

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	{
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   List[Any]									=				"gpt_neo"
			SCREAMING_SNAKE_CASE     :   Any									=				["past_key_values"]
			SCREAMING_SNAKE_CASE     :   Union[str, Any]									=				{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}







			def __init__(     self :				Optional[int]							,   _UpperCamelCase :				Dict=5_0_2_5_7							,   _UpperCamelCase :				Any=2_0_4_8							,   _UpperCamelCase :				Optional[Any]=2_0_4_8							,   _UpperCamelCase :				Optional[Any]=2_4							,   _UpperCamelCase :				Optional[Any]=[[["global", "local"], 1_2]]							,   _UpperCamelCase :				Optional[Any]=1_6							,   _UpperCamelCase :				List[Any]=None							,   _UpperCamelCase :				Union[str, Any]=2_5_6							,   _UpperCamelCase :				Optional[int]="gelu_new"							,   _UpperCamelCase :				Optional[int]=0.0							,   _UpperCamelCase :				Tuple=0.0							,   _UpperCamelCase :				List[str]=0.0							,   _UpperCamelCase :				Any=0.1							,   _UpperCamelCase :				List[Any]=1e-5							,   _UpperCamelCase :				Dict=0.02							,   _UpperCamelCase :				Union[str, Any]=True							,   _UpperCamelCase :				Dict=5_0_2_5_6							,   _UpperCamelCase :				int=5_0_2_5_6							,   **_UpperCamelCase :				Optional[int]							,   )      ->Optional[int]:
									snake_case_   =	vocab_size
									snake_case_   =	max_position_embeddings
									snake_case_   =	hidden_size
									snake_case_   =	num_layers
									snake_case_   =	num_heads
									snake_case_   =	intermediate_size
									snake_case_   =	window_size
									snake_case_   =	activation_function
									snake_case_   =	resid_dropout
									snake_case_   =	embed_dropout
									snake_case_   =	attention_dropout
									snake_case_   =	classifier_dropout
									snake_case_   =	layer_norm_epsilon
									snake_case_   =	initializer_range
									snake_case_   =	use_cache

									snake_case_   =	bos_token_id
									snake_case_   =	eos_token_id

									snake_case_   =	attention_types
									snake_case_   =	self.expand_attention_types_params(_UpperCamelCase							)

									if len(self.attention_layers							) != self.num_layers:
															raise ValueError(
															    '''Configuration for convolutional module is incorrect. '''
															    '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
															    f'''but is `len(config.attention_layers) = {len(self.attention_layers							)}`, '''
															    f'''`config.num_layers = {self.num_layers}`. '''
															    '''`config.attention_layers` is prepared using `config.attention_types`. '''
															    '''Please verify the value of `config.attention_types` argument.'''							)

									super().__init__(bos_token_id=_UpperCamelCase							,   eos_token_id=_UpperCamelCase							,   **_UpperCamelCase							)







			@staticmethod
			def 				snake_case__(     _UpperCamelCase :				Optional[int]							)      ->int:
									snake_case_   =	[]
									for item in attention_types:
															for _ in range(item[1]							):
																					attentions.extend(item[0]							)
									return attentions






def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						import torch

						snake_case_   =	input.size()
						snake_case_   =	len(SCREAMING_SNAKE_CASE__       )
						snake_case_   =	shape[dimension]

						snake_case_   =	torch.arange(0  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
						snake_case_   =	torch.div(sizedim - size  ,		SCREAMING_SNAKE_CASE__  ,		rounding_mode='''floor'''       ) + 1
						snake_case_   =	torch.arange(SCREAMING_SNAKE_CASE__       ) + low_indices[:min_length][:, None]

						snake_case_   =	[slice(SCREAMING_SNAKE_CASE__       )] * rank
						snake_case_   =	indices
						snake_case_   =	input[s]

						snake_case_   =	list(range(0  ,		rank + 1       )       )
						perm.append(perm.pop(dimension + 1       )       )

						return sliced.permute(SCREAMING_SNAKE_CASE__       )


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						import torch

						snake_case_   =	torch.arange(1  ,		SCREAMING_SNAKE_CASE__       )
						snake_case_   =	torch.remainder(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
						snake_case_   =	remainders == 0
						snake_case_   =	candidates[divisor_indices]
						snake_case_   =	torch.max(SCREAMING_SNAKE_CASE__       )
						return largest_divisor, torch.div(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		rounding_mode='''floor'''       )





class 	snake_case_   (		__A					):







