




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


UpperCamelCase								=	logging.get_logger(__name__)


if is_vision_available():
     import PIL





class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = ["pixel_values"]


    def __init__(			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	PILImageResampling = PILImageResampling.BICUBIC  ,      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__	:	bool = True  ,      **SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ,      ) ->			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__  ,      default_to_square=SCREAMING_SNAKE_CASE__  ,      param_name="crop_size"  )

         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   a (			self	:	Tuple  ,      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__	:	Union[str, Any]  ,      ) ->			np.ndarray:
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  ,      default_to_square=SCREAMING_SNAKE_CASE__  )
         if "shortest_edge" not in size:
              raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'  )
         lowerCAmelCase__            =					get_resize_output_image_size(SCREAMING_SNAKE_CASE__  ,      size=size["shortest_edge"]  ,      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   a (			self	:	int  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	Dict  ,      ) ->			np.ndarray:
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  )
         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(SCREAMING_SNAKE_CASE__  ,      size=(size["height"], size["width"])  ,      data_format=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Union[int, float]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	Tuple  ,      ) ->			str:
         return rescale(SCREAMING_SNAKE_CASE__  ,      scale=SCREAMING_SNAKE_CASE__  ,      data_format=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			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:
         return normalize(SCREAMING_SNAKE_CASE__  ,      mean=SCREAMING_SNAKE_CASE__  ,      std=SCREAMING_SNAKE_CASE__  ,      data_format=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	ImageInput  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	PILImageResampling = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	int = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	float = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, TensorType]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[ChannelDimension] = ChannelDimension.FIRST  ,      **SCREAMING_SNAKE_CASE__	:	List[Any]  ,      ) ->			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__  ,      param_name="size"  ,      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__  ,      param_name="crop_size"  ,      default_to_square=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__  )







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce

def   __snake_case    (       _lowerCAmelCase			:			Optional[int]						)			->    Dict:
   return (torch.arange(state.num_processes						) + 1.0 + (state.num_processes * state.process_index)).to(state.device						)

def   __snake_case    (       _lowerCAmelCase			:			Tuple						)			->    Optional[Any]:
   A_       :					Optional[Any]        =      create_tensor(_lowerCAmelCase						)
   A_       :					Dict        =      gather(_lowerCAmelCase						)
   assert gathered_tensor.tolist() == list(range(1					,					state.num_processes**2 + 1						)						)

def   __snake_case    (       _lowerCAmelCase			:			List[Any]						)			->    Any:
   A_       :					int        =      [state.process_index]
   A_       :					Union[str, Any]        =      gather_object(_lowerCAmelCase						)
   assert len(_lowerCAmelCase						) == state.num_processes, f"{gathered_obj}, {len(_lowerCAmelCase						)} != {state.num_processes}"
   assert gathered_obj == list(range(state.num_processes						)						), f"{gathered_obj} != {list(range(state.num_processes						)						)}"

def   __snake_case    (       _lowerCAmelCase			:			Union[str, Any]						)			->    Optional[Any]:
   A_       :					List[str]        =      create_tensor(_lowerCAmelCase						)
   A_       :					Optional[Any]        =      broadcast(_lowerCAmelCase						)
   assert broadcasted_tensor.shape == torch.Size([state.num_processes]						)
   assert broadcasted_tensor.tolist() == list(range(1					,					state.num_processes + 1						)						)

def   __snake_case    (       _lowerCAmelCase			:			Dict						)			->    str:
   # We need to pad the tensor with one more element if we are the main process
   # to ensure that we can pad
   if state.is_main_process:
      A_       :					Tuple        =      torch.arange(state.num_processes + 1						).to(state.device						)
   else:
      A_       :					List[str]        =      torch.arange(state.num_processes						).to(state.device						)
   A_       :					Any        =      pad_across_processes(_lowerCAmelCase						)
   assert padded_tensor.shape == torch.Size([state.num_processes + 1]						)
   if not state.is_main_process:
      assert padded_tensor.tolist() == list(range(0					,					state.num_processes						)						) + [0]

def   __snake_case    (       _lowerCAmelCase			:			List[Any]						)			->    Tuple:
   # For now runs on only two processes
   if state.num_processes != 2:
      return
   A_       :					str        =      create_tensor(_lowerCAmelCase						)
   A_       :					int        =      reduce(_lowerCAmelCase					,					"sum"						)
   A_       :					Optional[Any]        =      torch.tensor([4.0, 6]						).to(state.device						)
   assert torch.allclose(_lowerCAmelCase					,					_lowerCAmelCase						), f"{reduced_tensor} != {truth_tensor}"

def   __snake_case    (       _lowerCAmelCase			:			Union[str, Any]						)			->    Dict:
   # For now runs on only two processes
   if state.num_processes != 2:
      return
   A_       :					List[str]        =      create_tensor(_lowerCAmelCase						)
   A_       :					Tuple        =      reduce(_lowerCAmelCase					,					"mean"						)
   A_       :					List[str]        =      torch.tensor([2.0, 3]						).to(state.device						)
   assert torch.allclose(_lowerCAmelCase					,					_lowerCAmelCase						), f"{reduced_tensor} != {truth_tensor}"

def   __snake_case    (       _lowerCAmelCase			:			List[str]						)			->    Dict:
   # For xla_spawn (TPUs)
   main()

def   __snake_case    (       )			->    List[str]:
   A_       :					Tuple        =      PartialState()
   state.print(f"State: {state}"						)
   state.print("testing gather"						)
   test_gather(_lowerCAmelCase						)
   state.print("testing gather_object"						)
   test_gather_object(_lowerCAmelCase						)
   state.print("testing broadcast"						)
   test_broadcast(_lowerCAmelCase						)
   state.print("testing pad_across_processes"						)
   test_pad_across_processes(_lowerCAmelCase						)
   state.print("testing reduce_sum"						)
   test_reduce_sum(_lowerCAmelCase						)
   state.print("testing reduce_mean"						)
   test_reduce_mean(_lowerCAmelCase						)


if __name__ == "__main__":
 main()
