

"""simple docstring"""




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__A     :    Optional[int]    =		logging.get_logger(__name__)

__A     :    Union[str, Any]    =		{
    'google/long-t5-local-base': 'https://huggingface.co/google/long-t5-local-base/blob/main/config.json',
    'google/long-t5-local-large': 'https://huggingface.co/google/long-t5-local-large/blob/main/config.json',
    'google/long-t5-tglobal-base': 'https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json',
    'google/long-t5-tglobal-large': 'https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json',
}





class 			__UpperCamelCase (   _A						):
		SCREAMING_SNAKE_CASE								=    "longt5"
		SCREAMING_SNAKE_CASE								=    ["past_key_values"]
		SCREAMING_SNAKE_CASE								=    {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


		def __init__(self :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=3_2_1_2_8     ,     __SCREAMING_SNAKE_CASE :		str=5_1_2     ,     __SCREAMING_SNAKE_CASE :		List[str]=6_4     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=2_0_4_8     ,     __SCREAMING_SNAKE_CASE :		str=6     ,     __SCREAMING_SNAKE_CASE :		Tuple=None     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=8     ,     __SCREAMING_SNAKE_CASE :		Any=1_2_7     ,     __SCREAMING_SNAKE_CASE :		int=1_6     ,     __SCREAMING_SNAKE_CASE :		Dict=3_2     ,     __SCREAMING_SNAKE_CASE :		Dict=1_2_8     ,     __SCREAMING_SNAKE_CASE :		str=0.1     ,     __SCREAMING_SNAKE_CASE :		Tuple=1E-6     ,     __SCREAMING_SNAKE_CASE :		str=1.0     ,     __SCREAMING_SNAKE_CASE :		str="relu"     ,     __SCREAMING_SNAKE_CASE :		List[Any]=True     ,     __SCREAMING_SNAKE_CASE :		Any="local"     ,     __SCREAMING_SNAKE_CASE :		int=True     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=0     ,     __SCREAMING_SNAKE_CASE :		List[Any]=1     ,     **__SCREAMING_SNAKE_CASE :		List[Any]     ,     ):
							A			=					vocab_size
							A			=					d_model
							A			=					d_kv
							A			=					d_ff
							A			=					num_layers
							# default = symmetry
							A			=					num_decoder_layers if num_decoder_layers is not None else self.num_layers
							A			=					num_heads
							A			=					local_radius
							A			=					global_block_size
							A			=					relative_attention_num_buckets
							A			=					relative_attention_max_distance
							A			=					dropout_rate
							A			=					layer_norm_epsilon
							A			=					initializer_factor
							A			=					feed_forward_proj
							A			=					encoder_attention_type
							A			=					use_cache

							A			=					self.feed_forward_proj.split("-")
							A			=					act_info[-1]
							A			=					act_info[0] == "gated"

							if len(__SCREAMING_SNAKE_CASE) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE) > 2:
												raise ValueError(
												    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
												    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
												    "'gated-gelu' or 'relu'")

							# for backwards compatibility
							if feed_forward_proj == "gated-gelu":
												A			=					"gelu_new"

							super().__init__(
							    pad_token_id=__SCREAMING_SNAKE_CASE     ,     eos_token_id=__SCREAMING_SNAKE_CASE     ,     is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE     ,     )





class 			__UpperCamelCase (   _A						):


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]):
							A			=					{
							    "input_ids": {0: "batch", 1: "encoder_sequence"},
							    "attention_mask": {0: "batch", 1: "encoder_sequence"},
							}
							if self.use_past:
												A			=					"past_encoder_sequence + sequence"
												A			=					{0: "batch"}
												A			=					{0: "batch", 1: "past_decoder_sequence + sequence"}
							else:
												A			=					{0: "batch", 1: "decoder_sequence"}
												A			=					{0: "batch", 1: "decoder_sequence"}

							if self.use_past:
												self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE     ,     direction="inputs")

							return common_inputs


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		str):
							return 1_3







"""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


snake_case    =		logging.get_logger(__name__)

snake_case    =		{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

snake_case    =		{
    '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      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__=None		,	lowercase__=None		,	lowercase__=False		,	lowercase__=True		,	lowercase__=False		,	lowercase__="<s>"		,	lowercase__="</s>"		,	lowercase__="<unk>"		,	lowercase__="<sep>"		,	lowercase__="<pad>"		,	lowercase__="<cls>"		,	lowercase__="<mask>"		,	lowercase__=["<eop>", "<eod>"]		,	**lowercase__		,	)    ->    int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	AddedToken(lowercase__		,	lstrip=lowercase__		,	rstrip=lowercase__			) if isinstance(lowercase__		,	lowercase__			) else mask_token

									super().__init__(
									    vocab_file=lowercase__		,	tokenizer_file=lowercase__		,	do_lower_case=lowercase__		,	remove_space=lowercase__		,	keep_accents=lowercase__		,	bos_token=lowercase__		,	eos_token=lowercase__		,	unk_token=lowercase__		,	sep_token=lowercase__		,	pad_token=lowercase__		,	cls_token=lowercase__		,	mask_token=lowercase__		,	additional_special_tokens=lowercase__		,	**lowercase__		,	)

									SCREAMING_SNAKE_CASE				=	3
									SCREAMING_SNAKE_CASE				=	do_lower_case
									SCREAMING_SNAKE_CASE				=	remove_space
									SCREAMING_SNAKE_CASE				=	keep_accents
									SCREAMING_SNAKE_CASE				=	vocab_file
									SCREAMING_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.'			)
									SCREAMING_SNAKE_CASE				=	jieba
									SCREAMING_SNAKE_CASE				=	str.maketrans(' \n'		,	'\u2582\u2583'			)


				def 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    List[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[self.sep_token_id]
									SCREAMING_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 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    List[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[self.sep_token_id]
									SCREAMING_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 							A					(				self		,	lowercase__		,	lowercase__ = None			)    ->    Tuple[str]:
									"""simple docstring"""







									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(lowercase__			):
														logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
														return
									SCREAMING_SNAKE_CASE				=	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__			):
														copyfile(self.vocab_file		,	lowercase__			)

									return (out_vocab_file,)


				def 							A					(				self		,	lowercase__		,	*lowercase__		,	**lowercase__			)    ->    Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	[
									    ' '.join([x.translate(self.translator			) for x in self.jieba.cut(lowercase__		,	cut_all=lowercase__			)]			)
									    for text in batch_text_or_text_pairs
									]
									return super()._batch_encode_plus(lowercase__		,	*lowercase__		,	**lowercase__			)




				def 							A					(				self		,	*lowercase__		,	**lowercase__			)    ->    Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	super()._decode(*lowercase__		,	**lowercase__			)
									SCREAMING_SNAKE_CASE				=	text.replace(' '		,	''			).replace('\u2582'		,	' '			).replace('\u2583'		,	'\n'			)
									return text
