'''simple docstring'''
import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led 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






@require_tokenizers
class 			__lowercase		(						__lowerCAmelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						LEDTokenizer
     SCREAMING_SNAKE_CASE__		   =						LEDTokenizerFast
     SCREAMING_SNAKE_CASE__		   =						True







     def lowerCAmelCase     (		self    ):
         super().setUp()
         __UpperCamelCase	:	List[Any]		    =  [
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''\u0120''',
             '''\u0120l''',
             '''\u0120n''',
             '''\u0120lo''',
             '''\u0120low''',
             '''er''',
             '''\u0120lowest''',
             '''\u0120newer''',
             '''\u0120wider''',
             '''<unk>''',
         ]
         __UpperCamelCase	:	Tuple		    =  dict(zip(lowerCamelCase__     ,			range(len(lowerCamelCase__    )    )    )    )
         __UpperCamelCase	:	Optional[Any]		    =  ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
         __UpperCamelCase	:	Dict		    =  {'''unk_token''': '''<unk>'''}

         __UpperCamelCase	:	Any		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase	:	int		    =  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(lowerCamelCase__    ) + '\n'    )
         with open(self.merges_file     ,			'w'     ,			encoding='utf-8'    ) as fp:
             fp.write('\n'.join(lowerCamelCase__    )    )







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         kwargs.update(self.special_tokens_map    )
         return self.tokenizer_class.from_pretrained(self.tmpdirname     ,			**lowerCamelCase__    )







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         kwargs.update(self.special_tokens_map    )
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,			**lowerCamelCase__    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return "lower newer", "lower newer"







     @cached_property
     def lowerCAmelCase     (		self    ):
         return LEDTokenizer.from_pretrained('allenai/led-base-16384'    )







     @cached_property
     def lowerCAmelCase     (		self    ):
         return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'    )







     @require_torch
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Union[str, Any]		    =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         __UpperCamelCase	:	Union[str, Any]		    =  [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             __UpperCamelCase	:	str		    =  tokenizer(lowerCamelCase__     ,			max_length=len(lowerCamelCase__    )     ,			padding=lowerCamelCase__     ,			return_tensors='pt'    )
             self.assertIsInstance(lowerCamelCase__     ,			lowerCamelCase__    )

             self.assertEqual((2, 9)     ,			batch.input_ids.shape    )
             self.assertEqual((2, 9)     ,			batch.attention_mask.shape    )
             __UpperCamelCase	:	Dict		    =  batch.input_ids.tolist()[0]
             self.assertListEqual(lowerCamelCase__     ,			lowerCamelCase__    )







     @require_torch
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[Any]		    =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             __UpperCamelCase	:	Union[str, Any]		    =  tokenizer(lowerCamelCase__     ,			padding=lowerCamelCase__     ,			return_tensors='pt'    )
             self.assertIn('input_ids'     ,			lowerCamelCase__    )
             self.assertIn('attention_mask'     ,			lowerCamelCase__    )
             self.assertNotIn('labels'     ,			lowerCamelCase__    )
             self.assertNotIn('decoder_attention_mask'     ,			lowerCamelCase__    )







     @require_torch
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	int		    =  [
             '''Summary of the text.''',
             '''Another summary.''',
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             __UpperCamelCase	:	List[Any]		    =  tokenizer(text_target=lowerCamelCase__     ,			max_length=3_2     ,			padding='max_length'     ,			return_tensors='pt'    )
             self.assertEqual(3_2     ,			targets['input_ids'].shape[1]    )







     @require_torch
     def lowerCAmelCase     (		self    ):






         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             __UpperCamelCase	:	Dict		    =  tokenizer(
                 ['I am a small frog' * 1_0_2_4, 'I am a small frog']     ,			padding=lowerCamelCase__     ,			truncation=lowerCamelCase__     ,			return_tensors='pt'    )
             self.assertIsInstance(lowerCamelCase__     ,			lowerCamelCase__    )
             self.assertEqual(batch.input_ids.shape     ,			(2, 5_1_2_2)    )







