




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    : Optional[int]		= logging.get_logger(__name__)


if is_vision_available():
  import PIL
class       _UpperCamelCase   (					lowerCAmelCase		):
      UpperCAmelCase_          =		["""pixel_values"""]
      def __init__(      self :Dict			,   lowerCamelCase :bool = True			,   lowerCamelCase :Dict[str, int] = None			,   lowerCamelCase :PILImageResampling = PILImageResampling.BICUBIC			,   lowerCamelCase :bool = True			,   lowerCamelCase :Dict[str, int] = None			,   lowerCamelCase :bool = True			,   lowerCamelCase :Union[int, float] = 1 / 255			,   lowerCamelCase :bool = True			,   lowerCamelCase :Optional[Union[float, List[float]]] = None			,   lowerCamelCase :Optional[Union[float, List[float]]] = None			,   lowerCamelCase :bool = True			,   **lowerCamelCase :int			,   )  ->      None:
       super().__init__(**lowerCamelCase   )
       UpperCAmelCase__							=    size if size is not None else {"shortest_edge": 224}
       UpperCAmelCase__							=    get_size_dict(lowerCamelCase			,   default_to_square=lowerCamelCase   )
       UpperCAmelCase__							=    crop_size if crop_size is not None else {"height": 224, "width": 224}
       UpperCAmelCase__							=    get_size_dict(lowerCamelCase   )

       UpperCAmelCase__							=    do_resize
       UpperCAmelCase__							=    size
       UpperCAmelCase__							=    resample
       UpperCAmelCase__							=    do_center_crop
       UpperCAmelCase__							=    crop_size
       UpperCAmelCase__							=    do_rescale
       UpperCAmelCase__							=    rescale_factor
       UpperCAmelCase__							=    do_normalize
       UpperCAmelCase__							=    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       UpperCAmelCase__							=    image_std if image_std is not None else OPENAI_CLIP_STD
       UpperCAmelCase__							=    do_convert_rgb
      def 						UpperCAmelCase_    (      self :Dict			,   lowerCamelCase :np.ndarray			,   lowerCamelCase :Dict[str, int]			,   lowerCamelCase :PILImageResampling = PILImageResampling.BICUBIC			,   lowerCamelCase :Optional[Union[str, ChannelDimension]] = None			,   **lowerCamelCase :int			,   )  ->      np.ndarray:
       UpperCAmelCase__							=    get_size_dict(lowerCamelCase			,   default_to_square=lowerCamelCase   )
       UpperCAmelCase__							=    get_resize_output_image_size(
           lowerCamelCase			,   size=(size["height"], size["width"])			,   default_to_square=lowerCamelCase   )
       return resize(lowerCamelCase			,   size=lowerCamelCase			,   resample=lowerCamelCase			,   data_format=lowerCamelCase			,   **lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :Dict			,   lowerCamelCase :np.ndarray			,   lowerCamelCase :Dict[str, int]			,   lowerCamelCase :Optional[Union[str, ChannelDimension]] = None			,   **lowerCamelCase :Tuple			,   )  ->      np.ndarray:
       UpperCAmelCase__							=    get_size_dict(lowerCamelCase   )
       return center_crop(lowerCamelCase			,   size=(size["height"], size["width"])			,   data_format=lowerCamelCase			,   **lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :Union[str, Any]			,   lowerCamelCase :np.ndarray			,   lowerCamelCase :Union[int, float]			,   lowerCamelCase :Optional[Union[str, ChannelDimension]] = None			,   **lowerCamelCase :Any			,   )  ->      Union[str, Any]:
       return rescale(lowerCamelCase			,   scale=lowerCamelCase			,   data_format=lowerCamelCase			,   **lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :Union[str, Any]			,   lowerCamelCase :np.ndarray			,   lowerCamelCase :Union[float, List[float]]			,   lowerCamelCase :Union[float, List[float]]			,   lowerCamelCase :Optional[Union[str, ChannelDimension]] = None			,   **lowerCamelCase :List[str]			,   )  ->      np.ndarray:
       return normalize(lowerCamelCase			,   mean=lowerCamelCase			,   std=lowerCamelCase			,   data_format=lowerCamelCase			,   **lowerCamelCase   )







