


import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger




class       lowercase_					(lowercase__   ):





				def __init__(				self       ,  lowercase_       ,  lowercase_)  -> Tuple:
											a__      =params

											a__      =np.array(lowercase_)
											a__      =np.array([len(lowercase_) for t in data])

											self.check()
											self.remove_long_sequences()
											self.remove_empty_sequences()
											self.remove_unknown_sequences()
											self.check()
											self.print_statistics()





				def __getitem__(				self       ,  lowercase_)  -> List[str]:
											return (self.token_ids[index], self.lengths[index])





				def __len__(				self)  -> Any:
											return len(self.lengths)





				def 						__UpperCamelCase    (				self)  -> Any:
											assert len(self.token_ids) == len(self.lengths)
											assert all(self.lengths[i] == len(self.token_ids[i]) for i in range(len(self.lengths)))





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											a__      =self.params.max_model_input_size
											a__      =self.lengths > max_len
											logger.info(F"""Splitting {sum(lowercase_)} too long sequences.""")

											def divide_chunks(lowercase_       ,  lowercase_):
																		return [l[i : i + n] for i in range(0       ,  len(lowercase_)       ,  lowercase_)]

											a__      =[]
											a__      =[]
											if self.params.mlm:
																		a__    ,     a__      =self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
											else:
																		a__    ,     a__      =self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']

											for seq_, len_ in zip(self.token_ids       ,  self.lengths):
																		assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
																		if len_ <= max_len:
																									new_tok_ids.append(seq_)
																									new_lengths.append(len_)
																		else:
																									a__      =[]
																									for sub_s in divide_chunks(seq_       ,  max_len - 2):
																																if sub_s[0] != cls_id:
																																							a__      =np.insert(lowercase_       ,  0       ,  lowercase_)
																																if sub_s[-1] != sep_id:
																																							a__      =np.insert(lowercase_       ,  len(lowercase_)       ,  lowercase_)
																																assert len(lowercase_) <= max_len
																																assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																																sub_seqs.append(lowercase_)

																									new_tok_ids.extend(lowercase_)
																									new_lengths.extend([len(lowercase_) for l in sub_seqs])

											a__      =np.array(lowercase_)
											a__      =np.array(lowercase_)





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =len(self)
											a__      =self.lengths > 11
											a__      =self.token_ids[indices]
											a__      =self.lengths[indices]
											a__      =len(self)
											logger.info(F"""Remove {init_size - new_size} too short (<=11 tokens) sequences.""")





				def 						__UpperCamelCase    (				self)  -> Any:
											if "unk_token" not in self.params.special_tok_ids:
																		return
											else:
																		a__      =self.params.special_tok_ids['unk_token']
											a__      =len(self)
											a__      =np.array([np.count_nonzero(a == unk_token_id) for a in self.token_ids])
											a__      =(unk_occs / self.lengths) < 0.5
											a__      =self.token_ids[indices]
											a__      =self.lengths[indices]
											a__      =len(self)
											logger.info(F"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).""")





				def 						__UpperCamelCase    (				self)  -> List[str]:
											if not self.params.is_master:
																		return
											logger.info(F"""{len(self)} sequences""")
											# data_len = sum(self.lengths)
											# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
											# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

											# unk_idx = self.params.special_tok_ids['unk_token']
											# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
											# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> str:
											a__      =[t[0] for t in batch]
											a__      =[t[1] for t in batch]
											assert len(lowercase_) == len(lowercase_)

											# Max for paddings
											a__      =max(lowercase_)

											# Pad token ids
											if self.params.mlm:
																		a__      =self.params.special_tok_ids['pad_token']
											else:
																		a__      =self.params.special_tok_ids['unk_token']
											a__      =[list(t.astype(lowercase_)) + [pad_idx] * (max_seq_len_ - len(lowercase_)) for t in token_ids]
											assert len(tk_) == len(lowercase_)
											assert all(len(lowercase_) == max_seq_len_ for t in tk_)

											a__      =torch.tensor(tk_)  # (bs, max_seq_len_)
											a__      =torch.tensor(lowercase_)  # (bs)
											return tk_t, lg_t





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:
	A			:						List[Any]        =  None


A			:						Optional[Any]        =  logging.get_logger(__name__)


A			:						str        =  '▁'

A			:						Optional[Any]        =  {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A			:						Any        =  {
    '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'
    },
}

A			:						Optional[int]        =  {
    'google/pegasus-xsum': 5_1_2,
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					VOCAB_FILES_NAMES
					A__     =					PRETRAINED_VOCAB_FILES_MAP
					A__     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__     =					PegasusTokenizer
					A__     =					['''input_ids''', '''attention_mask''']

					def __init__(self       :		Tuple ,   _UpperCAmelCase       :		Union[str, Any]=None ,   _UpperCAmelCase       :		List[Any]=None ,   _UpperCAmelCase       :		Union[str, Any]="<pad>" ,   _UpperCAmelCase       :		int="</s>" ,   _UpperCAmelCase       :		int="<unk>" ,   _UpperCAmelCase       :		Optional[Any]="<mask_2>" ,   _UpperCAmelCase       :		Optional[Any]="<mask_1>" ,   _UpperCAmelCase       :		Optional[Any]=None ,   _UpperCAmelCase       :		Dict=103 ,   **_UpperCAmelCase       :		Dict ,   )		->  Dict:




							"""simple docstring"""


							lowercase__	     =						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						)}'''						)

									lowercase__	     =						(
									    ([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}.'''						)
									lowercase__	     =						additional_special_tokens_extended
							else:
									lowercase__	     =						[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 ,   )
							lowercase__	     =						vocab_file
							lowercase__	     =						False if not self.vocab_file else True

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		Tuple						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						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[Any] ,   _UpperCAmelCase       :		List ,   _UpperCAmelCase       :		Optional[List] = None ,   _UpperCAmelCase       :		bool = False						)		->  List[int]:




							"""simple docstring"""


							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       :		int ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		Union[str, Any]=None						)		->  List[int]:




							"""simple docstring"""


							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       :		str ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Optional[str] = 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(_UpperCAmelCase						):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
									return
							lowercase__	     =						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,)

