






'''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    __SCREAMING_SNAKE_CASE					(	_UpperCamelCase		, unittest.TestCase ):
 snake_case_     							=							LEDTokenizer
 snake_case_     							=							LEDTokenizerFast
 snake_case_     							=							True




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

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




 def    __magic_name__		(					self		:							List[Any]    ,					**__lowercase		:							List[Any]					)		->  List[Any]:
       kwargs.update(self.special_tokens_map					)
       return self.tokenizer_class.from_pretrained(self.tmpdirname    ,					**_UpperCAmelCase					)




 def    __magic_name__		(					self		:							Dict    ,					**__lowercase		:							str					)		->  Any:
       kwargs.update(self.special_tokens_map					)
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,					**_UpperCAmelCase					)




 def    __magic_name__		(					self		:							Any    ,					__lowercase		:							Any					)		->  int:
       return "lower newer", "lower newer"




 @cached_property
 def    __magic_name__		(					self		:							str					)		->  Optional[int]:
       return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''					)




 @cached_property
 def    __magic_name__		(					self		:							Optional[Any]					)		->  Dict:
       return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''					)




 @require_torch
 def    __magic_name__		(					self		:							List[str]					)		->  Dict:
       SCREAMING_SNAKE_CASE__   : Any													=['A long paragraph for summarization.', 'Another paragraph for summarization.']
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

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

             self.assertEqual((2, 9)    ,					batch.input_ids.shape					)
             self.assertEqual((2, 9)    ,					batch.attention_mask.shape					)
             SCREAMING_SNAKE_CASE__   : int													=batch.input_ids.tolist()[0]
             self.assertListEqual(_UpperCAmelCase    ,					_UpperCAmelCase					)




 @require_torch
 def    __magic_name__		(					self		:							Union[str, Any]					)		->  Any:
       SCREAMING_SNAKE_CASE__   : Dict													=['A long paragraph for summarization.', 'Another paragraph for summarization.']
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             SCREAMING_SNAKE_CASE__   : int													=tokenizer(_UpperCAmelCase    ,					padding=_UpperCAmelCase    ,					return_tensors='''pt'''					)
             self.assertIn('''input_ids'''    ,					_UpperCAmelCase					)
             self.assertIn('''attention_mask'''    ,					_UpperCAmelCase					)
             self.assertNotIn('''labels'''    ,					_UpperCAmelCase					)
             self.assertNotIn('''decoder_attention_mask'''    ,					_UpperCAmelCase					)




 @require_torch
 def    __magic_name__		(					self		:							str					)		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__   : List[Any]													=[
           'Summary of the text.',
           'Another summary.',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             SCREAMING_SNAKE_CASE__   : Dict													=tokenizer(text_target=_UpperCAmelCase    ,					max_length=32    ,					padding='''max_length'''    ,					return_tensors='''pt'''					)
             self.assertEqual(32    ,					targets['''input_ids'''].shape[1]					)




 @require_torch
 def    __magic_name__		(					self		:							int					)		->  int:
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             SCREAMING_SNAKE_CASE__   : Any													=tokenizer(
                 ['''I am a small frog''' * 10_24, '''I am a small frog''']    ,					padding=_UpperCAmelCase    ,					truncation=_UpperCAmelCase    ,					return_tensors='''pt'''					)
             self.assertIsInstance(_UpperCAmelCase    ,					_UpperCAmelCase					)
             self.assertEqual(batch.input_ids.shape    ,					(2, 51_22)					)




 @require_torch
 def    __magic_name__		(					self		:							List[str]					)		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__   : int													=['A long paragraph for summarization.']
       SCREAMING_SNAKE_CASE__   : int													=[
           'Summary of the text.',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             SCREAMING_SNAKE_CASE__   : Dict													=tokenizer(_UpperCAmelCase    ,					return_tensors='''pt'''					)
             SCREAMING_SNAKE_CASE__   : List[str]													=tokenizer(text_target=_UpperCAmelCase    ,					return_tensors='''pt'''					)
             SCREAMING_SNAKE_CASE__   : List[Any]													=inputs['input_ids']
             SCREAMING_SNAKE_CASE__   : Tuple													=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    __magic_name__		(					self		:							Union[str, Any]					)		->  Optional[Any]:
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             SCREAMING_SNAKE_CASE__   : Optional[Any]													=['Summary of the text.', 'Another summary.']
             SCREAMING_SNAKE_CASE__   : int													=[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

             SCREAMING_SNAKE_CASE__   : List[Any]													=tokenizer(_UpperCAmelCase    ,					padding=_UpperCAmelCase					)
             SCREAMING_SNAKE_CASE__   : int													=[[0] * len(_UpperCAmelCase					) for x in encoded_output['input_ids']]
             SCREAMING_SNAKE_CASE__   : List[Any]													=tokenizer.pad(_UpperCAmelCase					)
             self.assertSequenceEqual(outputs['''global_attention_mask''']    ,					_UpperCAmelCase					)




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




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

                   SCREAMING_SNAKE_CASE__   : Any													=tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']					)
                   SCREAMING_SNAKE_CASE__   : Optional[Any]													=tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']					)
                   self.assertSequenceEqual(tokens_p['''input_ids''']    ,					[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]					)
                   self.assertSequenceEqual(tokens_r['''input_ids''']    ,					[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]					)

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




'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase									=   {
    '''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''LILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LiltForQuestionAnswering''',
							    '''LiltForSequenceClassification''',
							    '''LiltForTokenClassification''',
							    '''LiltModel''',
							    '''LiltPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_lilt import (
														    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LiltForQuestionAnswering,
														    LiltForSequenceClassification,
														    LiltForTokenClassification,
														    LiltModel,
														    LiltPreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase									=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




