



'''simple docstring'''






import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


UpperCamelCase__:   Union[str, Any]								=			logging.get_logger(__name__)

class 				SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(						self			:     List[str]		,			__snake_case			:     Optional[int]		,			__snake_case			:     Tuple		)			->		str:
								UpperCAmelCase :       Any 							=		question_encoder
								UpperCAmelCase :       Dict 							=		generator
								UpperCAmelCase :       int 							=		self.question_encoder







	def      A	(						self			:     Optional[int]		,			__snake_case			:     Optional[int]		)			->		Optional[int]:
								if os.path.isfile(__snake_case		):
															raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""		)
								os.makedirs(__snake_case		,			exist_ok=__snake_case		)
								UpperCAmelCase :       str 							=		os.path.join(__snake_case		,			'''question_encoder_tokenizer'''		)
								UpperCAmelCase :       int 							=		os.path.join(__snake_case		,			'''generator_tokenizer'''		)
								self.question_encoder.save_pretrained(__snake_case		)
								self.generator.save_pretrained(__snake_case		)







	@classmethod
	def      A	(						cls			:     List[str]		,			__snake_case			:     str		,			**__snake_case			:     str		)			->		Dict:
								# dynamically import AutoTokenizer
								from ..auto.tokenization_auto import AutoTokenizer

								UpperCAmelCase :       str 							=		kwargs.pop('''config'''		,			__snake_case		)

								if config is None:
															UpperCAmelCase :       List[Any] 							=		RagConfig.from_pretrained(__snake_case		)

								UpperCAmelCase :       int 							=		AutoTokenizer.from_pretrained(
								    __snake_case		,			config=config.question_encoder		,			subfolder='''question_encoder_tokenizer'''		)
								UpperCAmelCase :       Optional[Any] 							=		AutoTokenizer.from_pretrained(
								    __snake_case		,			config=config.generator		,			subfolder='''generator_tokenizer'''		)
								return cls(question_encoder=__snake_case		,			generator=__snake_case		)







	def __call__(						self			:     int		,			*__snake_case			:     List[str]		,			**__snake_case			:     Union[str, Any]		)			->		List[str]:
								return self.current_tokenizer(*__snake_case		,			**__snake_case		)







	def      A	(						self			:     Tuple		,			*__snake_case			:     str		,			**__snake_case			:     int		)			->		int:
								return self.generator.batch_decode(*__snake_case		,			**__snake_case		)







	def      A	(						self			:     Union[str, Any]		,			*__snake_case			:     Union[str, Any]		,			**__snake_case			:     Optional[int]		)			->		List[Any]:
								return self.generator.decode(*__snake_case		,			**__snake_case		)







	def      A	(						self			:     Union[str, Any]		)			->		Dict:
								UpperCAmelCase :       Tuple 							=		self.question_encoder







	def      A	(						self			:     Optional[int]		)			->		Tuple:
								UpperCAmelCase :       int 							=		self.generator







	def      A	(						self			:     Any		,			__snake_case			:     List[str]		,			__snake_case			:     Optional[List[str]] = None		,			__snake_case			:     Optional[int] = None		,			__snake_case			:     Optional[int] = None		,			__snake_case			:     str = "longest"		,			__snake_case			:     str = None		,			__snake_case			:     bool = True		,			**__snake_case			:     List[str]		,			)			->		BatchEncoding:
								warnings.warn(
								    '''`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '''
								    '''regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '''
								    '''context manager to prepare your targets. See the documentation of your specific tokenizer for more '''
								    '''details'''		,			__snake_case		,			)
								if max_length is None:
															UpperCAmelCase :       Union[str, Any] 							=		self.current_tokenizer.model_max_length
								UpperCAmelCase :       int 							=		self(
								    __snake_case		,			add_special_tokens=__snake_case		,			return_tensors=__snake_case		,			max_length=__snake_case		,			padding=__snake_case		,			truncation=__snake_case		,			**__snake_case		,			)
								if tgt_texts is None:
															return model_inputs
								# Process tgt_texts
								if max_target_length is None:
															UpperCAmelCase :       Any 							=		self.current_tokenizer.model_max_length
								UpperCAmelCase :       List[Any] 							=		self(
								    text_target=__snake_case		,			add_special_tokens=__snake_case		,			return_tensors=__snake_case		,			padding=__snake_case		,			max_length=__snake_case		,			truncation=__snake_case		,			**__snake_case		,			)
								UpperCAmelCase :       Tuple 							=		labels['''input_ids''']
								return model_inputs










'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       42
	lowerCamelCase__			 =       42







	def __init__(						self			:     Union[str, Any]		,			__snake_case			:     UNetaDModel		,			__snake_case			:     ScoreSdeVeScheduler		)			->		int:
								super().__init__()
								self.register_modules(unet=__snake_case		,			scheduler=__snake_case		)







	@torch.no_grad()
	def __call__(						self			:     Optional[int]		,			__snake_case			:     int = 1		,			__snake_case			:     int = 2000		,			__snake_case			:     Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			__snake_case			:     Optional[str] = "pil"		,			__snake_case			:     bool = True		,			**__snake_case			:     Optional[int]		,			)			->		Union[ImagePipelineOutput, Tuple]:
								UpperCAmelCase :       str 							=		self.unet.config.sample_size
								UpperCAmelCase :       Union[str, Any] 							=		(batch_size, 3, img_size, img_size)

								UpperCAmelCase :       int 							=		self.unet

								UpperCAmelCase :       Any 							=		randn_tensor(__snake_case		,			generator=__snake_case		) * self.scheduler.init_noise_sigma
								UpperCAmelCase :       List[Any] 							=		sample.to(self.device		)

								self.scheduler.set_timesteps(__snake_case		)
								self.scheduler.set_sigmas(__snake_case		)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
															UpperCAmelCase :       Any 							=		self.scheduler.sigmas[i] * torch.ones(shape[0]		,			device=self.device		)

															# correction step
															for _ in range(self.scheduler.config.correct_steps		):
																						UpperCAmelCase :       Union[str, Any] 							=		self.unet(__snake_case		,			__snake_case		).sample
																						UpperCAmelCase :       Optional[Any] 							=		self.scheduler.step_correct(__snake_case		,			__snake_case		,			generator=__snake_case		).prev_sample

															# prediction step
															UpperCAmelCase :       Optional[Any] 							=		model(__snake_case		,			__snake_case		).sample
															UpperCAmelCase :       List[str] 							=		self.scheduler.step_pred(__snake_case		,			__snake_case		,			__snake_case		,			generator=__snake_case		)

															UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		output.prev_sample, output.prev_sample_mean

								UpperCAmelCase :       int 							=		sample_mean.clamp(0		,			1		)
								UpperCAmelCase :       Union[str, Any] 							=		sample.cpu().permute(0		,			2		,			3		,			1		).numpy()
								if output_type == "pil":
															UpperCAmelCase :       Optional[Any] 							=		self.numpy_to_pil(__snake_case		)

								if not return_dict:
															return (sample,)

								return ImagePipelineOutput(images=__snake_case		)





