


'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 						__lowerCAmelCase		(lowercase_  ):





      '''simple docstring'''






      lowerCAmelCase__      :  List[str]						      =							["""image_processor""", """tokenizer"""]
      lowerCAmelCase__      :  Tuple						      =							"""AutoImageProcessor"""
      lowerCAmelCase__      :  Optional[int]						      =							"""AutoTokenizer"""



      def __init__(self					:					Optional[Any]     ,		UpperCamelCase					:					Dict=None     ,		UpperCamelCase					:					Any=None     ,		**UpperCamelCase					:					Tuple				):

             '''simple docstring'''







             lowercase__       =				None
             if "feature_extractor" in kwargs:
                    warnings.warn(
                        '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                        ''' instead.'''     ,		UpperCamelCase     ,		)
                    lowercase__       =				kwargs.pop('''feature_extractor'''				)

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

             super().__init__(UpperCamelCase     ,		UpperCamelCase				)
             lowercase__       =				self.image_processor



      def __call__(self					:					Dict     ,		UpperCamelCase					:					Tuple=None     ,		UpperCamelCase					:					Optional[Any]=None     ,		UpperCamelCase					:					int=None     ,		**UpperCamelCase					:					Optional[Any]				):

             '''simple docstring'''







             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:
                    lowercase__       =				self.tokenizer(UpperCamelCase     ,		return_tensors=UpperCamelCase     ,		**UpperCamelCase				)

             if images is not None:
                    lowercase__       =				self.image_processor(UpperCamelCase     ,		return_tensors=UpperCamelCase     ,		**UpperCamelCase				)

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



      def    UpperCamelCase__	(self					:					Optional[Any]     ,		*UpperCamelCase					:					Optional[Any]     ,		**UpperCamelCase					:					Optional[Any]				):

             '''simple docstring'''







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



      def    UpperCamelCase__	(self					:					Union[str, Any]     ,		*UpperCamelCase					:					Optional[Any]     ,		**UpperCamelCase					:					List[str]				):

             '''simple docstring'''







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



      @property
      def    UpperCamelCase__	(self					:					Dict				):

             '''simple docstring'''







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



      @property
      def    UpperCamelCase__	(self					:					Union[str, Any]				):

             '''simple docstring'''







             warnings.warn(
                 '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''     ,		UpperCamelCase     ,		)
             return self.image_processor_class






      @property
      def    UpperCamelCase__	(self					:					Optional[int]				):

             '''simple docstring'''







             warnings.warn(
                 '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''     ,		UpperCamelCase     ,		)
             return self.image_processor








'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowerCamelCase							:     Optional[int]								=			logging.get_logger(__name__)
class 						__lowerCAmelCase		(lowercase_  ):





      '''simple docstring'''






      lowerCAmelCase__      :  List[str]						      =							["""pixel_values"""]



      def __init__(self					:					Optional[int]     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					PILImageResampling = PIL.Image.NEAREST     ,		UpperCamelCase					:					bool = False     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					Union[int, float] = 1 / 255     ,		UpperCamelCase					:					bool = False     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					bool = True     ,		**UpperCamelCase					:					Optional[int]     ,		):

             '''simple docstring'''







             super().__init__(**UpperCamelCase				)
             lowercase__       =				size if size is not None else {'''height''': 346, '''width''': 346}
             lowercase__       =				get_size_dict(UpperCamelCase				)
             lowercase__       =				crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
             lowercase__       =				get_size_dict(UpperCamelCase     ,		param_name='''crop_size'''				)

             lowercase__       =				do_resize
             lowercase__       =				size
             lowercase__       =				resample
             lowercase__       =				do_center_crop
             lowercase__       =				crop_size
             lowercase__       =				do_rescale
             lowercase__       =				rescale_factor
             lowercase__       =				rescale_offset
             lowercase__       =				do_normalize
             lowercase__       =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             lowercase__       =				image_std if image_std is not None else IMAGENET_STANDARD_STD
             lowercase__       =				include_top



      def    UpperCamelCase__	(self					:					Dict     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Dict[str, int]     ,		UpperCamelCase					:					PILImageResampling = PIL.Image.NEAREST     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					Dict     ,		):

             '''simple docstring'''







             lowercase__       =				get_size_dict(UpperCamelCase				)
             if "height" not in size or "width" not in size:
                    raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
             return resize(
                 UpperCamelCase     ,		size=(size['''height'''], size['''width'''])     ,		resample=UpperCamelCase     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)



      def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Dict[str, int]     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					Union[str, Any]     ,		):

