'''simple docstring'''

import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class   SCREAMING_SNAKE_CASE			(						__lowercase    ,			unittest.TestCase):







		"""simple docstring"""
		lowercase   :      Optional[int]       =							LxmertTokenizer
		lowercase   :      int       =							LxmertTokenizerFast
		lowercase   :      Union[str, Any]       =							True
		lowercase   :      str       =							True






		def 		UpperCamelCase__       (   self				) ->     str:
								super().setUp()

								_lowerCAmelCase										=[
								    '[UNK]',
								    '[CLS]',
								    '[SEP]',
								    'want',
								    '##want',
								    '##ed',
								    'wa',
								    'un',
								    'runn',
								    '##ing',
								    ',',
								    'low',
								    'lowest',
								]
								_lowerCAmelCase										=os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES['vocab_file']				)
								with open(self.vocab_file		,						'w'		,						encoding='utf-8'				) as vocab_writer:
														vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]				)				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     int:
								_lowerCAmelCase										='UNwant\u00E9d,running'
								_lowerCAmelCase										='unwanted, running'
								return input_text, output_text






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								_lowerCAmelCase										=self.tokenizer_class(self.vocab_file				)

								_lowerCAmelCase										=tokenizer.tokenize('UNwant\u00E9d,running'				)
								self.assertListEqual(__A		,						['un', '##want', '##ed', ',', 'runn', '##ing']				)
								self.assertListEqual(tokenizer.convert_tokens_to_ids(__A				)		,						[7, 4, 5, 10, 8, 9]				)






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								if not self.test_rust_tokenizer:
														return

								_lowerCAmelCase										=self.get_tokenizer()
								_lowerCAmelCase										=self.get_rust_tokenizer()

								_lowerCAmelCase										='I was born in 92000, and this is falsé.'

								_lowerCAmelCase										=tokenizer.tokenize(__A				)
								_lowerCAmelCase										=rust_tokenizer.tokenize(__A				)
								self.assertListEqual(__A		,						__A				)

								_lowerCAmelCase										=tokenizer.encode(__A		,						add_special_tokens=__A				)
								_lowerCAmelCase										=rust_tokenizer.encode(__A		,						add_special_tokens=__A				)
								self.assertListEqual(__A		,						__A				)

								_lowerCAmelCase										=self.get_rust_tokenizer()
								_lowerCAmelCase										=tokenizer.encode(__A				)
								_lowerCAmelCase										=rust_tokenizer.encode(__A				)
								self.assertListEqual(__A		,						__A				)






'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Any       =							['image_processor', 'tokenizer']
		lowercase   :      Any       =							'CLIPImageProcessor'
		lowercase   :      int       =							('CLIPTokenizer', 'CLIPTokenizerFast')






		def __init__(   self		,						__A=None		,						__A=None		,						**__A				) ->     str:
								_lowerCAmelCase										=None
								if "feature_extractor" in kwargs:
														warnings.warn(
														    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
														    ' instead.'		,						__A		,						)
														_lowerCAmelCase										=kwargs.pop('feature_extractor'				)

								_lowerCAmelCase										=image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
														raise ValueError('You need to specify an `image_processor`.'				)
								if tokenizer is None:
														raise ValueError('You need to specify a `tokenizer`.'				)

								super().__init__(__A		,						__A				)






		def __call__(   self		,						__A=None		,						__A=None		,						__A=None		,						**__A				) ->     Optional[int]:

								if text is None and images is None:
														raise ValueError('You have to specify either text or images. Both cannot be none.'				)

								if text is not None:
														_lowerCAmelCase										=self.tokenizer(__A		,						return_tensors=__A		,						**__A				)

								if images is not None:
														_lowerCAmelCase										=self.image_processor(__A		,						return_tensors=__A		,						**__A				)

								if text is not None and images is not None:
														_lowerCAmelCase										=image_features.pixel_values
														return encoding
								elif text is not None:
														return encoding
								else:
														return BatchEncoding(data=dict(**__A				)		,						tensor_type=__A				)






		def 		UpperCamelCase__       (   self		,						*__A		,						**__A				) ->     Any:
								return self.tokenizer.batch_decode(*__A		,						**__A				)






		def 		UpperCamelCase__       (   self		,						*__A		,						**__A				) ->     Optional[int]:
								return self.tokenizer.decode(*__A		,						**__A				)






		@property
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=self.tokenizer.model_input_names
								_lowerCAmelCase										=self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)






		@property
		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								warnings.warn(
								    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,						__A		,						)
								return self.image_processor_class






		@property
		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								warnings.warn(
								    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,						__A		,						)
								return self.image_processor





