





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

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


__lowercase							:Any							=	logging.get_logger(__name__)

__lowercase							:Dict							=	{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

__lowercase							:Dict							=	{
    "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"},
}

__lowercase							:List[str]							=	{
    "microsoft/biogpt": 1_024,
}







def    UpperCAmelCase				(  _lowerCamelCase   :	Optional[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       set()
						SCREAMING_SNAKE_CASE__  :					List[str] =       word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)  )
												SCREAMING_SNAKE_CASE__  :					List[Any] =       char
						return pairs



class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    VOCAB_FILES_NAMES
							snake_case_							=    PRETRAINED_VOCAB_FILES_MAP
							snake_case_							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case_							=    ["input_ids", "attention_mask"]

							def __init__(   self					:  int				,						a					:  List[Any]				,						a					:  Union[str, Any]				,						a					:  int="<unk>"				,						a					:  Dict="<s>"				,						a					:  List[str]="</s>"				,						a					:  List[str]="</s>"				,						a					:  int="<pad>"				,						**a					:  Dict				,						)						->Any:
													super().__init__(
													    bos_token=a				,						eos_token=a				,						sep_token=a				,						unk_token=a				,						pad_token=a				,						**a				,						)

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

							@property
							def   A_	(   self					:  Dict     )						->str:
													return len(self.encoder     )

							def   A_	(   self					:  Tuple     )						->str:
													return dict(self.encoder				,						**self.added_tokens_encoder     )

							def   A_	(   self					:  str				,						a					:  str				,						a					:  Dict     )						->Dict:
													if lang not in self.cache_moses_tokenizer:
																			SCREAMING_SNAKE_CASE__  :					str =       self.sm.MosesTokenizer(lang=a     )
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       moses_tokenizer
													return self.cache_moses_tokenizer[lang].tokenize(
													    a				,						aggressive_dash_splits=a				,						return_str=a				,						escape=a     )

							def   A_	(   self					:  Dict				,						a					:  List[str]				,						a					:  Tuple     )						->int:
													if lang not in self.cache_moses_detokenizer:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.sm.MosesDetokenizer(lang=a     )
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       moses_detokenizer
													return self.cache_moses_detokenizer[lang].detokenize(a     )

							def   A_	(   self					:  Dict				,						a					:  Optional[int]     )						->Dict:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       tuple(token[:-1]     ) + (token[-1] + "</w>",)
													if token in self.cache:
																			return self.cache[token]
													SCREAMING_SNAKE_CASE__  :					List[Any] =       get_pairs(a     )

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

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

																									if word[i] == first and i < len(a     ) - 1 and word[i + 1] == second:
																															new_word.append(first + second     )
																															i += 2
																									else:
																															new_word.append(word[i]     )
																															i += 1
																			SCREAMING_SNAKE_CASE__  :					Dict =       tuple(a     )
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       new_word
																			if len(a     ) == 1:
																									break
																			else:
																									SCREAMING_SNAKE_CASE__  :					Dict =       get_pairs(a     )
													SCREAMING_SNAKE_CASE__  :					List[str] =       " ".join(a     )
													if word == "\n  </w>":
																			SCREAMING_SNAKE_CASE__  :					str =       "\n</w>"
													SCREAMING_SNAKE_CASE__  :					Any =       word
													return word

							def   A_	(   self					:  Union[str, Any]				,						a					:  Optional[int]				,						a					:  List[str]=False     )						->List[Any]:
													if bypass_tokenizer:
																			SCREAMING_SNAKE_CASE__  :					str =       text.split()
													else:
																			SCREAMING_SNAKE_CASE__  :					str =       self.moses_tokenize(a				,						self.lang     )

													SCREAMING_SNAKE_CASE__  :					Tuple =       []
													for token in text:
																			if token:
																									split_tokens.extend(list(self.bpe(a     ).split(" "     )     )     )

													return split_tokens

							def   A_	(   self					:  List[Any]				,						a					:  List[str]     )						->Union[str, Any]:
													return self.encoder.get(a				,						self.encoder.get(self.unk_token     )     )

							def   A_	(   self					:  Dict				,						a					:  List[Any]     )						->Any:
													return self.decoder.get(a				,						self.unk_token     )

							def   A_	(   self					:  Optional[int]				,						a					:  Union[str, Any]     )						->Tuple:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       [t.replace(" "				,						""     ).replace("</w>"				,						" "     ) for t in tokens]
													SCREAMING_SNAKE_CASE__  :					Any =       "".join(a     ).split()
													# detokenize
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self.moses_detokenize(a				,						self.lang     )
													return text

