





"""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 			_a						(				_lowerCAmelCase     ,    unittest.TestCase    ):
				UpperCamelCase								=   LEDTokenizer
				UpperCamelCase								=   LEDTokenizerFast
				UpperCamelCase								=   True






				def     snake_case     (			self		:   str							)     ->				Any:




										'''simple docstring'''



										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      : List[Any]         =   dict(zip(lowerCAmelCase__,			range(len(lowerCAmelCase__							)							)							)							)
										_UpperCamelCase      : str         =   ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										_UpperCamelCase      : Optional[Any]         =   {'''unk_token''': '''<unk>'''}

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






				def     snake_case     (			self		:   List[Any],			**lowerCAmelCase__		:   int							)     ->				List[str]:




										'''simple docstring'''



										kwargs.update(self.special_tokens_map							)
										return self.tokenizer_class.from_pretrained(self.tmpdirname,			**lowerCAmelCase__							)






				def     snake_case     (			self		:   Dict,			**lowerCAmelCase__		:   Tuple							)     ->				Union[str, Any]:




										'''simple docstring'''



										kwargs.update(self.special_tokens_map							)
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,			**lowerCAmelCase__							)






				def     snake_case     (			self		:   str,			lowerCAmelCase__		:   str							)     ->				Tuple:




										'''simple docstring'''



										return "lower newer", "lower newer"






				@cached_property
				def     snake_case     (			self		:   Tuple							)     ->				Optional[int]:




										'''simple docstring'''



										return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''							)






				@cached_property
				def     snake_case     (			self		:   Any							)     ->				Dict:




										'''simple docstring'''



										return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''							)






				@require_torch
				def     snake_case     (			self		:   Tuple							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										_UpperCamelCase      : 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      : str         =   batch.input_ids.tolist()[0]
																self.assertListEqual(lowerCAmelCase__,			lowerCAmelCase__							)






				@require_torch
				def     snake_case     (			self		:   Any							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, 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     snake_case     (			self		:   str							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : str         =   [
										    '''Summary of the text.''',
										    '''Another summary.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																_UpperCamelCase      : 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     snake_case     (			self		:   List[Any]							)     ->				Optional[int]:




										'''simple docstring'''



										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																_UpperCamelCase      : Any         =   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     snake_case     (			self		:   Tuple							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   ['''A long paragraph for summarization.''']
										_UpperCamelCase      : int         =   [
										    '''Summary of the text.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																_UpperCamelCase      : str         =   tokenizer(lowerCAmelCase__,			return_tensors='''pt'''							)
																_UpperCamelCase      : List[str]         =   tokenizer(text_target=lowerCAmelCase__,			return_tensors='''pt'''							)
																_UpperCamelCase      : Tuple         =   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     snake_case     (			self		:   int							)     ->				List[str]:




										'''simple docstring'''



										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																_UpperCamelCase      : Any         =   ['''Summary of the text.''', '''Another summary.''']
																_UpperCamelCase      : int         =   [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																_UpperCamelCase      : int         =   tokenizer(lowerCAmelCase__,			padding=lowerCAmelCase__							)
																_UpperCamelCase      : Union[str, Any]         =   [[0] * len(lowerCAmelCase__							) for x in encoded_output['''input_ids''']]
																_UpperCamelCase      : Dict         =   tokenizer.pad(lowerCAmelCase__							)
																self.assertSequenceEqual(outputs['''global_attention_mask'''],			lowerCAmelCase__							)






				def     snake_case     (			self		:   List[str]							)     ->				int:




										'''simple docstring'''



										pass






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




										'''simple docstring'''



