





'''simple docstring'''

import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation

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

lowerCAmelCase__	:					Any	     =       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

lowerCAmelCase__	:					Any	     =       {
    'vocab_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
    },
    'merges_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
    },
}

lowerCAmelCase__	:					Union[str, Any]	     =       {
    'gpt2': 10_24,
    'gpt2-medium': 10_24,
    'gpt2-large': 10_24,
    'gpt2-xl': 10_24,
    'distilgpt2': 10_24,
}


@lru_cache()
def 		__UpperCamelCase		(       ):
		__UpperCAmelCase     :				Union[str, Any]					   =			(
		    list(range(ord("!"   ),    ord("~"   ) + 1   )   ) + list(range(ord("¡"   ),    ord("¬"   ) + 1   )   ) + list(range(ord("®"   ),    ord("ÿ"   ) + 1   )   )
		)
		__UpperCAmelCase     :				Dict					   =			bs[:]
		__UpperCAmelCase     :				int					   =			0
		for b in range(2**8   ):
				if b not in bs:
						bs.append(_UpperCamelCase   )
						cs.append(2**8 + n   )
						n += 1
		__UpperCAmelCase     :				Union[str, Any]					   =			[chr(_UpperCamelCase   ) for n in cs]
		return dict(zip(_UpperCamelCase,    _UpperCamelCase   )   )


def 		__UpperCamelCase		(       _UpperCAmelCase   ):
		__UpperCAmelCase     :				Union[str, Any]					   =			set()
		__UpperCAmelCase     :				str					   =			word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)   )
				__UpperCAmelCase     :				Tuple					   =			char
		return pairs






class 			SCREAMING_SNAKE_CASE__	(					_UpperCamelCase						):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE				=     VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE				=     PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE				=     ['input_ids', 'attention_mask']
					def __init__(					self    :	Optional[Any]						,     UpperCAmelCase_    :	Tuple						,     UpperCAmelCase_    :	List[Any]						,     UpperCAmelCase_    :	Tuple="replace"						,     UpperCAmelCase_    :	Tuple="<|endoftext|>"						,     UpperCAmelCase_    :	List[str]="<|endoftext|>"						,     UpperCAmelCase_    :	Tuple="<|endoftext|>"						,     UpperCAmelCase_    :	Any=None						,     UpperCAmelCase_    :	List[str]=False						,     UpperCAmelCase_    :	Dict=False						,     **UpperCAmelCase_    :	List[Any]						,     ):







							"""simple docstring"""
							__UpperCAmelCase     :				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
							__UpperCAmelCase     :				List[str]					   =			AddedToken(_SCREAMING_SNAKE_CASE						,     lstrip=_SCREAMING_SNAKE_CASE						,     rstrip=_SCREAMING_SNAKE_CASE						) if isinstance(_SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE						) else eos_token
							__UpperCAmelCase     :				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
							__UpperCAmelCase     :				str					   =			AddedToken(_SCREAMING_SNAKE_CASE						,     lstrip=_SCREAMING_SNAKE_CASE						,     rstrip=_SCREAMING_SNAKE_CASE						) if isinstance(_SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE						) else pad_token
							super().__init__(
							    errors=_SCREAMING_SNAKE_CASE						,     unk_token=_SCREAMING_SNAKE_CASE						,     bos_token=_SCREAMING_SNAKE_CASE						,     eos_token=_SCREAMING_SNAKE_CASE						,     pad_token=_SCREAMING_SNAKE_CASE						,     add_prefix_space=_SCREAMING_SNAKE_CASE						,     add_bos_token=_SCREAMING_SNAKE_CASE						,     **_SCREAMING_SNAKE_CASE						,     )
							__UpperCAmelCase     :				Union[str, Any]					   =			add_bos_token

							with open(_SCREAMING_SNAKE_CASE						,     encoding="utf-8"						) as vocab_handle:
									__UpperCAmelCase     :				Dict					   =			json.load(_SCREAMING_SNAKE_CASE						)
							__UpperCAmelCase     :				List[str]					   =			{v: k for k, v in self.encoder.items()}
							__UpperCAmelCase     :				int					   =			errors  # how to handle errors in decoding
							__UpperCAmelCase     :				Tuple					   =			bytes_to_unicode()
							__UpperCAmelCase     :				Any					   =			{v: k for k, v in self.byte_encoder.items()}
							with open(_SCREAMING_SNAKE_CASE						,     encoding="utf-8"						) as merges_handle:
									__UpperCAmelCase     :				List[Any]					   =			merges_handle.read().split("\n"						)[1:-1]
							__UpperCAmelCase     :				Optional[Any]					   =			[tuple(merge.split()						) for merge in bpe_merges]
							__UpperCAmelCase     :				str					   =			dict(zip(_SCREAMING_SNAKE_CASE						,     range(len(_SCREAMING_SNAKE_CASE						)						)						)						)
							__UpperCAmelCase     :				Any					   =			{}
							__UpperCAmelCase     :				List[Any]					   =			add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							__UpperCAmelCase     :				Any					   =			re.compile(R"\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"						)
					@property
					def 						lowerCamelCase_					(					self    :	List[str]						):







