenizer()
    __lowerCamelCase       :       Dict           = tokenizer.encode(_a   )
    __lowerCamelCase       :       int           = rust_tokenizer.encode(_a   )
    self.assertListEqual(_a			,	_a   )

    # With lower casing
    __lowerCamelCase       :       List[Any]           = self.get_tokenizer(do_lower_case=_a   )
    __lowerCamelCase       :       str           = self.get_rust_tokenizer(do_lower_case=_a   )

    __lowerCamelCase       :       Tuple           = 'UNwant\u00E9d,running'

    __lowerCamelCase       :       str           = tokenizer.tokenize(_a   )
    __lowerCamelCase       :       Optional[Any]           = rust_tokenizer.tokenize(_a   )
    self.assertListEqual(_a			,	_a   )

    __lowerCamelCase       :       Optional[Any]           = tokenizer.encode(_a			,	add_special_tokens=_a   )
    __lowerCamelCase       :       int           = rust_tokenizer.encode(_a			,	add_special_tokens=_a   )
    self.assertListEqual(_a			,	_a   )

    __lowerCamelCase       :       Union[str, Any]           = self.get_rust_tokenizer()
    __lowerCamelCase       :       Dict           = tokenizer.encode(_a   )
    __lowerCamelCase       :       str           = rust_tokenizer.encode(_a   )
    self.assertListEqual(_a			,	_a   )




  def 						_lowercase     ( self      :	Optional[int]   )					->					Union[str, Any]:
    __lowerCamelCase       :       List[Any]           = BasicTokenizer()

    self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'   )			,	['ah', '\u535A', '\u63A8', 'zz']   )




  def 						_lowercase     ( self      :	Optional[int]   )					->					Optional[Any]:
    __lowerCamelCase       :       List[Any]           = BasicTokenizer(do_lower_case=_a   )

    self.assertListEqual(
        tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '   )			,	['hello', '!', 'how', 'are', 'you', '?']   )
    self.assertListEqual(tokenizer.tokenize('H\u00E9llo'   )			,	['hello']   )




  def 						_lowercase     ( self      :	str   )					->					Union[str, Any]:
    __lowerCamelCase       :       int           = BasicTokenizer(do_lower_case=_a			,	strip_accents=_a   )

    self.assertListEqual(
        tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '   )			,	['hällo', '!', 'how', 'are', 'you', '?']   )
    self.assertListEqual(tokenizer.tokenize('H\u00E9llo'   )			,	['h\u00E9llo']   )




  def 						_lowercase     ( self      :	List[Any]   )					->					str:
    __lowerCamelCase       :       Any           = BasicTokenizer(do_lower_case=_a			,	strip_accents=_a   )

    self.assertListEqual(
        tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '   )			,	['hallo', '!', 'how', 'are', 'you', '?']   )
    self.assertListEqual(tokenizer.tokenize('H\u00E9llo'   )			,	['hello']   )




  def 						_lowercase     ( self      :	Tuple   )					->					str:
    __lowerCamelCase       :       List[Any]           = BasicTokenizer(do_lower_case=_a   )

    self.assertListEqual(
        tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '   )			,	['hallo', '!', 'how', 'are', 'you', '?']   )
    self.assertListEqual(tokenizer.tokenize('H\u00E9llo'   )			,	['hello']   )




  def 						_lowercase     ( self      :	int   )					->					Optional[int]:
    __lowerCamelCase       :       Union[str, Any]           = BasicTokenizer(do_lower_case=_a   )

    self.assertListEqual(
        tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '   )			,	['HeLLo', '!', 'how', 'Are', 'yoU', '?']   )




  def 						_lowercase     ( self      :	List[str]   )					->					int:
    __lowerCamelCase       :       List[Any]           = BasicTokenizer(do_lower_case=_a			,	strip_accents=_a   )

    self.assertListEqual(
        tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '   )			,	['HäLLo', '!', 'how', 'Are', 'yoU', '?']   )




  def 						_lowercase     ( self      :	Optional[int]   )					->					List[str]:
    __lowerCamelCase       :       Optional[Any]           = BasicTokenizer(do_lower_case=_a			,	strip_accents=_a   )

    self.assertListEqual(
        tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '   )			,	['HaLLo', '!', 'how', 'Are', 'yoU', '?']   )




  def 						_lowercase     ( self      :	Tuple   )					->					Optional[int]:
    __lowerCamelCase       :       Tuple           = BasicTokenizer(do_lower_case=_a			,	never_split=['[UNK]']   )

