




'''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     UpperCamelCase_     (a__     ):







						"""simple docstring"""


						_lowerCAmelCase    						=					42
						_lowerCAmelCase    						=					42






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

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

					elif alpha_transform_type == "exp":

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

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

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






class     UpperCamelCase_     (a__,      a__     ):







						"""simple docstring"""


						_lowerCAmelCase    						=					1







						@register_to_config
						def __init__(     self      :       List[Any]							, _lowerCamelCase      :       int = 1000							, _lowerCamelCase      :       float = 0.00_01							, _lowerCamelCase      :       float = 0.02							, _lowerCamelCase      :       str = "linear"							, _lowerCamelCase      :       float = 0.0							, _lowerCamelCase      :       Optional[np.ndarray] = None							, _lowerCamelCase      :       bool = True							, ):





											"""simple docstring"""







											if trained_betas is not None:
																A_ :     str =							torch.from_numpy(_lowerCamelCase    )
											elif beta_schedule == "linear":
																A_ :     Optional[int] =							torch.linspace(_lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase							, dtype=torch.floataa    )
											elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																A_ :     Optional[int] =							(
																    torch.linspace(beta_start**0.5							, beta_end**0.5							, _lowerCamelCase							, dtype=torch.floataa    ) ** 2
																)
											elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																A_ :     Optional[int] =							betas_for_alpha_bar(_lowerCamelCase    )
											elif beta_schedule == "sigmoid":
																# GeoDiff sigmoid schedule
																A_ :     str =							torch.linspace(-6							, 6							, _lowerCamelCase    )
																A_ :     Optional[Any] =							torch.sigmoid(_lowerCamelCase    ) * (beta_end - beta_start) + beta_start
											else:
																raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'    )

											A_ :     int =							1.0 - self.betas
											A_ :     Tuple =							torch.cumprod(self.alphas							, dim=0    )
											A_ :     Tuple =							torch.tensor(1.0    )

											A_ :     Tuple =							torch.tensor(1.0    )

											# standard deviation of the initial noise distribution
											A_ :     Optional[int] =							1.0

											# setable values
											A_ :     Any =							None
											A_ :     Tuple =							torch.from_numpy(np.arange(0							, _lowerCamelCase    )[::-1].copy()    )

											A_ :     Tuple =							eta







						def       _a    (     self      :       Any							, _lowerCamelCase      :       torch.FloatTensor							, _lowerCamelCase      :       Optional[int] = None    ):





											"""simple docstring"""







											return sample







						def       _a    (     self      :       Dict							, _lowerCamelCase      :       int							, _lowerCamelCase      :       int = 10							, _lowerCamelCase      :       int = 10							, _lowerCamelCase      :       Union[str, torch.device] = None							, ):





											"""simple docstring"""







											A_ :     Optional[Any] =							min(self.config.num_train_timesteps							, _lowerCamelCase    )
											A_ :     List[str] =							num_inference_steps

											A_ :     Optional[Any] =							[]

											A_ :     int =							{}
											for j in range(0							, num_inference_steps - jump_length							, _lowerCamelCase    ):
																A_ :     Optional[int] =							jump_n_sample - 1

											A_ :     Union[str, Any] =							num_inference_steps
											while t >= 1:
																A_ :     List[Any] =							t - 1
																timesteps.append(_lowerCamelCase    )

																if jumps.get(_lowerCamelCase							, 0    ) > 0:
																					A_ :     Union[str, Any] =							jumps[t] - 1
																					for _ in range(_lowerCamelCase    ):
																										A_ :     Any =							t + 1
																										timesteps.append(_lowerCamelCase    )

											A_ :     Tuple =							np.array(_lowerCamelCase    ) * (self.config.num_train_timesteps // self.num_inference_steps)
											A_ :     int =							torch.from_numpy(_lowerCamelCase    ).to(_lowerCamelCase    )







						def       _a    (     self      :       int							, _lowerCamelCase      :       Optional[int]    ):





											"""simple docstring"""







											A_ :     Optional[int] =							t - self.config.num_train_timesteps // self.num_inference_steps

