




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   (			lowerCAmelCase_	,		unittest.TestCase    ):
     _lowerCAmelCase		       =						GPTNeoXJapaneseTokenizer
     _lowerCAmelCase		       =						False
     _lowerCAmelCase		       =						{'''do_clean_text''': False, '''add_prefix_space''': False}



     def 				__magic_name__		(  self   :							Union[str, Any]       ):
       super().setUp()

       a_		    =  [
           '''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_		    =  {'''emoji''': {'''\ud83d\ude00''': '''<|emoji1|>'''}, '''emoji_inv''': {'''<|emoji1|>''': '''\ud83d\ude00'''}}  # 😀
       a_		    =  {'''unk_token''': '''<unk>'''}

       a_		    =  os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES['''vocab_file''']       )
       a_		    =  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(UpperCamelCase_       )       )



     def 				__magic_name__		(  self   :							str      ,		**lowercase__   :							List[str]       ):
       kwargs.update(self.special_tokens_map       )
       return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname      ,		**UpperCamelCase_       )



     def 				__magic_name__		(  self   :							int      ,		lowercase__   :							int       ):
       a_		    =  '''こんにちは、世界。 \nこんばんは、㔺界。😀'''
       a_		    =  '''こんにちは、世界。 \nこんばんは、世界。😀'''
       return input_text, output_text



     def 				__magic_name__		(  self   :							List[str]      ,		lowercase__   :							str       ):
       a_		    =  self.get_input_output_texts(UpperCamelCase_       )
       a_		    =  tokenizer.encode(UpperCamelCase_      ,		add_special_tokens=UpperCamelCase_       )
       a_		    =  tokenizer.decode(UpperCamelCase_      ,		clean_up_tokenization_spaces=UpperCamelCase_       )
       return text, ids



     def 				__magic_name__		(  self   :							List[str]       ):
       pass  # TODO add if relevant



     def 				__magic_name__		(  self   :							List[str]       ):
       pass  # TODO add if relevant



     def 				__magic_name__		(  self   :							Dict       ):
       pass  # TODO add if relevant



     def 				__magic_name__		(  self   :							Dict       ):
       a_		    =  self.get_tokenizer()

       # Testing tokenization
       a_		    =  '''こんにちは、世界。　こんばんは、㔺界。'''
       a_		    =  ['''こん''', '''にちは''', '''、''', '''世界''', '''。''', '''<SP>''', '''こん''', '''ばんは''', '''、''', '''㔺界''', '''。''']
       a_		    =  tokenizer.tokenize(UpperCamelCase_       )
       self.assertListEqual(UpperCamelCase_      ,		UpperCamelCase_       )

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

       # Testing conversion to ids with special tokens
       a_		    =  tokens + [tokenizer.unk_token]
       a_		    =  [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
       a_		    =  tokenizer.convert_tokens_to_ids(UpperCamelCase_       )
       self.assertListEqual(UpperCamelCase_      ,		UpperCamelCase_       )



     @slow
     def 				__magic_name__		(  self   :							Optional[int]       ):
       a_		    =  self.tokenizer_class.from_pretrained('''abeja/gpt-neox-japanese-2.7b'''       )

       a_		    =  tokenizer.encode('''ありがとう。'''      ,		add_special_tokens=UpperCamelCase_       )
       a_		    =  tokenizer.encode('''どういたしまして。'''      ,		add_special_tokens=UpperCamelCase_       )

       a_		    =  tokenizer.build_inputs_with_special_tokens(UpperCamelCase_       )
       a_		    =  tokenizer.build_inputs_with_special_tokens(UpperCamelCase_      ,		UpperCamelCase_       )

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



     def 				__magic_name__		(  self   :							List[Any]       ):
       # Intentionally convert some words to accommodate character fluctuations unique to Japanese
       pass



     def 				__magic_name__		(  self   :							Tuple       ):
       # tokenizer has no padding token
       pass


'''simple docstring'''






