

import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
lowerCamelCase 	=   '''stas/tiny-wmt19-en-ru'''







class 		__magic_name__     (				lowerCamelCase__						,			unittest.TestCase				):

   '''simple docstring'''




   lowerCamelCase__					:      Union[str, Any]        =  FSMTTokenizer
   lowerCamelCase__					:      Dict        =  False



   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     a__    =[
         '''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>''',
     ]
     a__    =dict(zip(lowercase_,       range(len(lowercase_			)			)			)			)
     a__    =['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

     a__    =['''en''', '''ru''']
     a__    ={
         '''langs''': self.langs,
         '''src_vocab_size''': 10,
         '''tgt_vocab_size''': 20,
     }

     a__    =os.path.join(self.tmpdirname,       VOCAB_FILES_NAMES['''src_vocab_file''']			)
     a__    =os.path.join(self.tmpdirname,       VOCAB_FILES_NAMES['''tgt_vocab_file''']			)
     a__    =os.path.join(self.tmpdirname,       '''tokenizer_config.json'''			)
     a__    =os.path.join(self.tmpdirname,       VOCAB_FILES_NAMES['''merges_file''']			)
     with open(self.src_vocab_file,       '''w'''			) as fp:
       fp.write(json.dumps(lowercase_			)			)
     with open(self.tgt_vocab_file,       '''w'''			) as fp:
       fp.write(json.dumps(lowercase_			)			)
     with open(self.merges_file,       '''w'''			) as fp:
       fp.write('''\n'''.join(lowercase_			)			)
     with open(lowercase_,       '''w'''			) as fp:
       fp.write(json.dumps(lowercase_			)			)



   @cached_property
   def 					_UpperCAmelCase					(							self			)				->       Dict:






     """simple docstring"""






     return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''			)



   @cached_property
   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






     return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''			)



   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     a__    =FSMTTokenizer.from_pretrained(lowercase_			)
     self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang],       ['''en''', '''ru''']			)
     self.assertEqual(tokenizer.src_vocab_size,       21			)
     self.assertEqual(tokenizer.tgt_vocab_size,       21			)



   def 					_UpperCAmelCase					(							self			)				->       Optional[Any]:






     """simple docstring"""






     a__    =FSMTTokenizer(self.langs,       self.src_vocab_file,       self.tgt_vocab_file,       self.merges_file			)

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

     a__    =tokens + ['''<unk>''']
     a__    =[14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_			),       lowercase_			)



   @slow
   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =self.tokenizer_ru_en

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

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

     assert encoded_sentence == text + [2]
     assert encoded_pair == text + [2] + text_a + [2]



   @slow
   def 					_UpperCAmelCase					(							self			)				->       Dict:






     """simple docstring"""






     a__    =self.tokenizer_en_ru
     a__    =self.tokenizer_ru_en

     a__    =[
         [
             '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
             [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 13160, 5, 26, 6445, 7, 2],
         ],
         ['''This is it. No more. I\'m done!''', [132, 21, 37, 7, 1434, 86, 7, 70, 6476, 1305, 427, 2]],
     ]

     # if data needs to be recreated or added, run:
     # import torch
     # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
     # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

     for src_text, tgt_input_ids in targets:
       a__    =tokenizer_enc.encode(lowercase_,       return_tensors=lowercase_			)
       self.assertListEqual(lowercase_,       lowercase_			)

       # and decode backward, using the reversed languages model
       a__    =tokenizer_dec.decode(lowercase_,       skip_special_tokens=lowercase_			)
       self.assertEqual(lowercase_,       lowercase_			)



   @slow
   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






     a__    =FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en''',       do_lower_case=lowercase_			)
     a__    =tokenizer.tokenize('''USA is United States of America'''			)
     a__    =['''us''', '''a</w>''', '''is</w>''', '''un''', '''i''', '''ted</w>''', '''st''', '''ates</w>''', '''of</w>''', '''am''', '''er''', '''ica</w>''']
     self.assertListEqual(lowercase_,       lowercase_			)



   @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''			)
   def 					_UpperCAmelCase					(							self			)				->       Dict:






     """simple docstring"""






     pass



   @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''			)
   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






     pass



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


lowerCamelCase 	=   logging.get_logger(__name__)

lowerCamelCase 	=   {
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Tuple        =  'yoso'



   def __init__(							self,       lowercase_=50265,       lowercase_=768,       lowercase_=12,       lowercase_=12,       lowercase_=3072,       lowercase_="gelu",       lowercase_=0.1,       lowercase_=0.1,       lowercase_=4096,       lowercase_=1,       lowercase_=0.02,       lowercase_=1E-12,       lowercase_="absolute",       lowercase_=True,       lowercase_=9,       lowercase_=64,       lowercase_=None,       lowercase_=True,       lowercase_=True,       lowercase_=1,       lowercase_=0,       lowercase_=2,       **lowercase_,       )				->       List[str]:






     """simple docstring"""






     super().__init__(pad_token_id=lowercase_,       bos_token_id=lowercase_,       eos_token_id=lowercase_,       **lowercase_			)

     a__    =vocab_size
     a__    =max_position_embeddings
     a__    =hidden_size
     a__    =num_hidden_layers
     a__    =num_attention_heads
     a__    =intermediate_size
     a__    =hidden_act
     a__    =hidden_dropout_prob
     a__    =attention_probs_dropout_prob
     a__    =initializer_range
     a__    =type_vocab_size
     a__    =layer_norm_eps
     a__    =position_embedding_type
     a__    =use_expectation
     a__    =hash_code_len
     a__    =num_hash
     a__    =conv_window
     a__    =use_fast_hash
     a__    =lsh_backward