    self.assertListEqual(
        tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'   )			,	['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']   )




  def 						_lowercase     ( self      :	Tuple   )					->					List[str]:
    __lowerCamelCase       :       Union[str, Any]           = BasicTokenizer()
    __lowerCamelCase       :       str           = 'a\n\'ll !!to?\'d of, can\'t.'
    __lowerCamelCase       :       List[Any]           = ['a', '\'', 'll', '!', '!', 'to', '?', '\'', 'd', 'of', ',', 'can', '\'', 't', '.']
    self.assertListEqual(tokenizer.tokenize(_a   )			,	_a   )




  def 						_lowercase     ( self      :	Tuple   )					->					Union[str, Any]:
    __lowerCamelCase       :       Any           = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

    __lowerCamelCase       :       List[Any]           = {}
    for i, token in enumerate(_a   ):
      __lowerCamelCase       :       List[Any]           = i
    __lowerCamelCase       :       List[str]           = WordpieceTokenizer(vocab=_a			,	unk_token='[UNK]'   )

    self.assertListEqual(tokenizer.tokenize(''   )			,	[]   )

    self.assertListEqual(tokenizer.tokenize('unwanted running'   )			,	['un', '##want', '##ed', 'runn', '##ing']   )

    self.assertListEqual(tokenizer.tokenize('unwantedX running'   )			,	['[UNK]', 'runn', '##ing']   )




  def 						_lowercase     ( self      :	List[str]   )					->					Union[str, Any]:
    self.assertTrue(_is_whitespace(' '   )   )
    self.assertTrue(_is_whitespace('\t'   )   )
    self.assertTrue(_is_whitespace('\r'   )   )
    self.assertTrue(_is_whitespace('\n'   )   )
    self.assertTrue(_is_whitespace('\u00A0'   )   )

    self.assertFalse(_is_whitespace('A'   )   )
    self.assertFalse(_is_whitespace('-'   )   )




  def 						_lowercase     ( self      :	Any   )					->					Tuple:
    self.assertTrue(_is_control('\u0005'   )   )

    self.assertFalse(_is_control('A'   )   )
    self.assertFalse(_is_control(' '   )   )
    self.assertFalse(_is_control('\t'   )   )
    self.assertFalse(_is_control('\r'   )   )




  def 						_lowercase     ( self      :	List[str]   )					->					Dict:
    self.assertTrue(_is_punctuation('-'   )   )
    self.assertTrue(_is_punctuation('$'   )   )
    self.assertTrue(_is_punctuation('`'   )   )
    self.assertTrue(_is_punctuation('.'   )   )

    self.assertFalse(_is_punctuation('A'   )   )
    self.assertFalse(_is_punctuation(' '   )   )




  def 						_lowercase     ( self      :	Tuple   )					->					Optional[Any]:
    __lowerCamelCase       :       Dict           = self.get_tokenizer()
    __lowerCamelCase       :       Any           = self.get_rust_tokenizer()

    # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
    self.assertListEqual([tokenizer.tokenize(_a   ) for t in ['Test', '\xad', 'test']]			,	[['[UNK]'], [], ['[UNK]']]   )

    self.assertListEqual(
        [rust_tokenizer.tokenize(_a   ) for t in ['Test', '\xad', 'test']]			,	[['[UNK]'], [], ['[UNK]']]   )




  @slow
  def 						_lowercase     ( self      :	Optional[Any]   )					->					int:
    __lowerCamelCase       :       List[str]           = self.tokenizer_class.from_pretrained('bert-base-uncased'   )

    __lowerCamelCase       :       Dict           = tokenizer.encode('sequence builders'			,	add_special_tokens=_a   )
    __lowerCamelCase       :       Optional[int]           = tokenizer.encode('multi-sequence build'			,	add_special_tokens=_a   )

    __lowerCamelCase       :       List[str]           = tokenizer.build_inputs_with_special_tokens(_a   )
    __lowerCamelCase       :       List[str]           = tokenizer.build_inputs_with_special_tokens(_a			,	_a   )

    assert encoded_sentence == [101] + text + [102]
    assert encoded_pair == [101] + text + [102] + text_a + [102]




  def 						_lowercase     ( self      :	int   )					->					Tuple:
    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
      with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
        __lowerCamelCase       :       Any           = self.rust_tokenizer_class.from_pretrained(_a			,	**_a   )

