'''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,
    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


_UpperCAmelCase    :     Tuple         =       logging.get_logger(__name__)


if is_vision_available():
   import PIL







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

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

        _A    		=  do_resize
        _A    		=  size
        _A    		=  resample
        _A    		=  do_center_crop
        _A    		=  crop_size
        _A    		=  do_rescale
        _A    		=  rescale_factor
        _A    		=  do_normalize
        _A    		=  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        _A    		=  image_std if image_std is not None else OPENAI_CLIP_STD
        _A    		=  do_convert_rgb
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			np.ndarray, UpperCamelCase__      :			Dict[str, int], UpperCamelCase__      :			PILImageResampling = PILImageResampling.BICUBIC, UpperCamelCase__      :			Optional[Union[str, ChannelDimension]] = None, **UpperCamelCase__      :			Tuple, )  ->			np.ndarray:
        _A    		=  get_size_dict(UpperCamelCase__, default_to_square=UpperCamelCase__     )
        if "shortest_edge" not in size:
             raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'     )
        _A    		=  get_resize_output_image_size(UpperCamelCase__, size=size['shortest_edge'], default_to_square=UpperCamelCase__     )
        return resize(UpperCamelCase__, size=UpperCamelCase__, resample=UpperCamelCase__, data_format=UpperCamelCase__, **UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			int, UpperCamelCase__      :			np.ndarray, UpperCamelCase__      :			Dict[str, int], UpperCamelCase__      :			Optional[Union[str, ChannelDimension]] = None, **UpperCamelCase__      :			Dict, )  ->			np.ndarray:
        _A    		=  get_size_dict(UpperCamelCase__     )
        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(UpperCamelCase__, size=(size['height'], size['width']), data_format=UpperCamelCase__, **UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any], UpperCamelCase__      :			np.ndarray, UpperCamelCase__      :			Union[int, float], UpperCamelCase__      :			Optional[Union[str, ChannelDimension]] = None, **UpperCamelCase__      :			str, )  ->			int:
        return rescale(UpperCamelCase__, scale=UpperCamelCase__, data_format=UpperCamelCase__, **UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			str, UpperCamelCase__      :			np.ndarray, UpperCamelCase__      :			Union[float, List[float]], UpperCamelCase__      :			Union[float, List[float]], UpperCamelCase__      :			Optional[Union[str, ChannelDimension]] = None, **UpperCamelCase__      :			List[str], )  ->			np.ndarray:
        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__      :			PILImageResampling = None, UpperCamelCase__      :			bool = None, UpperCamelCase__      :			int = None, UpperCamelCase__      :			bool = None, UpperCamelCase__      :			float = 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__      :			Optional[ChannelDimension] = ChannelDimension.FIRST, **UpperCamelCase__      :			Tuple, )  ->			PIL.Image.Image:
        _A    		=  do_resize if do_resize is not None else self.do_resize
        _A    		=  size if size is not None else self.size
        _A    		=  get_size_dict(UpperCamelCase__, param_name='size', default_to_square=UpperCamelCase__     )
        _A    		=  resample if resample is not None else self.resample
        _A    		=  do_center_crop if do_center_crop is not None else self.do_center_crop
        _A    		=  crop_size if crop_size is not None else self.crop_size
        _A    		=  get_size_dict(UpperCamelCase__, param_name='crop_size', default_to_square=UpperCamelCase__     )
        _A    		=  do_rescale if do_rescale is not None else self.do_rescale
        _A    		=  rescale_factor if rescale_factor is not None else self.rescale_factor
        _A    		=  do_normalize if do_normalize is not None else self.do_normalize
        _A    		=  image_mean if image_mean is not None else self.image_mean
        _A    		=  image_std if image_std is not None else self.image_std
        _A    		=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        _A    		=  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:
             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:
             _A    		=  [convert_to_rgb(UpperCamelCase__     ) for image in images]

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

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

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

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

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

        _A    		=  [to_channel_dimension_format(UpperCamelCase__, UpperCamelCase__     ) for image in images]

        _A    		=  {'pixel_values': images}
        return BatchFeature(data=UpperCamelCase__, tensor_type=UpperCamelCase__     )




'''simple docstring'''






_UpperCAmelCase    :     Any         =       '''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
   from .utils import rich



