
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

__SCREAMING_SNAKE_CASE     :				Tuple             =       logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :				Optional[int]             =       {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

__SCREAMING_SNAKE_CASE     :				str             =       {
    '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',
    },
}

__SCREAMING_SNAKE_CASE     :				Optional[int]             =       {
    'gpt2': 10_24,
    'gpt2-medium': 10_24,
    'gpt2-large': 10_24,
    'gpt2-xl': 10_24,
    'distilgpt2': 10_24,
}
@lru_cache()
def       UpperCAmelCase__							(    ):


					'''simple docstring'''

					lowerCAmelCase						:   Dict							=       (
					    list(range(ord('''!'''					)							,					ord('''~'''					) + 1					)					) + list(range(ord('''¡'''					)							,					ord('''¬'''					) + 1					)					) + list(range(ord('''®'''					)							,					ord('''ÿ'''					) + 1					)					)
					)
					lowerCAmelCase						:   Optional[Any]							=       bs[:]
					lowerCAmelCase						:   Any							=       0
					for b in range(2**8					):
										if b not in bs:
															bs.append(__magic_name__					)
															cs.append(2**8 + n					)
															n += 1
					lowerCAmelCase						:   Tuple							=       [chr(__magic_name__					) for n in cs]
					return dict(zip(__magic_name__							,					__magic_name__					)					)
def       UpperCAmelCase__							(    __magic_name__  :       List[Any]					):


					'''simple docstring'''

					lowerCAmelCase						:   Union[str, Any]							=       set()
					lowerCAmelCase						:   Optional[int]							=       word[0]
					for char in word[1:]:
										pairs.add((prev_char, char)					)
										lowerCAmelCase						:   Dict							=       char
					return pairs


class 						__magic_name__				(  snake_case      ):
		_lowerCAmelCase						=		VOCAB_FILES_NAMES
		_lowerCAmelCase						=		PRETRAINED_VOCAB_FILES_MAP
		_lowerCAmelCase						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_lowerCAmelCase						=		["input_ids", "attention_mask"]





		def __init__(							self      :		List[str]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Optional[Any]  ,					lowerCamelCase__      :		Dict="replace"  ,					lowerCamelCase__      :		Any="<|endoftext|>"  ,					lowerCamelCase__      :		Optional[Any]="<|endoftext|>"  ,					lowerCamelCase__      :		Any="<|endoftext|>"  ,					lowerCamelCase__      :		Tuple=None  ,					lowerCamelCase__      :		List[str]=False  ,					lowerCamelCase__      :		Optional[int]=False  ,					**lowerCamelCase__      :		str  ,					):
							lowerCAmelCase						:   List[str]							=       AddedToken(lowerCamelCase__  ,					lstrip=lowerCamelCase__  ,					rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__  ,					lowerCamelCase__			) else bos_token
							lowerCAmelCase						:   List[Any]							=       AddedToken(lowerCamelCase__  ,					lstrip=lowerCamelCase__  ,					rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__  ,					lowerCamelCase__			) else eos_token
							lowerCAmelCase						:   List[str]							=       AddedToken(lowerCamelCase__  ,					lstrip=lowerCamelCase__  ,					rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__  ,					lowerCamelCase__			) else unk_token
							lowerCAmelCase						:   str							=       AddedToken(lowerCamelCase__  ,					lstrip=lowerCamelCase__  ,					rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__  ,					lowerCamelCase__			) else pad_token
							super().__init__(
							    errors=lowerCamelCase__  ,					unk_token=lowerCamelCase__  ,					bos_token=lowerCamelCase__  ,					eos_token=lowerCamelCase__  ,					pad_token=lowerCamelCase__  ,					add_prefix_space=lowerCamelCase__  ,					add_bos_token=lowerCamelCase__  ,					**lowerCamelCase__  ,					)
							lowerCAmelCase						:   List[str]							=       add_bos_token

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

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





		@property
		def  _A							(							self      :		Dict			):
							return len(self.encoder			)





		def  _A							(							self      :		Tuple			):
							return dict(self.encoder  ,					**self.added_tokens_encoder			)





		def  _A							(							self      :		str  ,					lowerCamelCase__      :		Dict			):
							if token in self.cache:
												return self.cache[token]
							lowerCAmelCase						:   Union[str, Any]							=       tuple(lowerCamelCase__			)
							lowerCAmelCase						:   Optional[Any]							=       get_pairs(lowerCamelCase__			)

							if not pairs:
												return token

							while True:
												lowerCAmelCase						:   Tuple							=       min(lowerCamelCase__  ,					key=lambda lowerCamelCase__			: self.bpe_ranks.get(lowerCamelCase__  ,					float('''inf'''			)			)			)
												if bigram not in self.bpe_ranks:
																	break
												lowerCAmelCase		,							lowerCAmelCase						:   List[Any]							=       bigram
												lowerCAmelCase						:   Tuple							=       []
												lowerCAmelCase						:   Optional[int]							=       0
												while i < len(lowerCamelCase__			):
																	try:
																						lowerCAmelCase						:   str							=       word.index(lowerCamelCase__  ,					lowerCamelCase__			)
																	except ValueError:
																						new_word.extend(word[i:]			)
																						break
																	else:
																						new_word.extend(word[i:j]			)
																						lowerCAmelCase						:   str							=       j

