
'''simple docstring'''


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 	__lowercase				(  __SCREAMING_SNAKE_CASE    ):
			lowerCamelCase			:		int	      =			["image_processor", "tokenizer"]
			lowerCamelCase			:		str	      =			"FlavaImageProcessor"
			lowerCamelCase			:		Dict	      =			("BertTokenizer", "BertTokenizerFast")

			def __init__(self			,   A=None			,   A=None			,   **A      ):
								lowerCamelCase_					:      Dict     =		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_					:      List[str]     =		kwargs.pop('''feature_extractor'''      )

								lowerCamelCase_					:      List[str]     =		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      )
								lowerCamelCase_					:      Optional[Any]     =		self.image_processor

			def __call__(self			,   A = None			,   A = None			,   A = True			,   A = False			,   A = False			,   A = None			,   A = 0			,   A = None			,   A = None			,   A = None			,   A = None			,   A = None			,   A = False			,   A = False			,   A = False			,   A = False			,   A = True			,   A = None			,   **A			,   ):




								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_					:      Optional[int]     =		self.tokenizer(
													    text=_a			,   add_special_tokens=_a			,   padding=_a			,   truncation=_a			,   max_length=_a			,   stride=_a			,   pad_to_multiple_of=_a			,   return_token_type_ids=_a			,   return_attention_mask=_a			,   return_overflowing_tokens=_a			,   return_special_tokens_mask=_a			,   return_offsets_mapping=_a			,   return_length=_a			,   verbose=_a			,   return_tensors=_a			,   **_a			,   )
								if images is not None:
													lowerCamelCase_					:      Dict     =		self.image_processor(
													    _a			,   return_image_mask=_a			,   return_codebook_pixels=_a			,   return_tensors=_a			,   **_a			,   )

								if text is not None and images is not None:
													encoding.update(_a      )
													return encoding
								elif text is not None:
													return encoding
								else:
													return BatchEncoding(data=dict(**_a      )			,   tensor_type=_a      )

			def        UpperCAmelCase__ (self			,   *A			,   **A      ):
								return self.tokenizer.batch_decode(*_a			,   **_a      )

			def        UpperCAmelCase__ (self			,   *A			,   **A      ):
								return self.tokenizer.decode(*_a			,   **_a      )

			@property
			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Any     =		self.tokenizer.model_input_names
								lowerCamelCase_					:      Union[str, Any]     =		self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )

			@property
			def        UpperCAmelCase__ (self      ):
								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      ):
								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,   _a			,   )
								return self.image_processor




'''simple docstring'''


from collections import namedtuple

__lowercase							:   List[Any]					=     namedtuple('''from_to''', '''from_ to''')

__lowercase							:   Optional[Any]					=     {
    '''cubicmeter''': from_to(1, 1),
    '''litre''': from_to(0.0_01, 1000),
    '''kilolitre''': from_to(1, 1),
    '''gallon''': from_to(0.0_04_54, 2_64.1_72),
    '''cubicyard''': from_to(0.7_64_55, 1.3_07_95),
    '''cubicfoot''': from_to(0.0_28, 35.31_47),
    '''cup''': from_to(0.0_00_23_65_88, 42_26.75),
}




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							float:
					'''simple docstring'''

					if from_type not in METRIC_CONVERSION:
										raise ValueError(
										    F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
										    + ''', '''.join(_lowercase				)				)
					if to_type not in METRIC_CONVERSION:
										raise ValueError(
										    F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
										    + ''', '''.join(_lowercase				)				)
					return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
					import doctest

					doctest.testmod()