     @require_torch
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[Any]		    =  ['''A long paragraph for summarization.''']
         __UpperCamelCase	:	str		    =  [
             '''Summary of the text.''',
         ]
         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             __UpperCamelCase	:	Dict		    =  tokenizer(lowerCamelCase__     ,			return_tensors='pt'    )
             __UpperCamelCase	:	int		    =  tokenizer(text_target=lowerCamelCase__     ,			return_tensors='pt'    )
             __UpperCamelCase	:	Union[str, Any]		    =  inputs['''input_ids''']
             __UpperCamelCase	:	Dict		    =  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()    )







     @require_torch
     def lowerCAmelCase     (		self    ):






         for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             __UpperCamelCase	:	Union[str, Any]		    =  ['''Summary of the text.''', '''Another summary.''']
             __UpperCamelCase	:	Optional[Any]		    =  [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

             __UpperCamelCase	:	List[str]		    =  tokenizer(lowerCamelCase__     ,			padding=lowerCamelCase__    )
             __UpperCamelCase	:	Union[str, Any]		    =  [[0] * len(lowerCamelCase__    ) for x in encoded_output['''input_ids''']]
             __UpperCamelCase	:	Optional[Any]		    =  tokenizer.pad(lowerCamelCase__    )
             self.assertSequenceEqual(outputs['global_attention_mask']     ,			lowerCamelCase__    )







     def lowerCAmelCase     (		self    ):
         pass







     def lowerCAmelCase     (		self    ):






         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
                 __UpperCamelCase	:	Union[str, Any]		    =  self.rust_tokenizer_class.from_pretrained(lowerCamelCase__     ,			**lowerCamelCase__    )
                 __UpperCamelCase	:	Dict		    =  self.tokenizer_class.from_pretrained(lowerCamelCase__     ,			**lowerCamelCase__    )
                 __UpperCamelCase	:	Optional[Any]		    =  '''A, <mask> AllenNLP sentence.'''
                 __UpperCamelCase	:	Optional[Any]		    =  tokenizer_r.encode_plus(lowerCamelCase__     ,			add_special_tokens=lowerCamelCase__     ,			return_token_type_ids=lowerCamelCase__    )
                 __UpperCamelCase	:	int		    =  tokenizer_p.encode_plus(lowerCamelCase__     ,			add_special_tokens=lowerCamelCase__     ,			return_token_type_ids=lowerCamelCase__    )
                 self.assertEqual(sum(tokens_r['token_type_ids']    )     ,			sum(tokens_p['token_type_ids']    )    )
                 self.assertEqual(
                     sum(tokens_r['attention_mask']    ) / len(tokens_r['attention_mask']    )     ,			sum(tokens_p['attention_mask']    ) / len(tokens_p['attention_mask']    )     ,			)

                 __UpperCamelCase	:	int		    =  tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']    )
                 __UpperCamelCase	:	Optional[int]		    =  tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']    )
                 self.assertSequenceEqual(tokens_p['input_ids']     ,			[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]    )
                 self.assertSequenceEqual(tokens_r['input_ids']     ,			[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]    )

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




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a=  {
    '''configuration_bridgetower''': [
        '''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BridgeTowerConfig''',
        '''BridgeTowerTextConfig''',
        '''BridgeTowerVisionConfig''',
    ],
    '''processing_bridgetower''': ['''BridgeTowerProcessor'''],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  ['''BridgeTowerImageProcessor''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  [
      '''BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''BridgeTowerForContrastiveLearning''',
      '''BridgeTowerForImageAndTextRetrieval''',
      '''BridgeTowerForMaskedLM''',
      '''BridgeTowerModel''',
      '''BridgeTowerPreTrainedModel''',
  ]


if TYPE_CHECKING:
  from .configuration_bridgetower import (
      BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      BridgeTowerConfig,
      BridgeTowerTextConfig,
      BridgeTowerVisionConfig,
  )
  from .processing_bridgetower import BridgeTowerProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_bridgetower import BridgeTowerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bridgetower import (
        BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
        BridgeTowerForContrastiveLearning,
        BridgeTowerForImageAndTextRetrieval,
        BridgeTowerForMaskedLM,
        BridgeTowerModel,
        BridgeTowerPreTrainedModel,
    )


else:
  import sys

  a=  _LazyModule(__name__, globals()['''__file__'''], _import_structure)



