'''simple docstring'''







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


UpperCamelCase_      	=	logging.get_logger(__name__)

UpperCamelCase_      	=	{
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Union[str, Any]     		= 'gpt_neox'




						def __init__(    self :       List[str]							,							UpperCamelCase_ :       int=5_04_32							,							UpperCamelCase_ :       Dict=61_44							,							UpperCamelCase_ :       int=44							,							UpperCamelCase_ :       Any=64							,							UpperCamelCase_ :       Optional[int]=2_45_76							,							UpperCamelCase_ :       str="gelu"							,							UpperCamelCase_ :       Tuple=0.25							,							UpperCamelCase_ :       Tuple=1_00_00							,							UpperCamelCase_ :       Tuple=0.0							,							UpperCamelCase_ :       Optional[int]=0.0							,							UpperCamelCase_ :       str=0.1							,							UpperCamelCase_ :       List[Any]=20_48							,							UpperCamelCase_ :       List[str]=0.02							,							UpperCamelCase_ :       Optional[int]=1e-5							,							UpperCamelCase_ :       List[str]=True							,							UpperCamelCase_ :       Optional[Any]=0							,							UpperCamelCase_ :       str=2							,							UpperCamelCase_ :       str=False							,							UpperCamelCase_ :       Union[str, Any]=True							,							UpperCamelCase_ :       Tuple=None							,							**UpperCamelCase_ :       Any							,							)     ->				int:
													super().__init__(bos_token_id=UpperCamelCase_							,							eos_token_id=UpperCamelCase_							,							**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = vocab_size
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = max_position_embeddings
													SCREAMING_SNAKE_CASE__     :List[Any]						    = hidden_size
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = num_hidden_layers
													SCREAMING_SNAKE_CASE__     :Dict						    = num_attention_heads
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = intermediate_size
													SCREAMING_SNAKE_CASE__     :Dict						    = hidden_act
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = rotary_pct
													SCREAMING_SNAKE_CASE__     :Any						    = rotary_emb_base
													SCREAMING_SNAKE_CASE__     :Dict						    = attention_dropout
													SCREAMING_SNAKE_CASE__     :Tuple						    = hidden_dropout
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = classifier_dropout
													SCREAMING_SNAKE_CASE__     :Dict						    = initializer_range
													SCREAMING_SNAKE_CASE__     :List[str]						    = layer_norm_eps
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = use_cache
													SCREAMING_SNAKE_CASE__     :Any						    = tie_word_embeddings
													SCREAMING_SNAKE_CASE__     :List[str]						    = use_parallel_residual
													SCREAMING_SNAKE_CASE__     :int						    = rope_scaling
													self._rope_scaling_validation()

													if self.hidden_size % self.num_attention_heads != 0:
																				raise ValueError(
																				    'The hidden size is not divisble by the number of attention heads! Make sure to update them!'      )




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				Union[str, Any]:
													if self.rope_scaling is None:
																				return

													if not isinstance(self.rope_scaling							,							UpperCamelCase_      ) or len(self.rope_scaling      ) != 2:
																				raise ValueError(
																				    '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
																				    f'''got {self.rope_scaling}'''      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.rope_scaling.get('type'							,							UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.rope_scaling.get('factor'							,							UpperCamelCase_      )
													if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
																				raise ValueError(
																				    f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''      )
													if rope_scaling_factor is None or not isinstance(UpperCamelCase_							,							UpperCamelCase_      ) or rope_scaling_factor <= 1.0:
																				raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''      )







'''simple docstring'''







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

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


if is_sentencepiece_available():
		from .tokenization_pegasus import PegasusTokenizer
else:
		UpperCamelCase_      	=	None


UpperCamelCase_      	=	logging.get_logger(__name__)


UpperCamelCase_      	=	'''▁'''

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

UpperCamelCase_      	=	{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
    '''tokenizer_file''': {
        '''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
    },
}