			'''simple docstring'''

			@property
			def 				snake_case__(     self :				List[Any]							)      ->Mapping[str, Mapping[int, str]]:
									snake_case_   =	OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}							)
									if self.use_past:
															self.fill_with_past_key_values_(_UpperCamelCase							,   direction='''inputs'''							)
															snake_case_   =	{0: '''batch''', 1: '''past_sequence + sequence'''}
									else:
															snake_case_   =	{0: '''batch''', 1: '''sequence'''}

									return common_inputs







			@property
			def 				snake_case__(     self :				str							)      ->int:
									return self._config.num_heads







			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				PreTrainedTokenizer							,   _UpperCamelCase :				int = -1							,   _UpperCamelCase :				int = -1							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				Optional[TensorType] = None							,   )      ->Mapping[str, Any]:
									snake_case_   =	super(_UpperCamelCase							,   self							).generate_dummy_inputs(
									    _UpperCamelCase							,   batch_size=_UpperCamelCase							,   seq_length=_UpperCamelCase							,   is_pair=_UpperCamelCase							,   framework=_UpperCamelCase							)

									# We need to order the input in the way they appears in the forward()
									snake_case_   =	OrderedDict({'''input_ids''': common_inputs['''input_ids''']}							)

									# Need to add the past_keys
									if self.use_past:
															if not is_torch_available():
																					raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
															else:
																					import torch

																					snake_case_,    snake_case_   =	common_inputs['''input_ids'''].shape
																					# Not using the same length for past_key_values
																					snake_case_   =	seqlen + 2
																					snake_case_   =	(
																					    batch,
																					    self.num_attention_heads,
																					    past_key_values_length,
																					    self._config.hidden_size // self.num_attention_heads,
																					)
																					snake_case_   =	[
																					    (torch.zeros(_UpperCamelCase							), torch.zeros(_UpperCamelCase							)) for _ in range(self.num_layers							)
																					]

									snake_case_   =	common_inputs['''attention_mask''']
									if self.use_past:
															snake_case_   =	ordered_inputs['''attention_mask'''].dtype
															snake_case_   =	torch.cat(
															    [ordered_inputs['''attention_mask'''], torch.ones(_UpperCamelCase							,   _UpperCamelCase							,   dtype=_UpperCamelCase							)]							,   dim=1							)

									return ordered_inputs







			@property
			def 				snake_case__(     self :				int							)      ->int:
									return 1_3


import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	{'''vocab_file''': '''spiece.model'''}

lowerCAmelCase_       	=	{
    '''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'''
        ),
    }
}

lowerCAmelCase_       	=	{
    '''google/bigbird-roberta-base''': 40_96,
    '''google/bigbird-roberta-large''': 40_96,
    '''google/bigbird-base-trivia-itc''': 40_96,
}





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   List[Any]									=				VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE     :   List[Any]									=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE     :   List[Any]									=				["input_ids", "attention_mask"]
			SCREAMING_SNAKE_CASE     :   List[int]									=				[]







			def __init__(     self :				List[str]							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				Dict="<unk>"							,   _UpperCamelCase :				List[str]="<s>"							,   _UpperCamelCase :				Tuple="</s>"							,   _UpperCamelCase :				Any="<pad>"							,   _UpperCamelCase :				Any="[SEP]"							,   _UpperCamelCase :				Optional[Any]="[MASK]"							,   _UpperCamelCase :				Any="[CLS]"							,   _UpperCamelCase :				Optional[Dict[str, Any]] = None							,   **_UpperCamelCase :				Dict							,   )      ->None:
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else bos_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else eos_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else unk_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else pad_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else cls_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else sep_token

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

									snake_case_   =	{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=_UpperCamelCase							,   eos_token=_UpperCamelCase							,   unk_token=_UpperCamelCase							,   pad_token=_UpperCamelCase							,   sep_token=_UpperCamelCase							,   mask_token=_UpperCamelCase							,   cls_token=_UpperCamelCase							,   sp_model_kwargs=self.sp_model_kwargs							,   **_UpperCamelCase							,   )

									snake_case_   =	vocab_file

									snake_case_   =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(_UpperCamelCase							)







