





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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_UpperCAmelCase        =				logging.get_logger(__name__)



class 							_UpperCamelCase					(	_snake_case     ):
       _UpperCamelCase				:					List[Any]         =   ['''pixel_values''']

       def __init__(		self: List[Any]					,   _SCREAMING_SNAKE_CASE: bool = True					,   _SCREAMING_SNAKE_CASE: Optional[Dict[str, int]] = None					,   _SCREAMING_SNAKE_CASE: PILImageResampling = PILImageResampling.BILINEAR					,   _SCREAMING_SNAKE_CASE: bool = True					,   _SCREAMING_SNAKE_CASE: Dict[str, int] = None					,   _SCREAMING_SNAKE_CASE: bool = True					,   _SCREAMING_SNAKE_CASE: Union[int, float] = 1 / 255					,   _SCREAMING_SNAKE_CASE: bool = True					,   _SCREAMING_SNAKE_CASE: Optional[Union[float, List[float]]] = None					,   _SCREAMING_SNAKE_CASE: Optional[Union[float, List[float]]] = None					,   **_SCREAMING_SNAKE_CASE: List[str]					,   )       -> None:




              """simple docstring"""


              super().__init__(**lowerCAmelCase__		)
              UpperCamelCase_          =  size if size is not None else {"shortest_edge": 256}
              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 IMAGENET_STANDARD_MEAN
              UpperCamelCase_          =  image_std if image_std is not None else IMAGENET_STANDARD_STD

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: np.ndarray					,   _SCREAMING_SNAKE_CASE: Dict[str, int]					,   _SCREAMING_SNAKE_CASE: PILImageResampling = PILImageResampling.BICUBIC					,   _SCREAMING_SNAKE_CASE: Optional[Union[str, ChannelDimension]] = None					,   **_SCREAMING_SNAKE_CASE: Tuple					,   )       -> np.ndarray:




              """simple docstring"""


              UpperCamelCase_          =  get_size_dict(lowerCAmelCase__					,   default_to_square=lowerCAmelCase__		)
              if "shortest_edge" not in size:
                     raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''		)
              UpperCamelCase_          =  get_resize_output_image_size(lowerCAmelCase__					,   size=size["shortest_edge"]					,   default_to_square=lowerCAmelCase__		)
              return resize(lowerCAmelCase__					,   size=lowerCAmelCase__					,   resample=lowerCAmelCase__					,   data_format=lowerCAmelCase__					,   **lowerCAmelCase__		)

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: np.ndarray					,   _SCREAMING_SNAKE_CASE: Dict[str, int]					,   _SCREAMING_SNAKE_CASE: Optional[Union[str, ChannelDimension]] = None					,   **_SCREAMING_SNAKE_CASE: int					,   )       -> np.ndarray:




              """simple docstring"""


              UpperCamelCase_          =  get_size_dict(lowerCAmelCase__		)
              return center_crop(lowerCAmelCase__					,   size=(size["height"], size["width"])					,   data_format=lowerCAmelCase__					,   **lowerCAmelCase__		)

       def lowercase (		self: List[Any]					,   _SCREAMING_SNAKE_CASE: np.ndarray					,   _SCREAMING_SNAKE_CASE: float					,   _SCREAMING_SNAKE_CASE: Optional[Union[str, ChannelDimension]] = None					,   **_SCREAMING_SNAKE_CASE: Any		)       -> np.ndarray:




              """simple docstring"""


              return rescale(lowerCAmelCase__					,   scale=lowerCAmelCase__					,   data_format=lowerCAmelCase__					,   **lowerCAmelCase__		)

       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: np.ndarray					,   _SCREAMING_SNAKE_CASE: Union[float, List[float]]					,   _SCREAMING_SNAKE_CASE: Union[float, List[float]]					,   _SCREAMING_SNAKE_CASE: Optional[Union[str, ChannelDimension]] = None					,   **_SCREAMING_SNAKE_CASE: Optional[int]					,   )       -> np.ndarray:




              """simple docstring"""


              return normalize(lowerCAmelCase__					,   mean=lowerCAmelCase__					,   std=lowerCAmelCase__					,   data_format=lowerCAmelCase__					,   **lowerCAmelCase__		)



       def lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: ImageInput					,   _SCREAMING_SNAKE_CASE: Optional[bool] = None					,   _SCREAMING_SNAKE_CASE: Dict[str, int] = None					,   _SCREAMING_SNAKE_CASE: PILImageResampling = None					,   _SCREAMING_SNAKE_CASE: bool = None					,   _SCREAMING_SNAKE_CASE: Dict[str, int] = None					,   _SCREAMING_SNAKE_CASE: Optional[bool] = None					,   _SCREAMING_SNAKE_CASE: Optional[float] = None					,   _SCREAMING_SNAKE_CASE: Optional[bool] = None					,   _SCREAMING_SNAKE_CASE: Optional[Union[float, List[float]]] = None					,   _SCREAMING_SNAKE_CASE: Optional[Union[float, List[float]]] = None					,   _SCREAMING_SNAKE_CASE: Optional[Union[str, TensorType]] = None					,   _SCREAMING_SNAKE_CASE: Union[str, ChannelDimension] = ChannelDimension.FIRST					,   **_SCREAMING_SNAKE_CASE: str					,   )       -> Dict:




              """simple docstring"""


              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_          =  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."		)

              # 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 os
import sys
import tempfile

import torch

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




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

       try:
              UpperCamelCase_          =  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_          =  8

              UpperCamelCase_          =  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_          =  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_          =  "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  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_=()					,		UpperCamelCase_=2  )     ->		Optional[Any]:
       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_          =  PrepareForLaunch(UpperCamelCase_					,		debug=UpperCamelCase_  )
                     start_processes(UpperCamelCase_					,		args=UpperCamelCase_					,		nprocs=UpperCamelCase_					,		start_method="fork"  )