        __lowerCamelCase       :       Union[str, Any]           = f'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
        __lowerCamelCase       :       Dict           = tokenizer_r.encode_plus(
            _a			,	return_attention_mask=_a			,	return_token_type_ids=_a			,	return_offsets_mapping=_a			,	add_special_tokens=_a			,	)

        __lowerCamelCase       :       Tuple           = tokenizer_r.do_lower_case if hasattr(_a			,	'do_lower_case'   ) else False
        __lowerCamelCase       :       Tuple           = (
            [
                ((0, 0), tokenizer_r.cls_token),
                ((0, 1), 'A'),
                ((1, 2), ','),
                ((3, 5), 'na'),
                ((5, 6), '##ï'),
                ((6, 8), '##ve'),
                ((9, 15), tokenizer_r.mask_token),
                ((16, 21), 'Allen'),
                ((21, 23), '##NL'),
                ((23, 24), '##P'),
                ((25, 33), 'sentence'),
                ((33, 34), '.'),
                ((0, 0), tokenizer_r.sep_token),
            ]
            if not do_lower_case
            else [
                ((0, 0), tokenizer_r.cls_token),
                ((0, 1), 'a'),
                ((1, 2), ','),
                ((3, 8), 'naive'),
                ((9, 15), tokenizer_r.mask_token),
                ((16, 21), 'allen'),
                ((21, 23), '##nl'),
                ((23, 24), '##p'),
                ((25, 33), 'sentence'),
                ((33, 34), '.'),
                ((0, 0), tokenizer_r.sep_token),
            ]
        )

        self.assertEqual(
            [e[1] for e in expected_results]			,	tokenizer_r.convert_ids_to_tokens(tokens['input_ids']   )   )
        self.assertEqual([e[0] for e in expected_results]			,	tokens['offset_mapping']   )




  def 						_lowercase     ( self      :	str   )					->					List[str]:
    __lowerCamelCase       :       Optional[Any]           = ['的', '人', '有']
    __lowerCamelCase       :       List[str]           = ''.join(_a   )
    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
      with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
        __lowerCamelCase       :       Any           = True
        __lowerCamelCase       :       str           = self.tokenizer_class.from_pretrained(_a			,	**_a   )
        __lowerCamelCase       :       Dict           = self.rust_tokenizer_class.from_pretrained(_a			,	**_a   )

        __lowerCamelCase       :       Tuple           = tokenizer_p.encode(_a			,	add_special_tokens=_a   )
        __lowerCamelCase       :       str           = tokenizer_r.encode(_a			,	add_special_tokens=_a   )

        __lowerCamelCase       :       Any           = tokenizer_r.convert_ids_to_tokens(_a   )
        __lowerCamelCase       :       Any           = tokenizer_p.convert_ids_to_tokens(_a   )

        # it is expected that each Chinese character is not preceded by "##"
        self.assertListEqual(_a			,	_a   )
        self.assertListEqual(_a			,	_a   )

        __lowerCamelCase       :       List[str]           = False
        __lowerCamelCase       :       List[Any]           = self.rust_tokenizer_class.from_pretrained(_a			,	**_a   )
        __lowerCamelCase       :       str           = self.tokenizer_class.from_pretrained(_a			,	**_a   )

        __lowerCamelCase       :       int           = tokenizer_r.encode(_a			,	add_special_tokens=_a   )
        __lowerCamelCase       :       Union[str, Any]           = tokenizer_p.encode(_a			,	add_special_tokens=_a   )

        __lowerCamelCase       :       List[Any]           = tokenizer_r.convert_ids_to_tokens(_a   )
        __lowerCamelCase       :       List[str]           = tokenizer_p.convert_ids_to_tokens(_a   )

        # it is expected that only the first Chinese character is not preceded by "##".
        __lowerCamelCase       :       Any           = [
            f'##{token}' if idx != 0 else token for idx, token in enumerate(_a   )
        ]
        self.assertListEqual(_a			,	_a   )
        self.assertListEqual(_a			,	_a   )











'''simple docstring'''





from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_UpperCamelCase											=						8

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase=BITS       )      ->      Any:
  __lowerCamelCase       :       List[str]           = x.device

  __lowerCamelCase       :       int           = (x * 255).int().clamp(0       ,255       )

  __lowerCamelCase       :       Any           = 2 ** torch.arange(bits - 1       ,-1       ,-1       ,device=_lowerCAmelCase       )
  __lowerCamelCase       :       List[str]           = rearrange(_lowerCAmelCase       ,'d -> d 1 1'       )
  __lowerCamelCase       :       Optional[Any]           = rearrange(_lowerCAmelCase       ,'b c h w -> b c 1 h w'       )

