"""simple docstring"""


import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


UpperCAmelCase__  		=						logging.get_logger(__name__)

UpperCAmelCase__  		=						{"""vocab_file""": """vocab.txt""", """emoji_file""": """emoji.json"""}

UpperCAmelCase__  		=						{
    """vocab_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt""",
    },
    """emoji_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json""",
    },
}

UpperCAmelCase__  		=						{
    """abeja/gpt-neox-japanese-2.7b""": 2_0_4_8,
}






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







		"""simple docstring"""
		with open(lowercase   ,"""r"""   ,encoding="""utf-8"""		) as f:
				_UpperCAmelCase						 =      json.loads(f.read()		)

		_UpperCAmelCase						 =      collections.OrderedDict()
		_UpperCAmelCase						 =      collections.OrderedDict()
		_UpperCAmelCase						 =      collections.OrderedDict()
		with open(lowercase   ,"""r"""   ,encoding="""utf-8"""		) as f:
				_UpperCAmelCase						 =      f.readlines()
		_UpperCAmelCase						 =      [[t.rstrip("""\n"""		)] if (t == """,""" or """,""" not in t) else t.rstrip("""\n"""		).split(""","""		) for t in token]
		for idx, b in enumerate(lowercase		):
				_UpperCAmelCase						 =      b
				_UpperCAmelCase						 =      idx
				for wd in b:
						_UpperCAmelCase						 =      idx

		return vocab, raw_vocab, ids_to_tokens, emoji


class        a				(		lowerCAmelCase_   ):
				_snake_case     :  List[str]     							=							VOCAB_FILES_NAMES
				_snake_case     :  Union[str, Any]     							=							PRETRAINED_VOCAB_FILES_MAP
				_snake_case     :  Any     							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case     :  Tuple     							=							['input_ids', 'attention_mask']


				def __init__(					self	:  List[Any]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Tuple="<|endoftext|>"   ,				__lowerCAmelCase	:  List[Any]="<|endoftext|>"   ,				__lowerCAmelCase	:  str="<|startoftext|>"   ,				__lowerCAmelCase	:  int="<|endoftext|>"   ,				__lowerCAmelCase	:  int=False   ,				**__lowerCAmelCase	:  str   ,				):
						super().__init__(
						    unk_token=__lowerCAmelCase   ,				pad_token=__lowerCAmelCase   ,				bos_token=__lowerCAmelCase   ,				eos_token=__lowerCAmelCase   ,				do_clean_text=__lowerCAmelCase   ,				**__lowerCAmelCase   ,				)
						if not os.path.isfile(__lowerCAmelCase					):
								raise ValueError(
								    f'''Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'''
								    """ model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"""					)
						if not os.path.isfile(__lowerCAmelCase					):
								raise ValueError(
								    f'''Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'''
								    """ pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"""					)
						_UpperCAmelCase						 =      do_clean_text
						_UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase						 =      load_vocab_and_emoji(__lowerCAmelCase   ,				__lowerCAmelCase					)
						_UpperCAmelCase						 =      SubWordJapaneseTokenizer(
						    vocab=self.vocab   ,				ids_to_tokens=self.ids_to_tokens   ,				emoji=self.emoji					)


				@property
				def      lowerCAmelCase_  (					self	:  List[Any]					):
						# self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
						return len(self.raw_vocab					)


				def      lowerCAmelCase_  (					self	:  Tuple					):
						return dict(self.raw_vocab   ,				**self.added_tokens_encoder					)


				def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  Optional[Any]					):
						return self.subword_tokenizer.tokenize(__lowerCAmelCase   ,				clean=self.do_clean_text					)


				def      lowerCAmelCase_  (					self	:  Optional[int]   ,				__lowerCAmelCase	:  str					):
						return self.vocab.get(__lowerCAmelCase   ,				self.vocab.get(self.unk_token					)					)


				def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Tuple					):
						return self.subword_tokenizer.convert_id_to_token(__lowerCAmelCase					)


				def      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  List[str]					):
						_UpperCAmelCase						 =      """""".join(__lowerCAmelCase					).strip()
						return out_string


