

import warnings
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_		(	_UpperCAmelCase    ):
							_UpperCAmelCase   :  Optional[int]										=				['image_processor', 'tokenizer']
							_UpperCAmelCase   :  int										=				'FlavaImageProcessor'
							_UpperCAmelCase   :  Optional[Any]										=				('BertTokenizer', 'BertTokenizerFast')
							def __init__(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=None       ,SCREAMING_SNAKE_CASE__		:      Tuple=None       ,**SCREAMING_SNAKE_CASE__		:      int):
								__lowerCamelCase    :    Any 	=			None
								if "feature_extractor" in kwargs:
									warnings.warn(
									    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
									    ' instead.'       ,SCREAMING_SNAKE_CASE_       ,)
									__lowerCamelCase    :    Any 	=			kwargs.pop('feature_extractor')

								__lowerCamelCase    :    Optional[Any] 	=			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__(SCREAMING_SNAKE_CASE_       ,SCREAMING_SNAKE_CASE_)
								__lowerCamelCase    :    Any 	=			self.image_processor
							def __call__(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      List[str] = None       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any] = None       ,SCREAMING_SNAKE_CASE__		:      int = True       ,SCREAMING_SNAKE_CASE__		:      List[Any] = False       ,SCREAMING_SNAKE_CASE__		:      Optional[Any] = False       ,SCREAMING_SNAKE_CASE__		:      int = None       ,SCREAMING_SNAKE_CASE__		:      List[str] = 0       ,SCREAMING_SNAKE_CASE__		:      Dict = None       ,SCREAMING_SNAKE_CASE__		:      Any = None       ,SCREAMING_SNAKE_CASE__		:      str = None       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any] = None       ,SCREAMING_SNAKE_CASE__		:      Tuple = None       ,SCREAMING_SNAKE_CASE__		:      List[Any] = False       ,SCREAMING_SNAKE_CASE__		:      Optional[Any] = False       ,SCREAMING_SNAKE_CASE__		:      Any = False       ,SCREAMING_SNAKE_CASE__		:      str = False       ,SCREAMING_SNAKE_CASE__		:      Optional[Any] = True       ,SCREAMING_SNAKE_CASE__		:      Dict = None       ,**SCREAMING_SNAKE_CASE__		:      List[Any]       ,):

								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    :    List[Any] 	=			self.tokenizer(
									    text=SCREAMING_SNAKE_CASE_       ,add_special_tokens=SCREAMING_SNAKE_CASE_       ,padding=SCREAMING_SNAKE_CASE_       ,truncation=SCREAMING_SNAKE_CASE_       ,max_length=SCREAMING_SNAKE_CASE_       ,stride=SCREAMING_SNAKE_CASE_       ,pad_to_multiple_of=SCREAMING_SNAKE_CASE_       ,return_token_type_ids=SCREAMING_SNAKE_CASE_       ,return_attention_mask=SCREAMING_SNAKE_CASE_       ,return_overflowing_tokens=SCREAMING_SNAKE_CASE_       ,return_special_tokens_mask=SCREAMING_SNAKE_CASE_       ,return_offsets_mapping=SCREAMING_SNAKE_CASE_       ,return_length=SCREAMING_SNAKE_CASE_       ,verbose=SCREAMING_SNAKE_CASE_       ,return_tensors=SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_       ,)
								if images is not None:
									__lowerCamelCase    :    Optional[Any] 	=			self.image_processor(
									    SCREAMING_SNAKE_CASE_       ,return_image_mask=SCREAMING_SNAKE_CASE_       ,return_codebook_pixels=SCREAMING_SNAKE_CASE_       ,return_tensors=SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_       ,)

								if text is not None and images is not None:
									encoding.update(SCREAMING_SNAKE_CASE_)
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_)       ,tensor_type=SCREAMING_SNAKE_CASE_)
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,*SCREAMING_SNAKE_CASE__		:      List[Any]       ,**SCREAMING_SNAKE_CASE__		:      List[Any]):
								return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_)
							def    lowerCAmelCase					(		self		:      Any       ,*SCREAMING_SNAKE_CASE__		:      Optional[int]       ,**SCREAMING_SNAKE_CASE__		:      int):
								return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_       ,**SCREAMING_SNAKE_CASE_)
							@property
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    Dict 	=			self.tokenizer.model_input_names
								__lowerCamelCase    :    str 	=			self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
							@property
							def    lowerCAmelCase					(		self		:      int):
								warnings.warn(
								    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,SCREAMING_SNAKE_CASE_       ,)
								return self.image_processor_class


