

import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
					import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a		=logging.get_logger(__name__)

a		={"""vocab_file""": """vocab.txt"""}

a		={
    """vocab_file""": {
        """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt""",
    },
}

a		={
    """openbmb/cpm-ant-10b""": 1024,
}






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     Union[str, Any]:
	__lowerCamelCase				:				Optional[int]					       = collections.OrderedDict()
	with open(lowerCamelCase__	, 'r'	, encoding='utf-8'			) as reader:
		__lowerCamelCase				:				Optional[int]					       = reader.readlines()
	for index, token in enumerate(lowerCamelCase__			):
		__lowerCamelCase				:				Optional[Any]					       = token.rstrip('\n'			)
		__lowerCamelCase				:				Optional[Any]					       = index
	return vocab






class        A_		(       SCREAMING_SNAKE_CASE				):
							def __init__(		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			Dict="<unk>"   ,SCREAMING_SNAKE_CASE__		:			Tuple=2_0_0):
								__lowerCamelCase				:				Optional[Any]					       = vocab
								__lowerCamelCase				:				Optional[int]					       = unk_token
								__lowerCamelCase				:				str					       = max_input_chars_per_word


							def     lowerCAmelCase     (		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			List[Any]):
								__lowerCamelCase				:				int					       = list(SCREAMING_SNAKE_CASE__)
								if len(SCREAMING_SNAKE_CASE__) > self.max_input_chars_per_word:
									return [self.unk_token]

								__lowerCamelCase				:				List[Any]					       = 0
								__lowerCamelCase				:				List[Any]					       = []
								while start < len(SCREAMING_SNAKE_CASE__):
									__lowerCamelCase				:				List[str]					       = len(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase				:				int					       = None
									while start < end:
										__lowerCamelCase				:				Dict					       = ''.join(chars[start:end])
										if substr in self.vocab:
											__lowerCamelCase				:				str					       = substr
											break
										end -= 1
									if cur_substr is None:
										sub_tokens.append(self.unk_token)
										start += 1
									else:
										sub_tokens.append(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase				:				Dict					       = end

								return sub_tokens






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  List[Any]						      =      VOCAB_FILES_NAMES
							_UpperCAmelCase    :  int						      =      PRETRAINED_VOCAB_FILES_MAP
							_UpperCAmelCase    :  str						      =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_UpperCAmelCase    :  str						      =      ['''input_ids''', '''attention_mask''']
							_UpperCAmelCase    :  Union[str, Any]						      =      False
							def __init__(		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			Optional[int]="<d>"   ,SCREAMING_SNAKE_CASE__		:			str="</d>"   ,SCREAMING_SNAKE_CASE__		:			Dict="<s>"   ,SCREAMING_SNAKE_CASE__		:			Dict="</s>"   ,SCREAMING_SNAKE_CASE__		:			str="<pad>"   ,SCREAMING_SNAKE_CASE__		:			Any="<unk>"   ,SCREAMING_SNAKE_CASE__		:			Dict="</n>"   ,SCREAMING_SNAKE_CASE__		:			Any="</_>"   ,SCREAMING_SNAKE_CASE__		:			Any="left"   ,**SCREAMING_SNAKE_CASE__		:			str   ,):
								requires_backends(self   ,['jieba'])
								super().__init__(
								    bod_token=SCREAMING_SNAKE_CASE__   ,eod_token=SCREAMING_SNAKE_CASE__   ,bos_token=SCREAMING_SNAKE_CASE__   ,eos_token=SCREAMING_SNAKE_CASE__   ,pad_token=SCREAMING_SNAKE_CASE__   ,unk_token=SCREAMING_SNAKE_CASE__   ,line_token=SCREAMING_SNAKE_CASE__   ,space_token=SCREAMING_SNAKE_CASE__   ,padding_side=SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__   ,)
								__lowerCamelCase				:				List[Any]					       = bod_token
								__lowerCamelCase				:				int					       = eod_token
								__lowerCamelCase				:				Dict					       = load_vocab(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Tuple					       = self.encoder[space_token]
								__lowerCamelCase				:				Optional[Any]					       = self.encoder[line_token]

								del self.encoder[space_token]
								del self.encoder[line_token]

								__lowerCamelCase				:				Tuple					       = collections.OrderedDict(sorted(self.encoder.items()   ,key=lambda SCREAMING_SNAKE_CASE__: x[1]))
								__lowerCamelCase				:				Union[str, Any]					       = {v: k for k, v in self.encoder.items()}

