



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
UpperCAmelCase_          =				"stas/tiny-wmt19-en-ru"





class 		UpperCAmelCase    (    snake_case_							,unittest.TestCase					):
   SCREAMING_SNAKE_CASE__					      =  FSMTTokenizer
   SCREAMING_SNAKE_CASE__					      =  False





   def 						__lowerCAmelCase				(						self      ):
         super().setUp()

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

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

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





   @cached_property
   def 						__lowerCAmelCase				(						self      ):
         return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''      )





   @cached_property
   def 						__lowerCAmelCase				(						self      ):
         return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''      )





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	FSMTTokenizer.from_pretrained(_lowerCAmelCase      )
         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 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	FSMTTokenizer(self.langs							,  self.src_vocab_file							,  self.tgt_vocab_file							,  self.merges_file      )

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

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





   @slow
   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	self.tokenizer_ru_en

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

         _lowerCAmelCase			      =	tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase      )
         _lowerCAmelCase			      =	tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase							,  _lowerCAmelCase      )

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





   @slow
   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	self.tokenizer_en_ru
         _lowerCAmelCase			      =	self.tokenizer_ru_en

         _lowerCAmelCase			      =	[
             [
                 '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
                 [2_470, 39, 11, 2_349, 7_222, 70, 5_979, 7, 8_450, 1_050, 13_160, 5, 26, 6_445, 7, 2],
             ],
             ['''This is it. No more. I\'m done!''', [132, 21, 37, 7, 1_434, 86, 7, 70, 6_476, 1_305, 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:
               _lowerCAmelCase			      =	tokenizer_enc.encode(_lowerCAmelCase							,  return_tensors=_lowerCAmelCase      )
               self.assertListEqual(_lowerCAmelCase							,  _lowerCAmelCase      )

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





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





   @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''      )
   def 						__lowerCAmelCase				(						self      ):
         pass





   @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''      )
   def 						__lowerCAmelCase				(						self      ):
         pass




from typing import List, Union

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


UpperCAmelCase_          =				logging.get_logger(__name__)

UpperCAmelCase_          =				{
    "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/config.json",
    # See all LED models at https://huggingface.co/models?filter=led
}





class 		UpperCAmelCase    (    snake_case_					):
   SCREAMING_SNAKE_CASE__					      =  '''led'''
   SCREAMING_SNAKE_CASE__					      =  {
       '''num_attention_heads''': '''encoder_attention_heads''',
       '''hidden_size''': '''d_model''',
       '''attention_probs_dropout_prob''': '''attention_dropout''',
       '''initializer_range''': '''init_std''',
   }





   def __init__(						self							,  _lowerCAmelCase=50_265							,  _lowerCAmelCase=16_384							,  _lowerCAmelCase=1_024							,  _lowerCAmelCase=12							,  _lowerCAmelCase=4_096							,  _lowerCAmelCase=16							,  _lowerCAmelCase=12							,  _lowerCAmelCase=4_096							,  _lowerCAmelCase=16							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=True							,  _lowerCAmelCase=True							,  _lowerCAmelCase="gelu"							,  _lowerCAmelCase=1_024							,  _lowerCAmelCase=0.1							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=0.02							,  _lowerCAmelCase=2							,  _lowerCAmelCase=0.0							,  _lowerCAmelCase=1							,  _lowerCAmelCase=0							,  _lowerCAmelCase=2							,  _lowerCAmelCase = 512							,  **_lowerCAmelCase							,  ):
         _lowerCAmelCase			      =	vocab_size
         _lowerCAmelCase			      =	max_encoder_position_embeddings
         _lowerCAmelCase			      =	max_decoder_position_embeddings
         _lowerCAmelCase			      =	d_model
         _lowerCAmelCase			      =	encoder_ffn_dim
         _lowerCAmelCase			      =	encoder_layers
         _lowerCAmelCase			      =	encoder_attention_heads
         _lowerCAmelCase			      =	decoder_ffn_dim
         _lowerCAmelCase			      =	decoder_layers
         _lowerCAmelCase			      =	decoder_attention_heads
         _lowerCAmelCase			      =	dropout
         _lowerCAmelCase			      =	attention_dropout
         _lowerCAmelCase			      =	activation_dropout
         _lowerCAmelCase			      =	activation_function
         _lowerCAmelCase			      =	init_std
         _lowerCAmelCase			      =	encoder_layerdrop
         _lowerCAmelCase			      =	decoder_layerdrop
         _lowerCAmelCase			      =	classifier_dropout
         _lowerCAmelCase			      =	use_cache
         _lowerCAmelCase			      =	encoder_layers
         _lowerCAmelCase			      =	attention_window

         super().__init__(
             pad_token_id=_lowerCAmelCase							,  bos_token_id=_lowerCAmelCase							,  eos_token_id=_lowerCAmelCase							,  is_encoder_decoder=_lowerCAmelCase							,  decoder_start_token_id=_lowerCAmelCase							,  **_lowerCAmelCase							,  )