



from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
      import tensorflow as tf

      from transformers.models.openai.modeling_tf_openai import (
          TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFOpenAIGPTDoubleHeadsModel,
          TFOpenAIGPTForSequenceClassification,
          TFOpenAIGPTLMHeadModel,
          TFOpenAIGPTModel,
      )







class       _snake_case				:






   '''simple docstring'''






   def __init__(    self:     Union[str, Any]  ,lowerCamelCase_:     Union[str, Any]  ,)	->      Optional[Any]:
       UpperCAmelCase_     :       List[str]  		=					parent
       UpperCAmelCase_     :       Optional[int]  		=					13
       UpperCAmelCase_     :       Union[str, Any]  		=					7
       UpperCAmelCase_     :       Tuple  		=					True
       UpperCAmelCase_     :       Union[str, Any]  		=					True
       UpperCAmelCase_     :       List[str]  		=					True
       UpperCAmelCase_     :       Optional[Any]  		=					True
       UpperCAmelCase_     :       str  		=					True
       UpperCAmelCase_     :       Union[str, Any]  		=					99
       UpperCAmelCase_     :       List[str]  		=					32
       UpperCAmelCase_     :       int  		=					2
       UpperCAmelCase_     :       Optional[int]  		=					4
       UpperCAmelCase_     :       Dict  		=					37
       UpperCAmelCase_     :       Any  		=					"""gelu"""
       UpperCAmelCase_     :       str  		=					0.1
       UpperCAmelCase_     :       int  		=					0.1
       UpperCAmelCase_     :       List[str]  		=					512
       UpperCAmelCase_     :       List[str]  		=					16
       UpperCAmelCase_     :       List[str]  		=					2
       UpperCAmelCase_     :       Union[str, Any]  		=					0.0_2
       UpperCAmelCase_     :       Union[str, Any]  		=					3
       UpperCAmelCase_     :       Optional[Any]  		=					4
       UpperCAmelCase_     :       Tuple  		=					None
       UpperCAmelCase_     :       Union[str, Any]  		=					self.vocab_size - 1


   def       A__    (    self:     Tuple							)	->      Dict:
       UpperCAmelCase_     :       Optional[int]  		=					ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size							)

       UpperCAmelCase_     :       str  		=					None
       if self.use_input_mask:
           UpperCAmelCase_     :       str  		=					random_attention_mask([self.batch_size, self.seq_length]							)

       UpperCAmelCase_     :       Any  		=					None
       if self.use_token_type_ids:
           UpperCAmelCase_     :       List[Any]  		=					ids_tensor([self.batch_size, self.seq_length]  ,self.type_vocab_size							)

       UpperCAmelCase_     :       List[str]  		=					None
       if self.use_mc_token_ids:
           UpperCAmelCase_     :       Tuple  		=					ids_tensor([self.batch_size, self.num_choices]  ,self.seq_length							)

       UpperCAmelCase_     :       Tuple  		=					None
       UpperCAmelCase_     :       str  		=					None
       UpperCAmelCase_     :       int  		=					None
       if self.use_labels:
           UpperCAmelCase_     :       List[str]  		=					ids_tensor([self.batch_size]  ,self.type_sequence_label_size							)
           UpperCAmelCase_     :       Dict  		=					ids_tensor([self.batch_size, self.seq_length]  ,self.num_labels							)
           UpperCAmelCase_     :       Union[str, Any]  		=					ids_tensor([self.batch_size]  ,self.num_choices							)

       UpperCAmelCase_     :       Union[str, Any]  		=					OpenAIGPTConfig(
           vocab_size=self.vocab_size  ,n_embd=self.hidden_size  ,n_layer=self.num_hidden_layers  ,n_head=self.num_attention_heads  ,n_positions=self.max_position_embeddings  ,pad_token_id=self.pad_token_id  ,)

