




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

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


lowercase__    :					List[str]					=						logging.get_logger(__name__)

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

lowercase__    :					Optional[int]					=						{
    "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__    :					Optional[Any]					=						{
    "microsoft/biogpt": 1024,
}


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	snake_case_		   =					set()
	snake_case_		   =					word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)							)
		snake_case_		   =					char
	return pairs






class 		UpperCAmelCase       (    UpperCAmelCase__      ):







						'''simple docstring'''





						lowerCAmelCase_							=       VOCAB_FILES_NAMES
						lowerCAmelCase_							=       PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase_							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase_							=       ['''input_ids''', '''attention_mask''']






						def __init__(						self							:					Union[str, Any]	,     __lowercase							:					Dict	,     __lowercase							:					Tuple	,     __lowercase							:					List[Any]="<unk>"	,     __lowercase							:					Tuple="<s>"	,     __lowercase							:					Tuple="</s>"	,     __lowercase							:					Union[str, Any]="</s>"	,     __lowercase							:					str="<pad>"	,     **__lowercase							:					List[Any]	,     ):






							"""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."						)

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






						@property
						def 	snake_case__						(						self							:					List[Any]						):






							"""simple docstring"""






							return len(self.encoder						)






						def 	snake_case__						(						self							:					Optional[int]						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					int	,     __lowercase							:					Dict	,     __lowercase							:					int						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					str	,     __lowercase							:					List[str]						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					Tuple	,     __lowercase							:					Dict						):






							"""simple docstring"""






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

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

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






						def 	snake_case__						(						self							:					Optional[Any]	,     __lowercase							:					int	,     __lowercase							:					Dict=False						):






							"""simple docstring"""






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

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

							return split_tokens






						def 	snake_case__						(						self							:					Optional[int]	,     __lowercase							:					List[str]						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					List[Any]	,     __lowercase							:					List[str]						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					Union[str, Any]	,     __lowercase							:					str						):






							"""simple docstring"""






							snake_case_		   =					[t.replace(" "	,     ""						).replace("</w>"	,     " "						) for t in tokens]
							snake_case_		   =					"".join(__lowercase						).split()
							# detokenize
							snake_case_		   =					self.moses_detokenize(__lowercase	,     self.lang						)
							return text






						def 	snake_case__						(						self							:					Optional[Any]	,     __lowercase							:					List[int]	,     __lowercase							:					Optional[List[int]] = None						):






							"""simple docstring"""






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






						def 	snake_case__						(						self							:					str	,     __lowercase							:					List[int]	,     __lowercase							:					Optional[List[int]] = None	,     __lowercase							:					bool = 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 	snake_case__						(						self							:					Optional[Any]	,     __lowercase							:					List[int]	,     __lowercase							:					Optional[List[int]] = None						):






							"""simple docstring"""






							snake_case_		   =					[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 	snake_case__						(						self							:					int	,     __lowercase							:					str	,     __lowercase							:					Optional[str] = None						):






							"""simple docstring"""






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

							snake_case_		   =					0
							with open(__lowercase	,     "w"	,     encoding="utf-8"						) as writer:
								for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,     key=lambda __lowercase						: 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!"						)
										snake_case_		   =					token_index
									writer.write(" ".join(__lowercase						) + "\n"						)
									index += 1

							return vocab_file, merge_file






						def __getstate__(						self							:					Tuple						):






							"""simple docstring"""






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






						def __setstate__(						self							:					List[str]	,     __lowercase							:					List[str]						):






							"""simple docstring"""






							snake_case_		   =					d

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

							snake_case_		   =					sacremoses






from typing import TYPE_CHECKING

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


lowercase__    :					List[str]					=						{
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__    :					Optional[Any]					=						["XLMProphetNetTokenizer"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__    :					List[Any]					=						[
		    "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "XLMProphetNetDecoder",
		    "XLMProphetNetEncoder",
		    "XLMProphetNetForCausalLM",
		    "XLMProphetNetForConditionalGeneration",
		    "XLMProphetNetModel",
		    "XLMProphetNetPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xlm_prophetnet import (
				    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XLMProphetNetDecoder,
				    XLMProphetNetEncoder,
				    XLMProphetNetForCausalLM,
				    XLMProphetNetForConditionalGeneration,
				    XLMProphetNetModel,
				    XLMProphetNetPreTrainedModel,
				)

else:
		import sys

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