






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







class       UpperCAmelCase_						( lowercase,     unittest.TestCase			):



      """simple docstring"""
      UpperCamelCase_    : Any									=FSMTTokenizer
      UpperCamelCase_    : Optional[Any]									=False
      def    UpperCAmelCase						(    self			)    ->				List[str]:
          super().setUp()

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

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

          UpperCamelCase	:List[str]     							=       os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES['''src_vocab_file''']			)
          UpperCamelCase	:List[Any]     							=       os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES['''tgt_vocab_file''']			)
          UpperCamelCase	:Dict     							=       os.path.join(self.tmpdirname   ,    '''tokenizer_config.json'''			)
          UpperCamelCase	:List[str]     							=       os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES['''merges_file''']			)
          with open(self.src_vocab_file   ,    '''w'''			) as fp:
              fp.write(json.dumps(SCREAMING_SNAKE_CASE_			)			)
          with open(self.tgt_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_			)			)
          with open(SCREAMING_SNAKE_CASE_   ,    '''w'''			) as fp:
              fp.write(json.dumps(SCREAMING_SNAKE_CASE_			)			)
      @cached_property
      def    UpperCAmelCase						(    self			)    ->				Dict:
          return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''			)
      @cached_property
      def    UpperCAmelCase						(    self			)    ->				Optional[Any]:
          return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''			)
      def    UpperCAmelCase						(    self			)    ->				int:
          UpperCamelCase	:List[str]     							=       FSMTTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_			)
          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			)    ->				Tuple:
          UpperCamelCase	:str     							=       FSMTTokenizer(self.langs   ,    self.src_vocab_file   ,    self.tgt_vocab_file   ,    self.merges_file			)

          UpperCamelCase	:Dict     							=       '''lower'''
          UpperCamelCase	:Tuple     							=       ['''low''', '''er</w>''']
          UpperCamelCase	:Optional[Any]     							=       tokenizer.tokenize(SCREAMING_SNAKE_CASE_			)
          self.assertListEqual(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:Union[str, Any]     							=       tokens + ['''<unk>''']
          UpperCamelCase	:Tuple     							=       [14, 15, 20]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_			)   ,    SCREAMING_SNAKE_CASE_			)
      @slow
      def    UpperCAmelCase						(    self			)    ->				Optional[Any]:
          UpperCamelCase	:Optional[Any]     							=       self.tokenizer_ru_en

          UpperCamelCase	:Tuple     							=       tokenizer.encode('''sequence builders'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Dict     							=       tokenizer.encode('''multi-sequence build'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:int     							=       tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Union[str, Any]     							=       tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

          assert encoded_sentence == text + [2]
          assert encoded_pair == text + [2] + text_a + [2]
      @slow
      def    UpperCAmelCase						(    self			)    ->				Any:
          UpperCamelCase	:List[str]     							=       self.tokenizer_en_ru
          UpperCamelCase	:Optional[Any]     							=       self.tokenizer_ru_en

          UpperCamelCase	:Dict     							=       [
              [
                  '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
                  [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 1_3160, 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:
              UpperCamelCase	:Dict     							=       tokenizer_enc.encode(SCREAMING_SNAKE_CASE_   ,    return_tensors=SCREAMING_SNAKE_CASE_			)
              self.assertListEqual(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              # and decode backward, using the reversed languages model
              UpperCamelCase	:int     							=       tokenizer_dec.decode(SCREAMING_SNAKE_CASE_   ,    skip_special_tokens=SCREAMING_SNAKE_CASE_			)
              self.assertEqual(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
      @slow
      def    UpperCAmelCase						(    self			)    ->				Optional[Any]:
          UpperCamelCase	:List[Any]     							=       FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''   ,    do_lower_case=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Tuple     							=       tokenizer.tokenize('''USA is United States of America'''			)
          UpperCamelCase	:List[str]     							=       ['''us''', '''a</w>''', '''is</w>''', '''un''', '''i''', '''ted</w>''', '''st''', '''ates</w>''', '''of</w>''', '''am''', '''er''', '''ica</w>''']
          self.assertListEqual(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
      @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''			)
      def    UpperCAmelCase						(    self			)    ->				Union[str, Any]:
          pass




      @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''			)
      def    UpperCAmelCase						(    self			)    ->				List[Any]:
          pass













from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Optional[int]									=DPRContextEncoderTokenizer
      UpperCamelCase_    : Any									=DPRContextEncoderTokenizerFast
      UpperCamelCase_    : Optional[int]									=True







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=DPRQuestionEncoderTokenizer
      UpperCamelCase_    : Union[str, Any]									=DPRQuestionEncoderTokenizerFast
      UpperCamelCase_    : Any									=True







@require_tokenizers
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=DPRReaderTokenizer
      UpperCamelCase_    : List[Any]									=DPRReaderTokenizerFast
      UpperCamelCase_    : Union[str, Any]									=True
      @slow
      def    UpperCAmelCase						(    self			)    ->				Dict:
          UpperCamelCase	:Optional[int]     							=       self.tokenizer_class.from_pretrained('''bert-base-uncased'''			)

          UpperCamelCase	:Optional[Any]     							=       tokenizer.encode('''question sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[Any]     							=       tokenizer.encode('''title sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:List[str]     							=       tokenizer.encode('''text sequence ''' * 4   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Optional[int]     							=       [[101] + text_a + [102] + text_a + [102] + text_a]
          UpperCamelCase	:Tuple     							=       BatchEncoding({'''input_ids''': input_ids}			)

          UpperCamelCase	:Dict     							=       [[0] * len(input_ids[0]			)]
          UpperCamelCase	:List[Any]     							=       [[0] * len(input_ids[0]			)]
          UpperCamelCase	:Tuple     							=       [0]
          UpperCamelCase	:Union[str, Any]     							=       DPRReaderOutput(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

          UpperCamelCase							,       UpperCamelCase	:Union[str, Any]     							=       8, 9
          UpperCamelCase	:Any     							=       10
          UpperCamelCase	:Optional[Any]     							=       10
          UpperCamelCase	:Optional[int]     							=       tokenizer.decode_best_spans(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].start_index   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].end_index   ,    SCREAMING_SNAKE_CASE_			)
          self.assertEqual(predicted_spans[0].doc_id   ,    0			)




      @slow
      def    UpperCAmelCase						(    self			)    ->				Any:
          UpperCamelCase	:List[str]     							=       self.tokenizer_class.from_pretrained('''bert-base-uncased'''			)

          UpperCamelCase	:Any     							=       tokenizer.encode('''question sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:str     							=       tokenizer.encode('''title sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:Union[str, Any]     							=       tokenizer.encode('''text sequence'''   ,    add_special_tokens=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:List[Any]     							=       [101] + text_a + [102] + text_a + [102] + text_a
          UpperCamelCase	:Any     							=       tokenizer(questions=['''question sequence''']   ,    titles=['''title sequence''']   ,    texts=['''text sequence''']			)
          self.assertIn('''input_ids'''   ,    SCREAMING_SNAKE_CASE_			)
          self.assertIn('''attention_mask'''   ,    SCREAMING_SNAKE_CASE_			)
          self.assertListEqual(encoded_input['''input_ids'''][0]   ,    SCREAMING_SNAKE_CASE_			)





