
'''simple docstring'''







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

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


if is_sentencepiece_available():
       from .tokenization_big_bird import BigBirdTokenizer
else:
       _lowercase			:  str   							=	None

_lowercase			:  int   							=	logging.get_logger(__name__)
_lowercase			:  int   							=	{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase			:  Dict   							=	{
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    },
    "tokenizer_file": {
        "google/bigbird-roberta-base": (
            "https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
        ),
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
        ),
    },
}

_lowercase			:  Any   							=	{
    "google/bigbird-roberta-base": 4_0_9_6,
    "google/bigbird-roberta-large": 4_0_9_6,
    "google/bigbird-base-trivia-itc": 4_0_9_6,
}


_lowercase			:  List[str]   							=	"▁"






class 			lowerCAmelCase__       (  lowerCamelCase_					):
     lowerCAmelCase_					       =					VOCAB_FILES_NAMES
     lowerCAmelCase_					       =					PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase_					       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase_					       =					BigBirdTokenizer
     lowerCAmelCase_					       =					['''input_ids''', '''attention_mask''']
     lowerCAmelCase_					       =					[]






     def __init__(       self    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE="<unk>"    ,							__SCREAMING_SNAKE_CASE="<s>"    ,							__SCREAMING_SNAKE_CASE="</s>"    ,							__SCREAMING_SNAKE_CASE="<pad>"    ,							__SCREAMING_SNAKE_CASE="[SEP]"    ,							__SCREAMING_SNAKE_CASE="[MASK]"    ,							__SCREAMING_SNAKE_CASE="[CLS]"    ,							**__SCREAMING_SNAKE_CASE    ,							):


           """simple docstring"""





           lowercase_     :					Optional[int]													=	AddedToken(__SCREAMING_SNAKE_CASE    ,							lstrip=__SCREAMING_SNAKE_CASE    ,							rstrip=__SCREAMING_SNAKE_CASE		) if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) else bos_token
           lowercase_     :					Any													=	AddedToken(__SCREAMING_SNAKE_CASE    ,							lstrip=__SCREAMING_SNAKE_CASE    ,							rstrip=__SCREAMING_SNAKE_CASE		) if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) else eos_token
           lowercase_     :					List[Any]													=	AddedToken(__SCREAMING_SNAKE_CASE    ,							lstrip=__SCREAMING_SNAKE_CASE    ,							rstrip=__SCREAMING_SNAKE_CASE		) if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) else unk_token
           lowercase_     :					str													=	AddedToken(__SCREAMING_SNAKE_CASE    ,							lstrip=__SCREAMING_SNAKE_CASE    ,							rstrip=__SCREAMING_SNAKE_CASE		) if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) else pad_token
           lowercase_     :					Optional[int]													=	AddedToken(__SCREAMING_SNAKE_CASE    ,							lstrip=__SCREAMING_SNAKE_CASE    ,							rstrip=__SCREAMING_SNAKE_CASE		) if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) else cls_token
           lowercase_     :					Union[str, Any]													=	AddedToken(__SCREAMING_SNAKE_CASE    ,							lstrip=__SCREAMING_SNAKE_CASE    ,							rstrip=__SCREAMING_SNAKE_CASE		) if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) else sep_token

           # Mask token behave like a normal word, i.e. include the space before it
           lowercase_     :					Dict													=	AddedToken(__SCREAMING_SNAKE_CASE    ,							lstrip=__SCREAMING_SNAKE_CASE    ,							rstrip=__SCREAMING_SNAKE_CASE		) if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) else mask_token

           super().__init__(
               __SCREAMING_SNAKE_CASE    ,							tokenizer_file=__SCREAMING_SNAKE_CASE    ,							bos_token=__SCREAMING_SNAKE_CASE    ,							eos_token=__SCREAMING_SNAKE_CASE    ,							unk_token=__SCREAMING_SNAKE_CASE    ,							sep_token=__SCREAMING_SNAKE_CASE    ,							pad_token=__SCREAMING_SNAKE_CASE    ,							cls_token=__SCREAMING_SNAKE_CASE    ,							mask_token=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE    ,							)

           lowercase_     :					str													=	vocab_file
           lowercase_     :					int													=	False if not self.vocab_file else True






     def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None		):


