
import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class 					lowercase__(							UpperCAmelCase					,		unittest.TestCase				):



	"""simple docstring"""




	a     :Tuple       	=      XLMTokenizer
	a     :Any       	=      False
	def 		_lowercase    (			self    :     List[Any]			)			->	List[str]:
					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					lowercase_	    =   [
					    '''l''',
					    '''o''',
					    '''w''',
					    '''e''',
					    '''r''',
					    '''s''',
					    '''t''',
					    '''i''',
					    '''d''',
					    '''n''',
					    '''w</w>''',
					    '''r</w>''',
					    '''t</w>''',
					    '''lo''',
					    '''low''',
					    '''er</w>''',
					    '''low</w>''',
					    '''lowest</w>''',
					    '''newer</w>''',
					    '''wider</w>''',
					    '''<unk>''',
					]
					lowercase_	    =   dict(zip(SCREAMING_SNAKE_CASE_			,				range(len(SCREAMING_SNAKE_CASE_			)			)			)			)
					lowercase_	    =   ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

					lowercase_	    =   os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['''vocab_file''']			)
					lowercase_	    =   os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['''merges_file''']			)
					with open(self.vocab_file			,				'''w'''			) as fp:
									fp.write(json.dumps(SCREAMING_SNAKE_CASE_			)			)
					with open(self.merges_file			,				'''w'''			) as fp:
									fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_			)			)
	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	Optional[Any]:
					lowercase_	    =   '''lower newer'''
					lowercase_	    =   '''lower newer'''
					return input_text, output_text
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Any:
					lowercase_	    =   XLMTokenizer(self.vocab_file			,				self.merges_file			)

					lowercase_	    =   '''lower'''
					lowercase_	    =   ['''low''', '''er</w>''']
					lowercase_	    =   tokenizer.tokenize(SCREAMING_SNAKE_CASE_			)
					self.assertListEqual(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   tokens + ['''<unk>''']
					lowercase_	    =   [1_4, 1_5, 2_0]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_			)			,				SCREAMING_SNAKE_CASE_			)




	@slow
	def 		_lowercase    (			self    :     int			)			->	List[str]:
					lowercase_	    =   XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''			)

					lowercase_	    =   tokenizer.encode('''sequence builders'''			,				add_special_tokens=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   tokenizer.encode('''multi-sequence build'''			,				add_special_tokens=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

					assert encoded_sentence == [0] + text + [1]
					assert encoded_pair == [0] + text + [1] + text_a + [1]










'''simple docstring'''


import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class      SCREAMING_SNAKE_CASE    (						tf.keras.layers.Layer				):






      def __init__( self   :  List[Any]						,						lowercase__   :  Dict[str, int]						,						lowercase__   :  List[str]						,						lowercase__   :  int = None						,						lowercase__   :  int = None       ):


           '''simple docstring'''

           super().__init__()
           a_     :							Optional[Any]     						=  pad_token_id
           a_     :							str     						=  max_length
           a_     :							List[str]     						=  vocab
           a_     :							Union[str, Any]     						=  merges
           a_     :							Union[str, Any]     						=  BytePairTokenizer(lowercase__						,						lowercase__						,						sequence_length=lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  List[Any]						,						lowercase__   :  GPTaTokenizer						,						*lowercase__   :  Optional[Any]						,						**lowercase__   :  int       ):


           '''simple docstring'''

           a_     :							str     						=  [""" """.join(lowercase__       ) for m in tokenizer.bpe_ranks.keys()]
           a_     :							Any     						=  tokenizer.get_vocab()
           return cls(lowercase__						,						lowercase__						,						*lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						lowercase__   :  Union[str, os.PathLike]						,						*lowercase__   :  str						,						**lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  GPTaTokenizer.from_pretrained(lowercase__						,						*lowercase__						,						**lowercase__       )
           return cls.from_tokenizer(lowercase__						,						*lowercase__						,						**lowercase__       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						lowercase__   :  Optional[Any]       ):


           '''simple docstring'''

           return cls(**lowercase__       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  int = None       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.tf_tokenizer(lowercase__       )
           a_     :							int     						=  tf.ones_like(lowercase__       )

           if self.pad_token_id is not None:
                # pad the tokens up to max length
                a_     :							int     						=  max_length if max_length is not None else self.max_length

                if max_length is not None:
                     a_						,     a_     :							int     						=  pad_model_inputs(
                         lowercase__						,						max_seq_length=lowercase__						,						pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}
