



'''simple docstring'''





from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase=None       ,_lowerCAmelCase=None       )      ->      Optional[int]:
  if attention_mask is None:
    __lowerCamelCase       :       List[Any]           = tf.cast(tf.math.not_equal(_lowerCAmelCase       ,config.pad_token_id       )       ,tf.inta       )
  return {"input_ids": input_ids, "attention_mask": attention_mask}






@require_tf
class 					lowerCamelCase_    :
  """simple docstring"""




  a_												=OPTConfig
  a_												={}
  a_												="""gelu"""




  def __init__( self      :	List[str]			,	_a      :	Tuple			,	_a      :	List[Any]=13			,	_a      :	Any=7			,	_a      :	Optional[int]=True			,	_a      :	List[str]=False			,	_a      :	Optional[int]=99			,	_a      :	Optional[int]=16			,	_a      :	int=2			,	_a      :	List[Any]=4			,	_a      :	Union[str, Any]=4			,	_a      :	Union[str, Any]="gelu"			,	_a      :	Any=0.1			,	_a      :	int=0.1			,	_a      :	Dict=20			,	_a      :	Any=2			,	_a      :	Optional[Any]=1			,	_a      :	List[Any]=0			,	_a      :	Tuple=16			,	_a      :	Union[str, Any]=16			,	)					->					Tuple:
    __lowerCamelCase       :       Optional[int]           = parent
    __lowerCamelCase       :       Union[str, Any]           = batch_size
    __lowerCamelCase       :       Union[str, Any]           = seq_length
    __lowerCamelCase       :       Optional[Any]           = is_training
    __lowerCamelCase       :       str           = use_labels
    __lowerCamelCase       :       Tuple           = vocab_size
    __lowerCamelCase       :       List[str]           = hidden_size
    __lowerCamelCase       :       Dict           = num_hidden_layers
    __lowerCamelCase       :       int           = num_attention_heads
    __lowerCamelCase       :       List[str]           = intermediate_size
    __lowerCamelCase       :       int           = hidden_act
    __lowerCamelCase       :       Tuple           = hidden_dropout_prob
    __lowerCamelCase       :       List[str]           = attention_probs_dropout_prob
    __lowerCamelCase       :       str           = max_position_embeddings
    __lowerCamelCase       :       Dict           = eos_token_id
    __lowerCamelCase       :       int           = pad_token_id
    __lowerCamelCase       :       Dict           = bos_token_id
    __lowerCamelCase       :       Tuple           = embed_dim
    __lowerCamelCase       :       List[Any]           = word_embed_proj_dim
    __lowerCamelCase       :       Dict           = False




  def 						_lowercase     ( self      :	Tuple   )					->					str:
    __lowerCamelCase       :       Dict           = ids_tensor([self.batch_size, self.seq_length - 1]			,	self.vocab_size   )
    __lowerCamelCase       :       int           = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )			,	1   )
    __lowerCamelCase       :       Any           = tf.concat([input_ids, eos_tensor]			,	axis=1   )

    __lowerCamelCase       :       Optional[int]           = self.config_cls(
        vocab_size=self.vocab_size			,	hidden_size=self.hidden_size			,	num_hidden_layers=self.num_hidden_layers			,	num_attention_heads=self.num_attention_heads			,	ffn_dim=self.intermediate_size			,	dropout=self.hidden_dropout_prob			,	attention_dropout=self.attention_probs_dropout_prob			,	max_position_embeddings=self.max_position_embeddings			,	eos_token_id=self.eos_token_id			,	bos_token_id=self.bos_token_id			,	pad_token_id=self.pad_token_id			,	embed_dim=self.embed_dim			,	word_embed_proj_dim=self.word_embed_proj_dim			,	is_encoder_decoder=_a			,	**self.config_updates			,	)
    __lowerCamelCase       :       Any           = prepare_opt_inputs_dict(_a			,	_a   )
    return config, inputs_dict




  def 						_lowercase     ( self      :	Dict			,	_a      :	Optional[int]			,	_a      :	List[Any]   )					->					Any:
    __lowerCamelCase       :       List[Any]           = TFOPTModel(config=_a   )
    __lowerCamelCase       :       Optional[int]           = inputs_dict['input_ids']

    __lowerCamelCase       :       Dict           = input_ids[:1, :]
    __lowerCamelCase       :       int           = inputs_dict['attention_mask'][:1, :]
    __lowerCamelCase       :       Optional[int]           = 1

    # first forward pass
    __lowerCamelCase       :       Any           = model(_a			,	attention_mask=_a			,	use_cache=_a   )