											A_ :     List[str] =							self.alphas_cumprod[t]
											A_ :     Tuple =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
											A_ :     Dict =							1 - alpha_prod_t
											A_ :     Union[str, 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]
											A_ :     List[Any] =							(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

											return variance







						def       _a    (     self      :       Optional[Any]							, _lowerCamelCase      :       torch.FloatTensor							, _lowerCamelCase      :       int							, _lowerCamelCase      :       torch.FloatTensor							, _lowerCamelCase      :       torch.FloatTensor							, _lowerCamelCase      :       torch.FloatTensor							, _lowerCamelCase      :       Optional[torch.Generator] = None							, _lowerCamelCase      :       bool = True							, ):





											"""simple docstring"""







											A_ :     Dict =							timestep
											A_ :     List[Any] =							timestep - self.config.num_train_timesteps // self.num_inference_steps

											# 1. compute alphas, betas
											A_ :     Optional[Any] =							self.alphas_cumprod[t]
											A_ :     str =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
											A_ :     str =							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
											A_ :     Any =							(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
																A_ :     List[str] =							torch.clamp(_lowerCamelCase							, -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
											A_ :     Any =							model_output.device
											A_ :     Optional[int] =							randn_tensor(model_output.shape							, generator=_lowerCamelCase							, device=_lowerCamelCase							, dtype=model_output.dtype    )
											A_ :     List[str] =							self.eta * self._get_variance(_lowerCamelCase    ) ** 0.5

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

											# 6. compute "direction pointing to x_t" of formula (12)
											# from https://arxiv.org/pdf/2010.02502.pdf
											A_ :     Tuple =							(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
											A_ :     Optional[int] =							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
											A_ :     int =							(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
											A_ :     Tuple =							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=_lowerCamelCase							, pred_original_sample=_lowerCamelCase    )







						def       _a    (     self      :       Union[str, Any]							, _lowerCamelCase      :       List[Any]							, _lowerCamelCase      :       Union[str, Any]							, _lowerCamelCase      :       Any=None    ):





											"""simple docstring"""







											A_ :     int =							self.config.num_train_timesteps // self.num_inference_steps

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

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

											return sample







						def       _a    (     self      :       List[Any]							, _lowerCamelCase      :       torch.FloatTensor							, _lowerCamelCase      :       torch.FloatTensor							, _lowerCamelCase      :       torch.IntTensor							, ):





											"""simple docstring"""







											raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''    )







						def __len__(     self      :       Tuple    ):





											"""simple docstring"""







											return self.config.num_train_timesteps









'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__          =						logging.get_logger(__name__)

snake_case__          =						{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}






class     UpperCamelCase_     (a__     ):







						"""simple docstring"""


						_lowerCAmelCase    						=					'qdqbert'







						def __init__(     self      :       Union[str, Any]							, _lowerCamelCase      :       List[Any]=30522							, _lowerCamelCase      :       str=768							, _lowerCamelCase      :       Optional[Any]=12							, _lowerCamelCase      :       str=12							, _lowerCamelCase      :       List[Any]=3072							, _lowerCamelCase      :       Union[str, Any]="gelu"							, _lowerCamelCase      :       Dict=0.1							, _lowerCamelCase      :       Union[str, Any]=0.1							, _lowerCamelCase      :       Optional[int]=512							, _lowerCamelCase      :       Dict=2							, _lowerCamelCase      :       Optional[Any]=0.02							, _lowerCamelCase      :       List[Any]=1E-12							, _lowerCamelCase      :       Tuple=True							, _lowerCamelCase      :       Tuple=1							, _lowerCamelCase      :       Optional[Any]=0							, _lowerCamelCase      :       Optional[int]=2							, **_lowerCamelCase      :       List[str]							, ):





											"""simple docstring"""







											super().__init__(pad_token_id=_lowerCamelCase							, bos_token_id=_lowerCamelCase							, eos_token_id=_lowerCamelCase							, **_lowerCamelCase    )

											A_ :     Optional[Any] =							vocab_size
											A_ :     str =							max_position_embeddings
											A_ :     List[str] =							hidden_size
											A_ :     str =							num_hidden_layers
											A_ :     List[str] =							num_attention_heads
											A_ :     List[str] =							intermediate_size
											A_ :     int =							hidden_act
											A_ :     Tuple =							hidden_dropout_prob
											A_ :     Optional[Any] =							attention_probs_dropout_prob
											A_ :     Dict =							initializer_range
											A_ :     Optional[int] =							type_vocab_size
											A_ :     Any =							layer_norm_eps
											A_ :     Optional[int] =							use_cache