      def 						UpperCAmelCase_    (      self :List[str]			,   lowerCamelCase :ImageInput			,   lowerCamelCase :bool = None			,   lowerCamelCase :Dict[str, int] = None			,   lowerCamelCase :PILImageResampling = None			,   lowerCamelCase :bool = None			,   lowerCamelCase :int = None			,   lowerCamelCase :bool = None			,   lowerCamelCase :float = None			,   lowerCamelCase :bool = None			,   lowerCamelCase :Optional[Union[float, List[float]]] = None			,   lowerCamelCase :Optional[Union[float, List[float]]] = None			,   lowerCamelCase :bool = None			,   lowerCamelCase :Optional[Union[str, TensorType]] = None			,   lowerCamelCase :Optional[ChannelDimension] = ChannelDimension.FIRST			,   **lowerCamelCase :Optional[Any]			,   )  ->      PIL.Image.Image:
       UpperCAmelCase__							=    do_resize if do_resize is not None else self.do_resize
       UpperCAmelCase__							=    size if size is not None else self.size
       UpperCAmelCase__							=    get_size_dict(lowerCamelCase			,   default_to_square=lowerCamelCase   )
       UpperCAmelCase__							=    resample if resample is not None else self.resample
       UpperCAmelCase__							=    do_center_crop if do_center_crop is not None else self.do_center_crop
       UpperCAmelCase__							=    crop_size if crop_size is not None else self.crop_size
       UpperCAmelCase__							=    get_size_dict(lowerCamelCase   )
       UpperCAmelCase__							=    do_rescale if do_rescale is not None else self.do_rescale
       UpperCAmelCase__							=    rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCAmelCase__							=    do_normalize if do_normalize is not None else self.do_normalize
       UpperCAmelCase__							=    image_mean if image_mean is not None else self.image_mean
       UpperCAmelCase__							=    image_std if image_std is not None else self.image_std
       UpperCAmelCase__							=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       UpperCAmelCase__							=    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_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__							=    [convert_to_rgb(lowerCamelCase   ) for image in images]

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

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

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

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

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

       UpperCAmelCase__							=    [to_channel_dimension_format(lowerCamelCase			,   lowerCamelCase   ) for image in images]

       UpperCAmelCase__							=    {"pixel_values": images}
       return BatchFeature(data=lowerCamelCase			,   tensor_type=lowerCamelCase   )











import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm



def     lowerCAmelCase		(		_lowerCAmelCase				: int    ,				_lowerCAmelCase				: bool = False						):




 """simple docstring"""







 if device_specific:
  seed += AcceleratorState().process_index
 random.seed(_lowerCAmelCase						)
 np.random.seed(_lowerCAmelCase						)
 torch.manual_seed(_lowerCAmelCase						)
 if is_xpu_available():
  torch.xpu.manual_seed_all(_lowerCAmelCase						)
 elif is_npu_available():
  torch.npu.manual_seed_all(_lowerCAmelCase						)
 else:
  torch.cuda.manual_seed_all(_lowerCAmelCase						)
 # ^^ safe to call this function even if cuda is not available
 if is_tpu_available():
  xm.set_rng_state(_lowerCAmelCase						)



def     lowerCAmelCase		(		_lowerCAmelCase				: Optional[RNGType] = None    ,				_lowerCAmelCase				: Optional[torch.Generator] = None						):




 """simple docstring"""







 if rng_type == RNGType.TORCH:
  UpperCAmelCase__							=    torch.get_rng_state()
 elif rng_type == RNGType.CUDA:
  UpperCAmelCase__							=    torch.cuda.get_rng_state()
 elif rng_type == RNGType.XLA:
  assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
  UpperCAmelCase__							=    torch.tensor(xm.get_rng_state()						)
 elif rng_type == RNGType.NPU:
  assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
  UpperCAmelCase__							=    torch.npu.get_rng_state()
 elif rng_type == RNGType.XPU:
  assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
  UpperCAmelCase__							=    torch.xpu.get_rng_state()
 elif rng_type == RNGType.GENERATOR:
  assert generator is not None, "Need a generator to synchronize its seed."
  UpperCAmelCase__							=    generator.get_state()

 # Broadcast the rng state from device 0 to other devices
 UpperCAmelCase__							=    AcceleratorState()
 if state.distributed_type == DistributedType.TPU:
  UpperCAmelCase__							=    rng_state.to(xm.xla_device()						)
  xm.collective_broadcast([rng_state]						)
  xm.mark_step()
  UpperCAmelCase__							=    rng_state.cpu()
 elif (
     state.distributed_type in CUDA_DISTRIBUTED_TYPES
     or state.distributed_type == DistributedType.MULTI_NPU
     or state.distributed_type == DistributedType.MULTI_XPU
 ):
  UpperCAmelCase__							=    rng_state.to(state.device						)
  torch.distributed.broadcast(_lowerCAmelCase    ,				0						)
  UpperCAmelCase__							=    rng_state.cpu()
 elif state.distributed_type == DistributedType.MULTI_CPU:
  torch.distributed.broadcast(_lowerCAmelCase    ,				0						)

 # Set the broadcast rng state
 if rng_type == RNGType.TORCH:
  torch.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.CUDA:
  torch.cuda.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.NPU:
  torch.npu.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.XPU:
  torch.xpu.set_rng_state(_lowerCAmelCase						)
 elif rng_type == RNGType.XLA:
  xm.set_rng_state(rng_state.item()						)
 elif rng_type == RNGType.GENERATOR:
  generator.set_state(_lowerCAmelCase						)



def     lowerCAmelCase		(		_lowerCAmelCase				: List[Union[str, RNGType]]    ,				_lowerCAmelCase				: Optional[torch.Generator] = None						):




 """simple docstring"""







 for rng_type in rng_types:
  synchronize_rng_state(RNGType(_lowerCAmelCase						)    ,				generator=_lowerCAmelCase						)