UpperCamelCase_      	=	{
    '''google/pegasus-xsum''': 5_12,
}




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Union[str, Any]     		= VOCAB_FILES_NAMES
						A_			:							str     		= PRETRAINED_VOCAB_FILES_MAP
						A_			:							List[str]     		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A_			:							int     		= PegasusTokenizer
						A_			:							Dict     		= ['input_ids', 'attention_mask']




						def __init__(    self :       List[str]							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       Union[str, Any]="<pad>"							,							UpperCamelCase_ :       List[Any]="</s>"							,							UpperCamelCase_ :       Tuple="<unk>"							,							UpperCamelCase_ :       List[Any]="<mask_2>"							,							UpperCamelCase_ :       int="<mask_1>"							,							UpperCamelCase_ :       List[str]=None							,							UpperCamelCase_ :       Optional[Any]=1_03							,							**UpperCamelCase_ :       Optional[Any]							,							)     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Dict						    = offset

													if additional_special_tokens is not None:
																				if not isinstance(UpperCamelCase_							,							UpperCamelCase_      ):
																											raise TypeError(
																											    f'''additional_special_tokens should be of type {type(UpperCamelCase_      )}, but is'''
																											    f''' {type(UpperCamelCase_      )}'''      )

																				SCREAMING_SNAKE_CASE__     :List[str]						    = (
																				    ([mask_token_sent] + additional_special_tokens)
																				    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																				    else additional_special_tokens
																				)
																				# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																				additional_special_tokens_extended += [
																				    f'''<unk_{i}>''' for i in range(len(UpperCamelCase_      )							,							self.offset - 1      )
																				]

																				if len(set(UpperCamelCase_      )      ) != len(UpperCamelCase_      ):
																											raise ValueError(
																											    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
																											    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''      )
																				SCREAMING_SNAKE_CASE__     :List[str]						    = additional_special_tokens_extended
													else:
																				SCREAMING_SNAKE_CASE__     :Tuple						    = [mask_token_sent] if mask_token_sent is not None else []
																				additional_special_tokens += [f'''<unk_{i}>''' for i in range(2							,							self.offset      )]

													super().__init__(
													    UpperCamelCase_							,							tokenizer_file=UpperCamelCase_							,							pad_token=UpperCamelCase_							,							eos_token=UpperCamelCase_							,							unk_token=UpperCamelCase_							,							mask_token=UpperCamelCase_							,							mask_token_sent=UpperCamelCase_							,							offset=UpperCamelCase_							,							additional_special_tokens=UpperCamelCase_							,							**UpperCamelCase_							,							)
													SCREAMING_SNAKE_CASE__     :int						    = vocab_file
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = False if not self.vocab_file else True




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       Any      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :int						    = set(self.all_special_ids      )  # call it once instead of inside list comp
													all_special_ids.remove(self.unk_token_id      )  # <unk> is only sometimes special

													if all_special_ids != set(range(len(self.additional_special_tokens      ) + 3      )      ):
																				raise ValueError(
																				    'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
																				    f''' {len(self.additional_special_tokens      )} additional_special_tokens, but got {all_special_ids}'''      )

													return [1 if x in all_special_ids else 0 for x in seq]




						def      __lowerCamelCase       (    self :       Optional[int]							,							UpperCamelCase_ :       List							,							UpperCamelCase_ :       Optional[List] = None							,							UpperCamelCase_ :       bool = False      )     ->				List[int]:
													if already_has_special_tokens:
																				return self._special_token_mask(UpperCamelCase_      )
													elif token_ids_a is None:
																				return self._special_token_mask(UpperCamelCase_      ) + [1]
													else:
																				return self._special_token_mask(token_ids_a + token_ids_a      ) + [1]




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       Dict=None      )     ->				List[int]:
													if token_ids_a is None:
																				return token_ids_a + [self.eos_token_id]
													# We don't expect to process pairs, but leave the pair logic for API consistency
													return token_ids_a + token_ids_a + [self.eos_token_id]




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       Optional[str] = None      )     ->				Tuple[str]:
													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
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = 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,)