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


__lowercase	=    logging.get_logger(__name__)

__lowercase	=    {
    '''google/pegasus-x-base''': '''https://huggingface.co/google/pegasus-x-base/resolve/main/config.json''',
    '''google/pegasus-x-large''': '''https://huggingface.co/google/pegasus-x-large/resolve/main/config.json''',
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}

class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  Dict								=		'''pegasus_x'''
	_UpperCamelCase							:  Optional[int]								=		['''past_key_values''']
	_UpperCamelCase							:  str								=		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




	def __init__(						self				: Any					,						UpperCamelCase_				: Tuple=96103					,						UpperCamelCase_				: List[str]=16384					,						UpperCamelCase_				: Any=16					,						UpperCamelCase_				: Dict=4096					,						UpperCamelCase_				: str=16					,						UpperCamelCase_				: Union[str, Any]=16					,						UpperCamelCase_				: str=4096					,						UpperCamelCase_				: Any=16					,						UpperCamelCase_				: str=0.0					,						UpperCamelCase_				: Dict=0.0					,						UpperCamelCase_				: Union[str, Any]=True					,						UpperCamelCase_				: Union[str, Any]=True					,						UpperCamelCase_				: List[str]="gelu"					,						UpperCamelCase_				: Dict=1024					,						UpperCamelCase_				: str=0.1					,						UpperCamelCase_				: str=0.0					,						UpperCamelCase_				: Optional[Any]=0.0					,						UpperCamelCase_				: str=0.0_2					,						UpperCamelCase_				: Optional[Any]=0					,						UpperCamelCase_				: Tuple=True					,						UpperCamelCase_				: Tuple=0					,						UpperCamelCase_				: Optional[int]=1					,						UpperCamelCase_				: List[Any]=1					,						UpperCamelCase_				: List[Any]=32					,						UpperCamelCase_				: Optional[int]=512					,						UpperCamelCase_				: Any=True					,						**UpperCamelCase_				: Dict					,						):
			lowerCAmelCase_   :       List[Any]    							=vocab_size
			lowerCAmelCase_   :       Any    							=max_position_embeddings
			lowerCAmelCase_   :       int    							=d_model
			lowerCAmelCase_   :       List[Any]    							=encoder_ffn_dim
			lowerCAmelCase_   :       Union[str, Any]    							=encoder_layers
			lowerCAmelCase_   :       Tuple    							=encoder_attention_heads
			lowerCAmelCase_   :       List[Any]    							=decoder_ffn_dim
			lowerCAmelCase_   :       List[Any]    							=decoder_layers
			lowerCAmelCase_   :       List[str]    							=decoder_attention_heads
			lowerCAmelCase_   :       str    							=dropout
			lowerCAmelCase_   :       Any    							=attention_dropout
			lowerCAmelCase_   :       Any    							=activation_dropout
			lowerCAmelCase_   :       Optional[int]    							=activation_function
			lowerCAmelCase_   :       List[Any]    							=init_std
			lowerCAmelCase_   :       List[str]    							=encoder_layerdrop
			lowerCAmelCase_   :       Optional[int]    							=decoder_layerdrop
			lowerCAmelCase_   :       int    							=use_cache
			lowerCAmelCase_   :       Union[str, Any]    							=encoder_layers
			lowerCAmelCase_   :       Tuple    							=scale_embedding  # scale factor will be sqrt(d_model) if True

			lowerCAmelCase_   :       Optional[int]    							=num_global_tokens
			lowerCAmelCase_   :       List[str]    							=block_size
			lowerCAmelCase_   :       Optional[int]    							=stagger_local_blocks

			super().__init__(
			    pad_token_id=UpperCamelCase_					,						eos_token_id=UpperCamelCase_					,						is_encoder_decoder=UpperCamelCase_					,						decoder_start_token_id=UpperCamelCase_					,						forced_eos_token_id=UpperCamelCase_					,						**UpperCamelCase_					,						)




	@property
	def 	__A      (						self				: List[str]      ):
			return self.encoder_attention_heads





	@property
	def 	__A      (						self				: int      ):
			return self.d_model