           """simple docstring"""





           lowercase_     :					Optional[Any]													=	[self.sep_token_id]
           lowercase_     :					Optional[int]													=	[self.cls_token_id]
           if token_ids_a is None:
                 return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep






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


           """simple docstring"""





           if already_has_special_tokens:
                 if token_ids_a is not None:
                       raise ValueError(
                           '''You should not supply a second sequence if the provided sequence of '''
                           '''ids is already formatted with special tokens for the model.'''		)
                 return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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






     def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None		):


           """simple docstring"""





           lowercase_     :					Optional[Any]													=	[self.sep_token_id]
           lowercase_     :					Optional[Any]													=	[self.cls_token_id]

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






     def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None		):


           """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(__SCREAMING_SNAKE_CASE		):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
                 return
           lowercase_     :					Tuple													=	os.path.join(
               __SCREAMING_SNAKE_CASE    ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

           if os.path.abspath(self.vocab_file		) != os.path.abspath(__SCREAMING_SNAKE_CASE		):
                 copyfile(self.vocab_file    ,							__SCREAMING_SNAKE_CASE		)

           return (out_vocab_file,)





'''simple docstring'''







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


_lowercase			:  Any   							=	logging.get_logger(__name__)

_lowercase			:  Dict   							=	{
    "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 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					'''gpt_neox'''






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE=5_04_32    ,							__SCREAMING_SNAKE_CASE=61_44    ,							__SCREAMING_SNAKE_CASE=44    ,							__SCREAMING_SNAKE_CASE=64    ,							__SCREAMING_SNAKE_CASE=2_45_76    ,							__SCREAMING_SNAKE_CASE="gelu"    ,							__SCREAMING_SNAKE_CASE=0.25    ,							__SCREAMING_SNAKE_CASE=1_00_00    ,							__SCREAMING_SNAKE_CASE=0.0    ,							__SCREAMING_SNAKE_CASE=0.0    ,							__SCREAMING_SNAKE_CASE=0.1    ,							__SCREAMING_SNAKE_CASE=20_48    ,							__SCREAMING_SNAKE_CASE=0.02    ,							__SCREAMING_SNAKE_CASE=1E-5    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=0    ,							__SCREAMING_SNAKE_CASE=2    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=None    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE    ,							eos_token_id=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[Any]													=	vocab_size
											lowercase_     :					Optional[Any]													=	max_position_embeddings
											lowercase_     :					Optional[int]													=	hidden_size
											lowercase_     :					Tuple													=	num_hidden_layers
											lowercase_     :					Any													=	num_attention_heads
											lowercase_     :					str													=	intermediate_size
											lowercase_     :					Any													=	hidden_act
											lowercase_     :					Tuple													=	rotary_pct
											lowercase_     :					Optional[Any]													=	rotary_emb_base
											lowercase_     :					Any													=	attention_dropout
											lowercase_     :					str													=	hidden_dropout
											lowercase_     :					Dict													=	classifier_dropout
											lowercase_     :					Tuple													=	initializer_range
											lowercase_     :					List[str]													=	layer_norm_eps
											lowercase_     :					Union[str, Any]													=	use_cache
											lowercase_     :					int													=	tie_word_embeddings
											lowercase_     :					Tuple													=	use_parallel_residual
											lowercase_     :					Optional[Any]													=	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      _snake_case   (       self		):


											"""simple docstring"""





											if self.rope_scaling is None:
																	return

											if not isinstance(self.rope_scaling    ,							__SCREAMING_SNAKE_CASE		) 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}'''		)
											lowercase_     :					List[Any]													=	self.rope_scaling.get('''type'''    ,							__SCREAMING_SNAKE_CASE		)
											lowercase_     :					int													=	self.rope_scaling.get('''factor'''    ,							__SCREAMING_SNAKE_CASE		)
											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(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) or rope_scaling_factor <= 1.0:
																	raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''		)