				def      lowerCAmelCase_  (					self	:  Any   ,				__lowerCAmelCase	:  "Conversation"					):
						_UpperCAmelCase						 =      []
						for is_user, text in conversation.iter_texts():
								input_ids.extend(self.encode(__lowerCAmelCase   ,				add_special_tokens=__lowerCAmelCase					) + [self.eos_token_id]					)

						if len(__lowerCAmelCase					) > self.model_max_length:
								_UpperCAmelCase						 =      input_ids[-self.model_max_length :]
						return input_ids


				def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Optional[str] = None					):
						_UpperCAmelCase						 =      0
						if os.path.isdir(__lowerCAmelCase					):
								_UpperCAmelCase						 =      os.path.join(
								    __lowerCAmelCase   ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
								_UpperCAmelCase						 =      os.path.join(
								    __lowerCAmelCase   ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""emoji_file"""]					)
						else:
								_UpperCAmelCase						 =      (
								    (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""vocab_file"""]
								)
								_UpperCAmelCase						 =      (
								    (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""emoji_file"""]
								)
						with open(__lowerCAmelCase   ,				"""w"""   ,				encoding="""utf-8"""					) as writer:
								for token_index, token in self.ids_to_tokens.items():
										if index != token_index:
												logger.warning(
												    f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
												    """ Please check that the vocabulary is not corrupted!"""					)
												_UpperCAmelCase						 =      token_index
										writer.write(""",""".join(__lowerCAmelCase					) + """\n"""					)
										index += 1
						with open(__lowerCAmelCase   ,				"""w"""   ,				encoding="""utf-8"""					) as writer:
								json.dump(self.emoji   ,				__lowerCAmelCase					)
						return vocab_file, emoji_file



class        a				(		lowerCAmelCase_   ):


				def __init__(					self	:  Any   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Union[str, Any]					):
						_UpperCAmelCase						 =      vocab  # same as swe
						_UpperCAmelCase						 =      ids_to_tokens  # same as bpe
						_UpperCAmelCase						 =      emoji
						_UpperCAmelCase						 =      np.max([len(__lowerCAmelCase					) for w in self.vocab.keys()]					)
						_UpperCAmelCase						 =      re.compile(R"""(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"""					)
						_UpperCAmelCase						 =      re.compile(R"""[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"""					)
						_UpperCAmelCase						 =      re.compile(R"""[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"""					)
						_UpperCAmelCase						 =      re.compile(
						    R"""([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"""					)
						_UpperCAmelCase						 =      re.compile(
						    R"""(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"""					)
						_UpperCAmelCase						 =      re.compile(
						    R"""((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"""					)
						_UpperCAmelCase						 =      """─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"""
						_UpperCAmelCase						 =      """▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"""
						_UpperCAmelCase						 =      str.maketrans({k: """<BLOCK>""" for k in keisen + blocks}					)


				def __len__(					self	:  Union[str, Any]					):
						return len(self.ids_to_tokens					)


				def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  Tuple					):
						_UpperCAmelCase						 =      self.content_repattera.sub("""<URL>"""   ,				__lowerCAmelCase					)
						_UpperCAmelCase						 =      self.content_repattera.sub("""<EMAIL>"""   ,				__lowerCAmelCase					)
						_UpperCAmelCase						 =      self.content_repattera.sub("""<TEL>"""   ,				__lowerCAmelCase					)
						_UpperCAmelCase						 =      self.content_repattera.sub("""<DATE>"""   ,				__lowerCAmelCase					)
						_UpperCAmelCase						 =      self.content_repattera.sub("""<DATE>"""   ,				__lowerCAmelCase					)
						_UpperCAmelCase						 =      self.content_repattera.sub("""<PRICE>"""   ,				__lowerCAmelCase					)
						_UpperCAmelCase						 =      content.translate(self.content_transa					)
						while "<BLOCK><BLOCK>" in content:
								_UpperCAmelCase						 =      content.replace("""<BLOCK><BLOCK>"""   ,				"""<BLOCK>"""					)
						return content


