




"""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 __lowercase(      __SCREAMING_SNAKE_CASE				):




     '''simple docstring'''


     def __init__(    self						,	__a						,	__a     ):
        super().__init__()

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

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





     @torch.no_grad()
     def __call__(    self						,	__a = 1						,	__a = None						,	__a = 0.0						,	__a = 50						,	__a = None						,	__a = "pil"						,	__a = True						,	):

        # Sample gaussian noise to begin loop
        if isinstance(self.unet.config.sample_size						,	_a     ):
           __lowerCamelCase					:	Dict	       =				(
               batch_size,
               self.unet.config.in_channels,
               self.unet.config.sample_size,
               self.unet.config.sample_size,
           )
        else:
           __lowerCamelCase					:	Any	       =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

        __lowerCamelCase					:	List[str]	       =				randn_tensor(_a						,	generator=_a						,	device=self.device						,	dtype=self.unet.dtype     )

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

        for t in self.progress_bar(self.scheduler.timesteps     ):
           # 1. predict noise model_output
           __lowerCamelCase					:	Union[str, Any]	       =				self.unet(_a						,	_a     ).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
           __lowerCamelCase					:	Union[str, Any]	       =				self.scheduler.step(
               _a						,	_a						,	_a						,	eta=_a						,	use_clipped_model_output=_a						,	generator=_a     ).prev_sample

        __lowerCamelCase					:	List[str]	       =				(image / 2 + 0.5).clamp(0						,	1     )
        __lowerCamelCase					:	Tuple	       =				image.cpu().permute(0						,	2						,	3						,	1     ).numpy()
        if output_type == "pil":
           __lowerCamelCase					:	Any	       =				self.numpy_to_pil(_a     )

        if not return_dict:
           return (image,)

        return ImagePipelineOutput(images=_a     )









"""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_barthez import BarthezTokenizer
else:
					a_				:    List[str]										=     None

a_				:    Any										=     logging.get_logger(__name__)

a_				:    List[Any]										=     {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

a_				:    int										=     {
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

a_				:    Any										=     {
    '''moussaKam/mbarthez''': 10_24,
    '''moussaKam/barthez''': 10_24,
    '''moussaKam/barthez-orangesum-title''': 10_24,
}

a_				:    Union[str, Any]										=     '''▁'''

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						Optional[Any]     		= VOCAB_FILES_NAMES
					__a						:						Optional[int]     		= PRETRAINED_VOCAB_FILES_MAP
					__a						:						List[str]     		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a						:						Union[str, Any]     		= ['input_ids', 'attention_mask']
					__a						:						Optional[Any]     		= BarthezTokenizer
					def __init__(    self						,	__a=None						,	__a=None						,	__a="<s>"						,	__a="</s>"						,	__a="</s>"						,	__a="<s>"						,	__a="<unk>"						,	__a="<pad>"						,	__a="<mask>"						,	**__a						,	):
								# Mask token behave like a normal word, i.e. include the space before it
								__lowerCamelCase					:	Optional[Any]	       =				AddedToken(__a						,	lstrip=__a						,	rstrip=__a     ) if isinstance(__a						,	__a     ) else mask_token

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

								__lowerCamelCase					:	Tuple	       =				vocab_file
								__lowerCamelCase					:	List[str]	       =				False if not self.vocab_file else True
					def     snake_case_				(    self						,	__a						,	__a = None     ):

								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								__lowerCamelCase					:	Optional[Any]	       =				[self.cls_token_id]
								__lowerCamelCase					:	Dict	       =				[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep
					def     snake_case_				(    self						,	__a						,	__a = None     ):
								__lowerCamelCase					:	Optional[int]	       =				[self.sep_token_id]
								__lowerCamelCase					:	int	       =				[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     snake_case_				(    self						,	__a						,	__a = None     ):
								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(__a     ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
											return
								__lowerCamelCase					:	List[Any]	       =				os.path.join(
								    __a						,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

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

								return (out_vocab_file,)



