
'''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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


A							     = logging.get_logger(__name__)



class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			['pixel_values']

  def __init__(self	:			Tuple						,			lowerCAmelCase_	:			bool = True						,			lowerCAmelCase_	:			Dict[str, int] = None						,			lowerCAmelCase_	:			bool = True						,			lowerCAmelCase_	:			Dict[str, int] = None						,			lowerCAmelCase_	:			PILImageResampling = PILImageResampling.BICUBIC						,			lowerCAmelCase_	:			bool = True						,			lowerCAmelCase_	:			Union[int, float] = 1 / 2_55						,			lowerCAmelCase_	:			bool = True						,			lowerCAmelCase_	:			Optional[Union[float, List[float]]] = None						,			lowerCAmelCase_	:			Optional[Union[float, List[float]]] = None						,			**lowerCAmelCase_	:			Optional[Any]						,			) ->							None:
        super().__init__(**lowerCAmelCase_     )
        snake_case_    							=				crop_size if crop_size is not None else {"height": 2_56, "width": 2_56}
        snake_case_    							=				get_size_dict(lowerCAmelCase_						,			param_name="crop_size"     )
        snake_case_    							=				size if size is not None else {"height": 2_24, "width": 2_24}
        snake_case_    							=				get_size_dict(lowerCAmelCase_     )

        snake_case_    							=				do_center_crop
        snake_case_    							=				crop_size
        snake_case_    							=				do_resize
        snake_case_    							=				size
        snake_case_    							=				resample
        snake_case_    							=				do_rescale
        snake_case_    							=				rescale_factor
        snake_case_    							=				do_normalize
        snake_case_    							=				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        snake_case_    							=				image_std if image_std is not None else IMAGENET_DEFAULT_STD

  def 		_lowerCamelCase						(self	:			int						,			lowerCAmelCase_	:			np.ndarray						,			lowerCAmelCase_	:			Dict[str, int]						,			lowerCAmelCase_	:			Optional[int] = None						,			lowerCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None						,			**lowerCAmelCase_	:			List[Any]						,			) ->							np.ndarray:
        snake_case_    							=				self.size if size is None else size
        snake_case_    							=				get_size_dict(lowerCAmelCase_     )
        snake_case_    							=				get_size_dict(lowerCAmelCase_						,			param_name="crop_size"     )

        snake_case_							,						snake_case_    							=				get_image_size(lowerCAmelCase_     )
        snake_case_    							=				min(lowerCAmelCase_						,			lowerCAmelCase_     )
        snake_case_    							=				(size["height"] / crop_size["height"]) * min_dim
        snake_case_    							=				(size["width"] / crop_size["width"]) * min_dim
        return center_crop(lowerCAmelCase_						,			size=(cropped_height, cropped_width)						,			data_format=lowerCAmelCase_						,			**lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			np.ndarray						,			lowerCAmelCase_	:			Dict[str, int]						,			lowerCAmelCase_	:			PILImageResampling = PIL.Image.BICUBIC						,			lowerCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None						,			**lowerCAmelCase_	:			Any						,			) ->							np.ndarray:
        snake_case_    							=				get_size_dict(lowerCAmelCase_     )
        if "height" not in size or "width" not in size:
              raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''     )
        return resize(
            lowerCAmelCase_						,			size=(size["height"], size["width"])						,			resample=lowerCAmelCase_						,			data_format=lowerCAmelCase_						,			**lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			lowerCAmelCase_	:			np.ndarray						,			lowerCAmelCase_	:			Union[int, float]						,			lowerCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None						,			**lowerCAmelCase_	:			Optional[int]						,			) ->							Optional[int]:
        return rescale(lowerCAmelCase_						,			scale=lowerCAmelCase_						,			data_format=lowerCAmelCase_						,			**lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Any						,			lowerCAmelCase_	:			np.ndarray						,			lowerCAmelCase_	:			Union[float, List[float]]						,			lowerCAmelCase_	:			Union[float, List[float]]						,			lowerCAmelCase_	:			Optional[Union[str, ChannelDimension]] = None						,			**lowerCAmelCase_	:			Optional[int]						,			) ->							np.ndarray:
        return normalize(lowerCAmelCase_						,			mean=lowerCAmelCase_						,			std=lowerCAmelCase_						,			data_format=lowerCAmelCase_						,			**lowerCAmelCase_     )


  def 		_lowerCamelCase						(self	:			Tuple						,			lowerCAmelCase_	:			ImageInput						,			lowerCAmelCase_	:			Optional[bool] = None						,			lowerCAmelCase_	:			Optional[Dict[str, int]] = None						,			lowerCAmelCase_	:			Optional[bool] = None						,			lowerCAmelCase_	:			Optional[Dict[str, int]] = None						,			lowerCAmelCase_	:			PILImageResampling = None						,			lowerCAmelCase_	:			Optional[bool] = None						,			lowerCAmelCase_	:			Optional[float] = None						,			lowerCAmelCase_	:			Optional[bool] = None						,			lowerCAmelCase_	:			Optional[Union[float, List[float]]] = None						,			lowerCAmelCase_	:			Optional[Union[float, List[float]]] = None						,			lowerCAmelCase_	:			Optional[Union[str, TensorType]] = None						,			lowerCAmelCase_	:			ChannelDimension = ChannelDimension.FIRST						,			**lowerCAmelCase_	:			Optional[int]						,			) ->							PIL.Image.Image:
        snake_case_    							=				do_center_crop if do_center_crop is not None else self.do_center_crop
        snake_case_    							=				crop_size if crop_size is not None else self.crop_size
        snake_case_    							=				get_size_dict(lowerCAmelCase_						,			param_name="crop_size"     )
        snake_case_    							=				do_resize if do_resize is not None else self.do_resize
        snake_case_    							=				size if size is not None else self.size
        snake_case_    							=				get_size_dict(lowerCAmelCase_     )
        snake_case_    							=				resample if resample is not None else self.resample
        snake_case_    							=				do_rescale if do_rescale is not None else self.do_rescale
        snake_case_    							=				rescale_factor if rescale_factor is not None else self.rescale_factor
        snake_case_    							=				do_normalize if do_normalize is not None else self.do_normalize
        snake_case_    							=				image_mean if image_mean is not None else self.image_mean
        snake_case_    							=				image_std if image_std is not None else self.image_std

        snake_case_    							=				make_list_of_images(lowerCAmelCase_     )

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

        if do_center_crop and crop_size is None:
              raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided."     )

        if do_resize and size is None:
              raise ValueError("Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True."     )

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

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

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

        if do_rescale:
              snake_case_    							=				[self.rescale(image=lowerCAmelCase_						,			scale=lowerCAmelCase_     ) for image in images]

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

        snake_case_    							=				[to_channel_dimension_format(lowerCAmelCase_						,			lowerCAmelCase_     ) for image in images]

        snake_case_    							=				{"pixel_values": images}
        return BatchFeature(data=lowerCAmelCase_						,			tensor_type=lowerCAmelCase_     )


'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_A										= {
    'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A										= [
     'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
     'RwkvForCausalLM',
     'RwkvModel',
     'RwkvPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rwkv import (
      RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
      RwkvForCausalLM,
      RwkvModel,
      RwkvPreTrainedModel,
  )
else:
 import sys

 _A										= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




