




'''simple docstring'''




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      UpperCamelCase_  (a__,   unittest.TestCase  ):







						"""simple docstring"""


						_lowerCAmelCase         =       GPTNeoXJapaneseTokenizer
						_lowerCAmelCase         =       False
						_lowerCAmelCase         =       {'do_clean_text': False, 'add_prefix_space': False}







						def    _a     (  self   :							Tuple     ):





											"""simple docstring"""







											super().setUp()

											A_			:  Union[str, 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|>''',
											]
											A_			:  str			=				{'''emoji''': {'''\ud83d\ude00''': '''<|emoji1|>'''}, '''emoji_inv''': {'''<|emoji1|>''': '''\ud83d\ude00'''}}  # 😀
											A_			:  List[str]			=				{'''unk_token''': '''<unk>'''}

											A_			:  str			=				os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['''vocab_file''']     )
											A_			:  int			=				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    _a     (  self   :							Optional[Any]				,			**_lowerCamelCase   :							int     ):





											"""simple docstring"""







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







						def    _a     (  self   :							Tuple				,			_lowerCamelCase   :							Any     ):





											"""simple docstring"""







											A_			:  str			=				'''こんにちは、世界。 \nこんばんは、㔺界。😀'''
											A_			:  str			=				'''こんにちは、世界。 \nこんばんは、世界。😀'''
											return input_text, output_text







						def    _a     (  self   :							Optional[int]				,			_lowerCamelCase   :							int     ):





											"""simple docstring"""







											A_			,A_			:  Optional[int]			=				self.get_input_output_texts(_lowerCamelCase     )
											A_			:  Tuple			=				tokenizer.encode(_lowerCamelCase				,			add_special_tokens=_lowerCamelCase     )
											A_			:  List[Any]			=				tokenizer.decode(_lowerCamelCase				,			clean_up_tokenization_spaces=_lowerCamelCase     )
											return text, ids







						def    _a     (  self   :							List[Any]     ):





											"""simple docstring"""







											pass  # TODO add if relevant







						def    _a     (  self   :							Optional[Any]     ):





											"""simple docstring"""







											pass  # TODO add if relevant







						def    _a     (  self   :							Dict     ):





											"""simple docstring"""







											pass  # TODO add if relevant







						def    _a     (  self   :							Any     ):





											"""simple docstring"""







											A_			:  Tuple			=				self.get_tokenizer()

											# Testing tokenization
											A_			:  Tuple			=				'''こんにちは、世界。　こんばんは、㔺界。'''
											A_			:  Tuple			=				['''こん''', '''にちは''', '''、''', '''世界''', '''。''', '''<SP>''', '''こん''', '''ばんは''', '''、''', '''㔺界''', '''。''']
											A_			:  List[Any]			=				tokenizer.tokenize(_lowerCamelCase     )
											self.assertListEqual(_lowerCamelCase				,			_lowerCamelCase     )

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

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







						@slow
						def    _a     (  self   :							Dict     ):





											"""simple docstring"""







											A_			:  List[Any]			=				self.tokenizer_class.from_pretrained('''abeja/gpt-neox-japanese-2.7b'''     )

											A_			:  Union[str, Any]			=				tokenizer.encode('''ありがとう。'''				,			add_special_tokens=_lowerCamelCase     )
											A_			:  Dict			=				tokenizer.encode('''どういたしまして。'''				,			add_special_tokens=_lowerCamelCase     )

											A_			:  Optional[int]			=				tokenizer.build_inputs_with_special_tokens(_lowerCamelCase     )
											A_			:  Dict			=				tokenizer.build_inputs_with_special_tokens(_lowerCamelCase				,			_lowerCamelCase     )

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







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





											"""simple docstring"""







											pass







						def    _a     (  self   :							List[str]     ):





											"""simple docstring"""







											pass









'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__ 							=    logging.get_logger(__name__)

snake_case__ 							=    {
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}






class      UpperCamelCase_  (a__  ):







						"""simple docstring"""


						_lowerCAmelCase         =       'nystromformer'







						def __init__(  self   :							Optional[Any]				,			_lowerCamelCase   :							str=30000				,			_lowerCamelCase   :							Optional[Any]=768				,			_lowerCamelCase   :							Optional[Any]=12				,			_lowerCamelCase   :							str=12				,			_lowerCamelCase   :							List[str]=3072				,			_lowerCamelCase   :							Any="gelu_new"				,			_lowerCamelCase   :							List[Any]=0.1				,			_lowerCamelCase   :							List[str]=0.1				,			_lowerCamelCase   :							List[str]=510				,			_lowerCamelCase   :							Optional[Any]=2				,			_lowerCamelCase   :							List[Any]=64				,			_lowerCamelCase   :							str=64				,			_lowerCamelCase   :							str=65				,			_lowerCamelCase   :							List[Any]=False				,			_lowerCamelCase   :							List[Any]=0.02				,			_lowerCamelCase   :							List[Any]=1E-5				,			_lowerCamelCase   :							Dict=1				,			_lowerCamelCase   :							Tuple=0				,			_lowerCamelCase   :							List[str]=2				,			**_lowerCamelCase   :							List[str]				,			):





											"""simple docstring"""







											A_			:  List[Any]			=				vocab_size
											A_			:  Tuple			=				max_position_embeddings
											A_			:  Dict			=				hidden_size
											A_			:  Optional[Any]			=				num_hidden_layers
											A_			:  Dict			=				num_attention_heads
											A_			:  Tuple			=				intermediate_size
											A_			:  Dict			=				hidden_act
											A_			:  List[Any]			=				hidden_dropout_prob
											A_			:  str			=				attention_probs_dropout_prob
											A_			:  Optional[Any]			=				initializer_range
											A_			:  List[str]			=				type_vocab_size
											A_			:  List[Any]			=				segment_means_seq_len
											A_			:  Dict			=				num_landmarks
											A_			:  int			=				conv_kernel_size
											A_			:  Optional[Any]			=				inv_coeff_init_option
											A_			:  str			=				layer_norm_eps
											super().__init__(pad_token_id=_lowerCamelCase				,			bos_token_id=_lowerCamelCase				,			eos_token_id=_lowerCamelCase				,			**_lowerCamelCase     )



