

'''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


__snake_case					:      List[Any] 		=     8
def 		__lowerCamelCase			(  __snake_case	:						Optional[Any],						__snake_case	:						Optional[int]=BITS			)			->				List[str]:






						"""simple docstring"""





						A__	:  Dict           =x.device

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

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

						A__	:  Tuple           =((x & mask) != 0).float()
						A__	:  Any           =rearrange(__snake_case,						"""b c d h w -> b (c d) h w"""			)
						A__	:  List[Any]           =bits * 2 - 1
						return bits
def 		__lowerCamelCase			(  __snake_case	:						Union[str, Any],						__snake_case	:						Dict=BITS			)			->				List[str]:






						"""simple docstring"""





						A__	:  Optional[int]           =x.device

						A__	:  Union[str, Any]           =(x > 0).int()
						A__	:  int           =2 ** torch.arange(bits - 1,						-1,						-1,						device=__snake_case,						dtype=torch.intaa			)

						A__	:  Optional[int]           =rearrange(__snake_case,						"""d -> d 1 1"""			)
						A__	:  List[Any]           =rearrange(__snake_case,						"""b (c d) h w -> b c d h w""",						d=8			)
						A__	:  Tuple           =reduce(x * mask,						"""b c d h w -> b c h w""",						"""sum"""			)
						return (dec / 255).clamp(0.0,						1.0			)
def 		__lowerCamelCase			(  self	:						Union[str, Any],						__snake_case	:						torch.FloatTensor,						__snake_case	:						int,						__snake_case	:						torch.FloatTensor,						__snake_case	:						float = 0.0,						__snake_case	:						bool = True,						__snake_case	:						Tuple=None,						__snake_case	:						bool = True,						)			->				Any:






						"""simple docstring"""





						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)
						A__	:  Tuple           =timestep - self.config.num_train_timesteps // self.num_inference_steps

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

						A__	:  str           =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
						A__	:  List[Any]           =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

						# 4. Clip "predicted x_0"
						A__	:  Tuple           =self.bit_scale
						if self.config.clip_sample:
												A__	:  Any           =torch.clamp(__snake_case,						-scale,						__snake_case			)

						# 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)
						A__	:  int           =self._get_variance(__snake_case,						__snake_case			)
						A__	:  Tuple           =eta * variance ** 0.5

						if use_clipped_model_output:
												# the model_output is always re-derived from the clipped x_0 in Glide
												A__	:  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
						A__	:  Optional[int]           =(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
						A__	:  Union[str, 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
												A__	:  int           =model_output.device if torch.is_tensor(__snake_case			) else '''cpu'''
												A__	:  List[Any]           =torch.randn(model_output.shape,						dtype=model_output.dtype,						generator=__snake_case			).to(__snake_case			)
												A__	:  List[Any]           =self._get_variance(__snake_case,						__snake_case			) ** 0.5 * eta * noise

												A__	:  Dict           =prev_sample + variance

						if not return_dict:
												return (prev_sample,)

						return DDIMSchedulerOutput(prev_sample=__snake_case,						pred_original_sample=__snake_case			)
def 		__lowerCamelCase			(  self	:						Optional[int],						__snake_case	:						torch.FloatTensor,						__snake_case	:						int,						__snake_case	:						torch.FloatTensor,						__snake_case	:						Any="epsilon",						__snake_case	:						Optional[Any]=None,						__snake_case	:						bool = True,						)			->				str:






						"""simple docstring"""





						A__	:  Optional[int]           =timestep

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

						# 1. compute alphas, betas
						A__	:  List[Any]           =self.alphas_cumprod[t]
						A__	:  Tuple           =self.alphas_cumprod[t - 1] if t > 0 else self.one
						A__	:  Optional[int]           =1 - alpha_prod_t
						A__	:  List[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":
												A__	:  str           =(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						elif prediction_type == "sample":
												A__	:  List[str]           =model_output
						else:
												raise ValueError(f"Unsupported prediction_type {prediction_type}."			)

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

						# 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
						A__	:  Any           =(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
						A__	:  Tuple           =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
						A__	:  int           =pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

						A__	:  int           =pred_prev_sample + variance

						if not return_dict:
												return (pred_prev_sample,)

						return DDPMSchedulerOutput(prev_sample=__snake_case,						pred_original_sample=__snake_case			)



class lowerCamelCase					(    snake_case__		):



			'''simple docstring'''






			def __init__( self			:		int						,     lowerCAmelCase_			:		UNetaDConditionModel						,     lowerCAmelCase_			:		Union[DDIMScheduler, DDPMScheduler]						,     lowerCAmelCase_			:		Optional[float] = 1.0						,     )     ->						List[str]:





									'''simple docstring'''
									super().__init__()
									A__	:  int           =bit_scale
									A__	:  Dict           =(
									    ddim_bit_scheduler_step if isinstance(__lowercase						,     __lowercase					) else ddpm_bit_scheduler_step
									)

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



			@torch.no_grad()
			def __call__( self			:		str						,     lowerCAmelCase_			:		Optional[int] = 2_56						,     lowerCAmelCase_			:		Optional[int] = 2_56						,     lowerCAmelCase_			:		Optional[int] = 50						,     lowerCAmelCase_			:		Optional[torch.Generator] = None						,     lowerCAmelCase_			:		Optional[int] = 1						,     lowerCAmelCase_			:		Optional[str] = "pil"						,     lowerCAmelCase_			:		bool = True						,     **lowerCAmelCase_			:		Optional[int]						,     )     ->						Optional[int]:





									'''simple docstring'''
									A__	:  Optional[int]           =torch.randn(
									    (batch_size, self.unet.config.in_channels, height, width)						,     generator=__lowercase						,     )
									A__	:  Any           =decimal_to_bits(__lowercase					) * self.bit_scale
									A__	:  List[str]           =latents.to(self.device					)

									self.scheduler.set_timesteps(__lowercase					)

									for t in self.progress_bar(self.scheduler.timesteps					):
															# predict the noise residual
															A__	:  List[str]           =self.unet(__lowercase						,     __lowercase					).sample

															# compute the previous noisy sample x_t -> x_t-1
															A__	:  Dict           =self.scheduler.step(__lowercase						,     __lowercase						,     __lowercase					).prev_sample

									A__	:  str           =bits_to_decimal(__lowercase					)

									if output_type == "pil":
															A__	:  List[Any]           =self.numpy_to_pil(__lowercase					)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=__lowercase					)








"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							int									=		"""philschmid/bart-large-cnn-samsum"""
				__snake_case :							Optional[int]									=		(
				    """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
				    """and returns a summary of the text."""
				)
				__snake_case :							Dict									=		"""summarizer"""
				__snake_case :							str									=		AutoTokenizer
				__snake_case :							List[str]									=		AutoModelForSeqaSeqLM

				__snake_case :							List[Any]									=		["""text"""]
				__snake_case :							Dict									=		["""text"""]




				def        __lowercase	(  self	:int    ,     __lowercase	:List[Any]      ):
											return self.pre_processor(__lowercase    ,     return_tensors='''pt'''    ,     truncation=__lowercase      )




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:Optional[int]      ):
											return self.model.generate(**__lowercase      )[0]




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:str      ):
											return self.pre_processor.decode(__lowercase    ,     skip_special_tokens=__lowercase    ,     clean_up_tokenization_spaces=__lowercase      )




