"""simple docstring"""






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_camembert import CamembertTokenizer
else:
						_A   					=    None


_A   					=    logging.get_logger(__name__)

_A   					=    {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_A   					=    {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
    },
}

_A   					=    {
    'camembert-base': 5_1_2,
}

_A   					=    '▁'

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		VOCAB_FILES_NAMES
					a   	=		PRETRAINED_VOCAB_FILES_MAP
					a   	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a   	=		["input_ids", "attention_mask"]
					a   	=		CamembertTokenizer



					def __init__(						self			:				List[str]   , _snake_case			:				str=None   , _snake_case			:				Dict=None   , _snake_case			:				Dict="<s>"   , _snake_case			:				Optional[Any]="</s>"   , _snake_case			:				str="</s>"   , _snake_case			:				str="<s>"   , _snake_case			:				Union[str, Any]="<unk>"   , _snake_case			:				List[str]="<pad>"   , _snake_case			:				List[str]="<mask>"   , _snake_case			:				List[Any]=["<s>NOTUSED", "</s>NOTUSED"]   , **_snake_case			:				Union[str, Any]   , )							->       Tuple:
									# Mask token behave like a normal word, i.e. include the space before it
									SCREAMING_SNAKE_CASE__		  =				AddedToken(_snake_case   , lstrip=_snake_case   , rstrip=_snake_case		) if isinstance(_snake_case   , _snake_case		) else mask_token

									super().__init__(
									    _snake_case   , tokenizer_file=_snake_case   , bos_token=_snake_case   , eos_token=_snake_case   , sep_token=_snake_case   , cls_token=_snake_case   , unk_token=_snake_case   , pad_token=_snake_case   , mask_token=_snake_case   , additional_special_tokens=_snake_case   , **_snake_case   , )

									SCREAMING_SNAKE_CASE__		  =				vocab_file
									SCREAMING_SNAKE_CASE__		  =				False if not self.vocab_file else True



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				List[int]   , _snake_case			:				Optional[List[int]] = None		)							->       List[int]:

									if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									SCREAMING_SNAKE_CASE__		  =				[self.cls_token_id]
									SCREAMING_SNAKE_CASE__		  =				[self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				List[int]   , _snake_case			:				Optional[List[int]] = None		)							->       List[int]:
									SCREAMING_SNAKE_CASE__		  =				[self.sep_token_id]
									SCREAMING_SNAKE_CASE__		  =				[self.cls_token_id]

									if token_ids_a is None:
													return len(cls + token_ids_a + sep		) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				str   , _snake_case			:				Optional[str] = None		)							->       Tuple[str]:
									if not self.can_save_slow_tokenizer:
													raise ValueError(
													    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
													    "tokenizer."		)

									if not os.path.isdir(_snake_case		):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
													return
									SCREAMING_SNAKE_CASE__		  =				os.path.join(
									    _snake_case   , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

									if os.path.abspath(self.vocab_file		) != os.path.abspath(_snake_case		):
													copyfile(self.vocab_file   , _snake_case		)

									return (out_vocab_file,)





"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				int   , _snake_case			:				Optional[int]   , _snake_case			:				Dict		)							->       Dict:
									super().__init__()

									# make sure scheduler can always be converted to DDIM
									SCREAMING_SNAKE_CASE__		  =				DDIMScheduler.from_config(scheduler.config		)

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



					@torch.no_grad()
					def __call__(						self			:				int   , _snake_case			:				int = 1   , _snake_case			:				Optional[Union[torch.Generator, List[torch.Generator]]] = None   , _snake_case			:				float = 0.0   , _snake_case			:				int = 50   , _snake_case			:				Optional[bool] = None   , _snake_case			:				Optional[str] = "pil"   , _snake_case			:				bool = True   , )							->       Union[ImagePipelineOutput, Tuple]:

									# Sample gaussian noise to begin loop
									if isinstance(self.unet.config.sample_size   , _snake_case		):
													SCREAMING_SNAKE_CASE__		  =				(
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
									else:
													SCREAMING_SNAKE_CASE__		  =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

									if isinstance(_snake_case   , _snake_case		) and len(_snake_case		) != batch_size:
													raise ValueError(
													    F"""You have passed a list of generators of length {len(_snake_case		)}, but requested an effective batch"""
													    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""		)

									SCREAMING_SNAKE_CASE__		  =				randn_tensor(_snake_case   , generator=_snake_case   , device=self.device   , dtype=self.unet.dtype		)

									# set step values
									self.scheduler.set_timesteps(_snake_case		)

									for t in self.progress_bar(self.scheduler.timesteps		):
													# 1. predict noise model_output
													SCREAMING_SNAKE_CASE__		  =				self.unet(_snake_case   , _snake_case		).sample

													# 2. predict previous mean of image x_t-1 and add variance depending on eta
													# eta corresponds to η in paper and should be between [0, 1]
													# do x_t -> x_t-1
													SCREAMING_SNAKE_CASE__		  =				self.scheduler.step(
													    _snake_case   , _snake_case   , _snake_case   , eta=_snake_case   , use_clipped_model_output=_snake_case   , generator=_snake_case		).prev_sample

									SCREAMING_SNAKE_CASE__		  =				(image / 2 + 0.5).clamp(0   , 1		)
									SCREAMING_SNAKE_CASE__		  =				image.cpu().permute(0   , 2   , 3   , 1		).numpy()
									if output_type == "pil":
													SCREAMING_SNAKE_CASE__		  =				self.numpy_to_pil(_snake_case		)

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=_snake_case		)




