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

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


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE__							=    {
    """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"""},
}

SCREAMING_SNAKE_CASE__							=    {
    """microsoft/biogpt""": 1024,
}




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				List[Any]      )					->						Any:
				__lowercase								= set()
				__lowercase								= word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)      )
								__lowercase								= char
				return pairs
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Optional[Any]         =							VOCAB_FILES_NAMES
							lowerCAmelCase__		:						Dict         =							PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase__		:						int         =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase__		:						Union[str, Any]         =							["input_ids", "attention_mask"]





							def __init__(			self  :       Any					,			_UpperCAmelCase  :       List[Any]					,			_UpperCAmelCase  :       Optional[Any]					,			_UpperCAmelCase  :       List[Any]="<unk>"					,			_UpperCAmelCase  :       str="<s>"					,			_UpperCAmelCase  :       List[str]="</s>"					,			_UpperCAmelCase  :       Tuple="</s>"					,			_UpperCAmelCase  :       Tuple="<pad>"					,			**_UpperCAmelCase  :       Optional[Any]					,			)						->		Optional[Any]:



											"""simple docstring"""


											super().__init__(
											    bos_token=_UpperCAmelCase					,			eos_token=_UpperCAmelCase					,			sep_token=_UpperCAmelCase					,			unk_token=_UpperCAmelCase					,			pad_token=_UpperCAmelCase					,			**_UpperCAmelCase					,			)

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

											__lowercase								= 'en'
											__lowercase								= sacremoses
											# cache of sm.MosesTokenizer instance
											__lowercase								= {}
											__lowercase								= {}
											with open(_UpperCAmelCase					,			encoding='utf-8'    ) as vocab_handle:
															__lowercase								= json.load(_UpperCAmelCase    )
											__lowercase								= {v: k for k, v in self.encoder.items()}
											with open(_UpperCAmelCase					,			encoding='utf-8'    ) as merges_handle:
															__lowercase								= merges_handle.read().split('\n'    )[:-1]
											__lowercase								= [tuple(merge.split()[:2]    ) for merge in merges]
											__lowercase								= dict(zip(_UpperCAmelCase					,			range(len(_UpperCAmelCase    )    )    )    )
											__lowercase								= {}





							@property
							def       a__			(			self  :       int    )						->		Tuple:



											"""simple docstring"""


											return len(self.encoder    )





							def       a__			(			self  :       str    )						->		List[str]:



											"""simple docstring"""


											return dict(self.encoder					,			**self.added_tokens_encoder    )





							def       a__			(			self  :       Tuple					,			_UpperCAmelCase  :       List[Any]					,			_UpperCAmelCase  :       Tuple    )						->		Tuple:



											"""simple docstring"""


											if lang not in self.cache_moses_tokenizer:
															__lowercase								= self.sm.MosesTokenizer(lang=_UpperCAmelCase    )
															__lowercase								= moses_tokenizer
											return self.cache_moses_tokenizer[lang].tokenize(
											    _UpperCAmelCase					,			aggressive_dash_splits=_UpperCAmelCase					,			return_str=_UpperCAmelCase					,			escape=_UpperCAmelCase    )





							def       a__			(			self  :       Optional[int]					,			_UpperCAmelCase  :       int					,			_UpperCAmelCase  :       Dict    )						->		str:



											"""simple docstring"""


											if lang not in self.cache_moses_detokenizer:
															__lowercase								= self.sm.MosesDetokenizer(lang=_UpperCAmelCase    )
															__lowercase								= moses_detokenizer
											return self.cache_moses_detokenizer[lang].detokenize(_UpperCAmelCase    )





							def       a__			(			self  :       Tuple					,			_UpperCAmelCase  :       int    )						->		int:



											"""simple docstring"""


											__lowercase								= tuple(token[:-1]    ) + (token[-1] + '</w>',)
											if token in self.cache:
															return self.cache[token]
											__lowercase								= get_pairs(_UpperCAmelCase    )

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

											while True:
															__lowercase								= min(_UpperCAmelCase					,			key=lambda _UpperCAmelCase    : self.bpe_ranks.get(_UpperCAmelCase					,			float('inf'    )    )    )
															if bigram not in self.bpe_ranks:
																			break
															__lowercase		, __lowercase								= bigram
															__lowercase								= []
															__lowercase								= 0
															while i < len(_UpperCAmelCase    ):
																			try:
																							__lowercase								= word.index(_UpperCAmelCase					,			_UpperCAmelCase    )
																			except ValueError:
																							new_word.extend(word[i:]    )
																							break
																			else:
																							new_word.extend(word[i:j]    )
																							__lowercase								= j

																			if word[i] == first and i < len(_UpperCAmelCase    ) - 1 and word[i + 1] == second:
																							new_word.append(first + second    )
																							i += 2
																			else:
																							new_word.append(word[i]    )
																							i += 1
															__lowercase								= tuple(_UpperCAmelCase    )
															__lowercase								= new_word
															if len(_UpperCAmelCase    ) == 1:
																			break
															else:
																			__lowercase								= get_pairs(_UpperCAmelCase    )
											__lowercase								= ' '.join(_UpperCAmelCase    )
											if word == "\n  </w>":
															__lowercase								= '\n</w>'
											__lowercase								= word
											return word