				def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Dict=False					):
						_UpperCAmelCase						 =      text.replace(""" """   ,				"""<SP>"""					)
						_UpperCAmelCase						 =      text.replace("""　"""   ,				"""<SP>"""					)
						_UpperCAmelCase						 =      text.replace("""\r\n"""   ,				"""<BR>"""					)
						_UpperCAmelCase						 =      text.replace("""\n"""   ,				"""<BR>"""					)
						_UpperCAmelCase						 =      text.replace("""\r"""   ,				"""<BR>"""					)
						_UpperCAmelCase						 =      text.replace("""\t"""   ,				"""<TAB>"""					)
						_UpperCAmelCase						 =      text.replace("""—"""   ,				"""ー"""					)
						_UpperCAmelCase						 =      text.replace("""−"""   ,				"""ー"""					)
						for k, v in self.emoji["emoji"].items():
								if k in text:
										_UpperCAmelCase						 =      text.replace(__lowerCAmelCase   ,				__lowerCAmelCase					)
						if clean:
								_UpperCAmelCase						 =      self.clean_text(__lowerCAmelCase					)

						def check_simbol(__lowerCAmelCase	:  List[Any]					):
								_UpperCAmelCase						 =      x.encode()
								if len(__lowerCAmelCase					) == 1 and len(__lowerCAmelCase					) == 2:
										_UpperCAmelCase						 =      (int(e[0]					) << 8) + int(e[1]					)
										if (
										    (c >= 0xc2_a1 and c <= 0xc2_bf)
										    or (c >= 0xc7_80 and c <= 0xc7_83)
										    or (c >= 0xca_b9 and c <= 0xcb_bf)
										    or (c >= 0xcc_80 and c <= 0xcd_a2)
										):
												return True
								return False

						def checkuae(__lowerCAmelCase	:  List[Any]					):
								_UpperCAmelCase						 =      x.encode()
								if len(__lowerCAmelCase					) == 1 and len(__lowerCAmelCase					) == 3:
										_UpperCAmelCase						 =      (int(e[0]					) << 16) + (int(e[1]					) << 8) + int(e[2]					)
										if c >= 0xe2_80_80 and c <= 0xe2_b0_7f:
												return True
								return False

						_UpperCAmelCase						 =      0
						_UpperCAmelCase						 =      []
						while pos < len(__lowerCAmelCase					):
								_UpperCAmelCase						 =      min(len(__lowerCAmelCase					)   ,				pos + self.maxlen + 1					) if text[pos] == """<""" else pos + 3
								_UpperCAmelCase						 =      []  # (token_id, token, pos)
								for e in range(__lowerCAmelCase   ,				__lowerCAmelCase   ,				-1					):
										_UpperCAmelCase						 =      text[pos:e]
										if wd in self.vocab:
												if wd[0] == "<" and len(__lowerCAmelCase					) > 2:
														_UpperCAmelCase						 =      [(self.vocab[wd], wd, e)]
														break
												else:
														candidates.append((self.vocab[wd], wd, e)					)
								if len(__lowerCAmelCase					) > 0:
										# the smallest token_id is adopted
										_UpperCAmelCase ,				_UpperCAmelCase ,				_UpperCAmelCase						 =      sorted(__lowerCAmelCase   ,				key=lambda __lowerCAmelCase					: x[0]					)[0]
										result.append(__lowerCAmelCase					)
										_UpperCAmelCase						 =      e
								else:
										_UpperCAmelCase						 =      pos + 1
										_UpperCAmelCase						 =      text[pos:end]
										if check_simbol(__lowerCAmelCase					):
												result.append("""<KIGOU>"""					)
										elif checkuae(__lowerCAmelCase					):
												result.append("""<U2000U2BFF>"""					)
										else:
												for i in wd.encode("""utf-8"""					):
														result.append("""<|byte%d|>""" % i					)
										_UpperCAmelCase						 =      end
						return result