    __lowerCamelCase       ,__lowerCamelCase       :       Optional[Any]           = outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    __lowerCamelCase       :       Any           = ids_tensor((self.batch_size, 3)			,	config.vocab_size   )
    __lowerCamelCase       :       Optional[Any]           = tf.cast(ids_tensor((self.batch_size, 3)			,	2   )			,	tf.inta   )

    # append to next input_ids and
    __lowerCamelCase       :       Optional[int]           = tf.concat([input_ids, next_tokens]			,	axis=-1   )
    __lowerCamelCase       :       List[str]           = tf.concat([attention_mask, next_attn_mask]			,	axis=-1   )

    __lowerCamelCase       :       Optional[int]           = model(_a			,	attention_mask=_a   )[0]
    __lowerCamelCase       :       Union[str, Any]           = model(_a			,	attention_mask=_a			,	past_key_values=_a   )[0]

    self.parent.assertEqual(next_tokens.shape[1]			,	output_from_past.shape[1]   )

    # select random slice
    __lowerCamelCase       :       Dict           = int(ids_tensor((1,)			,	output_from_past.shape[-1]   )   )
    __lowerCamelCase       :       Union[str, Any]           = output_from_no_past[:, -3:, random_slice_idx]
    __lowerCamelCase       :       str           = output_from_past[:, :, random_slice_idx]

    # test that outputs are equal for slice
    tf.debugging.assert_near(_a			,	_a			,	rtol=1e-3   )






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




  a_												=(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
  a_												=(TFOPTForCausalLM,) if is_tf_available() else ()
  a_												=(
      {"""feature-extraction""": TFOPTModel, """text-generation""": TFOPTForCausalLM} if is_tf_available() else {}
  )
  a_												=False
  a_												=False
  a_												=False
  a_												=10




  def 						_lowercase     ( self      :	Any   )					->					str:
    __lowerCamelCase       :       int           = TFOPTModelTester(self   )
    __lowerCamelCase       :       int           = ConfigTester(self			,	config_class=_a   )




  def 						_lowercase     ( self      :	Dict   )					->					List[str]:
    self.config_tester.run_common_tests()




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					List[str]:
    __lowerCamelCase       :       int           = self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*_a   )




  def 						_lowercase     ( self      :	Optional[Any]   )					->					Any:
    __lowerCamelCase       ,__lowerCamelCase       :       Union[str, Any]           = self.model_tester.prepare_config_and_inputs_for_common()

    def _get_word_embedding_weight(_a      :	List[Any]			,	_a      :	Optional[int]   ):
      if hasattr(_a			,	'weight'   ):
        return embedding_layer.weight
      else:
        # Here we build the word embeddings weights if not exists.
        # And then we retry to get the attribute once built.
        model.build()
        if hasattr(_a			,	'weight'   ):
          return embedding_layer.weight
        else:
          return None

    for model_class in self.all_model_classes:
      for size in [config.vocab_size - 10, config.vocab_size + 10]:
        # build the embeddings
        __lowerCamelCase       :       int           = model_class(config=_a   )
        __lowerCamelCase       :       Any           = _get_word_embedding_weight(_a			,	model.get_input_embeddings()   )
        __lowerCamelCase       :       Dict           = _get_word_embedding_weight(_a			,	model.get_output_embeddings()   )

        # reshape the embeddings
        model.resize_token_embeddings(_a   )
        __lowerCamelCase       :       Optional[Any]           = _get_word_embedding_weight(_a			,	model.get_input_embeddings()   )
        __lowerCamelCase       :       Dict           = _get_word_embedding_weight(_a			,	model.get_output_embeddings()   )

        # check that the resized embeddings size matches the desired size.
        __lowerCamelCase       :       Optional[Any]           = size if size is not None else config.vocab_size

        self.assertEqual(new_input_embeddings.shape[0]			,	_a   )

        # check that weights remain the same after resizing
        __lowerCamelCase       :       Optional[Any]           = True
        for pa, pa in zip(old_input_embeddings.value()			,	new_input_embeddings.value()   ):
          if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
            __lowerCamelCase       :       List[str]           = False
        self.assertTrue(_a   )

        if old_output_embeddings is not None and new_output_embeddings is not None:
          self.assertEqual(new_output_embeddings.shape[0]			,	_a   )

