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


lowercase__    :       int						=			logging.get_logger(__name__)


if is_vision_available():
	import PIL




class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      Dict     =       ["""pixel_values"""]


	def __init__(							self	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 1 / 255	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = True	,    **__SCREAMING_SNAKE_CASE	,    )		->None:
								super().__init__(**__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	size if size is not None else {'''shortest_edge''': 224}
								lowerCAmelCase				=	get_size_dict(__SCREAMING_SNAKE_CASE	,    default_to_square=__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								lowerCAmelCase				=	get_size_dict(__SCREAMING_SNAKE_CASE       )

								lowerCAmelCase				=	do_resize
								lowerCAmelCase				=	size
								lowerCAmelCase				=	resample
								lowerCAmelCase				=	do_center_crop
								lowerCAmelCase				=	crop_size
								lowerCAmelCase				=	do_rescale
								lowerCAmelCase				=	rescale_factor
								lowerCAmelCase				=	do_normalize
								lowerCAmelCase				=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								lowerCAmelCase				=	image_std if image_std is not None else OPENAI_CLIP_STD
								lowerCAmelCase				=	do_convert_rgb


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC	,    __SCREAMING_SNAKE_CASE = None	,    **__SCREAMING_SNAKE_CASE	,    )		->np.ndarray:
								lowerCAmelCase				=	get_size_dict(__SCREAMING_SNAKE_CASE	,    default_to_square=__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	get_resize_output_image_size(
								    __SCREAMING_SNAKE_CASE	,    size=(size['''height'''], size['''width'''])	,    default_to_square=__SCREAMING_SNAKE_CASE       )
								return resize(__SCREAMING_SNAKE_CASE	,    size=__SCREAMING_SNAKE_CASE	,    resample=__SCREAMING_SNAKE_CASE	,    data_format=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None	,    **__SCREAMING_SNAKE_CASE	,    )		->np.ndarray:
								lowerCAmelCase				=	get_size_dict(__SCREAMING_SNAKE_CASE       )
								return center_crop(__SCREAMING_SNAKE_CASE	,    size=(size['''height'''], size['''width'''])	,    data_format=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None	,    **__SCREAMING_SNAKE_CASE	,    )		->List[str]:
								return rescale(__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	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None	,    **__SCREAMING_SNAKE_CASE	,    )		->np.ndarray:
								return normalize(__SCREAMING_SNAKE_CASE	,    mean=__SCREAMING_SNAKE_CASE	,    std=__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 = None	,    __SCREAMING_SNAKE_CASE = None	,    __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	,    )		->PIL.Image.Image:
								lowerCAmelCase				=	do_resize if do_resize is not None else self.do_resize
								lowerCAmelCase				=	size if size is not None else self.size
								lowerCAmelCase				=	get_size_dict(__SCREAMING_SNAKE_CASE	,    default_to_square=__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	resample if resample is not None else self.resample
								lowerCAmelCase				=	do_center_crop if do_center_crop is not None else self.do_center_crop
								lowerCAmelCase				=	crop_size if crop_size is not None else self.crop_size
								lowerCAmelCase				=	get_size_dict(__SCREAMING_SNAKE_CASE       )
								lowerCAmelCase				=	do_rescale if do_rescale is not None else self.do_rescale
								lowerCAmelCase				=	rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCAmelCase				=	do_normalize if do_normalize is not None else self.do_normalize
								lowerCAmelCase				=	image_mean if image_mean is not None else self.image_mean
								lowerCAmelCase				=	image_std if image_std is not None else self.image_std
								lowerCAmelCase				=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								lowerCAmelCase				=	make_list_of_images(__SCREAMING_SNAKE_CASE       )

								if not valid_images(__SCREAMING_SNAKE_CASE       ):
															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:
															lowerCAmelCase				=	[convert_to_rgb(__SCREAMING_SNAKE_CASE       ) for image in images]

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

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

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

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

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

								lowerCAmelCase				=	[to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ) for image in images]

								lowerCAmelCase				=	{'''pixel_values''': images}
								return BatchFeature(data=__SCREAMING_SNAKE_CASE	,    tensor_type=__SCREAMING_SNAKE_CASE       )




import torch

from accelerate import Accelerator, DistributedType




class      lowercase_						:



	"""simple docstring"""





	def __enter__(							self       )		->Dict:
								if self.enabled:
															lowerCAmelCase				=	self.model.no_sync()
															self.model_sync_obj.__enter__()

								return self


	def __exit__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->Any:
								if self.enabled:
															# Average all models on exit
															self._sync_and_avg_model_params()
															self.model_sync_obj.__exit__(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )


	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = True       )		->Tuple:
								if accelerator.distributed_type not in [
								    DistributedType.NO,
								    DistributedType.MULTI_CPU,
								    DistributedType.MULTI_GPU,
								]:
															raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''       )
								lowerCAmelCase				=	enabled and accelerator.distributed_type != DistributedType.NO
								lowerCAmelCase				=	0
								if self.enabled:
															lowerCAmelCase				=	accelerator
															lowerCAmelCase				=	model
															lowerCAmelCase				=	local_sgd_steps


	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[Any]:
								self.num_steps += 1
								if not self.enabled:
															return

								if self.num_steps % self.local_sgd_steps == 0:
															self._sync_and_avg_model_params()




	def     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[int]:
								self.accelerator.wait_for_everyone()
								with self.accelerator.autocast():
															for param in self.model.parameters():
																						lowerCAmelCase				=	self.accelerator.reduce(param.data	,    reduction='''mean'''       )