				def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  List[str]="\n"					):
						_UpperCAmelCase						 =      []
						_UpperCAmelCase						 =      []
						_UpperCAmelCase						 =      self.ids_to_tokens[index][0]
						if word[:6] == "<|byte" and word[-2:] == "|>":
								byte_tokens.append(int(word[6:-2]					)					)
						else:
								if len(__lowerCAmelCase					) > 0:
										words.append(bytearray(__lowerCAmelCase					).decode("""utf-8"""   ,				errors="""replace"""					)					)
										_UpperCAmelCase						 =      []
								if word[:7] == "<|emoji" and word[-2:] == "|>":
										words.append(self.emoji["""emoji_inv"""][word]					)
								elif word == "<SP>":
										words.append(""" """					)
								elif word == "<BR>":
										words.append(__lowerCAmelCase					)
								elif word == "<TAB>":
										words.append("""\t"""					)
								elif word == "<BLOCK>":
										words.append("""▀"""					)
								elif word == "<KIGOU>":
										words.append("""ǀ"""					)
								elif word == "<U2000U2BFF>":
										words.append("""‖"""					)
								else:
										words.append(__lowerCAmelCase					)
						if len(__lowerCAmelCase					) > 0:
								words.append(bytearray(__lowerCAmelCase					).decode("""utf-8"""   ,				errors="""replace"""					)					)
						_UpperCAmelCase						 =      """""".join(__lowerCAmelCase					)
						return text

"""simple docstring"""


from typing import TYPE_CHECKING

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


UpperCAmelCase__  		=						{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """RobertaPreLayerNormForCausalLM""",
        """RobertaPreLayerNormForMaskedLM""",
        """RobertaPreLayerNormForMultipleChoice""",
        """RobertaPreLayerNormForQuestionAnswering""",
        """RobertaPreLayerNormForSequenceClassification""",
        """RobertaPreLayerNormForTokenClassification""",
        """RobertaPreLayerNormModel""",
        """RobertaPreLayerNormPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFRobertaPreLayerNormForCausalLM""",
        """TFRobertaPreLayerNormForMaskedLM""",
        """TFRobertaPreLayerNormForMultipleChoice""",
        """TFRobertaPreLayerNormForQuestionAnswering""",
        """TFRobertaPreLayerNormForSequenceClassification""",
        """TFRobertaPreLayerNormForTokenClassification""",
        """TFRobertaPreLayerNormMainLayer""",
        """TFRobertaPreLayerNormModel""",
        """TFRobertaPreLayerNormPreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """FlaxRobertaPreLayerNormForCausalLM""",
        """FlaxRobertaPreLayerNormForMaskedLM""",
        """FlaxRobertaPreLayerNormForMultipleChoice""",
        """FlaxRobertaPreLayerNormForQuestionAnswering""",
        """FlaxRobertaPreLayerNormForSequenceClassification""",
        """FlaxRobertaPreLayerNormForTokenClassification""",
        """FlaxRobertaPreLayerNormModel""",
        """FlaxRobertaPreLayerNormPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        RobertaPreLayerNormConfig,
        RobertaPreLayerNormOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta_prelayernorm import (
            ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaPreLayerNormForCausalLM,
            RobertaPreLayerNormForMaskedLM,
            RobertaPreLayerNormForMultipleChoice,
            RobertaPreLayerNormForQuestionAnswering,
            RobertaPreLayerNormForSequenceClassification,
            RobertaPreLayerNormForTokenClassification,
            RobertaPreLayerNormModel,
            RobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta_prelayernorm import (
            TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaPreLayerNormForCausalLM,
            TFRobertaPreLayerNormForMaskedLM,
            TFRobertaPreLayerNormForMultipleChoice,
            TFRobertaPreLayerNormForQuestionAnswering,
            TFRobertaPreLayerNormForSequenceClassification,
            TFRobertaPreLayerNormForTokenClassification,
            TFRobertaPreLayerNormMainLayer,
            TFRobertaPreLayerNormModel,
            TFRobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta_prelayernorm import (
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormPreTrainedModel,
        )

else:
    import sys

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