							"""simple docstring"""
							return len(self.encoder						)
					def 						lowerCamelCase_					(					self    :	List[str]						):







							"""simple docstring"""
							return dict(self.encoder						,     **self.added_tokens_encoder						)
					def 						lowerCamelCase_					(					self    :	Optional[Any]						,     UpperCAmelCase_    :	Any						):







							"""simple docstring"""
							if token in self.cache:
									return self.cache[token]
							__UpperCAmelCase     :				List[Any]					   =			tuple(_SCREAMING_SNAKE_CASE						)
							__UpperCAmelCase     :				Any					   =			get_pairs(_SCREAMING_SNAKE_CASE						)

							if not pairs:
									return token

							while True:
									__UpperCAmelCase     :				Any					   =			min(_SCREAMING_SNAKE_CASE						,     key=lambda UpperCAmelCase_						: self.bpe_ranks.get(_SCREAMING_SNAKE_CASE						,     float("inf"						)						)						)
									if bigram not in self.bpe_ranks:
											break
									__UpperCAmelCase   ,       __UpperCAmelCase     :				Dict					   =			bigram
									__UpperCAmelCase     :				str					   =			[]
									__UpperCAmelCase     :				str					   =			0
									while i < len(_SCREAMING_SNAKE_CASE						):
											try:
													__UpperCAmelCase     :				str					   =			word.index(_SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE						)
											except ValueError:
													new_word.extend(word[i:]						)
													break
											else:
													new_word.extend(word[i:j]						)
													__UpperCAmelCase     :				Tuple					   =			j

											if word[i] == first and i < len(_SCREAMING_SNAKE_CASE						) - 1 and word[i + 1] == second:
													new_word.append(first + second						)
													i += 2
											else:
													new_word.append(word[i]						)
													i += 1
									__UpperCAmelCase     :				str					   =			tuple(_SCREAMING_SNAKE_CASE						)
									__UpperCAmelCase     :				Dict					   =			new_word
									if len(_SCREAMING_SNAKE_CASE						) == 1:
											break
									else:
											__UpperCAmelCase     :				Tuple					   =			get_pairs(_SCREAMING_SNAKE_CASE						)
							__UpperCAmelCase     :				int					   =			" ".join(_SCREAMING_SNAKE_CASE						)
							__UpperCAmelCase     :				Union[str, Any]					   =			word
							return word
					def 						lowerCamelCase_					(					self    :	Union[str, Any]						,     UpperCAmelCase_    :	Tuple						,     UpperCAmelCase_    :	Optional[int]=None						):







							"""simple docstring"""
							if self.add_bos_token:
									__UpperCAmelCase     :				Dict					   =			[self.bos_token_id]
							else:
									__UpperCAmelCase     :				Any					   =			[]

							__UpperCAmelCase     :				str					   =			bos_token_ids + token_ids_a

							if token_ids_a is None:
									return output

							return output + bos_token_ids + token_ids_a
					def 						lowerCamelCase_					(					self    :	Optional[Any]						,     UpperCAmelCase_    :	Union[str, Any]						,     UpperCAmelCase_    :	int = None						,     UpperCAmelCase_    :	Optional[Any] = False						):







							"""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 not self.add_bos_token:
									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 None:
									return [1] + ([0] * len(_SCREAMING_SNAKE_CASE						))
							return [1] + ([0] * len(_SCREAMING_SNAKE_CASE						)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE						))
					def 						lowerCamelCase_					(					self    :	Union[str, Any]						,     UpperCAmelCase_    :	Optional[Any]						):







							"""simple docstring"""
							__UpperCAmelCase     :				Union[str, Any]					   =			[]
							for token in re.findall(self.pat						,     _SCREAMING_SNAKE_CASE						):
									__UpperCAmelCase     :				Any					   =			"".join(
									    self.byte_encoder[b] for b in token.encode("utf-8"						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
									bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_SCREAMING_SNAKE_CASE						).split(" "						)						)
							return bpe_tokens
					def 						lowerCamelCase_					(					self    :	Union[str, Any]						,     UpperCAmelCase_    :	str						):







							"""simple docstring"""
							return self.encoder.get(_SCREAMING_SNAKE_CASE						,     self.encoder.get(self.unk_token						)						)
					def 						lowerCamelCase_					(					self    :	Optional[Any]						,     UpperCAmelCase_    :	int						):







