

'''simple docstring'''

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 			__UpperCamelCase							(		lowercase__  :					Tuple,   lowercase__  :					Dict = False	):






     '''simple docstring'''





     if device_specific:
          seed += AcceleratorState().process_index
     random.seed(lowercase_	)
     np.random.seed(lowercase_	)
     torch.manual_seed(lowercase_	)
     if is_xpu_available():
          torch.xpu.manual_seed_all(lowercase_	)
     elif is_npu_available():
          torch.npu.manual_seed_all(lowercase_	)
     else:
          torch.cuda.manual_seed_all(lowercase_	)
     # ^^ safe to call this function even if cuda is not available
     if is_tpu_available():
          xm.set_rng_state(lowercase_	)

def 			__UpperCamelCase							(		lowercase__  :					Optional[int] = None,   lowercase__  :					Union[str, Any] = None	):






     '''simple docstring'''





     if rng_type == RNGType.TORCH:
          __lowercase     						=torch.get_rng_state()
     elif rng_type == RNGType.CUDA:
          __lowercase     						=torch.cuda.get_rng_state()
     elif rng_type == RNGType.XLA:
          assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
          __lowercase     						=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."
          __lowercase     						=torch.npu.get_rng_state()
     elif rng_type == RNGType.XPU:
          assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
          __lowercase     						=torch.xpu.get_rng_state()
     elif rng_type == RNGType.GENERATOR:
          assert generator is not None, "Need a generator to synchronize its seed."
          __lowercase     						=generator.get_state()

     # Broadcast the rng state from device 0 to other devices
     __lowercase     						=AcceleratorState()
     if state.distributed_type == DistributedType.TPU:
          __lowercase     						=rng_state.to(xm.xla_device()	)
          xm.collective_broadcast([rng_state]	)
          xm.mark_step()
          __lowercase     						=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
     ):
          __lowercase     						=rng_state.to(state.device	)
          torch.distributed.broadcast(lowercase_,   0	)
          __lowercase     						=rng_state.cpu()
     elif state.distributed_type == DistributedType.MULTI_CPU:
          torch.distributed.broadcast(lowercase_,   0	)

     # Set the broadcast rng state
     if rng_type == RNGType.TORCH:
          torch.set_rng_state(lowercase_	)
     elif rng_type == RNGType.CUDA:
          torch.cuda.set_rng_state(lowercase_	)
     elif rng_type == RNGType.NPU:
          torch.npu.set_rng_state(lowercase_	)
     elif rng_type == RNGType.XPU:
          torch.xpu.set_rng_state(lowercase_	)
     elif rng_type == RNGType.XLA:
          xm.set_rng_state(rng_state.item()	)
     elif rng_type == RNGType.GENERATOR:
          generator.set_state(lowercase_	)







def 			__UpperCamelCase							(		lowercase__  :					List[str],   lowercase__  :					Tuple = None	):






     '''simple docstring'''





     for rng_type in rng_types:
          synchronize_rng_state(RNGType(lowercase_	),   generator=lowercase_	)





'''simple docstring'''

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 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 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	["pixel_values"]







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







            """simple docstring"""







            super().__init__(**__lowercase )
            __lowercase     						=size if size is not None else {'height': 224, 'width': 224}
            __lowercase     						=get_size_dict(__lowercase )
            __lowercase     						=do_resize
            __lowercase     						=do_rescale
            __lowercase     						=do_normalize
            __lowercase     						=size
            __lowercase     						=resample
            __lowercase     						=rescale_factor
            __lowercase     						=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            __lowercase     						=image_std if image_std is not None else IMAGENET_STANDARD_STD







       def      snake_case				(							self     :	Dict  , __lowercase     :	np.ndarray  , __lowercase     :	Dict[str, int]  , __lowercase     :	PILImageResampling = PILImageResampling.BILINEAR  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	Union[str, Any]  , ):







            """simple docstring"""







            __lowercase     						=get_size_dict(__lowercase )
            if "height" not in size or "width" not in size:
                 raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''' )
            return resize(
                __lowercase  , size=(size['height'], size['width'])  , resample=__lowercase  , data_format=__lowercase  , **__lowercase )







       def      snake_case				(							self     :	str  , __lowercase     :	np.ndarray  , __lowercase     :	float  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	Optional[Any] ):







            """simple docstring"""







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







       def      snake_case				(							self     :	Any  , __lowercase     :	np.ndarray  , __lowercase     :	Union[float, List[float]]  , __lowercase     :	Union[float, List[float]]  , __lowercase     :	Optional[Union[str, ChannelDimension]] = None  , **__lowercase     :	str  , ):







            """simple docstring"""







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







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







            """simple docstring"""







            __lowercase     						=do_resize if do_resize is not None else self.do_resize
            __lowercase     						=do_rescale if do_rescale is not None else self.do_rescale
            __lowercase     						=do_normalize if do_normalize is not None else self.do_normalize
            __lowercase     						=resample if resample is not None else self.resample
            __lowercase     						=rescale_factor if rescale_factor is not None else self.rescale_factor
            __lowercase     						=image_mean if image_mean is not None else self.image_mean
            __lowercase     						=image_std if image_std is not None else self.image_std

            __lowercase     						=size if size is not None else self.size
            __lowercase     						=get_size_dict(__lowercase )

            __lowercase     						=make_list_of_images(__lowercase )

            if not valid_images(__lowercase ):
                 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_rescale and rescale_factor is None:
                 raise ValueError('Rescale factor must be specified if do_rescale is True.' )

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

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

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

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

            __lowercase     						=[to_channel_dimension_format(__lowercase  , __lowercase ) for image in images]

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


