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				 =   logging.get_logger(__name__)


if is_vision_available():
  import PIL






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   UpperCamelCase_					:    Tuple			      = ['pixel_values']
   def __init__(     self				,  lowerCAmelCase_ = True				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = PILImageResampling.BICUBIC				,  lowerCAmelCase_ = True				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = True				,  lowerCAmelCase_ = 1 / 2_55				,  lowerCAmelCase_ = True				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = True				,  **lowerCAmelCase_				,  )						->None:
        super().__init__(**lowerCAmelCase_     )
        a	:     str						      =    size if size is not None else {'''shortest_edge''': 2_24}
        a	:     Optional[Any]						      =    get_size_dict(lowerCAmelCase_				,  default_to_square=lowerCAmelCase_     )
        a	:     Union[str, Any]						      =    crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
        a	:     int						      =    get_size_dict(lowerCAmelCase_     )

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


   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = ChannelDimension.FIRST				,  **lowerCAmelCase_				,  )						->PIL.Image.Image:
        a	:     List[Any]						      =    do_resize if do_resize is not None else self.do_resize
        a	:     List[Any]						      =    size if size is not None else self.size
        a	:     str						      =    get_size_dict(lowerCAmelCase_				,  default_to_square=lowerCAmelCase_     )
        a	:     Optional[int]						      =    resample if resample is not None else self.resample
        a	:     str						      =    do_center_crop if do_center_crop is not None else self.do_center_crop
        a	:     str						      =    crop_size if crop_size is not None else self.crop_size
        a	:     str						      =    get_size_dict(lowerCAmelCase_     )
        a	:     int						      =    do_rescale if do_rescale is not None else self.do_rescale
        a	:     Dict						      =    rescale_factor if rescale_factor is not None else self.rescale_factor
        a	:     Any						      =    do_normalize if do_normalize is not None else self.do_normalize
        a	:     str						      =    image_mean if image_mean is not None else self.image_mean
        a	:     str						      =    image_std if image_std is not None else self.image_std
        a	:     Optional[Any]						      =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        a	:     Union[str, Any]						      =    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:
             a	:     Union[str, Any]						      =    [convert_to_rgb(lowerCAmelCase_     ) for image in images]

        # All transformations expect numpy arrays.
        a	:     Optional[int]						      =    [to_numpy_array(lowerCAmelCase_     ) for image in images]

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

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

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

        if do_normalize:
             a	:     Union[str, Any]						      =    [self.normalize(image=lowerCAmelCase_				,  mean=lowerCAmelCase_				,  std=lowerCAmelCase_     ) for image in images]

        a	:     Optional[int]						      =    [to_channel_dimension_format(lowerCAmelCase_				,  lowerCAmelCase_     ) for image in images]

        a	:     Tuple						      =    {'''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						)