  __lowerCamelCase       :       Optional[Any]           = ((x & mask) != 0).float()
  __lowerCamelCase       :       Tuple           = rearrange(_lowerCAmelCase       ,'b c d h w -> b (c d) h w'       )
  __lowerCamelCase       :       Union[str, Any]           = bits * 2 - 1
  return bits

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase=BITS       )      ->      Optional[Any]:
  __lowerCamelCase       :       int           = x.device

  __lowerCamelCase       :       int           = (x > 0).int()
  __lowerCamelCase       :       List[str]           = 2 ** torch.arange(bits - 1       ,-1       ,-1       ,device=_lowerCAmelCase       ,dtype=torch.intaa       )

  __lowerCamelCase       :       Any           = rearrange(_lowerCAmelCase       ,'d -> d 1 1'       )
  __lowerCamelCase       :       Any           = rearrange(_lowerCAmelCase       ,'b (c d) h w -> b c d h w'       ,d=8       )
  __lowerCamelCase       :       List[str]           = reduce(x * mask       ,'b c d h w -> b c h w'       ,'sum'       )
  return (dec / 255).clamp(0.0       ,1.0       )

def        a_     (							self       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = 0.0       ,_lowerCAmelCase = True       ,_lowerCAmelCase=None       ,_lowerCAmelCase = True       ,)      ->      Union[DDIMSchedulerOutput, Tuple]:
  if self.num_inference_steps is None:
    raise ValueError(
        'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'       )

  # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
  # Ideally, read DDIM paper in-detail understanding

  # Notation (<variable name> -> <name in paper>
  # - pred_noise_t -> e_theta(x_t, t)
  # - pred_original_sample -> f_theta(x_t, t) or x_0
  # - std_dev_t -> sigma_t
  # - eta -> η
  # - pred_sample_direction -> "direction pointing to x_t"
  # - pred_prev_sample -> "x_t-1"

  # 1. get previous step value (=t-1)
  __lowerCamelCase       :       Optional[Any]           = timestep - self.config.num_train_timesteps // self.num_inference_steps

  # 2. compute alphas, betas
  __lowerCamelCase       :       str           = self.alphas_cumprod[timestep]
  __lowerCamelCase       :       Optional[Any]           = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

  __lowerCamelCase       :       Union[str, Any]           = 1 - alpha_prod_t

  # 3. compute predicted original sample from predicted noise also called
  # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  __lowerCamelCase       :       Optional[int]           = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

  # 4. Clip "predicted x_0"
  __lowerCamelCase       :       Dict           = self.bit_scale
  if self.config.clip_sample:
    __lowerCamelCase       :       Optional[Any]           = torch.clamp(_lowerCAmelCase       ,-scale       ,_lowerCAmelCase       )

  # 5. compute variance: "sigma_t(η)" -> see formula (16)
  # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
  __lowerCamelCase       :       int           = self._get_variance(_lowerCAmelCase       ,_lowerCAmelCase       )
  __lowerCamelCase       :       Tuple           = eta * variance ** 0.5

  if use_clipped_model_output:
    # the model_output is always re-derived from the clipped x_0 in Glide
    __lowerCamelCase       :       List[str]           = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

  # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  __lowerCamelCase       :       Union[str, Any]           = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

  # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  __lowerCamelCase       :       Any           = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

  if eta > 0:
    # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
    __lowerCamelCase       :       Dict           = model_output.device if torch.is_tensor(_lowerCAmelCase       ) else 'cpu'
    __lowerCamelCase       :       Dict           = torch.randn(model_output.shape       ,dtype=model_output.dtype       ,generator=_lowerCAmelCase       ).to(_lowerCAmelCase       )
    __lowerCamelCase       :       Optional[Any]           = self._get_variance(_lowerCAmelCase       ,_lowerCAmelCase       ) ** 0.5 * eta * noise

    __lowerCamelCase       :       int           = prev_sample + variance

  if not return_dict:
    return (prev_sample,)

  return DDIMSchedulerOutput(prev_sample=_lowerCAmelCase       ,pred_original_sample=_lowerCAmelCase       )

def        a_     (							self       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase="epsilon"       ,_lowerCAmelCase=None       ,_lowerCAmelCase = True       ,)      ->      Union[DDPMSchedulerOutput, Tuple]:
  __lowerCamelCase       :       Dict           = timestep

  if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
    __lowerCamelCase       ,__lowerCamelCase       :       int           = torch.split(_lowerCAmelCase       ,sample.shape[1]       ,dim=1       )
  else:
    __lowerCamelCase       :       Optional[Any]           = None

