




"""simple docstring"""

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

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


__A							=     logging.get_logger(__name__)

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

__A							=     {
    "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"},
}

__A							=     {
    "microsoft/biogpt": 1_024,
}



def 					UpperCamelCase__       (						lowercase__					:       Optional[int]				):
		snake_case						:  Tuple        =			set()
		snake_case						:  Tuple        =			word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)				)
				snake_case						:  Optional[Any]        =			char
		return pairs
class      lowerCamelCase__      (					lowerCamelCase_     ):
			a__	: str        =       VOCAB_FILES_NAMES
			a__	: List[Any]        =       PRETRAINED_VOCAB_FILES_MAP
			a__	: Optional[int]        =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a__	: Union[str, Any]        =       ["""input_ids""", """attention_mask"""]


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



					"""simple docstring"""







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

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


			@property
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					return len(self.encoder							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if lang not in self.cache_moses_tokenizer:
							snake_case						:  Dict        =			self.sm.MosesTokenizer(lang=SCREAMING_SNAKE_CASE							)
							snake_case						:  List[str]        =			moses_tokenizer
					return self.cache_moses_tokenizer[lang].tokenize(
					    SCREAMING_SNAKE_CASE			,	aggressive_dash_splits=SCREAMING_SNAKE_CASE			,	return_str=SCREAMING_SNAKE_CASE			,	escape=SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if lang not in self.cache_moses_detokenizer:
							snake_case						:  Optional[int]        =			self.sm.MosesDetokenizer(lang=SCREAMING_SNAKE_CASE							)
							snake_case						:  Optional[Any]        =			moses_detokenizer
					return self.cache_moses_detokenizer[lang].detokenize(SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			tuple(token[:-1]							) + (token[-1] + "</w>",)
					if token in self.cache:
							return self.cache[token]
					snake_case						:  str        =			get_pairs(SCREAMING_SNAKE_CASE							)

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

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

									if word[i] == first and i < len(SCREAMING_SNAKE_CASE							) - 1 and word[i + 1] == second:
											new_word.append(first + second							)
											i += 2
									else:
											new_word.append(word[i]							)
											i += 1
							snake_case						:  Optional[int]        =			tuple(SCREAMING_SNAKE_CASE							)
							snake_case						:  Optional[Any]        =			new_word
							if len(SCREAMING_SNAKE_CASE							) == 1:
									break
							else:
									snake_case						:  Union[str, Any]        =			get_pairs(SCREAMING_SNAKE_CASE							)
					snake_case						:  Optional[int]        =			" ".join(SCREAMING_SNAKE_CASE							)
					if word == "\n  </w>":
							snake_case						:  Tuple        =			"\n</w>"
					snake_case						:  Union[str, Any]        =			word
					return word


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=False							):



					"""simple docstring"""







					if bypass_tokenizer:
							snake_case						:  Dict        =			text.split()
					else:
							snake_case						:  int        =			self.moses_tokenize(SCREAMING_SNAKE_CASE			,	self.lang							)

					snake_case						:  str        =			[]
					for token in text:
							if token:
									split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE							).split(" "							)							)							)

					return split_tokens


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			[t.replace(" "			,	""							).replace("</w>"			,	" "							) for t in tokens]
					snake_case						:  Dict        =			"".join(SCREAMING_SNAKE_CASE							).split()
					# detokenize
					snake_case						:  str        =			self.moses_detokenize(SCREAMING_SNAKE_CASE			,	self.lang							)
					return text


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = None							):



					"""simple docstring"""







					if token_ids_a is None:
							return [self.sep_token_id] + token_ids_a
					snake_case						:  Union[str, Any]        =			[self.sep_token_id]
					return sep + token_ids_a + sep + token_ids_a


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = False							):



					"""simple docstring"""







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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = None							):



					"""simple docstring"""







					snake_case						:  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 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = None							):



					"""simple docstring"""







					if not os.path.isdir(SCREAMING_SNAKE_CASE							):
							logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
							return
					snake_case						:  List[Any]        =			os.path.join(
					    SCREAMING_SNAKE_CASE			,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)
					snake_case						:  Optional[int]        =			os.path.join(
					    SCREAMING_SNAKE_CASE			,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]							)

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

					snake_case						:  Dict        =			0
					with open(SCREAMING_SNAKE_CASE			,	"w"			,	encoding="utf-8"							) as writer:
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,	key=lambda SCREAMING_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!"							)
											snake_case						:  Optional[int]        =			token_index
									writer.write(" ".join(SCREAMING_SNAKE_CASE							) + "\n"							)
									index += 1

					return vocab_file, merge_file


			def __getstate__(       self							):



					"""simple docstring"""







					snake_case						:  int        =			self.__dict__.copy()
					snake_case						:  Optional[int]        =			None
					return state



			def __setstate__(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Dict        =			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						:  List[str]        =			sacremoses







"""simple docstring"""

from typing import TYPE_CHECKING

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


__A							=     {"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "GPTJForCausalLM",
							    "GPTJForQuestionAnswering",
							    "GPTJForSequenceClassification",
							    "GPTJModel",
							    "GPTJPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "TFGPTJForCausalLM",
							    "TFGPTJForQuestionAnswering",
							    "TFGPTJForSequenceClassification",
							    "TFGPTJModel",
							    "TFGPTJPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "FlaxGPTJForCausalLM",
							    "FlaxGPTJModel",
							    "FlaxGPTJPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gptj import (
														    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTJForCausalLM,
														    GPTJForQuestionAnswering,
														    GPTJForSequenceClassification,
														    GPTJModel,
														    GPTJPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_gptj import (
														    TFGPTJForCausalLM,
														    TFGPTJForQuestionAnswering,
														    TFGPTJForSequenceClassification,
														    TFGPTJModel,
														    TFGPTJPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
							import sys

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

