"""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     :		Optional[int]											=					logging.get_logger(__name__)

_UpperCamelCase     :		Union[str, Any]											=					{"vocab_file": "vocab.txt", "emoji_file": "emoji.json"}

_UpperCamelCase     :		List[Any]											=					{
    "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     :		Tuple											=					{
    "abeja/gpt-neox-japanese-2.7b": 20_48,
}





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Union[str, Any]  ):





       '''simple docstring'''

       with open(_lowerCAmelCase     ,				'r'     ,				encoding='utf-8'  ) as f:
              lowercase__    :	Optional[Any]     					=			json.loads(f.read()  )

       lowercase__    :	Dict     					=			collections.OrderedDict()
       lowercase__    :	Dict     					=			collections.OrderedDict()
       lowercase__    :	Dict     					=			collections.OrderedDict()
       with open(_lowerCAmelCase     ,				'r'     ,				encoding='utf-8'  ) as f:
              lowercase__    :	Tuple     					=			f.readlines()
       lowercase__    :	Optional[int]     					=			[[t.rstrip('\n'  )] if (t == ',' or ',' not in t) else t.rstrip('\n'  ).split(','  ) for t in token]
       for idx, b in enumerate(_lowerCAmelCase  ):
              lowercase__    :	Dict     					=			b
              lowercase__    :	Optional[int]     					=			idx
              for wd in b:
                     lowercase__    :	Dict     					=			idx

       return vocab, raw_vocab, ids_to_tokens, emoji


class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					List[str]										=						VOCAB_FILES_NAMES
      lowerCamelCase__	:					Any										=						PRETRAINED_VOCAB_FILES_MAP
      lowerCamelCase__	:					Any										=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowerCamelCase__	:					List[str]										=						["input_ids", "attention_mask"]


      def __init__(     self	,    a	,    a	,    a="<|endoftext|>"	,    a="<|endoftext|>"	,    a="<|startoftext|>"	,    a="<|endoftext|>"	,    a=False	,    **a	,    ) ->      Union[str, Any]:
             super().__init__(
                 unk_token=a	,    pad_token=a	,    bos_token=a	,    eos_token=a	,    do_clean_text=a	,    **a	,    )
             if not os.path.isfile(a							):
                    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(a							):
                    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)`'							)
             lowercase__    :	Dict     					=			do_clean_text
             lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	int     					=			load_vocab_and_emoji(a	,    a							)
             lowercase__    :	Dict     					=			SubWordJapaneseTokenizer(
                 vocab=self.vocab	,    ids_to_tokens=self.ids_to_tokens	,    emoji=self.emoji							)


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


      def 	_UpperCAmelCase (     self							) ->      Any:
             return dict(self.raw_vocab	,    **self.added_tokens_encoder							)


      def 	_UpperCAmelCase (     self	,    a							) ->      Union[str, Any]:
             return self.subword_tokenizer.tokenize(a	,    clean=self.do_clean_text							)


      def 	_UpperCAmelCase (     self	,    a							) ->      Any:
             return self.vocab.get(a	,    self.vocab.get(self.unk_token							)							)


      def 	_UpperCAmelCase (     self	,    a							) ->      Dict:
             return self.subword_tokenizer.convert_id_to_token(a							)


      def 	_UpperCAmelCase (     self	,    a							) ->      str:
             lowercase__    :	str     					=			''.join(a							).strip()
             return out_string


      def 	_UpperCAmelCase (     self	,    a							) ->      List[int]:
             lowercase__    :	List[Any]     					=			[]
             for is_user, text in conversation.iter_texts():
                    input_ids.extend(self.encode(a	,    add_special_tokens=a							) + [self.eos_token_id]							)

             if len(a							) > self.model_max_length:
                    lowercase__    :	str     					=			input_ids[-self.model_max_length :]
             return input_ids


      def 	_UpperCAmelCase (     self	,    a	,    a = None							) ->      Tuple[str]:
             lowercase__    :	Optional[int]     					=			0
             if os.path.isdir(a							):
                    lowercase__    :	Optional[Any]     					=			os.path.join(
                        a	,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)
                    lowercase__    :	List[Any]     					=			os.path.join(
                        a	,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']							)
             else:
                    lowercase__    :	Optional[Any]     					=			(
                        (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
                    )
                    lowercase__    :	str     					=			(
                        (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
                    )
             with open(a	,    '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!'							)
                                  lowercase__    :	Any     					=			token_index
                           writer.write(','.join(a							) + '\n'							)
                           index += 1
             with open(a	,    'w'	,    encoding='utf-8'							) as writer:
                    json.dump(self.emoji	,    a							)
             return vocab_file, emoji_file





class   UpperCAmelCase_			(					_a):