       UpperCAmelCase_     :       List[Any]  		=					ids_tensor([self.num_hidden_layers, self.num_attention_heads]  ,2							)

       return (
           config,
           input_ids,
           input_mask,
           head_mask,
           token_type_ids,
           mc_token_ids,
           sequence_labels,
           token_labels,
           choice_labels,
       )


   def       A__    (    self:     str  ,lowerCamelCase_:     int  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     Optional[int]  ,lowerCamelCase_:     Union[str, Any]  ,*lowerCamelCase_:     Dict							)	->      int:
       UpperCAmelCase_     :       Dict  		=					TFOpenAIGPTModel(config=lowerCamelCase_							)
       UpperCAmelCase_     :       Optional[int]  		=					{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
       UpperCAmelCase_     :       Optional[Any]  		=					model(lowerCamelCase_							)

       UpperCAmelCase_     :       Any  		=					[input_ids, input_mask]
       UpperCAmelCase_     :       int  		=					model(lowerCamelCase_							)

       UpperCAmelCase_     :       List[str]  		=					model(lowerCamelCase_							)

       self.parent.assertEqual(result.last_hidden_state.shape  ,(self.batch_size, self.seq_length, self.hidden_size)							)


   def       A__    (    self:     Any  ,lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     int  ,lowerCamelCase_:     str  ,lowerCamelCase_:     Union[str, Any]  ,lowerCamelCase_:     str  ,*lowerCamelCase_:     Dict							)	->      List[Any]:
       UpperCAmelCase_     :       Optional[Any]  		=					TFOpenAIGPTLMHeadModel(config=lowerCamelCase_							)
       UpperCAmelCase_     :       Optional[Any]  		=					{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
       UpperCAmelCase_     :       Union[str, Any]  		=					model(lowerCamelCase_							)
       self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.vocab_size)							)


   def       A__    (    self:     str  ,lowerCamelCase_:     Any  ,lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     Tuple  ,lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     List[str]  ,lowerCamelCase_:     int  ,*lowerCamelCase_:     str							)	->      int:
       UpperCAmelCase_     :       List[Any]  		=					TFOpenAIGPTDoubleHeadsModel(config=lowerCamelCase_							)

       UpperCAmelCase_     :       str  		=					tf.tile(tf.expand_dims(lowerCamelCase_  ,1							)  ,(1, self.num_choices, 1)							)
       UpperCAmelCase_     :       int  		=					tf.tile(tf.expand_dims(lowerCamelCase_  ,1							)  ,(1, self.num_choices, 1)							)
       UpperCAmelCase_     :       Tuple  		=					tf.tile(tf.expand_dims(lowerCamelCase_  ,1							)  ,(1, self.num_choices, 1)							)

       UpperCAmelCase_     :       Any  		=					{
           """input_ids""": multiple_choice_inputs_ids,
           """mc_token_ids""": mc_token_ids,
           """attention_mask""": multiple_choice_input_mask,
           """token_type_ids""": multiple_choice_token_type_ids,
       }
       UpperCAmelCase_     :       int  		=					model(lowerCamelCase_							)
       self.parent.assertEqual(
           result.logits.shape  ,(self.batch_size, self.num_choices, self.seq_length, self.vocab_size)							)
       self.parent.assertEqual(result.mc_logits.shape  ,(self.batch_size, self.num_choices)							)


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     Tuple  ,lowerCamelCase_:     int  ,lowerCamelCase_:     Tuple  ,lowerCamelCase_:     int  ,lowerCamelCase_:     Union[str, Any]  ,*lowerCamelCase_:     Dict							)	->      Union[str, Any]:
       UpperCAmelCase_     :       Union[str, Any]  		=					self.num_labels
       UpperCAmelCase_     :       str  		=					ids_tensor([self.batch_size]  ,self.type_sequence_label_size							)
       UpperCAmelCase_     :       List[Any]  		=					{
           """input_ids""": input_ids,
           """attention_mask""": input_mask,
           """token_type_ids""": token_type_ids,
           """labels""": sequence_labels,
       }
       UpperCAmelCase_     :       int  		=					TFOpenAIGPTForSequenceClassification(lowerCamelCase_							)
       UpperCAmelCase_     :       str  		=					model(lowerCamelCase_							)
       self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)							)






   def       A__    (    self:     Dict							)	->      Optional[Any]:
       UpperCAmelCase_     :       Dict  		=					self.prepare_config_and_inputs()