  # 1. compute alphas, betas
  __lowerCamelCase       :       Optional[int]           = self.alphas_cumprod[t]
  __lowerCamelCase       :       Optional[int]           = self.alphas_cumprod[t - 1] if t > 0 else self.one
  __lowerCamelCase       :       Any           = 1 - alpha_prod_t
  __lowerCamelCase       :       Any           = 1 - alpha_prod_t_prev

  # 2. compute predicted original sample from predicted noise also called
  # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
  if prediction_type == "epsilon":
    __lowerCamelCase       :       List[Any]           = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  elif prediction_type == "sample":
    __lowerCamelCase       :       Any           = model_output
  else:
    raise ValueError(F'Unsupported prediction_type {prediction_type}.'       )

  # 3. Clip "predicted x_0"
  __lowerCamelCase       :       Optional[Any]           = self.bit_scale
  if self.config.clip_sample:
    __lowerCamelCase       :       Union[str, Any]           = torch.clamp(_lowerCAmelCase       ,-scale       ,_lowerCAmelCase       )

  # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
  # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
  __lowerCamelCase       :       Union[str, Any]           = (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
  __lowerCamelCase       :       Dict           = self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

  # 5. Compute predicted previous sample µ_t
  # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
  __lowerCamelCase       :       Dict           = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

  # 6. Add noise
  __lowerCamelCase       :       Optional[Any]           = 0
  if t > 0:
    __lowerCamelCase       :       Any           = torch.randn(
        model_output.size()       ,dtype=model_output.dtype       ,layout=model_output.layout       ,generator=_lowerCAmelCase       ).to(model_output.device       )
    __lowerCamelCase       :       Optional[Any]           = (self._get_variance(_lowerCAmelCase       ,predicted_variance=_lowerCAmelCase       ) ** 0.5) * noise

  __lowerCamelCase       :       str           = pred_prev_sample + variance

  if not return_dict:
    return (pred_prev_sample,)

  return DDPMSchedulerOutput(prev_sample=_lowerCAmelCase       ,pred_original_sample=_lowerCAmelCase       )






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  def __init__( self      :	List[str]			,	_a      :	UNetaDConditionModel			,	_a      :	Union[DDIMScheduler, DDPMScheduler]			,	_a      :	Optional[float] = 1.0			,	)					->					Union[str, Any]:
    super().__init__()
    __lowerCamelCase       :       Dict           = bit_scale
    __lowerCamelCase       :       List[str]           = (
        ddim_bit_scheduler_step if isinstance(_a			,	_a   ) else ddpm_bit_scheduler_step
    )

    self.register_modules(unet=_a			,	scheduler=_a   )




  @torch.no_grad()
  def __call__( self      :	Tuple			,	_a      :	Optional[int] = 256			,	_a      :	Optional[int] = 256			,	_a      :	Optional[int] = 50			,	_a      :	Optional[torch.Generator] = None			,	_a      :	Optional[int] = 1			,	_a      :	Optional[str] = "pil"			,	_a      :	bool = True			,	**_a      :	Dict			,	)					->					Union[Tuple, ImagePipelineOutput]:
    __lowerCamelCase       :       str           = torch.randn(
        (batch_size, self.unet.config.in_channels, height, width)			,	generator=_a			,	)
    __lowerCamelCase       :       int           = decimal_to_bits(_a   ) * self.bit_scale
    __lowerCamelCase       :       Dict           = latents.to(self.device   )

    self.scheduler.set_timesteps(_a   )

    for t in self.progress_bar(self.scheduler.timesteps   ):
      # predict the noise residual
      __lowerCamelCase       :       int           = self.unet(_a			,	_a   ).sample

      # compute the previous noisy sample x_t -> x_t-1
      __lowerCamelCase       :       List[Any]           = self.scheduler.step(_a			,	_a			,	_a   ).prev_sample

    __lowerCamelCase       :       Dict           = bits_to_decimal(_a   )

    if output_type == "pil":
      __lowerCamelCase       :       int           = self.numpy_to_pil(_a   )

    if not return_dict:
      return (image,)

    return ImagePipelineOutput(images=_a   )






