from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE__		:					List[str]						=					logging.get_logger(__name__)

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     ['pixel_values']




  def __init__(      self     :	Optional[Any]     , a_     :	bool = True     , a_     :	Union[int, float] = 1 / 255     , a_     :	bool = True     , a_     :	int = 8     , **a_     :	List[str]     , )->       None:




       """simple docstring"""
       super().__init__(**a_				)

       SCREAMING_SNAKE_CASE__				:				str			=		do_rescale
       SCREAMING_SNAKE_CASE__				:				Any			=		rescale_factor
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		do_pad
       SCREAMING_SNAKE_CASE__				:				int			=		pad_size




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




       """simple docstring"""
       return rescale(a_     , scale=a_     , data_format=a_     , **a_				)




  def 	__lowercase(      self     :	List[str]     , a_     :	np.ndarray     , a_     :	int     , a_     :	Optional[Union[str, ChannelDimension]] = None				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[Any]			=		get_image_size(a_				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		(old_height // size + 1) * size - old_height
       SCREAMING_SNAKE_CASE__				:				int			=		(old_width // size + 1) * size - old_width

       return pad(a_     , ((0, pad_height), (0, pad_width))     , mode='symmetric'     , data_format=a_				)




  def 	__lowercase(      self     :	List[str]     , a_     :	ImageInput     , a_     :	Optional[bool] = None     , a_     :	Optional[float] = None     , a_     :	Optional[bool] = None     , a_     :	Optional[int] = None     , a_     :	Optional[Union[str, TensorType]] = None     , a_     :	Union[str, ChannelDimension] = ChannelDimension.FIRST     , **a_     :	Any     , )->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__				:				Dict			=		rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__				:				Tuple			=		do_pad if do_pad is not None else self.do_pad
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		pad_size if pad_size is not None else self.pad_size

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		make_list_of_images(a_				)

       if not valid_images(a_				):
            raise ValueError(
                'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                'torch.Tensor, tf.Tensor or jax.ndarray.'				)

       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.
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		[to_numpy_array(a_				) for image in images]

       if do_rescale:
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[self.rescale(image=a_     , scale=a_				) for image in images]

       if do_pad:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[self.pad(a_     , size=a_				) for image in images]

       SCREAMING_SNAKE_CASE__				:				Tuple			=		[to_channel_dimension_format(a_     , a_				) for image in images]

       SCREAMING_SNAKE_CASE__				:				Dict			=		{'pixel_values': images}
       return BatchFeature(data=a_     , tensor_type=a_				)




# 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     _a				(      lowercase__  :			Any ):







     '''simple docstring'''


     return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )







def     _a				(      lowercase__  :			Tuple ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		gather(lowercase__ )
     assert gathered_tensor.tolist() == list(range(1     ,  state.num_processes**2 + 1 ) )







def     _a				(      lowercase__  :			List[Any] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		[state.process_index]
     SCREAMING_SNAKE_CASE__				:				Any			=		gather_object(lowercase__ )
     assert len(lowercase__ ) == state.num_processes, f'''{gathered_obj}, {len(lowercase__ )} != {state.num_processes}'''
     assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		broadcast(lowercase__ )
     assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
     assert broadcasted_tensor.tolist() == list(range(1     ,  state.num_processes + 1 ) )







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if state.is_main_process:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.arange(state.num_processes + 1 ).to(state.device )
     else:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.arange(state.num_processes ).to(state.device )
     SCREAMING_SNAKE_CASE__				:				Any			=		pad_across_processes(lowercase__ )
     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     _a				(      lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if state.num_processes != 2:
          return
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				str			=		reduce(lowercase__     ,  'sum' )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.tensor([4.0, 6] ).to(state.device )
     assert torch.allclose(lowercase__     ,  lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if state.num_processes != 2:
          return
     SCREAMING_SNAKE_CASE__				:				Any			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		reduce(lowercase__     ,  'mean' )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.tensor([2.0, 3] ).to(state.device )
     assert torch.allclose(lowercase__     ,  lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     main()







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		PartialState()
     state.print(f'''State: {state}''' )
     state.print('testing gather' )
     test_gather(lowercase__ )
     state.print('testing gather_object' )
     test_gather_object(lowercase__ )
     state.print('testing broadcast' )
     test_broadcast(lowercase__ )
     state.print('testing pad_across_processes' )
     test_pad_across_processes(lowercase__ )
     state.print('testing reduce_sum' )
     test_reduce_sum(lowercase__ )
     state.print('testing reduce_mean' )
     test_reduce_mean(lowercase__ )


if __name__ == "__main__":
      main()