       (
           (
           UpperCAmelCase_
       )		, (
           UpperCAmelCase_
       )		, (
           UpperCAmelCase_
       )		, (
           UpperCAmelCase_
       )		, (
           UpperCAmelCase_
       )		, (
           UpperCAmelCase_
       )		, (
           UpperCAmelCase_
       )		, (
           UpperCAmelCase_
       )		, (
           UpperCAmelCase_
       )		, 
       )     :       List[Any]  		=					config_and_inputs

       UpperCAmelCase_     :       Union[str, Any]  		=					{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
       return config, inputs_dict







@require_tf
class       _snake_case				(     __snake_case ,							__snake_case ,							unittest.TestCase ):






   '''simple docstring'''






   A__      : List[Any]       =						(
       (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
       if is_tf_available()
       else ()
   )
   A__      : List[Any]       =						(
       (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
   )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
   A__      : int       =						(
       {
           "feature-extraction": TFOpenAIGPTModel,
           "text-classification": TFOpenAIGPTForSequenceClassification,
           "text-generation": TFOpenAIGPTLMHeadModel,
           "zero-shot": TFOpenAIGPTForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   A__      : List[Any]       =						False
   A__      : Union[str, Any]       =						False


   def       A__    (    self:     str  ,lowerCamelCase_:     Optional[int]  ,lowerCamelCase_:     Union[str, Any]  ,lowerCamelCase_:     Union[str, Any]  ,lowerCamelCase_:     Union[str, Any]  ,lowerCamelCase_:     Tuple							)	->      Tuple:
       if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
           # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
           # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
           # tiny config could not be created.
           return True

       return False


   def       A__    (    self:     Dict							)	->      str:
       UpperCAmelCase_     :       Any  		=					TFOpenAIGPTModelTester(self							)
       UpperCAmelCase_     :       List[Any]  		=					ConfigTester(self  ,config_class=lowerCamelCase_  ,n_embd=37							)


   def       A__    (    self:     List[Any]							)	->      Union[str, Any]:
       self.config_tester.run_common_tests()


   def       A__    (    self:     int							)	->      Union[str, Any]:
       UpperCAmelCase_     :       Union[str, Any]  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_model(*lowerCamelCase_							)


   def       A__    (    self:     int							)	->      Tuple:
       UpperCAmelCase_     :       Any  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_lm_head(*lowerCamelCase_							)


   def       A__    (    self:     Union[str, Any]							)	->      int:
       UpperCAmelCase_     :       Dict  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_double_head(*lowerCamelCase_							)


   def       A__    (    self:     List[str]							)	->      Optional[Any]:
       UpperCAmelCase_     :       Optional[int]  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowerCamelCase_							)






   @slow
   def       A__    (    self:     List[str]							)	->      str:
       for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCAmelCase_     :       str  		=					TFOpenAIGPTModel.from_pretrained(lowerCamelCase_							)
           self.assertIsNotNone(lowerCamelCase_							)







@require_tf
class       _snake_case				(     unittest.TestCase ):






   '''simple docstring'''






   @slow
   def       A__    (    self:     Dict							)	->      List[str]:
       UpperCAmelCase_     :       int  		=					TFOpenAIGPTLMHeadModel.from_pretrained("""openai-gpt"""							)
       UpperCAmelCase_     :       List[str]  		=					tf.convert_to_tensor([[481, 4735, 544]]  ,dtype=tf.intaa							)  # the president is
       UpperCAmelCase_     :       Union[str, Any]  		=					[
           481,
           4735,
           544,
           246,
           963,
           870,
           762,
           239,
           244,
           40477,
           244,
           249,
           719,
           881,
           487,
           544,
           240,
           244,
           603,
           481,
       ]  # the president is a very good man. " \n " i\'m sure he is, " said the

       UpperCAmelCase_     :       int  		=					model.generate(lowerCamelCase_  ,do_sample=lowerCamelCase_							)
       self.assertListEqual(output_ids[0].numpy().tolist()  ,lowerCamelCase_							)







'''simple docstring'''

