






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


A_		     =			logging.get_logger(__name__)


if is_vision_available():
   import PIL




class     _lowerCAmelCase(	_snake_case ):





       """simple docstring"""







       a    :		Union[str, Any]									=['''pixel_values''']





       def __init__( self  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = PILImageResampling.BICUBIC  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = True  ,      _lowerCamelCase = 1 / 2_5_5  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = True  ,      **_lowerCamelCase  ,      ):
         super().__init__(**UpperCamelCase__							)
         UpperCamelCase_: Tuple   				=    size if size is not None else {'shortest_edge': 2_2_4}
         UpperCamelCase_: int   				=    get_size_dict(UpperCamelCase__  ,      default_to_square=UpperCamelCase__							)
         UpperCamelCase_: Union[str, Any]   				=    crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_: Union[str, Any]   				=    get_size_dict(UpperCamelCase__  ,      default_to_square=UpperCamelCase__  ,      param_name='crop_size'							)

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





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = PILImageResampling.BICUBIC  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: str   				=    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()}'''							)
         UpperCamelCase_: Tuple   				=    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       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Dict   				=    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       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         return rescale(UpperCamelCase__  ,      scale=UpperCamelCase__  ,      data_format=UpperCamelCase__  ,      **UpperCamelCase__							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         return normalize(UpperCamelCase__  ,      mean=UpperCamelCase__  ,      std=UpperCamelCase__  ,      data_format=UpperCamelCase__  ,      **UpperCamelCase__							)





       def       _a   ( 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  ,      ):
         UpperCamelCase_: List[str]   				=    do_resize if do_resize is not None else self.do_resize
         UpperCamelCase_: Optional[Any]   				=    size if size is not None else self.size
         UpperCamelCase_: str   				=    get_size_dict(UpperCamelCase__  ,      param_name='size'  ,      default_to_square=UpperCamelCase__							)
         UpperCamelCase_: Any   				=    resample if resample is not None else self.resample
         UpperCamelCase_: Any   				=    do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCamelCase_: Dict   				=    crop_size if crop_size is not None else self.crop_size
         UpperCamelCase_: Any   				=    get_size_dict(UpperCamelCase__  ,      param_name='crop_size'  ,      default_to_square=UpperCamelCase__							)
         UpperCamelCase_: Optional[int]   				=    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_: int   				=    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_: List[str]   				=    image_std if image_std is not None else self.image_std
         UpperCamelCase_: List[str]   				=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         UpperCamelCase_: Any   				=    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:
           UpperCamelCase_: str   				=    [convert_to_rgb(UpperCamelCase__							) for image in images]

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

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

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

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

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

         UpperCamelCase_: Tuple   				=    [to_channel_dimension_format(UpperCamelCase__  ,      UpperCamelCase__							) for image in images]

         UpperCamelCase_: Optional[Any]   				=    {'pixel_values': images}
         return BatchFeature(data=UpperCamelCase__  ,      tensor_type=UpperCamelCase__							)







import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__=()     ,   UpperCAmelCase__=None     ,   UpperCAmelCase__="no"     ,   UpperCAmelCase__="29500"  )	->    List[Any]:
  UpperCamelCase_: Any   				=    False
  UpperCamelCase_: List[str]   				=    False
  if any(key.startswith('KAGGLE'  ) for key in os.environ.keys()  ):
    UpperCamelCase_: List[Any]   				=    True
  elif "IPython" in sys.modules:
    UpperCamelCase_: List[Any]   				=    'google.colab' in str(sys.modules['IPython'].get_ipython()  )

  try:
    UpperCamelCase_: Optional[int]   				=    PrecisionType(mixed_precision.lower()  )
  except ValueError:
    raise ValueError(
        F'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''  )

  if (in_colab or in_kaggle) and (os.environ.get('TPU_NAME'     ,   UpperCAmelCase__  ) is not None):
    # TPU launch
    import torch_xla.distributed.xla_multiprocessing as xmp

    if len(AcceleratorState._shared_state  ) > 0:
      raise ValueError(
          'To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '
          'your training function. Restart your notebook and make sure no cells initializes an '
          '`Accelerator`.'  )
    if num_processes is None:
      UpperCamelCase_: List[str]   				=    8

    UpperCamelCase_: str   				=    PrepareForLaunch(UpperCAmelCase__     ,   distributed_type='TPU'  )
    print(F'''Launching a training on {num_processes} TPU cores.'''  )
    xmp.spawn(UpperCAmelCase__     ,   args=UpperCAmelCase__     ,   nprocs=UpperCAmelCase__     ,   start_method='fork'  )
  elif in_colab:
    # No need for a distributed launch otherwise as it's either CPU or one GPU.
    if torch.cuda.is_available():
      print('Launching training on one GPU.'  )
    else:
      print('Launching training on one CPU.'  )
    function(*UpperCAmelCase__  )
  else:
    if num_processes is None:
      raise ValueError(
          'You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.'  )

    if num_processes > 1:
      # Multi-GPU launch
      from torch.multiprocessing import start_processes
      from torch.multiprocessing.spawn import ProcessRaisedException

      if len(AcceleratorState._shared_state  ) > 0:
        raise ValueError(
            'To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '
            'inside your training function. Restart your notebook and make sure no cells initializes an '
            '`Accelerator`.'  )

      if torch.cuda.is_initialized():
        raise ValueError(
            'To launch a multi-GPU training from your notebook, you need to avoid running any instruction '
            'using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '
            'function.'  )

      # torch.distributed will expect a few environment variable to be here. We set the ones common to each
      # process here (the other ones will be set be the launcher).
      with patch_environment(
          world_size=UpperCAmelCase__     ,   master_addr='127.0.01'     ,   master_port=UpperCAmelCase__     ,   mixed_precision=UpperCAmelCase__  ):
        UpperCamelCase_: str   				=    PrepareForLaunch(UpperCAmelCase__     ,   distributed_type='MULTI_GPU'  )
        print(F'''Launching training on {num_processes} GPUs.'''  )
        try:
          start_processes(UpperCAmelCase__     ,   args=UpperCAmelCase__     ,   nprocs=UpperCAmelCase__     ,   start_method='fork'  )
        except ProcessRaisedException as e:
          if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
            raise RuntimeError(
                'CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '
                'This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '
                'Please review your imports and test them when running the `notebook_launcher()` to identify '
                'which one is problematic.'  ) from e

    else:
      # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
      if is_mps_available():
        UpperCamelCase_: Tuple   				=    '1'
        print('Launching training on MPS.'  )
      elif torch.cuda.is_available():
        print('Launching training on one GPU.'  )
      else:
        print('Launching training on CPU.'  )
      function(*UpperCAmelCase__  )

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__=()     ,   UpperCAmelCase__=2  )	->    Optional[int]:
  from torch.multiprocessing import start_processes

  with tempfile.NamedTemporaryFile() as tmp_file:
    # torch.distributed will expect a few environment variable to be here. We set the ones common to each
    # process here (the other ones will be set be the launcher).
    with patch_environment(
        world_size=UpperCAmelCase__     ,   master_addr='127.0.01'     ,   master_port='29500'     ,   accelerate_mixed_precision='no'     ,   accelerate_debug_rdv_file=tmp_file.name     ,   accelerate_use_cpu='yes'     ,   ):
      UpperCamelCase_: str   				=    PrepareForLaunch(UpperCAmelCase__     ,   debug=UpperCAmelCase__  )
      start_processes(UpperCAmelCase__     ,   args=UpperCAmelCase__     ,   nprocs=UpperCAmelCase__     ,   start_method='fork'  )