from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


_lowercase:	Union[str, Any]       			=		logging.get_logger(__name__)  # pylint: disable=invalid-name



class        lowerCamelCase__				(   UpperCAmelCase				):





			def __init__(       self	:	Optional[Any]  ,  lowercase__	:	Optional[Any]  ,  lowercase__	:	Optional[int]						):
										super().__init__()
										self.register_modules(unet=lowercase__  ,  scheduler=lowercase__						)





			@torch.no_grad()
			def __call__(       self	:	List[str]  ,  lowercase__	:	int = 1  ,  lowercase__	:	int = 1_00  ,  lowercase__	:	Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,  lowercase__	:	Optional[float] = None  ,  lowercase__	:	bool = True  ,  ):

										if audio_length_in_s is None:
																	_lowerCAmelCase							=							self.unet.config.sample_size / self.unet.config.sample_rate

										_lowerCAmelCase							=							audio_length_in_s * self.unet.config.sample_rate

										_lowerCAmelCase							=							2 ** len(self.unet.up_blocks						)
										if sample_size < 3 * down_scale_factor:
																	raise ValueError(
																	    f'{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'
																	    f' {3 * down_scale_factor / self.unet.config.sample_rate}.'						)

										_lowerCAmelCase							=							int(lowercase__						)
										if sample_size % down_scale_factor != 0:
																	_lowerCAmelCase							=							(
																	    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
																	) * down_scale_factor
																	logger.info(
																	    f'{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'
																	    f' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'
																	    ' process.'						)
										_lowerCAmelCase							=							int(lowercase__						)

										_lowerCAmelCase							=							next(iter(self.unet.parameters()						)						).dtype
										_lowerCAmelCase							=							(batch_size, self.unet.config.in_channels, sample_size)
										if isinstance(lowercase__  ,  lowercase__						) and len(lowercase__						) != batch_size:
																	raise ValueError(
																	    f'You have passed a list of generators of length {len(lowercase__						)}, but requested an effective batch'
																	    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'						)

										_lowerCAmelCase							=							randn_tensor(lowercase__  ,  generator=lowercase__  ,  device=self.device  ,  dtype=lowercase__						)

										# set step values
										self.scheduler.set_timesteps(lowercase__  ,  device=audio.device						)
										_lowerCAmelCase							=							self.scheduler.timesteps.to(lowercase__						)

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

																	# 2. compute previous image: x_t -> t_t-1
																	_lowerCAmelCase							=							self.scheduler.step(lowercase__  ,  lowercase__  ,  lowercase__						).prev_sample

										_lowerCAmelCase							=							audio.clamp(-1  ,  1						).float().cpu().numpy()

										_lowerCAmelCase							=							audio[:, :, :original_sample_size]

										if not return_dict:
																	return (audio,)

										return AudioPipelineOutput(audios=lowercase__						)







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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowercase:	int       			=		logging.get_logger(__name__)

_lowercase:	Union[str, Any]       			=		{'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowercase:	Dict       			=		{
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    }
}

_lowercase:	List[Any]       			=		{
    '''camembert-base''': 5_1_2,
}

_lowercase:	Dict       			=		'''▁'''



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=VOCAB_FILES_NAMES
			UpperCamelCase__    							=PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__    							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__    							=["input_ids", "attention_mask"]





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

										_lowerCAmelCase							=							{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=lowercase__  ,  eos_token=lowercase__  ,  unk_token=lowercase__  ,  sep_token=lowercase__  ,  cls_token=lowercase__  ,  pad_token=lowercase__  ,  mask_token=lowercase__  ,  additional_special_tokens=lowercase__  ,  sp_model_kwargs=self.sp_model_kwargs  ,  **lowercase__  ,  )
										_lowerCAmelCase							=							spm.SentencePieceProcessor(**self.sp_model_kwargs						)
										self.sp_model.Load(str(lowercase__						)						)
										_lowerCAmelCase							=							vocab_file
										# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
										# sentencepiece vocabulary (this is the case for <s> and </s>
										_lowerCAmelCase							=							{'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
										_lowerCAmelCase							=							len(self.fairseq_tokens_to_ids						)
										_lowerCAmelCase							=							len(self.sp_model						) + len(self.fairseq_tokens_to_ids						)
										_lowerCAmelCase							=							{v: k for k, v in self.fairseq_tokens_to_ids.items()}





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]  ,  lowercase__	:	List[int]  ,  lowercase__	:	Optional[List[int]] = None						):

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





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any  ,  lowercase__	:	List[int]  ,  lowercase__	:	Optional[List[int]] = None  ,  lowercase__	:	bool = False						):
										if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=lowercase__  ,  token_ids_a=lowercase__  ,  already_has_special_tokens=lowercase__						)

										if token_ids_a is None:
																	return [1] + ([0] * len(lowercase__						)) + [1]
										return [1] + ([0] * len(lowercase__						)) + [1, 1] + ([0] * len(lowercase__						)) + [1]





			def   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  lowercase__	:	List[int]  ,  lowercase__	:	Optional[List[int]] = None						):
										_lowerCAmelCase							=							[self.sep_token_id]
										_lowerCAmelCase							=							[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]





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]						):
										return len(self.fairseq_tokens_to_ids						) + len(self.sp_model						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										_lowerCAmelCase							=							{self.convert_ids_to_tokens(lowercase__						): i for i in range(self.vocab_size						)}
										vocab.update(self.added_tokens_encoder						)
										return vocab





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	str						):
										return self.sp_model.encode(lowercase__  ,  out_type=lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	Optional[Any]						):
										if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
										elif self.sp_model.PieceToId(lowercase__						) == 0:
																	# Convert sentence piece unk token to fairseq unk token index
																	return self.unk_token_id
										return self.fairseq_offset + self.sp_model.PieceToId(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	Tuple						):
										if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(index - self.fairseq_offset						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  lowercase__	:	Optional[int]						):
										_lowerCAmelCase							=							[]
										_lowerCAmelCase							=							''
										_lowerCAmelCase							=							False
										for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																								if not prev_is_special:
																															out_string += " "
																								out_string += self.sp_model.decode(lowercase__						) + token
																								_lowerCAmelCase							=							True
																								_lowerCAmelCase							=							[]
																	else:
																								current_sub_tokens.append(lowercase__						)
																								_lowerCAmelCase							=							False
										out_string += self.sp_model.decode(lowercase__						)
										return out_string.strip()





			def __getstate__(       self	:	Any						):
										_lowerCAmelCase							=							self.__dict__.copy()
										_lowerCAmelCase							=							None
										return state





			def __setstate__(       self	:	Optional[Any]  ,  lowercase__	:	Union[str, Any]						):
										_lowerCAmelCase							=							d

										# for backward compatibility
										if not hasattr(self  ,  'sp_model_kwargs'						):
																	_lowerCAmelCase							=							{}

										_lowerCAmelCase							=							spm.SentencePieceProcessor(**self.sp_model_kwargs						)
										self.sp_model.Load(self.vocab_file						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	str  ,  lowercase__	:	Optional[str] = None						):
										if not os.path.isdir(lowercase__						):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory'						)
																	return
										_lowerCAmelCase							=							os.path.join(
										    lowercase__  ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

										if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase__						) and os.path.isfile(self.vocab_file						):
																	copyfile(self.vocab_file  ,  lowercase__						)
										elif not os.path.isfile(self.vocab_file						):
																	with open(lowercase__  ,  'wb'						) as fi:
																								_lowerCAmelCase							=							self.sp_model.serialized_model_proto()
																								fi.write(lowercase__						)

										return (out_vocab_file,)