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	42
					SCREAMING_SNAKE_CASE							=	42






def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase=0.999,       _UpperCAmelCase="cosine",       ):
		if alpha_transform_type == "cosine":

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

		elif alpha_transform_type == "exp":

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

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

		__UpperCAmelCase	:							Union[str, Any]     =     []
		for i in range(_UpperCAmelCase  ):
				__UpperCAmelCase	:							int     =     i / num_diffusion_timesteps
				__UpperCAmelCase	:							Any     =     (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      SCREAMING_SNAKE_CASE__      (   snake_case__							,snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	1
					@register_to_config
					def __init__(   self       :      Dict		,	UpperCAmelCase_       :      int = 1_000		,	UpperCAmelCase_       :      float = 0.0001		,	UpperCAmelCase_       :      float = 0.02		,	UpperCAmelCase_       :      str = "linear"		,	UpperCAmelCase_       :      float = 0.0		,	UpperCAmelCase_       :      Optional[np.ndarray] = None		,	UpperCAmelCase_       :      bool = True		,	):







							"""simple docstring"""
							if trained_betas is not None:
									__UpperCAmelCase	:							Tuple     =     torch.from_numpy(UpperCAmelCase_		)
							elif beta_schedule == "linear":
									__UpperCAmelCase	:							Dict     =     torch.linspace(UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		,	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		,	UpperCAmelCase_		,	dtype=torch.floataa		) ** 2
									)
							elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									__UpperCAmelCase	:							str     =     betas_for_alpha_bar(UpperCAmelCase_		)
							elif beta_schedule == "sigmoid":
									# GeoDiff sigmoid schedule
									__UpperCAmelCase	:							Optional[Any]     =     torch.linspace(-6		,	6		,	UpperCAmelCase_		)
									__UpperCAmelCase	:							Optional[int]     =     torch.sigmoid(UpperCAmelCase_		) * (beta_end - beta_start) + beta_start
							else:
									raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"		)

							__UpperCAmelCase	:							List[str]     =     1.0 - self.betas
							__UpperCAmelCase	:							List[str]     =     torch.cumprod(self.alphas		,	dim=0		)
							__UpperCAmelCase	:							List[Any]     =     torch.tensor(1.0		)

							__UpperCAmelCase	:							str     =     torch.tensor(1.0		)

							# standard deviation of the initial noise distribution
							__UpperCAmelCase	:							Optional[Any]     =     1.0

							# setable values
							__UpperCAmelCase	:							Optional[Any]     =     None
							__UpperCAmelCase	:							Optional[Any]     =     torch.from_numpy(np.arange(0		,	UpperCAmelCase_		)[::-1].copy()		)

							__UpperCAmelCase	:							Tuple     =     eta
					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      Optional[int] = None		):







							"""simple docstring"""
							return sample
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      int = 10		,	UpperCAmelCase_       :      int = 10		,	UpperCAmelCase_       :      Union[str, torch.device] = None		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							Union[str, Any]     =     min(self.config.num_train_timesteps		,	UpperCAmelCase_		)
							__UpperCAmelCase	:							List[Any]     =     num_inference_steps

							__UpperCAmelCase	:							Dict     =     []

							__UpperCAmelCase	:							int     =     {}
							for j in range(0		,	num_inference_steps - jump_length		,	UpperCAmelCase_		):
									__UpperCAmelCase	:							int     =     jump_n_sample - 1

							__UpperCAmelCase	:							Optional[int]     =     num_inference_steps
							while t >= 1:
									__UpperCAmelCase	:							int     =     t - 1
									timesteps.append(UpperCAmelCase_		)

									if jumps.get(UpperCAmelCase_		,	0		) > 0:
											__UpperCAmelCase	:							Any     =     jumps[t] - 1
											for _ in range(UpperCAmelCase_		):
													__UpperCAmelCase	:							Optional[Any]     =     t + 1
													timesteps.append(UpperCAmelCase_		)

