
import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


lowerCamelCase  =logging.get_logger(__name__)




class 					_lowerCamelCase			:





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	None


@experimental
def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      if ParallelBackendConfig.backend_name is None:
            return _map_with_multiprocessing_pool(
                UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		)

      return _map_with_joblib(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		)


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      UpperCamelCase__     : Any											=					num_proc if num_proc <= len(UpperCamelCase__		) else len(UpperCamelCase__		)
      UpperCamelCase__     : Any											=					[]  # We organize the splits ourselve (contiguous splits)
      for index in range(UpperCamelCase__		):
            UpperCamelCase__     : str											=					len(UpperCamelCase__		) // num_proc
            UpperCamelCase__     : Dict											=					len(UpperCamelCase__		) % num_proc
            UpperCamelCase__     : Optional[Any]											=					div * index + min(UpperCamelCase__			,		UpperCamelCase__		)
            UpperCamelCase__     : Optional[int]											=					start + div + (1 if index < mod else 0)
            split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)		)

      if len(UpperCamelCase__		) != sum(len(i[1]		) for i in split_kwds		):
            raise ValueError(
                f'''Error dividing inputs iterable among processes. '''
                f'''Total number of objects {len(UpperCamelCase__		)}, '''
                f'''length: {sum(len(i[1]		) for i in split_kwds		)}'''		)

      logger.info(
          f'''Spawning {num_proc} processes for {len(UpperCamelCase__		)} objects in slices of {[len(i[1]		) for i in split_kwds]}'''		)
      UpperCamelCase__					,UpperCamelCase__     : List[str]											=					None, None
      if not disable_tqdm:
            UpperCamelCase__					,UpperCamelCase__     : Tuple											=					(RLock(),), tqdm.set_lock
      with Pool(UpperCamelCase__			,		initargs=UpperCamelCase__			,		initializer=UpperCamelCase__		) as pool:
            UpperCamelCase__     : Optional[int]											=					pool.map(UpperCamelCase__			,		UpperCamelCase__		)
      logger.info(f'''Finished {num_proc} processes'''		)
      UpperCamelCase__     : Dict											=					[obj for proc_res in mapped for obj in proc_res]
      logger.info(f'''Unpacked {len(UpperCamelCase__		)} objects'''		)

      return mapped


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      # progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
      # and it requires monkey-patching joblib internal classes which is subject to change
      import joblib

      with joblib.parallel_backend(ParallelBackendConfig.backend_name			,		n_jobs=UpperCamelCase__		):
            return joblib.Parallel()(
                joblib.delayed(UpperCamelCase__		)((function, obj, types, None, True, None)		) for obj in iterable		)


@experimental
@contextlib.contextmanager
def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__		):
      UpperCamelCase__     : int											=					backend_name

      if backend_name == "spark":
            from joblibspark import register_spark

            register_spark()

            # TODO: call create_cache_and_write_probe if "download" in steps
            # TODO: raise NotImplementedError when Dataset.map etc is called

      try:
            yield
      finally:
            UpperCamelCase__     : List[str]											=					None




from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase  =logging.get_logger(__name__)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	['''pixel_values''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = 3_2    ,    __SCREAMING_SNAKE_CASE=PILImageResampling.BILINEAR    ,    __SCREAMING_SNAKE_CASE = True    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize
           UpperCamelCase__     : Tuple											=					do_rescale
           UpperCamelCase__     : Dict											=					size_divisor
           UpperCamelCase__     : str											=					resample
           super().__init__(**__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__     : Optional[Any]											=					get_image_size(__SCREAMING_SNAKE_CASE	)
           # Rounds the height and width down to the closest multiple of size_divisor
           UpperCamelCase__     : List[Any]											=					height // size_divisor * size_divisor
           UpperCamelCase__     : Dict											=					width // size_divisor * size_divisor
           UpperCamelCase__     : str											=					resize(__SCREAMING_SNAKE_CASE    ,    (new_h, new_w)    ,    resample=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           return image






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







           return rescale(image=__SCREAMING_SNAKE_CASE    ,    scale=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> BatchFeature:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__     : List[Any]											=					do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__     : Union[str, Any]											=					size_divisor if size_divisor is not None else self.size_divisor
           UpperCamelCase__     : List[str]											=					resample if resample is not None else self.resample

           if do_resize and size_divisor is None:
                 raise ValueError('''size_divisor is required for resizing'''	)

           UpperCamelCase__     : List[Any]											=					make_list_of_images(__SCREAMING_SNAKE_CASE	)

           if not valid_images(__SCREAMING_SNAKE_CASE	):
                 raise ValueError('''Invalid image(s)'''	)

           # All transformations expect numpy arrays.
           UpperCamelCase__     : Dict											=					[to_numpy_array(__SCREAMING_SNAKE_CASE	) for img in images]

           if do_resize:
                 UpperCamelCase__     : Optional[int]											=					[self.resize(__SCREAMING_SNAKE_CASE    ,    size_divisor=__SCREAMING_SNAKE_CASE    ,    resample=__SCREAMING_SNAKE_CASE	) for image in images]

           if do_rescale:
                 UpperCamelCase__     : Union[str, Any]											=					[self.rescale(__SCREAMING_SNAKE_CASE    ,    scale=1 / 2_5_5	) for image in images]

           UpperCamelCase__     : List[str]											=					[to_channel_dimension_format(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	) for image in images]

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