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


      def __len__(     self							) ->      Any:
             return len(self.ids_to_tokens							)


      def 	_UpperCAmelCase (     self	,    a							) ->      Any:
             lowercase__    :	Dict     					=			self.content_repattera.sub('<URL>'	,    a							)
             lowercase__    :	Optional[Any]     					=			self.content_repattera.sub('<EMAIL>'	,    a							)
             lowercase__    :	Any     					=			self.content_repattera.sub('<TEL>'	,    a							)
             lowercase__    :	Any     					=			self.content_repattera.sub('<DATE>'	,    a							)
             lowercase__    :	Tuple     					=			self.content_repattera.sub('<DATE>'	,    a							)
             lowercase__    :	Optional[Any]     					=			self.content_repattera.sub('<PRICE>'	,    a							)
             lowercase__    :	Union[str, Any]     					=			content.translate(self.content_transa							)
             while "<BLOCK><BLOCK>" in content:
                    lowercase__    :	Any     					=			content.replace('<BLOCK><BLOCK>'	,    '<BLOCK>'							)
             return content


      def 	_UpperCAmelCase (     self	,    a	,    a=False							) ->      int:
             lowercase__    :	Any     					=			text.replace(' '	,    '<SP>'							)
             lowercase__    :	int     					=			text.replace('　'	,    '<SP>'							)
             lowercase__    :	Tuple     					=			text.replace('\r\n'	,    '<BR>'							)
             lowercase__    :	Dict     					=			text.replace('\n'	,    '<BR>'							)
             lowercase__    :	List[str]     					=			text.replace('\r'	,    '<BR>'							)
             lowercase__    :	Optional[Any]     					=			text.replace('\t'	,    '<TAB>'							)
             lowercase__    :	Dict     					=			text.replace('—'	,    'ー'							)
             lowercase__    :	List[str]     					=			text.replace('−'	,    'ー'							)
             for k, v in self.emoji["emoji"].items():
                    if k in text:
                           lowercase__    :	Dict     					=			text.replace(a	,    a							)
             if clean:
                    lowercase__    :	Optional[Any]     					=			self.clean_text(a							)

             def check_simbol(a							):
                    lowercase__    :	int     					=			x.encode()
                    if len(a							) == 1 and len(a							) == 2:
                           lowercase__    :	str     					=			(int(e[0]							) << 8) + int(e[1]							)
                           if (
                               (c >= 0XC_2A1 and c <= 0XC_2BF)
                               or (c >= 0XC_780 and c <= 0XC_783)
                               or (c >= 0XC_AB9 and c <= 0XC_BBF)
                               or (c >= 0XC_C80 and c <= 0XC_DA2)
                           ):
                                  return True
                    return False

             def checkuae(a							):
                    lowercase__    :	Any     					=			x.encode()
                    if len(a							) == 1 and len(a							) == 3:
                           lowercase__    :	Any     					=			(int(e[0]							) << 1_6) + (int(e[1]							) << 8) + int(e[2]							)
                           if c >= 0XE28_080 and c <= 0XE2B_07F:
                                  return True
                    return False

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


      def 	_UpperCAmelCase (     self	,    a	,    a="\n"							) ->      Optional[int]:
             lowercase__    :	Optional[Any]     					=			[]
             lowercase__    :	Optional[int]     					=			[]
             lowercase__    :	List[Any]     					=			self.ids_to_tokens[index][0]
             if word[:6] == "<|byte" and word[-2:] == "|>":
                    byte_tokens.append(int(word[6:-2]							)							)
             else:
                    if len(a							) > 0:
                           words.append(bytearray(a							).decode('utf-8'	,    errors='replace'							)							)
                           lowercase__    :	Any     					=			[]
                    if word[:7] == "<|emoji" and word[-2:] == "|>":
                           words.append(self.emoji['emoji_inv'][word]							)
                    elif word == "<SP>":
                           words.append(' '							)
                    elif word == "<BR>":
                           words.append(a							)
                    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(a							)
             if len(a							) > 0:
                    words.append(bytearray(a							).decode('utf-8'	,    errors='replace'							)							)
             lowercase__    :	Union[str, Any]     					=			''.join(a							)
             return text