          __lowerCamelCase       :       int           = True
          for pa, pa in zip(old_output_embeddings.value()			,	new_output_embeddings.value()   ):
            if tf.math.reduce_sum(tf.math.abs(pa - pa   )   ) > 0:
              __lowerCamelCase       :       List[str]           = False
          self.assertTrue(_a   )




def        a_     (							_lowerCAmelCase       )      ->      List[Any]:
  return tf.constant(_lowerCAmelCase       ,dtype=tf.intaa       )






@require_tf
class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  a_												=99




  def 						_lowercase     ( self      :	Any   )					->					Union[str, Any]:
    __lowerCamelCase       :       Tuple           = tf.ones((4, 1)			,	dtype=tf.intaa   ) * 2
    __lowerCamelCase       :       Dict           = tf.concat([ids_tensor((4, 6)			,	self.vocab_size - 3   ) + 3, eos_column_vector]			,	axis=1   )
    __lowerCamelCase       :       Optional[Any]           = input_ids.shape[0]
    __lowerCamelCase       :       Optional[Any]           = OPTConfig(
        vocab_size=self.vocab_size			,	hidden_size=24			,	num_hidden_layers=2			,	num_attention_heads=2			,	ffn_dim=32			,	max_position_embeddings=48			,	eos_token_id=2			,	pad_token_id=1			,	bos_token_id=0			,	)
    return config, input_ids, batch_size






@require_sentencepiece
@require_tf
class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  @slow
  def 						_lowercase     ( self      :	Optional[int]   )					->					str:
    __lowerCamelCase       :       Optional[int]           = TFOPTModel.from_pretrained('facebook/opt-350m'   )
    __lowerCamelCase       :       Union[str, Any]           = _long_tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]   )
    __lowerCamelCase       :       Optional[int]           = tf.not_equal(_a			,	model.config.pad_token_id   )
    with tf.GradientTape():
      __lowerCamelCase       :       Optional[Any]           = model(input_ids=_a			,	attention_mask=_a   ).last_hidden_state
    __lowerCamelCase       :       List[str]           = (1, 11, 512)
    self.assertEqual(output.shape			,	_a   )
    __lowerCamelCase       :       List[Any]           = tf.constant(
        [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]   )
    self.assertTrue(np.allclose(output[:, :3, :3]			,	_a			,	atol=4e-3   )   )

    __lowerCamelCase       :       Optional[int]           = tf.function(_a			,	jit_compile=_a   )
    __lowerCamelCase       :       Optional[Any]           = xla_generate(_a			,	_a   )[0]
    self.assertTrue(np.allclose(output[:, :3, :3]			,	_a			,	atol=4e-2   )   )






@require_tf
@slow
class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  def 						_lowercase     ( self      :	List[str]   )					->					List[str]:
    super().setUp()
    __lowerCamelCase       :       Union[str, Any]           = 'facebook/opt-350m'




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					Tuple:
    __lowerCamelCase       :       Union[str, Any]           = TFOPTForCausalLM.from_pretrained(self.path_model   )
    __lowerCamelCase       :       Dict           = GPTaTokenizer.from_pretrained(self.path_model   )

    __lowerCamelCase       :       List[Any]           = [
        'Today is a beautiful day and I want to',
        'In the city of',
        'Paris is the capital of France and',
        'Computers and mobile phones have taken',
    ]
    # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
    __lowerCamelCase       :       int           = tokenizer(_a			,	return_tensors='tf'			,	padding=_a			,	add_special_tokens=_a   )
    __lowerCamelCase       :       List[Any]           = tf.math.reduce_mean(model(inputs.input_ids			,	attention_mask=inputs.attention_mask   )[0]			,	axis=-1   )
    __lowerCamelCase       :       int           = tf.constant(
        [
            [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
            [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
            [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
            [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
        ]   )
    self.assertTrue(np.allclose(_a			,	_a			,	atol=1e-4   )   )

    __lowerCamelCase       :       int           = tf.function(_a			,	jit_compile=_a   )
    __lowerCamelCase       :       Optional[Any]           = tf.math.reduce_mean(xla_generate(inputs.input_ids			,	attention_mask=inputs.attention_mask   )[0]			,	axis=-1   )
    self.assertTrue(np.allclose(_a			,	_a			,	atol=1e-4   )   )






@require_tf
@slow
class 					lowerCamelCase_    (				unittest.TestCase     ):
  """simple docstring"""




  @property
  def 						_lowercase     ( self      :	Tuple   )					->					List[str]:
    return [
        "Today is a beautiful day and I want",
        "In the city of",
        "Paris is the capital of France and",
        "Computers and mobile phones have taken",
    ]




  def 						_lowercase     ( self      :	Tuple   )					->					str:
    __lowerCamelCase       :       Tuple           = 'facebook/opt-125m'

