



'''simple docstring'''







_UpperCamelCase  						=			"""\n# Transformers installation\n! pip install transformers datasets evaluate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"""

_UpperCamelCase  						=			[{"""type""": """code""", """content""": INSTALL_CONTENT}]
_UpperCamelCase  						=			{
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}






'''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_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				torch.FloatTensor
			__magic_name__ :				torch.FloatTensor







def        UpperCAmelCase__   (				_lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			List[Any]=0.999,      _lowerCAmelCase    :			Any="cosine",      )    ->		List[Any]:



						"""simple docstring"""

						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_lowerCAmelCase    :			Union[str, Any]):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2

						elif alpha_transform_type == "exp":

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

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

						snake_case :       List[Any]				=    []
						for i in range(_lowerCAmelCase):
												snake_case :       int				=    i / num_diffusion_timesteps
												snake_case :       Optional[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 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase							,				_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Optional[Any]   							=       1



			@register_to_config
			def __init__(							self				,			snake_case_ = 1_000				,			snake_case_ = 0.0_001				,			snake_case_ = 0.02				,			snake_case_ = "linear"				,			snake_case_ = 0.0				,			snake_case_ = None				,			snake_case_ = True				,			):
									if trained_betas is not None:
															snake_case :       Optional[Any]				=    torch.from_numpy(snake_case_					)
									elif beta_schedule == "linear":
															snake_case :       Union[str, Any]				=    torch.linspace(snake_case_				,			snake_case_				,			snake_case_				,			dtype=torch.floataa					)
									elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															snake_case :       Optional[int]				=    (
															    torch.linspace(beta_start**0.5				,			beta_end**0.5				,			snake_case_				,			dtype=torch.floataa					) ** 2
															)
									elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															snake_case :       str				=    betas_for_alpha_bar(snake_case_					)
									elif beta_schedule == "sigmoid":
															# GeoDiff sigmoid schedule
															snake_case :       int				=    torch.linspace(-6				,			6				,			snake_case_					)
															snake_case :       Union[str, Any]				=    torch.sigmoid(snake_case_					) * (beta_end - beta_start) + beta_start
									else:
															raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""					)

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

									snake_case :       Tuple				=    torch.tensor(1.0					)

									# standard deviation of the initial noise distribution
									snake_case :       str				=    1.0

									# setable values
									snake_case :       Optional[int]				=    None
									snake_case :       str				=    torch.from_numpy(np.arange(0				,			snake_case_					)[::-1].copy()					)

									snake_case :       Tuple				=    eta



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_ = None					):
									return sample



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_ = 10				,			snake_case_ = 10				,			snake_case_ = None				,			):
									snake_case :       Optional[Any]				=    min(self.config.num_train_timesteps				,			snake_case_					)
									snake_case :       Tuple				=    num_inference_steps

									snake_case :       Optional[int]				=    []

									snake_case :       Tuple				=    {}
									for j in range(0				,			num_inference_steps - jump_length				,			snake_case_					):
															snake_case :       Union[str, Any]				=    jump_n_sample - 1

									snake_case :       Optional[int]				=    num_inference_steps
									while t >= 1:
															snake_case :       Optional[Any]				=    t - 1
															timesteps.append(snake_case_					)

															if jumps.get(snake_case_				,			0					) > 0:
																					snake_case :       List[Any]				=    jumps[t] - 1
																					for _ in range(snake_case_					):
																											snake_case :       int				=    t + 1
																											timesteps.append(snake_case_					)

									snake_case :       Optional[Any]				=    np.array(snake_case_					) * (self.config.num_train_timesteps // self.num_inference_steps)
									snake_case :       int				=    torch.from_numpy(snake_case_					).to(snake_case_					)



			def   lowerCamelCase (							self				,			snake_case_					):
									snake_case :       Union[str, Any]				=    t - self.config.num_train_timesteps // self.num_inference_steps

									snake_case :       Optional[int]				=    self.alphas_cumprod[t]
									snake_case :       Any				=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
									snake_case :       List[Any]				=    1 - alpha_prod_t
									snake_case :       int				=    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]
									snake_case :       List[str]				=    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

									return variance



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_ = None				,			snake_case_ = True				,			):
									snake_case :       Optional[int]				=    timestep
									snake_case :       List[Any]				=    timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

									snake_case :       Optional[int]				=    0
									if t > 0 and self.eta > 0:
															snake_case :       str				=    std_dev_t * noise

									# 6. compute "direction pointing to x_t" of formula (12)
									# from https://arxiv.org/pdf/2010.02502.pdf
									snake_case :       Optional[Any]				=    (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
									snake_case :       Union[str, 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
									snake_case :       Optional[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
									snake_case :       Optional[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=snake_case_				,			pred_original_sample=snake_case_					)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_=None					):
									snake_case :       Union[str, Any]				=    self.config.num_train_timesteps // self.num_inference_steps

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

															# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
															snake_case :       Union[str, Any]				=    (1 - beta) ** 0.5 * sample + beta**0.5 * noise

									return sample



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			):
									raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."					)






			def __len__(							self					):
									return self.config.num_train_timesteps