"""simple docstring"""





import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.albert.modeling_flax_albert import (
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForPreTraining,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertModel,
       )


class   UpperCAmelCase_			(					unittest.TestCase):


      def __init__(     self	,    a	,    a=1_3	,    a=7	,    a=True	,    a=True	,    a=True	,    a=True	,    a=9_9	,    a=3_2	,    a=5	,    a=4	,    a=3_7	,    a="gelu"	,    a=0.1	,    a=0.1	,    a=5_1_2	,    a=1_6	,    a=2	,    a=0.02	,    a=4	,    ) ->      Dict:
             lowercase__    :	Optional[Any]     					=			parent
             lowercase__    :	Dict     					=			batch_size
             lowercase__    :	List[Any]     					=			seq_length
             lowercase__    :	int     					=			is_training
             lowercase__    :	str     					=			use_attention_mask
             lowercase__    :	Dict     					=			use_token_type_ids
             lowercase__    :	Optional[int]     					=			use_labels
             lowercase__    :	Tuple     					=			vocab_size
             lowercase__    :	List[str]     					=			hidden_size
             lowercase__    :	Union[str, Any]     					=			num_hidden_layers
             lowercase__    :	int     					=			num_attention_heads
             lowercase__    :	Dict     					=			intermediate_size
             lowercase__    :	List[str]     					=			hidden_act
             lowercase__    :	Dict     					=			hidden_dropout_prob
             lowercase__    :	Tuple     					=			attention_probs_dropout_prob
             lowercase__    :	List[str]     					=			max_position_embeddings
             lowercase__    :	int     					=			type_vocab_size
             lowercase__    :	List[str]     					=			type_sequence_label_size
             lowercase__    :	Union[str, Any]     					=			initializer_range
             lowercase__    :	Optional[int]     					=			num_choices


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	List[Any]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size							)

             lowercase__    :	str     					=			None
             if self.use_attention_mask:
                    lowercase__    :	Optional[Any]     					=			random_attention_mask([self.batch_size, self.seq_length]							)

             lowercase__    :	List[str]     					=			None
             if self.use_token_type_ids:
                    lowercase__    :	Optional[int]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size							)

             lowercase__    :	Any     					=			AlbertConfig(
                 vocab_size=self.vocab_size	,    hidden_size=self.hidden_size	,    num_hidden_layers=self.num_hidden_layers	,    num_attention_heads=self.num_attention_heads	,    intermediate_size=self.intermediate_size	,    hidden_act=self.hidden_act	,    hidden_dropout_prob=self.hidden_dropout_prob	,    attention_probs_dropout_prob=self.attention_probs_dropout_prob	,    max_position_embeddings=self.max_position_embeddings	,    type_vocab_size=self.type_vocab_size	,    is_decoder=a	,    initializer_range=self.initializer_range	,    )

             return config, input_ids, token_type_ids, attention_mask


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Optional[int]     					=			self.prepare_config_and_inputs()
             lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	Tuple     					=			config_and_inputs
             lowercase__    :	Union[str, Any]     					=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
             return config, inputs_dict





@require_flax
class   UpperCAmelCase_			(					_a				,							unittest.TestCase):
      lowerCamelCase__	:					Tuple										=						(
          (
              FlaxAlbertModel,
              FlaxAlbertForPreTraining,
              FlaxAlbertForMaskedLM,
              FlaxAlbertForMultipleChoice,
              FlaxAlbertForQuestionAnswering,
              FlaxAlbertForSequenceClassification,
              FlaxAlbertForTokenClassification,
              FlaxAlbertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )


      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             lowercase__    :	Union[str, Any]     					=			FlaxAlbertModelTester(self							)


      @slow
      def 	_UpperCAmelCase (     self							) ->      str:
             for model_class_name in self.all_model_classes:
                    lowercase__    :	str     					=			model_class_name.from_pretrained('albert-base-v2'							)
                    lowercase__    :	Tuple     					=			model(np.ones((1, 1)							)							)
                    self.assertIsNotNone(a							)





@require_flax
class   UpperCAmelCase_			(					unittest.TestCase):


      @slow
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	str     					=			FlaxAlbertModel.from_pretrained('albert-base-v2'							)
             lowercase__    :	Optional[int]     					=			np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
             lowercase__    :	Optional[Any]     					=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
             lowercase__    :	Any     					=			model(a	,    attention_mask=a							)[0]
             lowercase__    :	Tuple     					=			(1, 1_1, 7_6_8)
             self.assertEqual(output.shape	,    a							)
             lowercase__    :	Optional[Any]     					=			np.array(
                 [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]]							)

             self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]	,    a	,    atol=1e-4							)							)

