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

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


lowercase						:      int							=							logging.get_logger(__name__)

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

lowercase						:      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"""},
}

lowercase						:      str							=							{
    """microsoft/biogpt""": 1024,
}







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Dict:
							lowercase							:  int 			=   set()
							lowercase							:  Any 			=   word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)       )
														lowercase							:  List[str] 			=   char
							return pairs




class __snake_case	(   __UpperCAmelCase  ):
				_a	:		Tuple=       VOCAB_FILES_NAMES
				_a	:		Dict=       PRETRAINED_VOCAB_FILES_MAP
				_a	:		Union[str, Any]=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_a	:		List[str]=       ["input_ids", "attention_mask"]
				def __init__(						self	,snake_case	,snake_case	,snake_case="<unk>"	,snake_case="<s>"	,snake_case="</s>"	,snake_case="</s>"	,snake_case="<pad>"	,**snake_case	,):



											'''simple docstring'''







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

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

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



											'''simple docstring'''







											return len(self.encoder   )
				def    _SCREAMING_SNAKE_CASE (						self   ):



											'''simple docstring'''







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



											'''simple docstring'''







											if lang not in self.cache_moses_tokenizer:
																		lowercase							:  Union[str, Any] 			=   self.sm.MosesTokenizer(lang=lowercase_   )
																		lowercase							:  List[Any] 			=   moses_tokenizer
											return self.cache_moses_tokenizer[lang].tokenize(
											    lowercase_	,aggressive_dash_splits=lowercase_	,return_str=lowercase_	,escape=lowercase_   )
				def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case   ):



											'''simple docstring'''







											if lang not in self.cache_moses_detokenizer:
																		lowercase							:  Dict 			=   self.sm.MosesDetokenizer(lang=lowercase_   )
																		lowercase							:  List[Any] 			=   moses_detokenizer
											return self.cache_moses_detokenizer[lang].detokenize(lowercase_   )
				def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



											'''simple docstring'''







											lowercase							:  List[str] 			=   tuple(token[:-1]   ) + (token[-1] + '''</w>''',)
											if token in self.cache:
																		return self.cache[token]
											lowercase							:  List[Any] 			=   get_pairs(lowercase_   )

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

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

																									if word[i] == first and i < len(lowercase_   ) - 1 and word[i + 1] == second:
																																new_word.append(first + second   )
																																i += 2
																									else:
																																new_word.append(word[i]   )
																																i += 1
																		lowercase							:  int 			=   tuple(lowercase_   )
																		lowercase							:  List[str] 			=   new_word
																		if len(lowercase_   ) == 1:
																									break
																		else:
																									lowercase							:  Optional[Any] 			=   get_pairs(lowercase_   )
											lowercase							:  int 			=   ''' '''.join(lowercase_   )
											if word == "\n  </w>":
																		lowercase							:  int 			=   '''\n</w>'''
											lowercase							:  Any 			=   word
											return word
				def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case=False   ):



											'''simple docstring'''







											if bypass_tokenizer:
																		lowercase							:  Dict 			=   text.split()
											else:
																		lowercase							:  Any 			=   self.moses_tokenize(lowercase_	,self.lang   )

											lowercase							:  Any 			=   []
											for token in text:
																		if token:
																									split_tokens.extend(list(self.bpe(lowercase_   ).split(""" """   )   )   )

											return split_tokens
				def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



											'''simple docstring'''







											return self.encoder.get(lowercase_	,self.encoder.get(self.unk_token   )   )
				def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



											'''simple docstring'''







											return self.decoder.get(lowercase_	,self.unk_token   )
				def    _SCREAMING_SNAKE_CASE (						self	,snake_case   ):



											'''simple docstring'''







											lowercase							:  Dict 			=   [t.replace(""" """	,""""""   ).replace("""</w>"""	,""" """   ) for t in tokens]
											lowercase							:  str 			=   ''''''.join(lowercase_   ).split()
											# detokenize
											lowercase							:  Union[str, Any] 			=   self.moses_detokenize(lowercase_	,self.lang   )
											return text
				def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case = None   ):



											'''simple docstring'''







											if token_ids_a is None:
																		return [self.sep_token_id] + token_ids_a
											lowercase							:  Any 			=   [self.sep_token_id]
											return sep + token_ids_a + sep + token_ids_a
				def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case = None	,snake_case = False   ):



											'''simple docstring'''







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



											'''simple docstring'''







											lowercase							:  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	,snake_case	,snake_case = None   ):



											'''simple docstring'''







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

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

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

											return vocab_file, merge_file
				def __getstate__(						self   ):



											'''simple docstring'''







											lowercase							:  List[str] 			=   self.__dict__.copy()
											lowercase							:  Any 			=   None
											return state
				def __setstate__(						self	,snake_case   ):



											'''simple docstring'''







											lowercase							:  Optional[Any] 			=   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							:  Optional[int] 			=   sacremoses


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase						:      Any							=							{
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      List[Any]							=							["""GPT2TokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      str							=							[
      """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPT2DoubleHeadsModel""",
      """GPT2ForQuestionAnswering""",
      """GPT2ForSequenceClassification""",
      """GPT2ForTokenClassification""",
      """GPT2LMHeadModel""",
      """GPT2Model""",
      """GPT2PreTrainedModel""",
      """load_tf_weights_in_gpt2""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      Tuple							=							[
      """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFGPT2DoubleHeadsModel""",
      """TFGPT2ForSequenceClassification""",
      """TFGPT2LMHeadModel""",
      """TFGPT2MainLayer""",
      """TFGPT2Model""",
      """TFGPT2PreTrainedModel""",
  ]

try:
  if not is_keras_nlp_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      List[str]							=							["""TFGPT2Tokenizer"""]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase						:      str							=							["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
  from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
  from .tokenization_gpta import GPTaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_fast import GPTaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpta import (
        GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTaDoubleHeadsModel,
        GPTaForQuestionAnswering,
        GPTaForSequenceClassification,
        GPTaForTokenClassification,
        GPTaLMHeadModel,
        GPTaModel,
        GPTaPreTrainedModel,
        load_tf_weights_in_gpta,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_gpta import (
        TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFGPTaDoubleHeadsModel,
        TFGPTaForSequenceClassification,
        TFGPTaLMHeadModel,
        TFGPTaMainLayer,
        TFGPTaModel,
        TFGPTaPreTrainedModel,
    )

  try:
    if not is_keras_nlp_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_tf import TFGPTaTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
  import sys

  lowercase						:      Union[str, Any]							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
