






import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class       UpperCAmelCase_						( lowercase,     unittest.TestCase			):



      """simple docstring"""
      UpperCamelCase_    : Tuple									=BartTokenizer
      UpperCamelCase_    : List[str]									=BartTokenizerFast
      UpperCamelCase_    : str									=True
      UpperCamelCase_    : Any									=filter_roberta_detectors
      def    UpperCAmelCase						(    self			)    ->				Union[str, Any]:
          super().setUp()
          UpperCamelCase	:Optional[int]     							=       [
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''\u0120''',
              '''\u0120l''',
              '''\u0120n''',
              '''\u0120lo''',
              '''\u0120low''',
              '''er''',
              '''\u0120lowest''',
              '''\u0120newer''',
              '''\u0120wider''',
              '''<unk>''',
          ]
          UpperCamelCase	:Any     							=       dict(zip(SCREAMING_SNAKE_CASE_   ,    range(len(SCREAMING_SNAKE_CASE_			)			)			)			)
          UpperCamelCase	:List[Any]     							=       ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
          UpperCamelCase	:str     							=       {'''unk_token''': '''<unk>'''}

          UpperCamelCase	:Optional[Any]     							=       os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES['''vocab_file''']			)
          UpperCamelCase	:List[str]     							=       os.path.join(self.tmpdirname   ,    VOCAB_FILES_NAMES['''merges_file''']			)
          with open(self.vocab_file   ,    '''w'''   ,    encoding='''utf-8'''			) as fp:
              fp.write(json.dumps(SCREAMING_SNAKE_CASE_			) + '''\n'''			)
          with open(self.merges_file   ,    '''w'''   ,    encoding='''utf-8'''			) as fp:
              fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_			)			)
      def    UpperCAmelCase						(    self   ,    **SCREAMING_SNAKE_CASE_			)    ->				Union[str, Any]:
          kwargs.update(self.special_tokens_map			)
          return self.tokenizer_class.from_pretrained(self.tmpdirname   ,    **SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self   ,    **SCREAMING_SNAKE_CASE_			)    ->				List[Any]:
          kwargs.update(self.special_tokens_map			)
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,    **SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_			)    ->				int:
          return "lower newer", "lower newer"
      @cached_property
      def    UpperCAmelCase						(    self			)    ->				Any:
          return BartTokenizer.from_pretrained('''facebook/bart-large'''			)
      @cached_property
      def    UpperCAmelCase						(    self			)    ->				Dict:
          return BartTokenizerFast.from_pretrained('''facebook/bart-large'''			)
      @require_torch
      def    UpperCAmelCase						(    self			)    ->				List[str]:
          UpperCamelCase	:Tuple     							=       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          UpperCamelCase	:Optional[Any]     							=       [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCamelCase	:Any     							=       tokenizer(SCREAMING_SNAKE_CASE_   ,    max_length=len(SCREAMING_SNAKE_CASE_			)   ,    padding=SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''			)
              self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

              self.assertEqual((2, 9)   ,    batch.input_ids.shape			)
              self.assertEqual((2, 9)   ,    batch.attention_mask.shape			)
              UpperCamelCase	:Tuple     							=       batch.input_ids.tolist()[0]
              self.assertListEqual(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
              # Test that special tokens are reset
      @require_torch
      def    UpperCAmelCase						(    self			)    ->				Tuple:
          UpperCamelCase	:Tuple     							=       ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCamelCase	:str     							=       tokenizer(SCREAMING_SNAKE_CASE_   ,    padding=SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''			)
              # check if input_ids are returned and no labels
              self.assertIn('''input_ids'''   ,    SCREAMING_SNAKE_CASE_			)
              self.assertIn('''attention_mask'''   ,    SCREAMING_SNAKE_CASE_			)
              self.assertNotIn('''labels'''   ,    SCREAMING_SNAKE_CASE_			)
              self.assertNotIn('''decoder_attention_mask'''   ,    SCREAMING_SNAKE_CASE_			)
      @require_torch
      def    UpperCAmelCase						(    self			)    ->				int:
          UpperCamelCase	:Dict     							=       [
              '''Summary of the text.''',
              '''Another summary.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCamelCase	:Union[str, Any]     							=       tokenizer(text_target=SCREAMING_SNAKE_CASE_   ,    max_length=32   ,    padding='''max_length'''   ,    return_tensors='''pt'''			)
              self.assertEqual(32   ,    targets['''input_ids'''].shape[1]			)
      @require_torch
      def    UpperCAmelCase						(    self			)    ->				Dict:
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCamelCase	:Optional[int]     							=       tokenizer(
                  ['''I am a small frog''' * 1024, '''I am a small frog''']   ,    padding=SCREAMING_SNAKE_CASE_   ,    truncation=SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''			)
              self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
              self.assertEqual(batch.input_ids.shape   ,    (2, 1024)			)
      @require_torch
      def    UpperCAmelCase						(    self			)    ->				str:
          UpperCamelCase	:List[str]     							=       ['''A long paragraph for summarization.''']
          UpperCamelCase	:Optional[Any]     							=       [
              '''Summary of the text.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              UpperCamelCase	:Any     							=       tokenizer(SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''			)
              UpperCamelCase	:str     							=       tokenizer(text_target=SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''			)
              UpperCamelCase	:Optional[int]     							=       inputs['''input_ids''']
              UpperCamelCase	:int     							=       targets['''input_ids''']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()			)
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()			)
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()			)
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()			)
      def    UpperCAmelCase						(    self			)    ->				Any:
          pass




      def    UpperCAmelCase						(    self			)    ->				Any:
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
                  UpperCamelCase	:List[Any]     							=       self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_   ,    **SCREAMING_SNAKE_CASE_			)
                  UpperCamelCase	:Union[str, Any]     							=       self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_   ,    **SCREAMING_SNAKE_CASE_			)
                  UpperCamelCase	:Union[str, Any]     							=       '''A, <mask> AllenNLP sentence.'''
                  UpperCamelCase	:Dict     							=       tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_   ,    add_special_tokens=SCREAMING_SNAKE_CASE_   ,    return_token_type_ids=SCREAMING_SNAKE_CASE_			)
                  UpperCamelCase	:Optional[Any]     							=       tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE_   ,    add_special_tokens=SCREAMING_SNAKE_CASE_   ,    return_token_type_ids=SCREAMING_SNAKE_CASE_			)

                  # token_type_ids should put 0 everywhere
                  self.assertEqual(sum(tokens_r['''token_type_ids''']			)   ,    sum(tokens_p['''token_type_ids''']			)			)

                  # attention_mask should put 1 everywhere, so sum over length should be 1
                  self.assertEqual(
                      sum(tokens_r['''attention_mask''']			) / len(tokens_r['''attention_mask''']			)   ,    sum(tokens_p['''attention_mask''']			) / len(tokens_p['''attention_mask''']			)   ,    )

                  UpperCamelCase	:Union[str, Any]     							=       tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']			)
                  UpperCamelCase	:Dict     							=       tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']			)

                  # Rust correctly handles the space before the mask while python doesnt
                  self.assertSequenceEqual(tokens_p['''input_ids''']   ,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]			)
                  self.assertSequenceEqual(tokens_r['''input_ids''']   ,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]			)

                  self.assertSequenceEqual(
                      SCREAMING_SNAKE_CASE_   ,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)
                  self.assertSequenceEqual(
                      SCREAMING_SNAKE_CASE_   ,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)













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_			)





