

import json
import os
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase							:						Tuple     			=		logging.get_logger(__name__)

lowerCAmelCase							:						List[Any]     			=		{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

lowerCAmelCase							:						Any     			=		{
    """vocab_file""": {
        """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json""",
    },
    """merges_file""": {"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"""},
}

lowerCAmelCase							:						List[Any]     			=		{
    """microsoft/biogpt""": 1024,
}







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



class 						__lowercase       (				_a			):




			"""simple docstring"""







			_UpperCAmelCase     :							Any    							=  VOCAB_FILES_NAMES
			_UpperCAmelCase     :							Optional[Any]    							=  PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase     :							List[str]    							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase     :							Optional[int]    							=  ['''input_ids''', '''attention_mask''']



			def __init__(				self						:  Tuple     ,		lowerCAmelCase__						:  Optional[Any]     ,		lowerCAmelCase__						:  int     ,		lowerCAmelCase__						:  int="<unk>"     ,		lowerCAmelCase__						:  Tuple="<s>"     ,		lowerCAmelCase__						:  str="</s>"     ,		lowerCAmelCase__						:  str="</s>"     ,		lowerCAmelCase__						:  Optional[Any]="<pad>"     ,		**lowerCAmelCase__						:  List[str]     ,		):
									super().__init__(
									    bos_token=lowerCamelCase__     ,		eos_token=lowerCamelCase__     ,		sep_token=lowerCamelCase__     ,		unk_token=lowerCamelCase__     ,		pad_token=lowerCamelCase__     ,		**lowerCamelCase__     ,		)

									try:
															import sacremoses
									except ImportError:
															raise ImportError(
															    "You need to install sacremoses to use BioGptTokenizer. "
															    "See https://pypi.org/project/sacremoses/ for installation.")

									SCREAMING_SNAKE_CASE_:				List[str]							= "en"
									SCREAMING_SNAKE_CASE_:				str							= sacremoses
									# cache of sm.MosesTokenizer instance
									SCREAMING_SNAKE_CASE_:				int							= {}
									SCREAMING_SNAKE_CASE_:				Optional[int]							= {}
									with open(lowerCamelCase__     ,		encoding="utf-8") as vocab_handle:
															SCREAMING_SNAKE_CASE_:				Tuple							= json.load(lowerCamelCase__)
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= {v: k for k, v in self.encoder.items()}
									with open(lowerCamelCase__     ,		encoding="utf-8") as merges_handle:
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= merges_handle.read().split("\n")[:-1]
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= [tuple(merge.split()[:2]) for merge in merges]
									SCREAMING_SNAKE_CASE_:				Tuple							= dict(zip(lowerCamelCase__     ,		range(len(lowerCamelCase__))))
									SCREAMING_SNAKE_CASE_:				str							= {}



			@property
			def  _SCREAMING_SNAKE_CASE    (				self						:  int):
									return len(self.encoder)



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



			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]     ,		lowerCAmelCase__						:  Optional[int]     ,		lowerCAmelCase__						:  Tuple):
									if lang not in self.cache_moses_tokenizer:
															SCREAMING_SNAKE_CASE_:				Any							= self.sm.MosesTokenizer(lang=lowerCamelCase__)
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= moses_tokenizer
									return self.cache_moses_tokenizer[lang].tokenize(
									    lowerCamelCase__     ,		aggressive_dash_splits=lowerCamelCase__     ,		return_str=lowerCamelCase__     ,		escape=lowerCamelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]     ,		lowerCAmelCase__						:  Any     ,		lowerCAmelCase__						:  Union[str, Any]):
									if lang not in self.cache_moses_detokenizer:
															SCREAMING_SNAKE_CASE_:				str							= self.sm.MosesDetokenizer(lang=lowerCamelCase__)
															SCREAMING_SNAKE_CASE_:				List[Any]							= moses_detokenizer
									return self.cache_moses_detokenizer[lang].detokenize(lowerCamelCase__)



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[Any]     ,		lowerCAmelCase__						:  Optional[int]):
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= tuple(token[:-1]) + (token[-1] + "</w>",)
									if token in self.cache:
															return self.cache[token]
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= get_pairs(lowerCamelCase__)

									if not pairs:
															return token + "</w>"

									while True:
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= min(lowerCamelCase__     ,		key=lambda lowerCAmelCase__: self.bpe_ranks.get(lowerCamelCase__     ,		float("inf")))
															if bigram not in self.bpe_ranks:
																					break
															SCREAMING_SNAKE_CASE_:				Optional[int]							= bigram
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= []
															SCREAMING_SNAKE_CASE_:				List[str]							= 0
															while i < len(lowerCamelCase__):
																					try:
																											SCREAMING_SNAKE_CASE_:				int							= word.index(lowerCamelCase__     ,		lowerCamelCase__)
																					except ValueError:
																											new_word.extend(word[i:])
																											break
																					else:
																											new_word.extend(word[i:j])
																											SCREAMING_SNAKE_CASE_:				int							= 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
															SCREAMING_SNAKE_CASE_:				Any							= tuple(lowerCamelCase__)
															SCREAMING_SNAKE_CASE_:				Any							= new_word
															if len(lowerCamelCase__) == 1:
																					break
															else:
																					SCREAMING_SNAKE_CASE_:				Any							= get_pairs(lowerCamelCase__)
									SCREAMING_SNAKE_CASE_:				Tuple							= " ".join(lowerCamelCase__)
									if word == "\n  </w>":
															SCREAMING_SNAKE_CASE_:				Tuple							= "\n</w>"
									SCREAMING_SNAKE_CASE_:				Optional[int]							= word
									return word