							__UpperCAmelCase	:							Optional[Any]     =     np.array(UpperCAmelCase_		) * (self.config.num_train_timesteps // self.num_inference_steps)
							__UpperCAmelCase	:							str     =     torch.from_numpy(UpperCAmelCase_		).to(UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      Optional[Any]		,	UpperCAmelCase_       :      Tuple		):







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[int]     =     t - self.config.num_train_timesteps // self.num_inference_steps

							__UpperCAmelCase	:							str     =     self.alphas_cumprod[t]
							__UpperCAmelCase	:							int     =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							__UpperCAmelCase	:							Dict     =     1 - alpha_prod_t
							__UpperCAmelCase	:							Optional[Any]     =     1 - alpha_prod_t_prev

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from
							# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
							# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
							# variance to pred_sample
							# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
							# without eta.
							# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
							__UpperCAmelCase	:							Dict     =     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance
					def 		lowerCamelCase_   (   self       :      Any		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      Optional[torch.Generator] = None		,	UpperCAmelCase_       :      bool = True		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     timestep
							__UpperCAmelCase	:							Optional[Any]     =     timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 1. compute alphas, betas
							__UpperCAmelCase	:							Any     =     self.alphas_cumprod[t]
							__UpperCAmelCase	:							Optional[int]     =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							__UpperCAmelCase	:							Optional[Any]     =     1 - alpha_prod_t

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							__UpperCAmelCase	:							List[Any]     =     (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
									__UpperCAmelCase	:							int     =     torch.clamp(UpperCAmelCase_		,	-1		,	1		)

							# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
							# substitute formula (7) in the algorithm coming from DDPM paper
							# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
							# DDIM schedule gives the same results as DDPM with eta = 1.0
							# Noise is being reused in 7. and 8., but no impact on quality has
							# been observed.

							# 5. Add noise
							__UpperCAmelCase	:							Optional[Any]     =     model_output.device
							__UpperCAmelCase	:							Optional[int]     =     randn_tensor(model_output.shape		,	generator=UpperCAmelCase_		,	device=UpperCAmelCase_		,	dtype=model_output.dtype		)
							__UpperCAmelCase	:							List[Any]     =     self.eta * self._get_variance(UpperCAmelCase_		) ** 0.5

							__UpperCAmelCase	:							str     =     0
							if t > 0 and self.eta > 0:
									__UpperCAmelCase	:							Union[str, Any]     =     std_dev_t * noise

							# 6. compute "direction pointing to x_t" of formula (12)
							# from https://arxiv.org/pdf/2010.02502.pdf
							__UpperCAmelCase	:							int     =     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

							# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							__UpperCAmelCase	:							Any     =     alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

							# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
							__UpperCAmelCase	:							str     =     (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

							# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
							__UpperCAmelCase	:							Union[str, Any]     =     mask * prev_known_part + (1.0 - mask) * prev_unknown_part

							if not return_dict:
									return (
									    pred_prev_sample,
									    pred_original_sample,
									)

							return RePaintSchedulerOutput(prev_sample=UpperCAmelCase_		,	pred_original_sample=UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      List[Any]		,	UpperCAmelCase_       :      Any		,	UpperCAmelCase_       :      List[str]=None		):







							"""simple docstring"""
							__UpperCAmelCase	:							Dict     =     self.config.num_train_timesteps // self.num_inference_steps

							for i in range(UpperCAmelCase_		):
									__UpperCAmelCase	:							Any     =     self.betas[timestep + i]
									if sample.device.type == "mps":
											# randn does not work reproducibly on mps
											__UpperCAmelCase	:							Tuple     =     randn_tensor(sample.shape		,	dtype=sample.dtype		,	generator=UpperCAmelCase_		)
											__UpperCAmelCase	:							str     =     noise.to(sample.device		)
									else:
											__UpperCAmelCase	:							str     =     randn_tensor(sample.shape		,	generator=UpperCAmelCase_		,	device=sample.device		,	dtype=sample.dtype		)

									# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
									__UpperCAmelCase	:							Any     =     (1 - beta) ** 0.5 * sample + beta**0.5 * noise

							return sample
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.IntTensor		,	):







							"""simple docstring"""
							raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."		)






					def __len__(   self       :      List[str]		):







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




