


import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger

def  lowerCAmelCase__				(    a__:	List[Any]    ,						a__:	Optional[int] )						->	str:


							'''simple docstring'''





							_UpperCAmelCase					=    copy.deepcopy(a__ )
							_UpperCAmelCase					=    sorted(a__ )
							_UpperCAmelCase					=    [bisect.bisect_right(a__    ,						a__ ) for y in x]
							return quantized

def  lowerCAmelCase__				(    a__:	int    ,						a__:	List[str]=0 )						->	Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=    np.arange(start=3    ,						stop=a__    ,						step=4 ).tolist() if k > 0 else [1_0]
							_UpperCAmelCase					=    _quantize(a__    ,						a__ )
							# count number of elements per group
							_UpperCAmelCase					=    np.unique(a__    ,						return_counts=a__ )[1]
							_UpperCAmelCase					=    [0] + bins + [np.inf]
							logger.info('Using {} as bins for aspect lengths quantization'.format(a__ ) )
							logger.info('Count of instances per bin: {}'.format(a__ ) )
							return groups







class 	__a   (       UpperCAmelCase    ):
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)				-> Optional[Any]:




									"""simple docstring"""





									if not isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		):
																raise ValueError(
																    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(_SCREAMING_SNAKE_CASE		)		)
									_UpperCAmelCase					=    sampler
									_UpperCAmelCase					=    group_ids
									_UpperCAmelCase					=    batch_size
		def __iter__(    self		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    defaultdict(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    defaultdict(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    0
									for idx in self.sampler:
																_UpperCAmelCase					=    self.group_ids[idx]
																buffer_per_group[group_id].append(_SCREAMING_SNAKE_CASE		)
																samples_per_group[group_id].append(_SCREAMING_SNAKE_CASE		)
																if len(buffer_per_group[group_id]		) == self.batch_size:
																							yield buffer_per_group[group_id]  # TODO
																							num_batches += 1
																							del buffer_per_group[group_id]
																assert len(buffer_per_group[group_id]		) < self.batch_size

									# now we have run out of elements that satisfy
									# the group criteria, let's return the remaining
									# elements so that the size of the sampler is
									# deterministic
									_UpperCAmelCase					=    len(self		)
									_UpperCAmelCase					=    expected_num_batches - num_batches
									if num_remaining > 0:
																# for the remaining batches, group the batches by similar lengths
																_UpperCAmelCase					=    []
																for group_id, idxs in sorted(buffer_per_group.items()    ,					key=lambda _SCREAMING_SNAKE_CASE		: x[0]		):
																							batch_idx.extend(_SCREAMING_SNAKE_CASE		)
																							if len(_SCREAMING_SNAKE_CASE		) >= self.batch_size:
																														yield batch_idx[: self.batch_size]
																														_UpperCAmelCase					=    batch_idx[self.batch_size :]
																														num_remaining -= 1
																if len(_SCREAMING_SNAKE_CASE		) > 0:
																							yield batch_idx
																							num_remaining -= 1
									assert num_remaining == 0






		def __len__(    self		)				-> Optional[Any]:




									"""simple docstring"""





									return (len(self.sampler		) + self.batch_size - 1) // self.batch_size




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

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


if is_sentencepiece_available():
		from .tokenization_deberta_va import DebertaVaTokenizer
else:
		lowerCAmelCase__						:int         =							None

lowerCAmelCase__						:List[str]         =							logging.get_logger(__name__)

lowerCAmelCase__						:List[str]         =							{'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase__						:List[str]         =							{
    '''vocab_file''': {
        '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'''
        ),
        '''microsoft/deberta-v2-xxlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'''
        ),
    }
}

lowerCAmelCase__						:List[str]         =							{
    '''microsoft/deberta-v2-xlarge''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge''': 5_1_2,
    '''microsoft/deberta-v2-xlarge-mnli''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge-mnli''': 5_1_2,
}

lowerCAmelCase__						:Any         =							{
    '''microsoft/deberta-v2-xlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xlarge-mnli''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge-mnli''': {'''do_lower_case''': False},
}

class 	__a   (       UpperCAmelCase    ):
		_a			:						Tuple    			=			VOCAB_FILES_NAMES
		_a			:						int    			=			PRETRAINED_VOCAB_FILES_MAP
		_a			:						Optional[Any]    			=			PRETRAINED_INIT_CONFIGURATION
		_a			:						str    			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a			:						Optional[int]    			=			DebertaVaTokenizer
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE="[CLS]"    ,					_SCREAMING_SNAKE_CASE="[SEP]"    ,					_SCREAMING_SNAKE_CASE="[UNK]"    ,					_SCREAMING_SNAKE_CASE="[SEP]"    ,					_SCREAMING_SNAKE_CASE="[PAD]"    ,					_SCREAMING_SNAKE_CASE="[CLS]"    ,					_SCREAMING_SNAKE_CASE="[MASK]"    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> None:




									"""simple docstring"""





									super().__init__(
									    _SCREAMING_SNAKE_CASE    ,					tokenizer_file=_SCREAMING_SNAKE_CASE    ,					do_lower_case=_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    ,					split_by_punct=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE    ,					)

									_UpperCAmelCase					=    do_lower_case
									_UpperCAmelCase					=    split_by_punct
									_UpperCAmelCase					=    vocab_file
									_UpperCAmelCase					=    False if not self.vocab_file else True
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None		)				-> str:




									"""simple docstring"""





									if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									_UpperCAmelCase					=    [self.cls_token_id]
									_UpperCAmelCase					=    [self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=False		)				-> Any:




									"""simple docstring"""





									if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=_SCREAMING_SNAKE_CASE    ,					token_ids_a=_SCREAMING_SNAKE_CASE    ,					already_has_special_tokens=_SCREAMING_SNAKE_CASE		)

									if token_ids_a is not None:
																return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
									return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    [self.sep_token_id]
									_UpperCAmelCase					=    [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 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = 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(_SCREAMING_SNAKE_CASE		):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
																return
									_UpperCAmelCase					=    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,)