										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
																						_UpperCamelCase      : List[str]         =   self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__,			**lowerCAmelCase__							)
																						_UpperCamelCase      : Optional[Any]         =   self.tokenizer_class.from_pretrained(lowerCAmelCase__,			**lowerCAmelCase__							)
																						_UpperCamelCase      : Tuple         =   '''A, <mask> AllenNLP sentence.'''
																						_UpperCamelCase      : 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      : Optional[Any]         =   tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']							)
																						_UpperCamelCase      : Tuple         =   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 List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   ['''image_processor''', '''tokenizer''']
				UpperCamelCase								=   '''BlipImageProcessor'''
				UpperCamelCase								=   '''AutoTokenizer'''






				def __init__(			self		:   List[str],			lowerCAmelCase__		:   Optional[int],			lowerCAmelCase__		:   Optional[int]							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   False
										super().__init__(lowerCAmelCase__,			lowerCAmelCase__							)
										_UpperCamelCase      : Tuple         =   self.image_processor






				def __call__(			self		:   str,			lowerCAmelCase__		:   ImageInput = None,			lowerCAmelCase__		:   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,			lowerCAmelCase__		:   bool = True,			lowerCAmelCase__		:   Union[bool, str, PaddingStrategy] = False,			lowerCAmelCase__		:   Union[bool, str, TruncationStrategy] = None,			lowerCAmelCase__		:   Optional[int] = None,			lowerCAmelCase__		:   int = 0,			lowerCAmelCase__		:   Optional[int] = None,			lowerCAmelCase__		:   Optional[bool] = None,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   bool = False,			lowerCAmelCase__		:   bool = True,			lowerCAmelCase__		:   Optional[Union[str, TensorType]] = None,			**lowerCAmelCase__		:   Optional[Any],			)     ->				BatchEncoding:




										'''simple docstring'''



										if images is None and text is None:
																raise ValueError('''You have to specify either images or text.'''							)

										# Get only text
										if images is None:
																_UpperCamelCase      : int         =   self.tokenizer
																_UpperCamelCase      : List[str]         =   self.tokenizer(
																    text=lowerCAmelCase__,			add_special_tokens=lowerCAmelCase__,			padding=lowerCAmelCase__,			truncation=lowerCAmelCase__,			max_length=lowerCAmelCase__,			stride=lowerCAmelCase__,			pad_to_multiple_of=lowerCAmelCase__,			return_attention_mask=lowerCAmelCase__,			return_overflowing_tokens=lowerCAmelCase__,			return_special_tokens_mask=lowerCAmelCase__,			return_offsets_mapping=lowerCAmelCase__,			return_token_type_ids=lowerCAmelCase__,			return_length=lowerCAmelCase__,			verbose=lowerCAmelCase__,			return_tensors=lowerCAmelCase__,			**lowerCAmelCase__,			)
																return text_encoding

										# add pixel_values
										_UpperCamelCase      : List[str]         =   self.image_processor(lowerCAmelCase__,			return_tensors=lowerCAmelCase__							)

										if text is not None:
																_UpperCamelCase      : Any         =   self.tokenizer(
																    text=lowerCAmelCase__,			add_special_tokens=lowerCAmelCase__,			padding=lowerCAmelCase__,			truncation=lowerCAmelCase__,			max_length=lowerCAmelCase__,			stride=lowerCAmelCase__,			pad_to_multiple_of=lowerCAmelCase__,			return_attention_mask=lowerCAmelCase__,			return_overflowing_tokens=lowerCAmelCase__,			return_special_tokens_mask=lowerCAmelCase__,			return_offsets_mapping=lowerCAmelCase__,			return_token_type_ids=lowerCAmelCase__,			return_length=lowerCAmelCase__,			verbose=lowerCAmelCase__,			return_tensors=lowerCAmelCase__,			**lowerCAmelCase__,			)
										else:
																_UpperCamelCase      : List[Any]         =   None

										if text_encoding is not None:
																encoding_image_processor.update(lowerCAmelCase__							)

										return encoding_image_processor






				def     snake_case     (			self		:   List[Any],			*lowerCAmelCase__		:   List[str],			**lowerCAmelCase__		:   str							)     ->				Optional[int]:




										'''simple docstring'''



										return self.tokenizer.batch_decode(*lowerCAmelCase__,			**lowerCAmelCase__							)






				def     snake_case     (			self		:   List[Any],			*lowerCAmelCase__		:   Dict,			**lowerCAmelCase__		:   Any							)     ->				List[str]:




										'''simple docstring'''



										return self.tokenizer.decode(*lowerCAmelCase__,			**lowerCAmelCase__							)






				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def     snake_case     (			self		:   Any							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.tokenizer.model_input_names
										_UpperCamelCase      : Union[str, Any]         =   self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



