






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase :      Optional[int]										=				logging.get_logger(__name__)



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		["pixel_values"]



					def __init__( self					:  Any							,		__A					:  bool = True							,		__A					:  int = 3_2							,		__A					:  Tuple=PILImageResampling.BILINEAR							,		__A					:  bool = True							,		**__A					:  Optional[Any]							,		):
						snake_case__			:				List[str]       =  do_resize
						snake_case__			:				Optional[Any]       =  do_rescale
						snake_case__			:				int       =  size_divisor
						snake_case__			:				int       =  resample
						super().__init__(**__A	)



					def 	_lowercase					( self					:  List[str]							,		__A					:  np.ndarray							,		__A					:  int							,		__A					:  Union[str, Any]							,		__A					:  Optional[ChannelDimension] = None							,		**__A					:  Any	):
						snake_case__,						snake_case__			:				Optional[Any]       =  get_image_size(__A	)
						# Rounds the height and width down to the closest multiple of size_divisor
						snake_case__			:				Optional[int]       =  height // size_divisor * size_divisor
						snake_case__			:				Any       =  width // size_divisor * size_divisor
						snake_case__			:				Optional[Any]       =  resize(__A							,		(new_h, new_w)							,		resample=__A							,		data_format=__A							,		**__A	)
						return image



					def 	_lowercase					( self					:  Any							,		__A					:  np.ndarray							,		__A					:  float							,		__A					:  Optional[ChannelDimension] = None							,		**__A					:  List[str]	):
						return rescale(image=__A							,		scale=__A							,		data_format=__A							,		**__A	)






					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]							,		__A					:  Optional[bool] = None							,		__A					:  Optional[int] = None							,		__A					:  Dict=None							,		__A					:  Optional[bool] = None							,		__A					:  Optional[Union[TensorType, str]] = None							,		__A					:  ChannelDimension = ChannelDimension.FIRST							,		**__A					:  Tuple							,		):
						snake_case__			:				str       =  do_resize if do_resize is not None else self.do_resize
						snake_case__			:				Union[str, Any]       =  do_rescale if do_rescale is not None else self.do_rescale
						snake_case__			:				List[str]       =  size_divisor if size_divisor is not None else self.size_divisor
						snake_case__			:				List[Any]       =  resample if resample is not None else self.resample

						if do_resize and size_divisor is None:
							raise ValueError("size_divisor is required for resizing"	)

						snake_case__			:				Dict       =  make_list_of_images(__A	)

						if not valid_images(__A	):
							raise ValueError("Invalid image(s)"	)

						# All transformations expect numpy arrays.
						snake_case__			:				int       =  [to_numpy_array(__A	) for img in images]

						if do_resize:
							snake_case__			:				Tuple       =  [self.resize(__A							,		size_divisor=__A							,		resample=__A	) for image in images]

						if do_rescale:
							snake_case__			:				str       =  [self.rescale(__A							,		scale=1 / 2_5_5	) for image in images]

						snake_case__			:				Dict       =  [to_channel_dimension_format(__A							,		__A	) for image in images]

						snake_case__			:				int       =  {"pixel_values": images}
						return BatchFeature(data=__A							,		tensor_type=__A	)











import warnings
from typing import List, Optional, Union

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



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		["image_processor", "tokenizer"]
					a_     =		"ViltImageProcessor"
					a_     =		("BertTokenizer", "BertTokenizerFast")



					def __init__( self					:  Optional[int]							,		__A					:  Optional[int]=None							,		__A					:  Optional[Any]=None							,		**__A					:  int	):
						snake_case__			:				Union[str, 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."							,		__A							,		)
							snake_case__			:				Tuple       =  kwargs.pop("feature_extractor"	)

						snake_case__			:				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__(__A							,		__A	)
						snake_case__			:				Tuple       =  self.image_processor



					def __call__( self					:  List[Any]							,		__A					:  int							,		__A					:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,		__A					:  bool = True							,		__A					:  Union[bool, str, PaddingStrategy] = False							,		__A					:  Union[bool, str, TruncationStrategy] = None							,		__A					:  Optional[int] = None							,		__A					:  int = 0							,		__A					:  Optional[int] = None							,		__A					:  Optional[bool] = None							,		__A					:  Optional[bool] = None							,		__A					:  bool = False							,		__A					:  bool = False							,		__A					:  bool = False							,		__A					:  bool = False							,		__A					:  bool = True							,		__A					:  Optional[Union[str, TensorType]] = None							,		**__A					:  List[Any]							,		):
						snake_case__			:				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							,		)
						# add pixel_values + pixel_mask
						snake_case__			:				Optional[Any]       =  self.image_processor(__A							,		return_tensors=__A	)
						encoding.update(__A	)

						return encoding



					def 	_lowercase					( self					:  Optional[Any]							,		*__A					:  List[str]							,		**__A					:  Optional[int]	):
						return self.tokenizer.batch_decode(*__A							,		**__A	)



					def 	_lowercase					( self					:  Dict							,		*__A					:  str							,		**__A					:  str	):
						return self.tokenizer.decode(*__A							,		**__A	)



					@property
					def 	_lowercase					( self					:  str	):
						snake_case__			:				Optional[Any]       =  self.tokenizer.model_input_names
						snake_case__			:				Any       =  self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)



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