							def   A_	(   self					:  Dict				,						a					:  List[int]				,						a					:  Optional[List[int]] = None     )						->List[int]:
													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   A_	(   self					:  Any				,						a					:  List[int]				,						a					:  Optional[List[int]] = None				,						a					:  bool = False     )						->List[int]:
													if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=a				,						token_ids_a=a				,						already_has_special_tokens=a     )
													# no bos used in fairseq
													if token_ids_a is not None:
																			return [1] + ([0] * len(a     )) + [1] + ([0] * len(a     ))
													return [1] + ([0] * len(a     ))

							def   A_	(   self					:  List[Any]				,						a					:  List[int]				,						a					:  Optional[List[int]] = None     )						->List[int]:
													SCREAMING_SNAKE_CASE__  :					Dict =       [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   A_	(   self					:  Optional[int]				,						a					:  str				,						a					:  Optional[str] = None     )						->Tuple[str]:
													if not os.path.isdir(a     ):
																			logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""     )
																			return
													SCREAMING_SNAKE_CASE__  :					Tuple =       os.path.join(
													    a				,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       os.path.join(
													    a				,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]     )

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

													SCREAMING_SNAKE_CASE__  :					List[str] =       0
													with open(a				,						"w"				,						encoding="utf-8"     ) as writer:
																			for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,						key=lambda a     : 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__  :					List[Any] =       token_index
																									writer.write(" ".join(a     ) + "\n"     )
																									index += 1

													return vocab_file, merge_file

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






							def __setstate__(   self					:  Optional[Any]				,						a					:  List[Any]     )						->Dict:
													SCREAMING_SNAKE_CASE__  :					int =       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 random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm



def     lowerCAmelCase		(		_lowerCAmelCase				: int    ,				_lowerCAmelCase				: bool = False						):




 """simple docstring"""







 if device_specific:
  seed += AcceleratorState().process_index
 random.seed(_lowerCAmelCase						)
 np.random.seed(_lowerCAmelCase						)
 torch.manual_seed(_lowerCAmelCase						)
 if is_xpu_available():
  torch.xpu.manual_seed_all(_lowerCAmelCase						)
 elif is_npu_available():
  torch.npu.manual_seed_all(_lowerCAmelCase						)
 else:
  torch.cuda.manual_seed_all(_lowerCAmelCase						)
 # ^^ safe to call this function even if cuda is not available
 if is_tpu_available():
  xm.set_rng_state(_lowerCAmelCase						)



def     lowerCAmelCase		(		_lowerCAmelCase				: Optional[RNGType] = None    ,				_lowerCAmelCase				: Optional[torch.Generator] = None						):




 """simple docstring"""







 if rng_type == RNGType.TORCH:
  UpperCAmelCase__							=    torch.get_rng_state()
 elif rng_type == RNGType.CUDA:
  UpperCAmelCase__							=    torch.cuda.get_rng_state()
 elif rng_type == RNGType.XLA:
  assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
  UpperCAmelCase__							=    torch.tensor(xm.get_rng_state()						)
 elif rng_type == RNGType.NPU:
  assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
  UpperCAmelCase__							=    torch.npu.get_rng_state()
 elif rng_type == RNGType.XPU:
  assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
  UpperCAmelCase__							=    torch.xpu.get_rng_state()
 elif rng_type == RNGType.GENERATOR:
  assert generator is not None, "Need a generator to synchronize its seed."
  UpperCAmelCase__							=    generator.get_state()

 # Broadcast the rng state from device 0 to other devices
 UpperCAmelCase__							=    AcceleratorState()
 if state.distributed_type == DistributedType.TPU:
  UpperCAmelCase__							=    rng_state.to(xm.xla_device()						)
  xm.collective_broadcast([rng_state]						)
  xm.mark_step()
  UpperCAmelCase__							=    rng_state.cpu()
 elif (
     state.distributed_type in CUDA_DISTRIBUTED_TYPES
     or state.distributed_type == DistributedType.MULTI_NPU
     or state.distributed_type == DistributedType.MULTI_XPU
 ):
  UpperCAmelCase__							=    rng_state.to(state.device						)
  torch.distributed.broadcast(_lowerCAmelCase    ,				0						)
  UpperCAmelCase__							=    rng_state.cpu()
 elif state.distributed_type == DistributedType.MULTI_CPU:
  torch.distributed.broadcast(_lowerCAmelCase    ,				0						)

 # Set the broadcast rng state
 if rng_type == RNGType.TORCH:
  torch.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.CUDA:
  torch.cuda.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.NPU:
  torch.npu.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.XPU:
  torch.xpu.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.XLA:
  xm.set_rng_state(rng_state.item()						)
 elif rng_type == RNGType.GENERATOR:
  generator.set_state(_lowerCAmelCase						)



def     lowerCAmelCase		(		_lowerCAmelCase				: List[Union[str, RNGType]]    ,				_lowerCAmelCase				: Optional[torch.Generator] = None						):




 """simple docstring"""







 for rng_type in rng_types:
  synchronize_rng_state(RNGType(_lowerCAmelCase						)    ,				generator=_lowerCAmelCase						)