             '''simple docstring'''







             lowercase__       =				get_size_dict(UpperCamelCase				)
             if "height" not in size or "width" not in size:
                    raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"				)
             return center_crop(UpperCamelCase     ,		size=(size['''height'''], size['''width'''])     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)



      def    UpperCamelCase__	(self					:					Tuple     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Union[int, float]     ,		UpperCamelCase					:					bool = True     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					List[str]     ,		):

             '''simple docstring'''







             if offset:
                    lowercase__       =				(image - 1_27.5) * scale
                    if data_format is not None:
                           lowercase__       =				to_channel_dimension_format(UpperCamelCase     ,		UpperCamelCase				)
                    lowercase__       =				rescaled_image.astype(np.floataa				)
             else:
                    lowercase__       =				rescale(UpperCamelCase     ,		scale=UpperCamelCase     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)
             return rescaled_image



      def    UpperCamelCase__	(self					:					List[Any]     ,		UpperCamelCase					:					np.ndarray     ,		UpperCamelCase					:					Union[float, List[float]]     ,		UpperCamelCase					:					Union[float, List[float]]     ,		UpperCamelCase					:					Optional[Union[str, ChannelDimension]] = None     ,		**UpperCamelCase					:					List[Any]     ,		):

             '''simple docstring'''







             return normalize(UpperCamelCase     ,		mean=UpperCamelCase     ,		std=UpperCamelCase     ,		data_format=UpperCamelCase     ,		**UpperCamelCase				)






      def    UpperCamelCase__	(self					:					Optional[int]     ,		UpperCamelCase					:					ImageInput     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					Union[str, Any]=None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Dict[str, int] = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					float = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					Optional[Union[float, List[float]]] = None     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Optional[Union[str, TensorType]] = None     ,		UpperCamelCase					:					ChannelDimension = ChannelDimension.FIRST     ,		**UpperCamelCase					:					List[Any]     ,		):

             '''simple docstring'''







             lowercase__       =				do_resize if do_resize is not None else self.do_resize
             lowercase__       =				resample if resample is not None else self.resample
             lowercase__       =				do_center_crop if do_center_crop is not None else self.do_center_crop
             lowercase__       =				do_rescale if do_rescale is not None else self.do_rescale
             lowercase__       =				rescale_factor if rescale_factor is not None else self.rescale_factor
             lowercase__       =				rescale_offset if rescale_offset is not None else self.rescale_offset
             lowercase__       =				do_normalize if do_normalize is not None else self.do_normalize
             lowercase__       =				image_mean if image_mean is not None else self.image_mean
             lowercase__       =				image_std if image_std is not None else self.image_std
             lowercase__       =				include_top if include_top is not None else self.include_top

             lowercase__       =				size if size is not None else self.size
             lowercase__       =				get_size_dict(UpperCamelCase				)
             lowercase__       =				crop_size if crop_size is not None else self.crop_size
             lowercase__       =				get_size_dict(UpperCamelCase     ,		param_name='''crop_size'''				)

             lowercase__       =				make_list_of_images(UpperCamelCase				)

             if not valid_images(UpperCamelCase				):
                    raise ValueError(
                        '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                        '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)

             if do_resize and size is None or resample is None:
                    raise ValueError('''Size and resample must be specified if do_resize is True.'''				)

             if do_center_crop and crop_size is None:
                    raise ValueError('''Crop size must be specified if do_center_crop is True.'''				)

             if do_rescale and rescale_factor is None:
                    raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)

             if do_normalize and (image_mean is None or image_std is None):
                    raise ValueError('''Image mean and std must be specified if do_normalize is True.'''				)

             # All transformations expect numpy arrays.
             lowercase__       =				[to_numpy_array(UpperCamelCase				) for image in images]

             if do_resize:
                    lowercase__       =				[self.resize(image=UpperCamelCase     ,		size=UpperCamelCase     ,		resample=UpperCamelCase				) for image in images]

             if do_center_crop:
                    lowercase__       =				[self.center_crop(image=UpperCamelCase     ,		size=UpperCamelCase				) for image in images]

             if do_rescale:
                    lowercase__       =				[self.rescale(image=UpperCamelCase     ,		scale=UpperCamelCase     ,		offset=UpperCamelCase				) for image in images]

             if do_normalize:
                    lowercase__       =				[self.normalize(image=UpperCamelCase     ,		mean=UpperCamelCase     ,		std=UpperCamelCase				) for image in images]

             if include_top:
                    lowercase__       =				[self.normalize(image=UpperCamelCase     ,		mean=[0, 0, 0]     ,		std=UpperCamelCase				) for image in images]

             lowercase__       =				[to_channel_dimension_format(UpperCamelCase     ,		UpperCamelCase				) for image in images]

             lowercase__       =				{'''pixel_values''': images}
             return BatchFeature(data=UpperCamelCase     ,		tensor_type=UpperCamelCase				)




