
import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad






class   UpperCAmelCase		(						unittest.TestCase ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Dict:


          '''simple docstring'''


          snake_case       :   Tuple							=      10



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Tuple:


          '''simple docstring'''


          snake_case       :   List[Any]							=      [1, 2, 3, 4]
          snake_case       :   Optional[Any]							=      [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
          self.assertEqual(truncate_or_pad(snake_case__    ,						self.block_size    ,						0 )    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   str							=      [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
          snake_case       :   List[str]							=      [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
          self.assertEqual(truncate_or_pad(snake_case__    ,						self.block_size    ,						0 )    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Dict:


          '''simple docstring'''


          snake_case       :   str							=      [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
          snake_case       :   str							=      [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
          self.assertEqual(truncate_or_pad(snake_case__    ,						self.block_size    ,						0 )    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Tuple:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      "It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this."
          snake_case				,					snake_case       :   int							=      process_story(snake_case__ )
          self.assertEqual(snake_case__    ,						[] )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Any:


          '''simple docstring'''


          snake_case       :   List[str]							=      ""
          snake_case				,					snake_case       :   Any							=      process_story(snake_case__ )
          self.assertEqual(snake_case__    ,						[] )
          self.assertEqual(snake_case__    ,						[] )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      (
              "It was the year of Our Lord one thousand seven hundred and "
              "seventy-five\n\nSpiritual revelations were conceded to England "
              "at that favoured period, as at this.\n@highlight\n\nIt was the best of times"
          )
          snake_case				,					snake_case       :   Dict							=      process_story(snake_case__ )

          snake_case       :   Union[str, Any]							=      [
              "It was the year of Our Lord one thousand seven hundred and seventy-five.",
              "Spiritual revelations were conceded to England at that favoured period, as at this.",
          ]
          self.assertEqual(snake_case__    ,						snake_case__ )

          snake_case       :   Union[str, Any]							=      ["It was the best of times."]
          self.assertEqual(snake_case__    ,						snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	List[str]:


          '''simple docstring'''


          snake_case       :   int							=      torch.tensor([1, 2, 3, 4] )
          snake_case       :   Tuple							=      torch.tensor([1, 1, 1, 1] )
          np.testing.assert_array_equal(build_mask(snake_case__    ,						0 ).numpy()    ,						expected.numpy() )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      torch.tensor([1, 2, 3, 4, 23, 23, 23] )
          snake_case       :   int							=      torch.tensor([1, 1, 1, 1, 0, 0, 0] )
          np.testing.assert_array_equal(build_mask(snake_case__    ,						23 ).numpy()    ,						expected.numpy() )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Tuple:


          '''simple docstring'''


          snake_case       :   int							=      torch.tensor([8, 2, 3, 4, 1, 1, 1] )
          snake_case       :   int							=      torch.tensor([1, 1, 1, 1, 0, 0, 0] )
          np.testing.assert_array_equal(build_mask(snake_case__    ,						1 ).numpy()    ,						expected.numpy() )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      1_01
          snake_case       :   Union[str, Any]							=      torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_01, 5, 6], [1, 1_01, 3, 4, 1_01, 6]] )
          snake_case       :   Any							=      torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )

          snake_case       :   int							=      compute_token_type_ids(snake_case__    ,						snake_case__ )
          np.testing.assert_array_equal(snake_case__    ,						snake_case__ )







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


__lowerCamelCase							=       {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase							=       {
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

__lowerCamelCase							=       {
    """unc-nlp/lxmert-base-uncased""": 5_12,
}

__lowerCamelCase							=       {
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}






class   UpperCAmelCase		(						A_ ):
   A__					:		Any						     =     VOCAB_FILES_NAMES
   A__					:		Optional[int]						     =     PRETRAINED_VOCAB_FILES_MAP
   A__					:		Tuple						     =     PRETRAINED_INIT_CONFIGURATION
   A__					:		Tuple						     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__					:		List[Any]						     =     LxmertTokenizer



   def __init__(self		:					Dict    ,						snake_case__		:					Tuple=None    ,						snake_case__		:					Optional[Any]=None    ,						snake_case__		:					Optional[Any]=True    ,						snake_case__		:					Tuple="[UNK]"    ,						snake_case__		:					Optional[Any]="[SEP]"    ,						snake_case__		:					Optional[Any]="[PAD]"    ,						snake_case__		:					List[Any]="[CLS]"    ,						snake_case__		:					Tuple="[MASK]"    ,						snake_case__		:					Dict=True    ,						snake_case__		:					Union[str, Any]=None    ,						**snake_case__		:					Dict    ,						)		->	Optional[int]:


          '''simple docstring'''


          super().__init__(
              snake_case__    ,						tokenizer_file=snake_case__    ,						do_lower_case=snake_case__    ,						unk_token=snake_case__    ,						sep_token=snake_case__    ,						pad_token=snake_case__    ,						cls_token=snake_case__    ,						mask_token=snake_case__    ,						tokenize_chinese_chars=snake_case__    ,						strip_accents=snake_case__    ,						**snake_case__    ,						)

          snake_case       :   int							=      json.loads(self.backend_tokenizer.normalizer.__getstate__() )
          if (
              normalizer_state.get("lowercase"    ,						snake_case__ ) != do_lower_case
              or normalizer_state.get("strip_accents"    ,						snake_case__ ) != strip_accents
              or normalizer_state.get("handle_chinese_chars"    ,						snake_case__ ) != tokenize_chinese_chars
          ):
                 snake_case       :   Union[str, Any]							=      getattr(snake_case__    ,						normalizer_state.pop("type" ) )
                 snake_case       :   str							=      do_lower_case
                 snake_case       :   List[Any]							=      strip_accents
                 snake_case       :   Optional[int]							=      tokenize_chinese_chars
                 snake_case       :   int							=      normalizer_class(**snake_case__ )

          snake_case       :   Optional[Any]							=      do_lower_case



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Dict=None )		->	Any:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
                 output += token_ids_a + [self.sep_token_id]

          return output



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					List[int]    ,						snake_case__		:					Optional[List[int]] = None )		->	List[int]:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      [self.sep_token_id]
          snake_case       :   Optional[Any]							=      [self.cls_token_id]
          if token_ids_a is None:
                 return len(cls + token_ids_a + sep ) * [0]
          return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					str    ,						snake_case__		:					Optional[str] = None )		->	Tuple[str]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self._tokenizer.model.save(snake_case__    ,						name=snake_case__ )
          return tuple(snake_case__ )





