





import os
import unicodedata
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 SPIECE_UNDERLINE, logging


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

UpperCAmelCase__  :     Union[str, Any]   			={'''vocab_file''': '''spiece.model'''}

UpperCAmelCase__  :     Any   			={
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}

class  __A       (  a      ):


				def __init__(						self				,	UpperCAmelCase_				,	UpperCAmelCase_=False				,	UpperCAmelCase_=True				,	UpperCAmelCase_=False				,	UpperCAmelCase_="<s>"				,	UpperCAmelCase_="</s>"				,	UpperCAmelCase_="<unk>"				,	UpperCAmelCase_="<sep>"				,	UpperCAmelCase_="<pad>"				,	UpperCAmelCase_="<cls>"				,	UpperCAmelCase_="<mask>"				,	UpperCAmelCase_=["<eop>", "<eod>"]				,	UpperCAmelCase_ = None				,	**UpperCAmelCase_				,	):
								lowerCamelCase			=AddedToken(UpperCAmelCase_				,	lstrip=UpperCAmelCase_				,	rstrip=UpperCAmelCase_							) if isinstance(UpperCAmelCase_				,	UpperCAmelCase_							) else mask_token

								lowerCamelCase			={} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    do_lower_case=UpperCAmelCase_				,	remove_space=UpperCAmelCase_				,	keep_accents=UpperCAmelCase_				,	bos_token=UpperCAmelCase_				,	eos_token=UpperCAmelCase_				,	unk_token=UpperCAmelCase_				,	sep_token=UpperCAmelCase_				,	pad_token=UpperCAmelCase_				,	cls_token=UpperCAmelCase_				,	mask_token=UpperCAmelCase_				,	additional_special_tokens=UpperCAmelCase_				,	sp_model_kwargs=self.sp_model_kwargs				,	**UpperCAmelCase_				,	)

								lowerCamelCase			=3

								lowerCamelCase			=do_lower_case
								lowerCamelCase			=remove_space
								lowerCamelCase			=keep_accents
								lowerCamelCase			=vocab_file

								lowerCamelCase			=spm.SentencePieceProcessor(**self.sp_model_kwargs							)
								self.sp_model.Load(UpperCAmelCase_							)

								try:
												import jieba
								except ModuleNotFoundError as error:
												raise error.__class__(
												    """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
												    """See https://pypi.org/project/jieba/ for installation."""							)
								lowerCamelCase			=jieba
								lowerCamelCase			=str.maketrans(""" \n"""				,	"""\u2582\u2583"""							)


				@property
				# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
				def _snake_case    (						self							):
								return len(self.sp_model							)


				def _snake_case    (						self							):
								lowerCamelCase			={self.convert_ids_to_tokens(UpperCAmelCase_							): i for i in range(self.vocab_size							)}
								vocab.update(self.added_tokens_encoder							)
								return vocab


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


				def __setstate__(						self				,	UpperCAmelCase_							):
								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 _snake_case    (						self				,	UpperCAmelCase_							):
								if self.remove_space:
												lowerCamelCase			=""" """.join(inputs.strip().split()							)
								else:
												lowerCamelCase			=inputs
								lowerCamelCase			=outputs.replace("""``"""				,	"""\""""							).replace("""''"""				,	"""\""""							)

								if not self.keep_accents:
												lowerCamelCase			=unicodedata.normalize("""NFKD"""				,	UpperCAmelCase_							)
												lowerCamelCase			="""""".join([c for c in outputs if not unicodedata.combining(UpperCAmelCase_							)]							)
								if self.do_lower_case:
												lowerCamelCase			=outputs.lower()

								return outputs


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			=self.preprocess_text(UpperCAmelCase_							)
								lowerCamelCase			=self.sp_model.encode(UpperCAmelCase_				,	out_type=UpperCAmelCase_							)
								lowerCamelCase			=[]
								for piece in pieces:
												if len(UpperCAmelCase_							) > 1 and piece[-1] == str(""","""							) and piece[-2].isdigit():
																lowerCamelCase			=self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCAmelCase_				,	""""""							)							)
																if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																				if len(cur_pieces[0]							) == 1:
																								lowerCamelCase			=cur_pieces[1:]
																				else:
																								lowerCamelCase			=cur_pieces[0][1:]
																cur_pieces.append(piece[-1]							)
																new_pieces.extend(UpperCAmelCase_							)
												else:
																new_pieces.append(UpperCAmelCase_							)

								return new_pieces


				def _snake_case    (						self				,	UpperCAmelCase_							):
								return self.sp_model.PieceToId(UpperCAmelCase_							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								return self.sp_model.IdToPiece(UpperCAmelCase_							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			="""""".join(UpperCAmelCase_							).replace(UpperCAmelCase_				,	""" """							).strip()
								return out_string


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_ = None							):
								lowerCamelCase			=[self.sep_token_id]
								lowerCamelCase			=[self.cls_token_id]
								if token_ids_a is None:
												return token_ids_a + sep + cls
								return token_ids_a + sep + token_ids_a + sep + cls


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = False							):

								if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=UpperCAmelCase_				,	token_ids_a=UpperCAmelCase_				,	already_has_special_tokens=UpperCAmelCase_							)

								if token_ids_a is not None:
												return ([0] * len(UpperCAmelCase_							)) + [1] + ([0] * len(UpperCAmelCase_							)) + [1, 1]
								return ([0] * len(UpperCAmelCase_							)) + [1, 1]


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_ = None							):
								lowerCamelCase			=[self.sep_token_id]
								lowerCamelCase			=[2]

								if token_ids_a is None:
												return len(token_ids_a + sep							) * [0] + cls_segment_id
								return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_ = None							):
								if not os.path.isdir(UpperCAmelCase_							):
												logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
												return
								lowerCamelCase			=os.path.join(
								    UpperCAmelCase_				,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

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

								return (out_vocab_file,)


				def _snake_case    (						self				,	*UpperCAmelCase_				,	**UpperCAmelCase_							):
								lowerCamelCase			=super()._decode(*UpperCAmelCase_				,	**UpperCAmelCase_							)
								lowerCamelCase			=text.replace(""" """				,	""""""							).replace("""\u2582"""				,	""" """							).replace("""\u2583"""				,	"""\n"""							)
								return text












from typing import List, Optional, Tuple, Union

import torch

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


UpperCAmelCase__  :     Dict   			=logging.get_logger(__name__)  # pylint: disable=invalid-name

class  __A       (  a      ):


				def __init__(						self				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								super().__init__()
								self.register_modules(unet=UpperCAmelCase_				,	scheduler=UpperCAmelCase_							)


				@torch.no_grad()
				def __call__(						self				,	UpperCAmelCase_ = 1				,	UpperCAmelCase_ = 100				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = None				,	UpperCAmelCase_ = 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(UpperCAmelCase_							)
								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(UpperCAmelCase_							)

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

								lowerCamelCase			=randn_tensor(UpperCAmelCase_				,	generator=UpperCAmelCase_				,	device=self.device				,	dtype=UpperCAmelCase_							)

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

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

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

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

								lowerCamelCase			=audio[:, :, :original_sample_size]

								if not return_dict:
												return (audio,)

								return AudioPipelineOutput(audios=UpperCAmelCase_							)