    __lowerCamelCase       :       Any           = [
        'Today is a beautiful day and I want to',
        'In the city of New York, the city',
        'Paris is the capital of France and the capital',
        'Computers and mobile phones have taken over the',
    ]

    __lowerCamelCase       :       Optional[Any]           = []
    __lowerCamelCase       :       Union[str, Any]           = GPTaTokenizer.from_pretrained(_a   )
    __lowerCamelCase       :       str           = TFOPTForCausalLM.from_pretrained(_a   )

    for prompt in self.prompts:
      __lowerCamelCase       :       Tuple           = tokenizer(_a			,	return_tensors='tf'   ).input_ids

      __lowerCamelCase       :       List[Any]           = model.generate(_a			,	max_length=10   )

      __lowerCamelCase       :       Dict           = tokenizer.batch_decode(_a			,	skip_special_tokens=_a   )
      predicted_outputs += generated_string

    self.assertListEqual(_a			,	_a   )




  def 						_lowercase     ( self      :	Dict   )					->					Any:
    __lowerCamelCase       :       Tuple           = 'facebook/opt-350m'

    __lowerCamelCase       :       Any           = GPTaTokenizer.from_pretrained(_a   )
    __lowerCamelCase       :       Union[str, Any]           = TFOPTForCausalLM.from_pretrained(_a   )

    __lowerCamelCase       :       Tuple           = 'left'

    # use different length sentences to test batching
    __lowerCamelCase       :       List[str]           = [
        'Hello, my dog is a little',
        'Today, I',
    ]

    __lowerCamelCase       :       str           = tokenizer(_a			,	return_tensors='tf'			,	padding=_a   )
    __lowerCamelCase       :       Optional[Any]           = inputs['input_ids']

    __lowerCamelCase       :       Optional[Any]           = model.generate(input_ids=_a			,	attention_mask=inputs['attention_mask']   )

    __lowerCamelCase       :       Union[str, Any]           = tokenizer(sentences[0]			,	return_tensors='tf'   ).input_ids
    __lowerCamelCase       :       Union[str, Any]           = model.generate(input_ids=_a   )

    __lowerCamelCase       :       Dict           = inputs_non_padded.shape[-1] - tf.math.reduce_sum(
        tf.cast(inputs['attention_mask'][-1]			,	tf.intaa   )   )
    __lowerCamelCase       :       List[str]           = tokenizer(sentences[1]			,	return_tensors='tf'   ).input_ids
    __lowerCamelCase       :       Tuple           = model.generate(input_ids=_a			,	max_length=model.config.max_length - num_paddings   )

    __lowerCamelCase       :       Any           = tokenizer.batch_decode(_a			,	skip_special_tokens=_a   )
    __lowerCamelCase       :       Tuple           = tokenizer.decode(output_non_padded[0]			,	skip_special_tokens=_a   )
    __lowerCamelCase       :       str           = tokenizer.decode(output_padded[0]			,	skip_special_tokens=_a   )

    __lowerCamelCase       :       int           = [
        'Hello, my dog is a little bit of a dork.\nI\'m a little bit',
        'Today, I was in the middle of a conversation with a friend about the',
    ]
    self.assertListEqual(_a			,	_a   )
    self.assertListEqual(_a			,	[non_padded_sentence, padded_sentence]   )




  def 						_lowercase     ( self      :	List[Any]   )					->					List[Any]:
    __lowerCamelCase       :       Union[str, Any]           = 'facebook/opt-350m'

    __lowerCamelCase       :       Optional[Any]           = [
        'Today is a beautiful day and I want to',
        'In the city of San Francisco, the city',
        'Paris is the capital of France and the capital',
        'Computers and mobile phones have taken over the',
    ]

    __lowerCamelCase       :       List[Any]           = []
    __lowerCamelCase       :       Any           = GPTaTokenizer.from_pretrained(_a   )
    __lowerCamelCase       :       int           = TFOPTForCausalLM.from_pretrained(_a   )

    for prompt in self.prompts:
      __lowerCamelCase       :       Optional[Any]           = tokenizer(_a			,	return_tensors='tf'   ).input_ids

      __lowerCamelCase       :       Tuple           = model.generate(_a			,	max_length=10   )

      __lowerCamelCase       :       int           = tokenizer.batch_decode(_a			,	skip_special_tokens=_a   )
      predicted_outputs += generated_string

    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   )






