

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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase__								=		logging.get_logger(__name__)


if is_vision_available():
       import PIL







class 	lowercase    (				_lowercase							):


  """simple docstring"""




  a__					       =  ["pixel_values"]


  def __init__(				self	,       __snake_case = True	,       __snake_case = None	,       __snake_case = PILImageResampling.BICUBIC	,       __snake_case = True	,       __snake_case = None	,       __snake_case = True	,       __snake_case = 1 / 2_55	,       __snake_case = True	,       __snake_case = None	,       __snake_case = None	,       __snake_case = True	,       **__snake_case	,       ):
   super().__init__(**lowercase_)
   _UpperCamelCase   :		List[str]			=						size if size is not None else {'shortest_edge': 2_24}
   _UpperCamelCase   :		str			=						get_size_dict(lowercase_	,       default_to_square=lowercase_)
   _UpperCamelCase   :		Union[str, Any]			=						crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
   _UpperCamelCase   :		Any			=						get_size_dict(lowercase_	,       default_to_square=lowercase_	,       param_name='crop_size')

   _UpperCamelCase   :		Optional[Any]			=						do_resize
   _UpperCamelCase   :		str			=						size
   _UpperCamelCase   :		Union[str, Any]			=						resample
   _UpperCamelCase   :		List[str]			=						do_center_crop
   _UpperCamelCase   :		Optional[int]			=						crop_size
   _UpperCamelCase   :		int			=						do_rescale
   _UpperCamelCase   :		List[str]			=						rescale_factor
   _UpperCamelCase   :		Optional[Any]			=						do_normalize
   _UpperCamelCase   :		str			=						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   _UpperCamelCase   :		List[Any]			=						image_std if image_std is not None else OPENAI_CLIP_STD
   _UpperCamelCase   :		Optional[Any]			=						do_convert_rgb


  def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case = PILImageResampling.BICUBIC	,       __snake_case = None	,       **__snake_case	,       ):
   _UpperCamelCase   :		int			=						get_size_dict(lowercase_	,       default_to_square=lowercase_)
   if "shortest_edge" not in size:
    raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''')
   _UpperCamelCase   :		Union[str, Any]			=						get_resize_output_image_size(lowercase_	,       size=size['shortest_edge']	,       default_to_square=lowercase_)
   return resize(lowercase_	,       size=lowercase_	,       resample=lowercase_	,       data_format=lowercase_	,       **lowercase_)


  def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case = None	,       **__snake_case	,       ):
   _UpperCamelCase   :		Any			=						get_size_dict(lowercase_)
   if "height" not in size or "width" not in size:
    raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''')
   return center_crop(lowercase_	,       size=(size['height'], size['width'])	,       data_format=lowercase_	,       **lowercase_)


  def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case = None	,       **__snake_case	,       ):
   return rescale(lowercase_	,       scale=lowercase_	,       data_format=lowercase_	,       **lowercase_)


  def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case = None	,       **__snake_case	,       ):
   return normalize(lowercase_	,       mean=lowercase_	,       std=lowercase_	,       data_format=lowercase_	,       **lowercase_)






  def 		A__					(				self	,       __snake_case	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = ChannelDimension.FIRST	,       **__snake_case	,       ):
   _UpperCamelCase   :		Tuple			=						do_resize if do_resize is not None else self.do_resize
   _UpperCamelCase   :		List[Any]			=						size if size is not None else self.size
   _UpperCamelCase   :		Optional[int]			=						get_size_dict(lowercase_	,       param_name='size'	,       default_to_square=lowercase_)
   _UpperCamelCase   :		Tuple			=						resample if resample is not None else self.resample
   _UpperCamelCase   :		Optional[Any]			=						do_center_crop if do_center_crop is not None else self.do_center_crop
   _UpperCamelCase   :		str			=						crop_size if crop_size is not None else self.crop_size
   _UpperCamelCase   :		Optional[int]			=						get_size_dict(lowercase_	,       param_name='crop_size'	,       default_to_square=lowercase_)
   _UpperCamelCase   :		str			=						do_rescale if do_rescale is not None else self.do_rescale
   _UpperCamelCase   :		Any			=						rescale_factor if rescale_factor is not None else self.rescale_factor
   _UpperCamelCase   :		str			=						do_normalize if do_normalize is not None else self.do_normalize
   _UpperCamelCase   :		Dict			=						image_mean if image_mean is not None else self.image_mean
   _UpperCamelCase   :		Optional[Any]			=						image_std if image_std is not None else self.image_std
   _UpperCamelCase   :		Tuple			=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   _UpperCamelCase   :		Dict			=						make_list_of_images(lowercase_)

   if not valid_images(lowercase_):
    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:
    raise ValueError('Size 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.')

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
    _UpperCamelCase   :		List[Any]			=						[convert_to_rgb(lowercase_) for image in images]

   # All transformations expect numpy arrays.
   _UpperCamelCase   :		int			=						[to_numpy_array(lowercase_) for image in images]

   if do_resize:
    _UpperCamelCase   :		Optional[Any]			=						[self.resize(image=lowercase_	,       size=lowercase_	,       resample=lowercase_) for image in images]

   if do_center_crop:
    _UpperCamelCase   :		Tuple			=						[self.center_crop(image=lowercase_	,       size=lowercase_) for image in images]

   if do_rescale:
    _UpperCamelCase   :		Optional[int]			=						[self.rescale(image=lowercase_	,       scale=lowercase_) for image in images]

   if do_normalize:
    _UpperCamelCase   :		Any			=						[self.normalize(image=lowercase_	,       mean=lowercase_	,       std=lowercase_) for image in images]

   _UpperCamelCase   :		Optional[Any]			=						[to_channel_dimension_format(lowercase_	,       lowercase_) for image in images]

   _UpperCamelCase   :		Dict			=						{'pixel_values': images}
   return BatchFeature(data=lowercase_	,       tensor_type=lowercase_)







from ...processing_utils import ProcessorMixin







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  ["image_processor", "feature_extractor"]
		a__					       =  "TvltImageProcessor"
		a__					       =  "TvltFeatureExtractor"


		def __init__(				self	,       __snake_case	,       __snake_case):
			super().__init__(image_processor=__snake_case	,       feature_extractor=__snake_case)

			_UpperCamelCase   :		List[str]			=						image_processor
			_UpperCamelCase   :		Dict			=						feature_extractor


		def __call__(				self	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=False	,       __snake_case=False	,       *__snake_case	,       **__snake_case	,       ):

			if images is None and audio is None:
				raise ValueError('You need to specify either an `images` or `audio` input to process.')

			_UpperCamelCase   :		Union[str, Any]			=						None
			if images is not None:
				_UpperCamelCase   :		Tuple			=						self.image_processor(__snake_case	,       mask_pixel=__snake_case	,       *__snake_case	,       **__snake_case)
			if images_mixed is not None:
				_UpperCamelCase   :		Union[str, Any]			=						self.image_processor(__snake_case	,       is_mixed=__snake_case	,       *__snake_case	,       **__snake_case)
			if audio is not None:
				_UpperCamelCase   :		Tuple			=						self.feature_extractor(
				    __snake_case	,       *__snake_case	,       sampling_rate=__snake_case	,       mask_audio=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		Tuple			=						{}
			if audio is not None:
				output_dict.update(__snake_case)
			if images is not None:
				output_dict.update(__snake_case)
			if images_mixed_dict is not None:
				output_dict.update(__snake_case)
			return output_dict






		@property
		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						self.image_processor.model_input_names
			_UpperCamelCase   :		List[Any]			=						self.feature_extractor.model_input_names
			return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names))