							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       List[str]					,			_UpperCAmelCase  :       Union[str, Any]=False    )						->		Optional[Any]:



											"""simple docstring"""


											if bypass_tokenizer:
															__lowercase								= text.split()
											else:
															__lowercase								= self.moses_tokenize(_UpperCAmelCase					,			self.lang    )

											__lowercase								= []
											for token in text:
															if token:
																			split_tokens.extend(list(self.bpe(_UpperCAmelCase    ).split(' '    )    )    )

											return split_tokens





							def       a__			(			self  :       str					,			_UpperCAmelCase  :       Any    )						->		List[Any]:



											"""simple docstring"""


											return self.encoder.get(_UpperCAmelCase					,			self.encoder.get(self.unk_token    )    )





							def       a__			(			self  :       Dict					,			_UpperCAmelCase  :       List[Any]    )						->		str:



											"""simple docstring"""


											return self.decoder.get(_UpperCAmelCase					,			self.unk_token    )





							def       a__			(			self  :       int					,			_UpperCAmelCase  :       Tuple    )						->		List[Any]:



											"""simple docstring"""


											__lowercase								= [t.replace(' '					,			''    ).replace('</w>'					,			' '    ) for t in tokens]
											__lowercase								= ''.join(_UpperCAmelCase    ).split()
											# detokenize
											__lowercase								= self.moses_detokenize(_UpperCAmelCase					,			self.lang    )
											return text





							def       a__			(			self  :       Dict					,			_UpperCAmelCase  :       List[int]					,			_UpperCAmelCase  :       Optional[List[int]] = None    )						->		List[int]:



											"""simple docstring"""


											if token_ids_a is None:
															return [self.sep_token_id] + token_ids_a
											__lowercase								= [self.sep_token_id]
											return sep + token_ids_a + sep + token_ids_a





							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       List[int]					,			_UpperCAmelCase  :       Optional[List[int]] = None					,			_UpperCAmelCase  :       bool = False    )						->		List[int]:



											"""simple docstring"""


											if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=_UpperCAmelCase					,			token_ids_a=_UpperCAmelCase					,			already_has_special_tokens=_UpperCAmelCase    )
											# no bos used in fairseq
											if token_ids_a is not None:
															return [1] + ([0] * len(_UpperCAmelCase    )) + [1] + ([0] * len(_UpperCAmelCase    ))
											return [1] + ([0] * len(_UpperCAmelCase    ))





							def       a__			(			self  :       List[str]					,			_UpperCAmelCase  :       List[int]					,			_UpperCAmelCase  :       Optional[List[int]] = None    )						->		List[int]:



											"""simple docstring"""


											__lowercase								= [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  :       int					,			_UpperCAmelCase  :       str					,			_UpperCAmelCase  :       Optional[str] = None    )						->		Tuple[str]:



											"""simple docstring"""


											if not os.path.isdir(_UpperCAmelCase    ):
															logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""    )
															return
											__lowercase								= os.path.join(
											    _UpperCAmelCase					,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
											__lowercase								= os.path.join(
											    _UpperCAmelCase					,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']    )

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

											__lowercase								= 0
											with open(_UpperCAmelCase					,			'w'					,			encoding='utf-8'    ) as writer:
															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!'    )
																							__lowercase								= token_index
																			writer.write(' '.join(_UpperCAmelCase    ) + '\n'    )
																			index += 1

											return vocab_file, merge_file





							def __getstate__(			self  :       Dict    )						->		List[Any]:



											"""simple docstring"""


											__lowercase								= self.__dict__.copy()
											__lowercase								= None
											return state





							def __setstate__(			self  :       Optional[Any]					,			_UpperCAmelCase  :       List[Any]    )						->		Optional[Any]:



											"""simple docstring"""


											__lowercase								= d

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

											__lowercase								= sacremoses







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__							=    {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__							=    [
							    """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """HubertForCTC""",
							    """HubertForSequenceClassification""",
							    """HubertModel""",
							    """HubertPreTrainedModel""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__							=    [
							    """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFHubertForCTC""",
							    """TFHubertModel""",
							    """TFHubertPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_hubert import (
														    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    HubertForCTC,
														    HubertForSequenceClassification,
														    HubertModel,
														    HubertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_hubert import (
														    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFHubertForCTC,
														    TFHubertModel,
														    TFHubertPreTrainedModel,
														)


else:
							import sys

							SCREAMING_SNAKE_CASE__							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