							@property
							def    lowerCAmelCase					(		self		:      List[str]):
								warnings.warn(
								    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       ,SCREAMING_SNAKE_CASE_       ,)
								return self.image_processor







import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar






class 	A_		(	unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    List[Any] 	=			logging.get_logger()

								# the current default level is logging.WARNING
								__lowerCamelCase    :    Dict 	=			logging.get_verbosity()

								logging.set_verbosity_error()
								self.assertEqual(logger.getEffectiveLevel()       ,logging.get_verbosity())

								logging.set_verbosity_warning()
								self.assertEqual(logger.getEffectiveLevel()       ,logging.get_verbosity())

								logging.set_verbosity_info()
								self.assertEqual(logger.getEffectiveLevel()       ,logging.get_verbosity())

								logging.set_verbosity_debug()
								self.assertEqual(logger.getEffectiveLevel()       ,logging.get_verbosity())

								# restore to the original level
								logging.set_verbosity(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[int] 	=			logging.get_verbosity()

								__lowerCamelCase    :    str 	=			logging.get_logger('transformers.models.bart.tokenization_bart')
								__lowerCamelCase    :    Tuple 	=			'Testing 1, 2, 3'

								# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
								if level_origin <= logging.WARNING:
									with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
										logger.warning(SCREAMING_SNAKE_CASE__)
									self.assertEqual(cl.out       ,msg + '\n')

								# this is setting the level for all of `transformers.*` loggers
								logging.set_verbosity_error()

								# should not be able to log warnings
								with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
									logger.warning(SCREAMING_SNAKE_CASE__)
								self.assertEqual(cl.out       ,'')

								# should be able to log warnings again
								logging.set_verbosity_warning()
								with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
									logger.warning(SCREAMING_SNAKE_CASE__)
								self.assertEqual(cl.out       ,msg + '\n')

								# restore to the original level
								logging.set_verbosity(SCREAMING_SNAKE_CASE__)
							@mockenv(TRANSFORMERS_VERBOSITY='error')
							def    lowerCAmelCase					(		self		:      Tuple):
								# reset for the env var to take effect, next time some logger call is made
								transformers.utils.logging._reset_library_root_logger()
								# this action activates the env var
								__lowerCamelCase    :    int 	=			logging.get_logger('transformers.models.bart.tokenization_bart')

								__lowerCamelCase    :    int 	=			os.getenv('TRANSFORMERS_VERBOSITY'       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[Any] 	=			logging.log_levels[env_level_str]

								__lowerCamelCase    :    Tuple 	=			logging.get_verbosity()
								self.assertEqual(
								    SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,F"TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"       ,)

								# restore to the original level
								__lowerCamelCase    :    List[str] 	=			''
								transformers.utils.logging._reset_library_root_logger()
							@mockenv(TRANSFORMERS_VERBOSITY='super-error')
							def    lowerCAmelCase					(		self		:      List[Any]):
								# reset for the env var to take effect, next time some logger call is made
								transformers.utils.logging._reset_library_root_logger()
								__lowerCamelCase    :    List[str] 	=			logging.logging.getLogger()
								with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
									# this action activates the env var
									logging.get_logger('transformers.models.bart.tokenization_bart')
								self.assertIn('Unknown option TRANSFORMERS_VERBOSITY=super-error'       ,cl.out)

								# no need to restore as nothing was changed


							def    lowerCAmelCase					(		self		:      Any):
								# testing `logger.warning_advice()`
								transformers.utils.logging._reset_library_root_logger()

								__lowerCamelCase    :    Tuple 	=			logging.get_logger('transformers.models.bart.tokenization_bart')
								__lowerCamelCase    :    Optional[int] 	=			'Testing 1, 2, 3'

								with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='1'):
									# nothing should be logged as env var disables this method
									with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
										logger.warning_advice(SCREAMING_SNAKE_CASE__)
									self.assertEqual(cl.out       ,'')

								with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''):
									# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
									with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
										logger.warning_advice(SCREAMING_SNAKE_CASE__)
									self.assertEqual(cl.out       ,msg + '\n')






def        SCREAMING_SNAKE_CASE__	(  )  ->					Any:
	disable_progress_bar()
	assert are_progress_bars_disabled()

	enable_progress_bar()
	assert not are_progress_bars_disabled()




