




'''simple docstring'''







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

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


_lowerCamelCase							:						Tuple								=  logging.get_logger(__name__)

_lowerCamelCase							:						int								=  {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowerCamelCase							:						Optional[Any]								=  {
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json",
    },
}





class      lowerCamelCase_							(       _a							):




					'''simple docstring'''






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

							super().__init__(
							    vocab_file=_UpperCAmelCase,  tokenizer_file=_UpperCAmelCase,  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,  **_UpperCAmelCase,  )

							_snake_case	       =       3
							_snake_case	       =       do_lower_case
							_snake_case	       =       remove_space
							_snake_case	       =       keep_accents
							_snake_case	       =       vocab_file
							_snake_case	       =       False if not self.vocab_file else True

							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." )
							_snake_case	       =       jieba
							_snake_case	       =       str.maketrans(" \n",  "\u2582\u2583" )






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = None ):
							_snake_case	       =       [self.sep_token_id]
							_snake_case	       =       [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 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = None ):
							_snake_case	       =       [self.sep_token_id]
							_snake_case	       =       [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 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  __UpperCamelCase = 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(_UpperCAmelCase ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
							_snake_case	       =       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 ):
									copyfile(self.vocab_file,  _UpperCAmelCase )

							return (out_vocab_file,)






					def 				__SCREAMING_SNAKE_CASE	(    self,  __UpperCamelCase,  *__UpperCamelCase,  **__UpperCamelCase ):
							_snake_case	       =       [
							    """ """.join([x.translate(self.translator ) for x in self.jieba.cut(_UpperCAmelCase,  cut_all=_UpperCAmelCase )] )
							    for text in batch_text_or_text_pairs
							]
							return super()._batch_encode_plus(_UpperCAmelCase,  *_UpperCAmelCase,  **_UpperCAmelCase )






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











from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase					:     List[str]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     List[str]					  =  {
    "ZinengTang/tvlt-base": "https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json",
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   "tvlt"



				def __init__(					self					:						Dict						,		_UpperCAmelCase					:						Any=224						,		_UpperCAmelCase					:						Any=2048						,		_UpperCAmelCase					:						Optional[int]=128						,		_UpperCAmelCase					:						Tuple=[16, 16]						,		_UpperCAmelCase					:						Optional[int]=[16, 16]						,		_UpperCAmelCase					:						Tuple=3						,		_UpperCAmelCase					:						str=1						,		_UpperCAmelCase					:						Dict=8						,		_UpperCAmelCase					:						Any=768						,		_UpperCAmelCase					:						Dict=12						,		_UpperCAmelCase					:						Any=12						,		_UpperCAmelCase					:						Optional[Any]=3072						,		_UpperCAmelCase					:						Tuple="gelu"						,		_UpperCAmelCase					:						List[str]=0.0						,		_UpperCAmelCase					:						Any=0.0						,		_UpperCAmelCase					:						Optional[int]=0.02						,		_UpperCAmelCase					:						int=1E-6						,		_UpperCAmelCase					:						str=True						,		_UpperCAmelCase					:						Dict=False						,		_UpperCAmelCase					:						str=16						,		_UpperCAmelCase					:						List[Any]=512						,		_UpperCAmelCase					:						Optional[Any]=8						,		_UpperCAmelCase					:						Optional[Any]=2048						,		_UpperCAmelCase					:						Optional[int]=0.75						,		_UpperCAmelCase					:						List[Any]=0.15						,		_UpperCAmelCase					:						int="frame-level"						,		_UpperCAmelCase					:						Union[str, Any]=True						,		_UpperCAmelCase					:						Optional[int]=True						,		_UpperCAmelCase					:						Any="classification"						,		**_UpperCAmelCase					:						Tuple						,		)     ->			List[str]:





								'''simple docstring'''




								super().__init__(**_UpperCAmelCase					)

								if audio_mask_type not in ("frame-level", "patch_level"):
												raise ValueError(
												    """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
												    f"got {audio_mask_type}"					)

								_lowerCAmelCase			:			Tuple        =					image_size
								_lowerCAmelCase			:			List[str]        =					spectrogram_length
								_lowerCAmelCase			:			str        =					frequency_length
								_lowerCAmelCase			:			Dict        =					image_patch_size
								_lowerCAmelCase			:			Tuple        =					audio_patch_size
								_lowerCAmelCase			:			str        =					num_image_channels
								_lowerCAmelCase			:			Dict        =					num_audio_channels
								_lowerCAmelCase			:			str        =					num_frames

								_lowerCAmelCase			:			Any        =					hidden_size
								_lowerCAmelCase			:			List[str]        =					num_hidden_layers
								_lowerCAmelCase			:			Union[str, Any]        =					num_attention_heads
								_lowerCAmelCase			:			List[Any]        =					intermediate_size
								_lowerCAmelCase			:			Any        =					hidden_act
								_lowerCAmelCase			:			Any        =					hidden_dropout_prob
								_lowerCAmelCase			:			Union[str, Any]        =					attention_probs_dropout_prob
								_lowerCAmelCase			:			str        =					initializer_range
								_lowerCAmelCase			:			Tuple        =					layer_norm_eps
								_lowerCAmelCase			:			str        =					qkv_bias
								_lowerCAmelCase			:			Tuple        =					use_mean_pooling

								_lowerCAmelCase			:			Union[str, Any]        =					decoder_num_attention_heads
								_lowerCAmelCase			:			int        =					decoder_hidden_size
								_lowerCAmelCase			:			List[str]        =					decoder_num_hidden_layers
								_lowerCAmelCase			:			Optional[int]        =					decoder_intermediate_size
								_lowerCAmelCase			:			Any        =					pixel_mask_ratio
								_lowerCAmelCase			:			int        =					audio_mask_ratio
								_lowerCAmelCase			:			Optional[int]        =					audio_mask_type

								_lowerCAmelCase			:			Dict        =					task_matching
								_lowerCAmelCase			:			Any        =					task_mae
								_lowerCAmelCase			:			int        =					loss_type