																	if word[i] == first and i < len(lowerCamelCase__			) - 1 and word[i + 1] == second:
																						new_word.append(first + second			)
																						i += 2
																	else:
																						new_word.append(word[i]			)
																						i += 1
												lowerCAmelCase						:   List[str]							=       tuple(lowerCamelCase__			)
												lowerCAmelCase						:   int							=       new_word
												if len(lowerCamelCase__			) == 1:
																	break
												else:
																	lowerCAmelCase						:   Any							=       get_pairs(lowerCamelCase__			)
							lowerCAmelCase						:   Optional[int]							=       ''' '''.join(lowerCamelCase__			)
							lowerCAmelCase						:   Optional[Any]							=       word
							return word





		def  _A							(							self      :		str  ,					lowerCamelCase__      :		Tuple  ,					lowerCamelCase__      :		Optional[int]=None			):
							if self.add_bos_token:
												lowerCAmelCase						:   Union[str, Any]							=       [self.bos_token_id]
							else:
												lowerCAmelCase						:   Tuple							=       []

							lowerCAmelCase						:   str							=       bos_token_ids + token_ids_a

							if token_ids_a is None:
												return output

							return output + bos_token_ids + token_ids_a





		def  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		List[int]  ,					lowerCamelCase__      :		Optional[List[int]] = None  ,					lowerCamelCase__      :		bool = False			):
							if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=lowerCamelCase__  ,					token_ids_a=lowerCamelCase__  ,					already_has_special_tokens=lowerCamelCase__			)

							if not self.add_bos_token:
												return super().get_special_tokens_mask(
												    token_ids_a=lowerCamelCase__  ,					token_ids_a=lowerCamelCase__  ,					already_has_special_tokens=lowerCamelCase__			)

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





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		Tuple			):
							lowerCAmelCase						:   Any							=       []
							for token in re.findall(self.pat  ,					lowerCamelCase__			):
												lowerCAmelCase						:   Dict							=       ''''''.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(lowerCamelCase__			).split(''' '''			)			)
							return bpe_tokens





		def  _A							(							self      :		Optional[Any]  ,					lowerCamelCase__      :		List[Any]			):
							return self.encoder.get(lowerCamelCase__  ,					self.encoder.get(self.unk_token			)			)





		def  _A							(							self      :		Any  ,					lowerCamelCase__      :		Optional[Any]			):
							return self.decoder.get(lowerCamelCase__			)





		def  _A							(							self      :		str  ,					lowerCamelCase__      :		Optional[int]			):
							lowerCAmelCase						:   Optional[Any]							=       ''''''.join(lowerCamelCase__			)
							lowerCAmelCase						:   Any							=       bytearray([self.byte_decoder[c] for c in text]			).decode('''utf-8'''  ,					errors=self.errors			)
							return text





		def  _A							(							self      :		Optional[int]  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		Optional[str] = None			):
							if not os.path.isdir(lowerCamelCase__			):
												logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
												return
							lowerCAmelCase						:   Union[str, Any]							=       os.path.join(
							    lowerCamelCase__  ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)
							lowerCAmelCase						:   Dict							=       os.path.join(
							    lowerCamelCase__  ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']			)

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

							lowerCAmelCase						:   Dict							=       0
							with open(lowerCamelCase__  ,					'''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 lowerCamelCase__			: 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!'''			)
																						lowerCAmelCase						:   str							=       token_index
																	writer.write(''' '''.join(lowerCamelCase__			) + '''\n'''			)
																	index += 1

							return vocab_file, merge_file





		def  _A							(							self      :		Optional[int]  ,					lowerCamelCase__      :		List[str]  ,					lowerCamelCase__      :		Dict=False  ,					**lowerCamelCase__      :		List[Any]			):
							lowerCAmelCase						:   Any							=       kwargs.pop('''add_prefix_space'''  ,					self.add_prefix_space			)
							if is_split_into_words or add_prefix_space:
												lowerCAmelCase						:   List[Any]							=       ''' ''' + text
							return (text, kwargs)





		def  _A							(							self      :		int  ,					lowerCamelCase__      :		"Conversation"			):
							lowerCAmelCase						:   int							=       []
							for is_user, text in conversation.iter_texts():
												input_ids.extend(self.encode(lowerCamelCase__  ,					add_special_tokens=lowerCamelCase__			) + [self.eos_token_id]			)
							if len(lowerCamelCase__			) > self.model_max_length:
												lowerCAmelCase						:   Optional[int]							=       input_ids[-self.model_max_length :]
							return input_ids


from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
def       UpperCAmelCase__							(    __magic_name__  :       str=10					):


					'''simple docstring'''

					for _ in tqdm(range(0							,					args.n_examples							,					__magic_name__					)					):
										yield [next(__magic_name__					)[args.text_column] for _ in range(__magic_name__					)]


# Configuration
__SCREAMING_SNAKE_CASE     :				Dict             =       HfArgumentParser(TokenizerTrainingArguments)
__SCREAMING_SNAKE_CASE     :				str             =       parser.parse_args()

# Base tokenizer
__SCREAMING_SNAKE_CASE     :				Tuple             =       AutoTokenizer.from_pretrained(args.base_tokenizer)
__SCREAMING_SNAKE_CASE     :				List[str]             =       list(bytes_to_unicode().values())

# Load dataset
__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       load_dataset(args.dataset_name, split='train', streaming=True)
__SCREAMING_SNAKE_CASE     :				Any             =       iter(dataset)


# Training and saving
__SCREAMING_SNAKE_CASE     :				Optional[int]             =       tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