							"""simple docstring"""
							return self.decoder.get(_SCREAMING_SNAKE_CASE						)
					def 						lowerCamelCase_					(					self    :	Optional[int]						,     UpperCAmelCase_    :	Optional[int]						):







							"""simple docstring"""
							__UpperCAmelCase     :				Any					   =			"".join(_SCREAMING_SNAKE_CASE						)
							__UpperCAmelCase     :				int					   =			bytearray([self.byte_decoder[c] for c in text]						).decode("utf-8"						,     errors=self.errors						)
							return text
					def 						lowerCamelCase_					(					self    :	List[str]						,     UpperCAmelCase_    :	Dict						,     UpperCAmelCase_    :	List[str] = None						):







							"""simple docstring"""
							if not os.path.isdir(_SCREAMING_SNAKE_CASE						):
									logger.error(f"Vocabulary path ({save_directory}) should be a directory"						)
									return
							__UpperCAmelCase     :				List[str]					   =			os.path.join(
							    _SCREAMING_SNAKE_CASE						,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
							__UpperCAmelCase     :				Union[str, Any]					   =			os.path.join(
							    _SCREAMING_SNAKE_CASE						,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]						)

							with open(_SCREAMING_SNAKE_CASE						,     "w"						,     encoding="utf-8"						) as f:
									f.write(json.dumps(self.encoder						,     indent=2						,     sort_keys=_SCREAMING_SNAKE_CASE						,     ensure_ascii=_SCREAMING_SNAKE_CASE						) + "\n"						)

							__UpperCAmelCase     :				Optional[Any]					   =			0
							with open(_SCREAMING_SNAKE_CASE						,     "w"						,     encoding="utf-8"						) as writer:
									writer.write("#version: 0.2\n"						)
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items()						,     key=lambda UpperCAmelCase_						: kv[1]						):
											if index != token_index:
													logger.warning(
													    f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
													    " Please check that the tokenizer is not corrupted!"						)
													__UpperCAmelCase     :				List[Any]					   =			token_index
											writer.write(" ".join(_SCREAMING_SNAKE_CASE						) + "\n"						)
											index += 1

							return vocab_file, merge_file
					def 						lowerCamelCase_					(					self    :	Tuple						,     UpperCAmelCase_    :	Dict						,     UpperCAmelCase_    :	int=False						,     **UpperCAmelCase_    :	Optional[int]						):







							"""simple docstring"""
							__UpperCAmelCase     :				str					   =			kwargs.pop("add_prefix_space"						,     self.add_prefix_space						)
							if is_split_into_words or add_prefix_space:
									__UpperCAmelCase     :				str					   =			" " + text
							return (text, kwargs)






					def 						lowerCamelCase_					(					self    :	List[str]						,     UpperCAmelCase_    :	Any						):







							"""simple docstring"""
							__UpperCAmelCase     :				Dict					   =			[]
							for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(_SCREAMING_SNAKE_CASE						,     add_special_tokens=_SCREAMING_SNAKE_CASE						) + [self.eos_token_id]						)
							if len(_SCREAMING_SNAKE_CASE						) > self.model_max_length:
									__UpperCAmelCase     :				Tuple					   =			input_ids[-self.model_max_length :]
							return input_ids





'''simple docstring'''




import bisect
import copy
from collections import defaultdict

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

from utils import logger





def      _UpperCAmelCase    (							_UpperCamelCase    :	Optional[Any],   _UpperCamelCase    :	int   )	->      Union[str, Any]:
    A_     =   copy.deepcopy(_UpperCamelCase   )
    A_     =   sorted(_UpperCamelCase   )
    A_     =   [bisect.bisect_right(_UpperCamelCase,   _UpperCamelCase   ) for y in x]
    return quantized





def      _UpperCAmelCase    (							_UpperCamelCase    :	str,   _UpperCamelCase    :	Tuple=0   )	->      str:
    A_     =   np.arange(start=3,   stop=_UpperCamelCase,   step=4   ).tolist() if k > 0 else [10]
    A_     =   _quantize(_UpperCamelCase,   _UpperCamelCase   )
    # count number of elements per group
    A_     =   np.unique(_UpperCamelCase,   return_counts=_UpperCamelCase   )[1]
    A_     =   [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    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Dict:
           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 ) )
           A_     =   sampler
           A_     =   group_ids
           A_     =   batch_size

       def __iter__(      self )		->     Tuple:
           A_     =   defaultdict(_SCREAMING_SNAKE_CASE )
           A_     =   defaultdict(_SCREAMING_SNAKE_CASE )

           A_     =   0
           for idx in self.sampler:
               A_     =   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
           A_     =   len(self )
           A_     =   expected_num_batches - num_batches
           if num_remaining > 0:
               # for the remaining batches, group the batches by similar lengths
               A_     =   []
               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]
                       A_     =   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[int]:
           return (len(self.sampler ) + self.batch_size - 1) // self.batch_size






