


import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class      _lowercase					(				lowercase__				,    unittest.TestCase):


       """simple docstring"""




       A__   		=     GPTNeoXJapaneseTokenizer
       A__   		=     False
       A__   		=     {"do_clean_text": False, "add_prefix_space": False}





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             super().setUp()

             lowerCamelCase__						:							Optional[Any]       =    [
                 "こん",
                 "こんに",
                 "にちは",
                 "ばんは",
                 "世界,㔺界",
                 "、",
                 "。",
                 "<BR>",
                 "<SP>",
                 "<TAB>",
                 "<URL>",
                 "<EMAIL>",
                 "<TEL>",
                 "<DATE>",
                 "<PRICE>",
                 "<BLOCK>",
                 "<KIGOU>",
                 "<U2000U2BFF>",
                 "<|emoji1|>",
                 "<unk>",
                 "<|startoftext|>",
                 "<|endoftext|>",
             ]
             lowerCamelCase__						:							List[str]       =    {"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
             lowerCamelCase__						:							int       =    {"unk_token": "<unk>"}

             lowerCamelCase__						:							Dict       =    os.path.join(self.tmpdirname   , VOCAB_FILES_NAMES["vocab_file"]			)
             lowerCamelCase__						:							Dict       =    os.path.join(self.tmpdirname   , VOCAB_FILES_NAMES["emoji_file"]			)
             with open(self.vocab_file   , "w"   , encoding="utf-8"			) as vocab_writer:
                   vocab_writer.write("".join([x + "\n" for x in vocab_tokens]			)			)
             with open(self.emoji_file   , "w"			) as emoji_writer:
                   emoji_writer.write(json.dumps(__lowerCamelCase			)			)





       def 		lowerCAmelCase	(  self				:     Optional[Any]   , **__lowerCamelCase				:     Tuple			):


             '''simple docstring'''



             kwargs.update(self.special_tokens_map			)
             return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname   , **__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     str   , __lowerCamelCase				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[int]       =    "こんにちは、世界。 \nこんばんは、㔺界。😀"
             lowerCamelCase__						:							Optional[Any]       =    "こんにちは、世界。 \nこんばんは、世界。😀"
             return input_text, output_text





       def 		lowerCAmelCase	(  self				:     Any   , __lowerCamelCase				:     Union[str, Any]			):


             '''simple docstring'''



             lowerCamelCase__       ,  lowerCamelCase__						:							str       =    self.get_input_output_texts(__lowerCamelCase			)
             lowerCamelCase__						:							Tuple       =    tokenizer.encode(__lowerCamelCase   , add_special_tokens=__lowerCamelCase			)
             lowerCamelCase__						:							Optional[int]       =    tokenizer.decode(__lowerCamelCase   , clean_up_tokenization_spaces=__lowerCamelCase			)
             return text, ids





       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             pass  # TODO add if relevant





       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             pass  # TODO add if relevant





       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             pass  # TODO add if relevant





       def 		lowerCAmelCase	(  self				:     Union[str, Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    self.get_tokenizer()

             # Testing tokenization
             lowerCamelCase__						:							Optional[int]       =    "こんにちは、世界。　こんばんは、㔺界。"
             lowerCamelCase__						:							Dict       =    ["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
             lowerCamelCase__						:							List[str]       =    tokenizer.tokenize(__lowerCamelCase			)
             self.assertListEqual(__lowerCamelCase   , __lowerCamelCase			)

             # Testing conversion to ids without special tokens
             lowerCamelCase__						:							str       =    [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
             lowerCamelCase__						:							str       =    tokenizer.convert_tokens_to_ids(__lowerCamelCase			)
             self.assertListEqual(__lowerCamelCase   , __lowerCamelCase			)

             # Testing conversion to ids with special tokens
             lowerCamelCase__						:							Optional[int]       =    tokens + [tokenizer.unk_token]
             lowerCamelCase__						:							List[str]       =    [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
             lowerCamelCase__						:							str       =    tokenizer.convert_tokens_to_ids(__lowerCamelCase			)
             self.assertListEqual(__lowerCamelCase   , __lowerCamelCase			)