			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict     ,		lowerCAmelCase__						:  str     ,		lowerCAmelCase__						:  List[str]=False):
									if bypass_tokenizer:
															SCREAMING_SNAKE_CASE_:				List[str]							= text.split()
									else:
															SCREAMING_SNAKE_CASE_:				int							= self.moses_tokenize(lowerCamelCase__     ,		self.lang)

									SCREAMING_SNAKE_CASE_:				str							= []
									for token in text:
															if token:
																					split_tokens.extend(list(self.bpe(lowerCamelCase__).split(" ")))

									return split_tokens



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



			def  _SCREAMING_SNAKE_CASE    (				self						:  str     ,		lowerCAmelCase__						:  Optional[Any]):
									return self.decoder.get(lowerCamelCase__     ,		self.unk_token)



			def  _SCREAMING_SNAKE_CASE    (				self						:  int     ,		lowerCAmelCase__						:  Optional[int]):
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= [t.replace(" "     ,		"").replace("</w>"     ,		" ") for t in tokens]
									SCREAMING_SNAKE_CASE_:				Any							= "".join(lowerCamelCase__).split()
									# detokenize
									SCREAMING_SNAKE_CASE_:				Any							= self.moses_detokenize(lowerCamelCase__     ,		self.lang)
									return text



			def  _SCREAMING_SNAKE_CASE    (				self						:  Union[str, Any]     ,		lowerCAmelCase__						:  List[str]     ,		lowerCAmelCase__						:  Dict = None):
									if token_ids_a is None:
															return [self.sep_token_id] + token_ids_a
									SCREAMING_SNAKE_CASE_:				str							= [self.sep_token_id]
									return sep + token_ids_a + sep + token_ids_a



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]     ,		lowerCAmelCase__						:  str     ,		lowerCAmelCase__						:  Any = None     ,		lowerCAmelCase__						:  str = 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__)
									# no bos used in fairseq
									if token_ids_a is not None:
															return [1] + ([0] * len(lowerCamelCase__)) + [1] + ([0] * len(lowerCamelCase__))
									return [1] + ([0] * len(lowerCamelCase__))



			def  _SCREAMING_SNAKE_CASE    (				self						:  Any     ,		lowerCAmelCase__						:  Any     ,		lowerCAmelCase__						:  Any = None):
									SCREAMING_SNAKE_CASE_:				List[str]							= [self.sep_token_id]

									# no bos used in fairseq
									if token_ids_a is None:
															return len(token_ids_a + sep) * [0]
									return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]     ,		lowerCAmelCase__						:  Tuple     ,		lowerCAmelCase__						:  Optional[int] = None):
									if not os.path.isdir(lowerCamelCase__):
															logger.error(F"Vocabulary path ({save_directory}) should be a directory")
															return
									SCREAMING_SNAKE_CASE_:				Optional[int]							= os.path.join(
									    lowerCamelCase__     ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= 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")

									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= 0
									with open(lowerCamelCase__     ,		"w"     ,		encoding="utf-8") as writer:
															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!")
																											SCREAMING_SNAKE_CASE_:				Optional[Any]							= token_index
																					writer.write(" ".join(lowerCamelCase__) + "\n")
																					index += 1

									return vocab_file, merge_file



			def __getstate__(				self						:  Optional[int]):
									SCREAMING_SNAKE_CASE_:				List[str]							= self.__dict__.copy()
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= None
									return state




			def __setstate__(				self						:  Optional[Any]     ,		lowerCAmelCase__						:  Dict):
									SCREAMING_SNAKE_CASE_:				List[str]							= d

									try:
															import sacremoses
									except ImportError:
															raise ImportError(
															    "You need to install sacremoses to use XLMTokenizer. "
															    "See https://pypi.org/project/sacremoses/ for installation.")

									SCREAMING_SNAKE_CASE_:				List[str]							= sacremoses









import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp







def     A_   (					):
						SCREAMING_SNAKE_CASE_:				Tuple							= ArgumentParser(
						    description=(
						        "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
						    )					)

						# Optional arguments for the launch helper
						parser.add_argument("--num_cores"       ,			type=_UpperCAmelCase       ,			default=1       ,			help="Number of TPU cores to use (1 or 8)."					)

						# positional
						parser.add_argument(
						    "training_script"       ,			type=_UpperCAmelCase       ,			help=(
						        "The full path to the single TPU training "
						        "program/script to be launched in parallel, "
						        "followed by all the arguments for the "
						        "training script"
						    )       ,			)

						# rest from the training program
						parser.add_argument("training_script_args"       ,			nargs=_UpperCAmelCase					)

						return parser.parse_args()







def     A_   (					):
						SCREAMING_SNAKE_CASE_:				List[str]							= parse_args()

						# Import training_script as a module.
						SCREAMING_SNAKE_CASE_:				Tuple							= Path(args.training_script					)
						sys.path.append(str(script_fpath.parent.resolve()					)					)
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= script_fpath.stem
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= importlib.import_module(_UpperCAmelCase					)

						# Patch sys.argv
						SCREAMING_SNAKE_CASE_:				int							= [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores					)]

						xmp.spawn(mod._mp_fn       ,			args=()       ,			nprocs=args.num_cores					)


if __name__ == "__main__":
						main()






