




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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       logging.get_logger(__name__)

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						b.T
			SCREAMING_SNAKE_CASE_								=						np.sum(np.square(SCREAMING_SNAKE_CASE )       ,    axis=1 )
			SCREAMING_SNAKE_CASE_								=						np.sum(np.square(SCREAMING_SNAKE_CASE )       ,    axis=0 )
			SCREAMING_SNAKE_CASE_								=						np.matmul(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						aa[:, None] - 2 * ab + ba[None, :]
			return d

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Optional[int]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						x.reshape(-1       ,    3 )
			SCREAMING_SNAKE_CASE_								=						squared_euclidean_distance(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
			return np.argmin(SCREAMING_SNAKE_CASE       ,    axis=1 )



class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= ['''pixel_values''']





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




				"""simple docstring"""



				super().__init__(**SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						size if size is not None else {'height': 256, 'width': 256}
				SCREAMING_SNAKE_CASE_								=						get_size_dict(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						np.array(SCREAMING_SNAKE_CASE      ) if clusters is not None else None
				SCREAMING_SNAKE_CASE_								=						do_resize
				SCREAMING_SNAKE_CASE_								=						size
				SCREAMING_SNAKE_CASE_								=						resample
				SCREAMING_SNAKE_CASE_								=						do_normalize
				SCREAMING_SNAKE_CASE_								=						do_color_quantize





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR      ,     SCREAMING_SNAKE_CASE = None      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					np.ndarray:




				"""simple docstring"""



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





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      ,     )  ->					np.ndarray:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						rescale(image=SCREAMING_SNAKE_CASE      ,     scale=1 / 1_2_7.5      ,     data_format=SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						image - 1
				return image





	def 						A_(							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 = ChannelDimension.FIRST      ,     **SCREAMING_SNAKE_CASE      ,     )  ->					PIL.Image.Image:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						do_resize if do_resize is not None else self.do_resize
				SCREAMING_SNAKE_CASE_								=						size if size is not None else self.size
				SCREAMING_SNAKE_CASE_								=						get_size_dict(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						resample if resample is not None else self.resample
				SCREAMING_SNAKE_CASE_								=						do_normalize if do_normalize is not None else self.do_normalize
				SCREAMING_SNAKE_CASE_								=						do_color_quantize if do_color_quantize is not None else self.do_color_quantize
				SCREAMING_SNAKE_CASE_								=						clusters if clusters is not None else self.clusters
				SCREAMING_SNAKE_CASE_								=						np.array(SCREAMING_SNAKE_CASE      )

				SCREAMING_SNAKE_CASE_								=						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 or resample is None:
							raise ValueError('Size and resample must be specified if do_resize is True.'      )

				if do_color_quantize and clusters is None:
							raise ValueError('Clusters must be specified if do_color_quantize is True.'      )

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

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

				if do_normalize:
							SCREAMING_SNAKE_CASE_								=						[self.normalize(image=SCREAMING_SNAKE_CASE      ) for image in images]

				if do_color_quantize:
							SCREAMING_SNAKE_CASE_								=						[to_channel_dimension_format(SCREAMING_SNAKE_CASE      ,     ChannelDimension.LAST      ) for image in images]
							# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
							SCREAMING_SNAKE_CASE_								=						np.array(SCREAMING_SNAKE_CASE      )
							SCREAMING_SNAKE_CASE_								=						color_quantize(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ).reshape(images.shape[:-1]      )

							# flatten to (batch_size, height*width)
							SCREAMING_SNAKE_CASE_								=						images.shape[0]
							SCREAMING_SNAKE_CASE_								=						images.reshape(SCREAMING_SNAKE_CASE      ,     -1      )

							# We need to convert back to a list of images to keep consistent behaviour across processors.
							SCREAMING_SNAKE_CASE_								=						list(SCREAMING_SNAKE_CASE      )
				else:
							SCREAMING_SNAKE_CASE_								=						[to_channel_dimension_format(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ) for image in images]

				SCREAMING_SNAKE_CASE_								=						{'input_ids': 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 lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







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

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						gather(SCREAMING_SNAKE_CASE )
			assert gathered_tensor.tolist() == list(range(1       ,    state.num_processes**2 + 1 ) )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[str]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						[state.process_index]
			SCREAMING_SNAKE_CASE_								=						gather_object(SCREAMING_SNAKE_CASE )
			assert len(SCREAMING_SNAKE_CASE ) == state.num_processes, F'{gathered_obj}, {len(SCREAMING_SNAKE_CASE )} != {state.num_processes}'
			assert gathered_obj == list(range(state.num_processes ) ), F'{gathered_obj} != {list(range(state.num_processes ) )}'

def lowercase    (  SCREAMING_SNAKE_CASE )			->							int:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						broadcast(SCREAMING_SNAKE_CASE )
			assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
			assert broadcasted_tensor.tolist() == list(range(1       ,    state.num_processes + 1 ) )

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			if state.is_main_process:
						SCREAMING_SNAKE_CASE_								=						torch.arange(state.num_processes + 1 ).to(state.device )
			else:
						SCREAMING_SNAKE_CASE_								=						torch.arange(state.num_processes ).to(state.device )
			SCREAMING_SNAKE_CASE_								=						pad_across_processes(SCREAMING_SNAKE_CASE )
			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 lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			if state.num_processes != 2:
						return
			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						reduce(SCREAMING_SNAKE_CASE       ,    'sum' )
			SCREAMING_SNAKE_CASE_								=						torch.tensor([4.0, 6] ).to(state.device )
			assert torch.allclose(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ), F'{reduced_tensor} != {truth_tensor}'

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			if state.num_processes != 2:
						return
			SCREAMING_SNAKE_CASE_								=						create_tensor(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						reduce(SCREAMING_SNAKE_CASE       ,    'mean' )
			SCREAMING_SNAKE_CASE_								=						torch.tensor([2.0, 3] ).to(state.device )
			assert torch.allclose(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ), F'{reduced_tensor} != {truth_tensor}'

def lowercase    (  SCREAMING_SNAKE_CASE )			->							List[str]:


			'''simple docstring'''







			main()

def lowercase    (  )			->							str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						PartialState()
			state.print(F'State: {state}' )
			state.print('testing gather' )
			test_gather(SCREAMING_SNAKE_CASE )
			state.print('testing gather_object' )
			test_gather_object(SCREAMING_SNAKE_CASE )
			state.print('testing broadcast' )
			test_broadcast(SCREAMING_SNAKE_CASE )
			state.print('testing pad_across_processes' )
			test_pad_across_processes(SCREAMING_SNAKE_CASE )
			state.print('testing reduce_sum' )
			test_reduce_sum(SCREAMING_SNAKE_CASE )
			state.print('testing reduce_mean' )
			test_reduce_mean(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				main()