       @slow
       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							int       =    self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b"			)

             lowerCamelCase__						:							Optional[int]       =    tokenizer.encode("ありがとう。"   , add_special_tokens=__lowerCamelCase			)
             lowerCamelCase__						:							Union[str, Any]       =    tokenizer.encode("どういたしまして。"   , add_special_tokens=__lowerCamelCase			)

             lowerCamelCase__						:							List[str]       =    tokenizer.build_inputs_with_special_tokens(__lowerCamelCase			)
             lowerCamelCase__						:							Tuple       =    tokenizer.build_inputs_with_special_tokens(__lowerCamelCase   , __lowerCamelCase			)

             assert encoded_sentence == ids_a
             assert encoded_pair == ids_a + ids_a





       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             pass





       def 		lowerCAmelCase	(  self				:     str			):


             '''simple docstring'''



             pass






import torch

from transformers import AutoTokenizer







class      _lowercase					(				lowercase__):


       """simple docstring"""




       def __init__(  self				:     Optional[Any]   , __lowerCamelCase				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							int       =    AutoTokenizer.from_pretrained(__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Union[str, Any]   , __lowerCamelCase				:     str			):


             '''simple docstring'''



             if isinstance(__lowerCamelCase   , __lowerCamelCase			) and all(isinstance(_x   , __lowerCamelCase			) for _x in x			):
                   lowerCamelCase__						:							List[Any]       =    None
                   for l in x:
                         lowerCamelCase__						:							str       =    self.tokenizer(
                             __lowerCamelCase   , padding="max_length"   , max_length=384   , truncation=__lowerCamelCase   , return_tensors="pt"   , )
                         lowerCamelCase__						:							Dict       =    torch.tensor([len(__lowerCamelCase			)]			)
                         if d is not None:
                               for k in d.keys():
                                     lowerCamelCase__						:							Tuple       =    torch.cat((d[k], t[k])   , 0			)
                         else:
                               lowerCamelCase__						:							Union[str, Any]       =    t

                   lowerCamelCase__						:							List[Any]       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"			)			)
                   lowerCamelCase__						:							Dict       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"			)			)

             elif isinstance(__lowerCamelCase   , __lowerCamelCase			) and all(isinstance(_x   , __lowerCamelCase			) for _x in x			):
                   lowerCamelCase__						:							Tuple       =    self.tokenizer(
                       __lowerCamelCase   , padding="max_length"   , max_length=384   , truncation=__lowerCamelCase   , return_tensors="pt"   , )

             else:
                   raise Exception(
                       "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
                       " strings` for supports are supported."			)

             return d





       def 		lowerCAmelCase	(  self				:     List[Any]   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     Dict   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     Optional[Any]=0.7_0			):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    []
             for idx in range(len(W_query["input_ids"]			)			):
                   lowerCamelCase__						:							str       =    range(p_start.shape[1]			)

                   lowerCamelCase__						:							int       =    []
                   for start_id in start_indexes:
                         for end_id in end_indexes:
                               if start_id < end_id:
                                     output.append(
                                         (
                                             start_id,
                                             end_id,
                                             p_start[idx][start_id].item(),
                                             p_end[idx][end_id].item(),
                                         )			)

                   output.sort(key=lambda __lowerCamelCase			: (tup[2] * tup[3])   , reverse=__lowerCamelCase			)
                   lowerCamelCase__						:							str       =    []
                   for k in range(len(__lowerCamelCase			)			):
                         if output[k][2] * output[k][3] >= thresh:
                               lowerCamelCase__       ,  lowerCamelCase__						:							Optional[int]       =    output[k][0], output[k][1]
                               lowerCamelCase__						:							Tuple       =    self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]			)
                               temp.append((decoded, output[k][2] * output[k][3])			)

                   final_outputs.append(__lowerCamelCase			)

             return final_outputs


