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 __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							["image_processor", "tokenizer"]
   lowerCAmelCase_														=							"BridgeTowerImageProcessor"
   lowerCAmelCase_														=							("RobertaTokenizer", "RobertaTokenizerFast")




   def __init__(    self			:				Dict       ,	_lowercase			:				List[Any]       ,	_lowercase			:				List[Any]      ):



     """simple docstring"""






     super().__init__(_lowercase       ,	_lowercase      )




   def __call__(    self			:				Any       ,	_lowercase			:				Optional[Any]       ,	_lowercase			:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,	_lowercase			:				bool = True       ,	_lowercase			:				Union[bool, str, PaddingStrategy] = False       ,	_lowercase			:				Union[bool, str, TruncationStrategy] = None       ,	_lowercase			:				Optional[int] = None       ,	_lowercase			:				int = 0       ,	_lowercase			:				Optional[int] = None       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				bool = False       ,	_lowercase			:				bool = False       ,	_lowercase			:				bool = False       ,	_lowercase			:				bool = False       ,	_lowercase			:				bool = True       ,	_lowercase			:				Optional[Union[str, TensorType]] = None       ,	**_lowercase			:				Dict       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.tokenizer(
         text=_lowercase       ,	add_special_tokens=_lowercase       ,	padding=_lowercase       ,	truncation=_lowercase       ,	max_length=_lowercase       ,	stride=_lowercase       ,	pad_to_multiple_of=_lowercase       ,	return_token_type_ids=_lowercase       ,	return_attention_mask=_lowercase       ,	return_overflowing_tokens=_lowercase       ,	return_special_tokens_mask=_lowercase       ,	return_offsets_mapping=_lowercase       ,	return_length=_lowercase       ,	verbose=_lowercase       ,	return_tensors=_lowercase       ,	**_lowercase       ,	)
     # add pixel_values + pixel_mask
     SCREAMING_SNAKE_CASE__										=      self.image_processor(
         _lowercase       ,	return_tensors=_lowercase       ,	do_normalize=_lowercase       ,	do_center_crop=_lowercase       ,	**_lowercase      )
     encoding.update(_lowercase      )

     return encoding




   def 	__a						(    self			:				Optional[int]       ,	*_lowercase			:				Union[str, Any]       ,	**_lowercase			:				Optional[Any]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Optional[int]       ,	*_lowercase			:				Optional[int]       ,	**_lowercase			:				Any      ):



     """simple docstring"""






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




   @property
   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.tokenizer.model_input_names
     SCREAMING_SNAKE_CASE__										=      self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )

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			: Union[str, Any]							   =		logging.get_logger(__name__)







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(    self			:				Any       ,	_lowercase			:				bool = True       ,	_lowercase			:				int = 32       ,	_lowercase			:				Any=PILImageResampling.BILINEAR       ,	_lowercase			:				bool = True       ,	**_lowercase			:				Union[str, Any]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      do_resize
     SCREAMING_SNAKE_CASE__										=      do_rescale
     SCREAMING_SNAKE_CASE__										=      size_divisor
     SCREAMING_SNAKE_CASE__										=      resample
     super().__init__(**_lowercase      )




   def 	__a						(    self			:				int       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				int       ,	_lowercase			:				int       ,	_lowercase			:				Optional[ChannelDimension] = None       ,	**_lowercase			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      get_image_size(_lowercase      )
     # Rounds the height and width down to the closest multiple of size_divisor
     SCREAMING_SNAKE_CASE__										=      height // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__										=      width // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__										=      resize(_lowercase       ,	(new_h, new_w)       ,	resample=_lowercase       ,	data_format=_lowercase       ,	**_lowercase      )
     return image




   def 	__a						(    self			:				int       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				float       ,	_lowercase			:				Optional[ChannelDimension] = None       ,	**_lowercase			:				str      ):



     """simple docstring"""






     return rescale(image=_lowercase       ,	scale=_lowercase       ,	data_format=_lowercase       ,	**_lowercase      )




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				Optional[int] = None       ,	_lowercase			:				int=None       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				Optional[Union[TensorType, str]] = None       ,	_lowercase			:				ChannelDimension = ChannelDimension.FIRST       ,	**_lowercase			:				Optional[int]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__										=      do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__										=      size_divisor if size_divisor is not None else self.size_divisor
     SCREAMING_SNAKE_CASE__										=      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"""      )

     SCREAMING_SNAKE_CASE__										=      make_list_of_images(_lowercase      )

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

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__										=      [to_numpy_array(_lowercase      ) for img in images]

     if do_resize:
       SCREAMING_SNAKE_CASE__										=      [self.resize(_lowercase       ,	size_divisor=_lowercase       ,	resample=_lowercase      ) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE__										=      [self.rescale(_lowercase       ,	scale=1 / 2_55      ) for image in images]

     SCREAMING_SNAKE_CASE__										=      [to_channel_dimension_format(_lowercase       ,	_lowercase      ) for image in images]

     SCREAMING_SNAKE_CASE__										=      {"""pixel_values""": images}
     return BatchFeature(data=_lowercase       ,	tensor_type=_lowercase      )