								__lowerCamelCase				:				int					       = WordpieceTokenizer(vocab=self.encoder   ,unk_token=self.unk_token)
							@property
							def     lowerCAmelCase     (		self		:			Union[str, Any]):
								return self.encoder[self.bod_token]
							@property
							def     lowerCAmelCase     (		self		:			Dict):
								return self.encoder[self.eod_token]
							@property
							def     lowerCAmelCase     (		self		:			int):
								return self.encoder["\n"]
							@property
							def     lowerCAmelCase     (		self		:			List[str]):
								return len(self.encoder)
							def     lowerCAmelCase     (		self		:			Optional[int]):
								return dict(self.encoder   ,**self.added_tokens_encoder)
							def     lowerCAmelCase     (		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			str):
								__lowerCamelCase				:				Tuple					       = []
								for x in jieba.cut(SCREAMING_SNAKE_CASE__   ,cut_all=SCREAMING_SNAKE_CASE__):
									output_tokens.extend(self.wordpiece_tokenizer.tokenize(SCREAMING_SNAKE_CASE__))
								return output_tokens
							def     lowerCAmelCase     (		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			List[Any]   ,**SCREAMING_SNAKE_CASE__		:			Optional[int]):
								__lowerCamelCase				:				Dict					       = [i for i in token_ids if i >= 0]
								__lowerCamelCase				:				Optional[int]					       = [
								    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
								]
								return super()._decode(SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			List[str]):
								return token in self.encoder
							def     lowerCAmelCase     (		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			List[str]):
								return "".join(SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			int   ,SCREAMING_SNAKE_CASE__		:			List[str]):
								return self.encoder.get(SCREAMING_SNAKE_CASE__   ,self.encoder.get(self.unk_token))
							def     lowerCAmelCase     (		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Tuple):
								return self.decoder.get(SCREAMING_SNAKE_CASE__   ,self.unk_token)
							def     lowerCAmelCase     (		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			Optional[str] = None):
								if os.path.isdir(SCREAMING_SNAKE_CASE__):
									__lowerCamelCase				:				str					       = os.path.join(
									    SCREAMING_SNAKE_CASE__   ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
								else:
									__lowerCamelCase				:				Dict					       = (filename_prefix + '-' if filename_prefix else '') + save_directory
								__lowerCamelCase				:				Optional[Any]					       = 0
								if " " in self.encoder:
									__lowerCamelCase				:				Dict					       = self.encoder[' ']
									del self.encoder[" "]
								if "\n" in self.encoder:
									__lowerCamelCase				:				List[str]					       = self.encoder['\n']
									del self.encoder["\n"]
								__lowerCamelCase				:				Any					       = collections.OrderedDict(sorted(self.encoder.items()   ,key=lambda SCREAMING_SNAKE_CASE__: x[1]))
								with open(SCREAMING_SNAKE_CASE__   ,'w'   ,encoding='utf-8') as writer:
									for token, token_index in self.encoder.items():
										if index != token_index:
											logger.warning(
											    F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
											    ' Please check that the vocabulary is not corrupted!')
											__lowerCamelCase				:				Optional[Any]					       = token_index
										writer.write(token + '\n')
										index += 1
								return (vocab_file,)
							def     lowerCAmelCase     (		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			List[int]   ,SCREAMING_SNAKE_CASE__		:			List[int] = None):
								if token_ids_a is None:
									return [self.bos_token_id] + token_ids_a
								return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a


							def     lowerCAmelCase     (		self		:			Optional[Any]   ,SCREAMING_SNAKE_CASE__		:			List[int]   ,SCREAMING_SNAKE_CASE__		:			Optional[List[int]] = None   ,SCREAMING_SNAKE_CASE__		:			bool = False):

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

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











from typing import List, Optional, Tuple, Union

import torch

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


lowercase      :       Any									=  logging.get_logger(__name__)  # pylint: disable=invalid-name





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase)			->						Optional[Any]:







          '''simple docstring'''

          super().__init__()
          self.register_modules(unet=lowercase					,  scheduler=lowercase)

   @torch.no_grad()
   def __call__(					self					,  lowercase = 1					,  lowercase = 100					,  lowercase = None					,  lowercase = None					,  lowercase = True					,  )			->						Union[AudioPipelineOutput, Tuple]:







          '''simple docstring'''

          if audio_length_in_s is None:
                 a__						:	Optional[int] =				self.unet.config.sample_size / self.unet.config.sample_rate

          a__						:	int =				audio_length_in_s * self.unet.config.sample_rate

          a__						:	Union[str, Any] =				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}.')

          a__						:	str =				int(lowercase)
          if sample_size % down_scale_factor != 0:
                 a__						:	List[str] =				(
                     (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.')
          a__						:	List[Any] =				int(lowercase)

          a__						:	int =				next(iter(self.unet.parameters())).dtype
          a__						:	Tuple =				(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.')

          a__						:	Optional[Any] =				randn_tensor(lowercase					,  generator=lowercase					,  device=self.device					,  dtype=lowercase)

          # set step values
          self.scheduler.set_timesteps(lowercase					,  device=audio.device)
          a__						:	Union[str, Any] =				self.scheduler.timesteps.to(lowercase)

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

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

          a__						:	str =				audio.clamp(-1					,  1).float().cpu().numpy()

          a__						:	List[Any] =				audio[:, :, :original_sample_size]

          if not return_dict:
                 return (audio,)

          return AudioPipelineOutput(audios=lowercase)


