



import bisect
import copy
from collections import defaultdict

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

from utils import logger

def 	a						(				_UpperCAmelCase							: List[Any]			,       _UpperCAmelCase							: Optional[int]						):







		'''simple docstring'''





		__UpperCAmelCase   :					List[Any]													=     copy.deepcopy(_UpperCAmelCase						)
		__UpperCAmelCase   :					int													=     sorted(_UpperCAmelCase						)
		__UpperCAmelCase   :					str													=     [bisect.bisect_right(_UpperCAmelCase			,       _UpperCAmelCase						) for y in x]
		return quantized

def 	a						(				_UpperCAmelCase							: Any			,       _UpperCAmelCase							: str=0						):







		'''simple docstring'''





		__UpperCAmelCase   :					List[str]													=     np.arange(start=3			,       stop=_UpperCAmelCase			,       step=4						).tolist() if k > 0 else [10]
		__UpperCAmelCase   :					Tuple													=     _quantize(_UpperCAmelCase			,       _UpperCAmelCase						)
		# count number of elements per group
		__UpperCAmelCase   :					Tuple													=     np.unique(_UpperCAmelCase			,       return_counts=_UpperCAmelCase						)[1]
		__UpperCAmelCase   :					Optional[Any]													=     [0] + bins + [np.inf]
		logger.info('''Using {} as bins for aspect lengths quantization'''.format(_UpperCAmelCase						)						)
		logger.info('''Count of instances per bin: {}'''.format(_UpperCAmelCase						)						)
		return groups

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			def __init__(       self							:			str	,	a_							:			Optional[int]	,	a_							:			int	,	a_							:			Dict			):
					'''simple docstring'''





					if not isinstance(a_	,	a_			):
							raise ValueError(
							    '''sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'''.format(a_			)			)
					__UpperCAmelCase   :					Optional[int]													=     sampler
					__UpperCAmelCase   :					int													=     group_ids
					__UpperCAmelCase   :					str													=     batch_size




			def __iter__(       self							:			int			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     defaultdict(a_			)
					__UpperCAmelCase   :					Tuple													=     defaultdict(a_			)

					__UpperCAmelCase   :					str													=     0
					for idx in self.sampler:
							__UpperCAmelCase   :					Optional[int]													=     self.group_ids[idx]
							buffer_per_group[group_id].append(a_			)
							samples_per_group[group_id].append(a_			)
							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   :					Optional[int]													=     len(self			)
					__UpperCAmelCase   :					Optional[int]													=     expected_num_batches - num_batches
					if num_remaining > 0:
							# for the remaining batches, group the batches by similar lengths
							__UpperCAmelCase   :					Optional[Any]													=     []
							for group_id, idxs in sorted(buffer_per_group.items()	,	key=lambda a_			: x[0]			):
									batch_idx.extend(a_			)
									if len(a_			) >= self.batch_size:
											yield batch_idx[: self.batch_size]
											__UpperCAmelCase   :					List[Any]													=     batch_idx[self.batch_size :]
											num_remaining -= 1
							if len(a_			) > 0:
									yield batch_idx
									num_remaining -= 1
					assert num_remaining == 0




			def __len__(       self							:			Dict			):
					'''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 ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_reformer import ReformerTokenizer
else:
				__A             =None


__A             =logging.get_logger(__name__)


__A             ="▁"

__A             ={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__A             ={
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

__A             ={
    "google/reformer-crime-and-punishment": 5_2_4_2_8_8,
}

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	VOCAB_FILES_NAMES
			UpperCamelCase      						=	PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase      						=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase      						=	["""input_ids""", """attention_mask"""]
			UpperCamelCase      						=	ReformerTokenizer




			def __init__(       self							:			Tuple	,	a_							:			Optional[int]=None	,	a_							:			int=None	,	a_							:			Dict="</s>"	,	a_							:			Dict="<unk>"	,	a_							:			List[Any]=[]	,	**a_							:			int	,	):
					'''simple docstring'''





					super().__init__(
					    a_	,	tokenizer_file=a_	,	eos_token=a_	,	unk_token=a_	,	additional_special_tokens=a_	,	**a_	,	)

					__UpperCAmelCase   :					Optional[Any]													=     vocab_file
					__UpperCAmelCase   :					Optional[Any]													=     False if not self.vocab_file else True




			def 			snake_case__					(       self							:			Dict	,	a_							:			str	,	a_							:			Optional[str] = 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(a_			):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
							return
					__UpperCAmelCase   :					Tuple													=     os.path.join(
					    a_	,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

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

					return (out_vocab_file,)





