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

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


snake_case				:	Any							   =						logging.get_logger(__name__)

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

snake_case				:	List[str]							   =						{
    "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"},
}

snake_case				:	Optional[Any]							   =						{
    "microsoft/biogpt": 1_024,
}






def 						lowerCAmelCase_		(      _snake_case   :						Optional[int]     )       ->	Any:







	'''simple docstring'''


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





class    _snake_case      (  snake_case       ):
			UpperCamelCase__					   =					VOCAB_FILES_NAMES
			UpperCamelCase__					   =					PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__					   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__					   =					['input_ids', 'attention_mask']







			def __init__(       self		,     _a		,     _a		,     _a="<unk>"		,     _a="<s>"		,     _a="</s>"		,     _a="</s>"		,     _a="<pad>"		,     **_a		,     ):
				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."	)

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







			@property
			def     SCREAMING_SNAKE_CASE					(       self	):
				return len(self.encoder	)







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







			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a	):
				if lang not in self.cache_moses_tokenizer:
					__magic_name__	:	Optional[int]			     =  self.sm.MosesTokenizer(lang=_a	)
					__magic_name__	:	Optional[Any]			     =  moses_tokenizer
				return self.cache_moses_tokenizer[lang].tokenize(
				    _a		,     aggressive_dash_splits=_a		,     return_str=_a		,     escape=_a	)







			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a	):
				if lang not in self.cache_moses_detokenizer:
					__magic_name__	:	List[Any]			     =  self.sm.MosesDetokenizer(lang=_a	)
					__magic_name__	:	Any			     =  moses_detokenizer
				return self.cache_moses_detokenizer[lang].detokenize(_a	)







			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
				__magic_name__	:	List[Any]			     =  tuple(token[:-1]	) + (token[-1] + "</w>",)
				if token in self.cache:
					return self.cache[token]
				__magic_name__	:	int			     =  get_pairs(_a	)

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

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







			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a=False	):
				if bypass_tokenizer:
					__magic_name__	:	Dict			     =  text.split()
				else:
					__magic_name__	:	Any			     =  self.moses_tokenize(_a		,     self.lang	)

				__magic_name__	:	Dict			     =  []
				for token in text:
					if token:
						split_tokens.extend(list(self.bpe(_a	).split(" "	)	)	)

				return split_tokens







			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
				return self.encoder.get(_a		,     self.encoder.get(self.unk_token	)	)







			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
				return self.decoder.get(_a		,     self.unk_token	)







			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
				__magic_name__	:	Any			     =  [t.replace(" "		,     ""	).replace("</w>"		,     " "	) for t in tokens]
				__magic_name__	:	Optional[Any]			     =  "".join(_a	).split()
				# detokenize
				__magic_name__	:	List[Any]			     =  self.moses_detokenize(_a		,     self.lang	)
				return text







			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
				if token_ids_a is None:
					return [self.sep_token_id] + token_ids_a
				__magic_name__	:	str			     =  [self.sep_token_id]
				return sep + token_ids_a + sep + token_ids_a







			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None		,     _a = False	):
				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     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
				__magic_name__	:	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     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
				if not os.path.isdir(_a	):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
					return
				__magic_name__	:	str			     =  os.path.join(
				    _a		,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
				__magic_name__	:	List[Any]			     =  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"	)

				__magic_name__	:	Optional[int]			     =  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!"	)
							__magic_name__	:	int			     =  token_index
						writer.write(" ".join(_a	) + "\n"	)
						index += 1

				return vocab_file, merge_file







			def __getstate__(       self	):
				__magic_name__	:	str			     =  self.__dict__.copy()
				__magic_name__	:	List[Any]			     =  None
				return state







			def __setstate__(       self		,     _a	):
				__magic_name__	:	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."	)

				__magic_name__	:	Tuple			     =  sacremoses


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


snake_case				:	Optional[int]							   =						{
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case				:	Optional[int]							   =						[
				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "MobileViTV2ForImageClassification",
				    "MobileViTV2ForSemanticSegmentation",
				    "MobileViTV2Model",
				    "MobileViTV2PreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileViTVaConfig,
				    MobileViTVaOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilevitva import (
								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileViTVaForImageClassification,
								    MobileViTVaForSemanticSegmentation,
								    MobileViTVaModel,
								    MobileViTVaPreTrainedModel,
								)

else:
				import sys

				snake_case				:	Tuple							   =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