			@property
			def 				snake_case__(     self :				str							)      ->List[Any]:
									return self.sp_model.get_piece_size()







			def 				snake_case__(     self :				int							)      ->Union[str, Any]:
									snake_case_   =	{self.convert_ids_to_tokens(_UpperCamelCase							): i for i in range(self.vocab_size							)}
									vocab.update(self.added_tokens_encoder							)
									return vocab







			def __getstate__(     self :				Tuple							)      ->Any:
									snake_case_   =	self.__dict__.copy()
									snake_case_   =	None
									return state







			def __setstate__(     self :				str							,   _UpperCamelCase :				List[Any]							)      ->List[str]:
									snake_case_   =	d

									# for backward compatibility
									if not hasattr(self							,   '''sp_model_kwargs'''							):
															snake_case_   =	{}

									snake_case_   =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(self.vocab_file							)







			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				str							)      ->List[str]:
									return self.sp_model.encode(_UpperCamelCase							,   out_type=_UpperCamelCase							)







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				List[str]							)      ->Tuple:
									return self.sp_model.piece_to_id(_UpperCamelCase							)







			def 				snake_case__(     self :				Union[str, Any]							,   _UpperCamelCase :				str							)      ->List[Any]:
									snake_case_   =	self.sp_model.IdToPiece(_UpperCamelCase							)
									return token







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				Optional[int]							)      ->List[str]:
									snake_case_   =	[]
									snake_case_   =	''''''
									snake_case_   =	False
									for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																					if not prev_is_special:
																											out_string += " "
																					out_string += self.sp_model.decode(_UpperCamelCase							) + token
																					snake_case_   =	True
																					snake_case_   =	[]
															else:
																					current_sub_tokens.append(_UpperCamelCase							)
																					snake_case_   =	False
									out_string += self.sp_model.decode(_UpperCamelCase							)
									return out_string.strip()







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				bool = None							,   _UpperCamelCase :				bool = True							,   **_UpperCamelCase :				List[str]							,   )      ->str:
									snake_case_   =	kwargs.pop('''use_source_tokenizer'''							,   _UpperCamelCase							)

									snake_case_   =	self.convert_ids_to_tokens(_UpperCamelCase							,   skip_special_tokens=_UpperCamelCase							)

									# To avoid mixing byte-level and unicode for byte-level BPT
									# we need to build string separately for added tokens and byte-level tokens
									# cf. https://github.com/huggingface/transformers/issues/1133
									snake_case_   =	[]
									snake_case_   =	[]
									for token in filtered_tokens:
															if skip_special_tokens and token in self.all_special_ids:
																					continue
															if token in self.added_tokens_encoder:
																					if current_sub_text:
																											sub_texts.append(self.convert_tokens_to_string(_UpperCamelCase							)							)
																											snake_case_   =	[]
																					sub_texts.append(_UpperCamelCase							)
															else:
																					current_sub_text.append(_UpperCamelCase							)
									if current_sub_text:
															sub_texts.append(self.convert_tokens_to_string(_UpperCamelCase							)							)

									# Mimic the behavior of the Rust tokenizer:
									# No space before [MASK] and [SEP]
									if spaces_between_special_tokens:
															snake_case_   =	re.sub(R''' (\[(MASK|SEP)\])'''							,   R'''\1'''							,   ''' '''.join(_UpperCamelCase							)							)
									else:
															snake_case_   =	''''''.join(_UpperCamelCase							)

									snake_case_   =	(
									    clean_up_tokenization_spaces
									    if clean_up_tokenization_spaces is not None
									    else self.clean_up_tokenization_spaces
									)
									if clean_up_tokenization_spaces:
															snake_case_   =	self.clean_up_tokenization(_UpperCamelCase							)
															return clean_text
									else:
															return text







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Optional[str] = None							)      ->Tuple[str]:
									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							) and os.path.isfile(self.vocab_file							):
															copyfile(self.vocab_file							,   _UpperCamelCase							)
									elif not os.path.isfile(self.vocab_file							):
															with open(_UpperCamelCase							,   '''wb'''							) as fi:
																					snake_case_   =	self.sp_model.serialized_model_proto()
																					fi.write(_UpperCamelCase							)

									return (out_vocab_file,)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							)      ->List[int]:
									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									snake_case_   =	[self.cls_token_id]
									snake_case_   =	[self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep







			def 				snake_case__(     self :				List[str]							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							,   _UpperCamelCase :				bool = False							)      ->List[int]:
									if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=_UpperCamelCase							,   token_ids_a=_UpperCamelCase							,   already_has_special_tokens=_UpperCamelCase							)

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







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							)      ->List[int]:
									snake_case_   =	[self.sep_token_id]
									snake_case_   =	[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]