






'''simple docstring'''




# Copyright 2021 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 warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType







class       __lowercase					(__lowerCamelCase    ):

	def __init__(      self   :						List[Any]  ,					UpperCAmelCase_   :						Optional[int]):
			UpperCamelCase__				:					int					=					data





	def __iter__(      self   :						Optional[Any]):
			for element in self.data:
					yield element







def       __UpperCAmelCase						(  lowerCamelCase_=True)							->     List[str]:
		UpperCamelCase__				:					List[str]					=					Accelerator(even_batches=lowerCamelCase_)
		assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
		return accelerator







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_ = False)							->     Any:
		if iterable:
				UpperCamelCase__				:					Tuple					=					DummyIterableDataset(torch.as_tensor(range(lowerCamelCase_)))
		else:
				UpperCamelCase__				:					List[str]					=					TensorDataset(torch.as_tensor(range(lowerCamelCase_)))

		UpperCamelCase__				:					Any					=					DataLoader(lowerCamelCase_     ,      batch_size=lowerCamelCase_)
		UpperCamelCase__				:					List[str]					=					accelerator.prepare(lowerCamelCase_)

		return dl







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      )							->     int:
		UpperCamelCase__				:					Optional[int]					=					create_dataloader(accelerator=lowerCamelCase_     ,      dataset_size=lowerCamelCase_     ,      batch_size=lowerCamelCase_)

		UpperCamelCase__				:					Any					=					[len(batch[0]) for batch in dl]

		if accelerator.process_index == 0:
				assert batch_sizes == process_0_expected_batch_sizes
		elif accelerator.process_index == 1:
				assert batch_sizes == process_1_expected_batch_sizes







def       __UpperCAmelCase						(  )							->     str:
		UpperCamelCase__				:					Any					=					create_accelerator()

		# without padding, we would expect a different number of batches
		verify_dataloader_batch_sizes(
		    lowerCamelCase_     ,      dataset_size=3     ,      batch_size=1     ,      process_0_expected_batch_sizes=[1, 1]     ,      process_1_expected_batch_sizes=[1, 1]     ,      )

		# without padding, we would expect the same number of batches, but different sizes
		verify_dataloader_batch_sizes(
		    lowerCamelCase_     ,      dataset_size=7     ,      batch_size=2     ,      process_0_expected_batch_sizes=[2, 2]     ,      process_1_expected_batch_sizes=[2, 2]     ,      )







def       __UpperCAmelCase						(  )							->     Tuple:
		UpperCamelCase__				:					int					=					create_accelerator(even_batches=lowerCamelCase_)

		verify_dataloader_batch_sizes(
		    lowerCamelCase_     ,      dataset_size=3     ,      batch_size=1     ,      process_0_expected_batch_sizes=[1, 1]     ,      process_1_expected_batch_sizes=[1]     ,      )

		verify_dataloader_batch_sizes(
		    lowerCamelCase_     ,      dataset_size=7     ,      batch_size=2     ,      process_0_expected_batch_sizes=[2, 2]     ,      process_1_expected_batch_sizes=[2, 1]     ,      )







def       __UpperCAmelCase						(  )							->     str:
		UpperCamelCase__				:					List[str]					=					create_accelerator(even_batches=lowerCamelCase_)

		UpperCamelCase__				:					Dict					=					torch.nn.Linear(1     ,      1)
		UpperCamelCase__				:					Union[str, Any]					=					accelerator.prepare(lowerCamelCase_)

		UpperCamelCase__				:					List[str]					=					create_dataloader(lowerCamelCase_     ,      dataset_size=3     ,      batch_size=1)

		UpperCamelCase__				:					Optional[Any]					=					[]
		with accelerator.join_uneven_inputs([ddp_model]):
				for batch_idx, batch in enumerate(lowerCamelCase_):
						UpperCamelCase__				:					List[Any]					=					ddp_model(batch[0].float())
						UpperCamelCase__				:					Any					=					output.sum()
						loss.backward()
						batch_idxs.append(lowerCamelCase_)

		accelerator.wait_for_everyone()

		if accelerator.process_index == 0:
				assert batch_idxs == [0, 1]
		elif accelerator.process_index == 1:
				assert batch_idxs == [0]







def       __UpperCAmelCase						(  lowerCamelCase_)							->     Any:
		with warnings.catch_warnings(record=lowerCamelCase_) as w:
				with accelerator.join_uneven_inputs([Mock()]):
						pass

				assert issubclass(w[-1].category     ,      lowerCamelCase_)
				assert "only supported for multi-GPU" in str(w[-1].message)







def       __UpperCAmelCase						(  )							->     List[Any]:
		UpperCamelCase__				:					List[str]					=					True
		UpperCamelCase__				:					Optional[Any]					=					False
		UpperCamelCase__				:					str					=					create_accelerator(even_batches=lowerCamelCase_)
		UpperCamelCase__				:					Optional[Any]					=					torch.nn.Linear(1     ,      1)
		UpperCamelCase__				:					List[Any]					=					accelerator.prepare(lowerCamelCase_)
		UpperCamelCase__				:					int					=					create_dataloader(lowerCamelCase_     ,      dataset_size=3     ,      batch_size=1)
		UpperCamelCase__				:					Optional[Any]					=					create_dataloader(lowerCamelCase_     ,      dataset_size=3     ,      batch_size=1)

		with accelerator.join_uneven_inputs([ddp_model]     ,      even_batches=lowerCamelCase_):
				UpperCamelCase__				:					Dict					=					train_dl.batch_sampler.even_batches
				UpperCamelCase__				:					Union[str, Any]					=					valid_dl.batch_sampler.even_batches

		assert train_dl_overridden_value == overridden_even_batches
		assert valid_dl_overridden_value == overridden_even_batches
		assert train_dl.batch_sampler.even_batches == default_even_batches
		assert valid_dl.batch_sampler.even_batches == default_even_batches







def       __UpperCAmelCase						(  )							->     List[str]:
		UpperCamelCase__				:					List[str]					=					True
		UpperCamelCase__				:					Optional[Any]					=					False
		UpperCamelCase__				:					Dict					=					create_accelerator(even_batches=lowerCamelCase_)
		UpperCamelCase__				:					Dict					=					torch.nn.Linear(1     ,      1)
		UpperCamelCase__				:					List[str]					=					accelerator.prepare(lowerCamelCase_)
		create_dataloader(lowerCamelCase_     ,      dataset_size=3     ,      batch_size=1     ,      iterable=lowerCamelCase_)
		UpperCamelCase__				:					Optional[Any]					=					create_dataloader(lowerCamelCase_     ,      dataset_size=3     ,      batch_size=1)

		with warnings.catch_warnings():
				warnings.filterwarnings('ignore')
				try:
						with accelerator.join_uneven_inputs([ddp_model]     ,      even_batches=lowerCamelCase_):
								UpperCamelCase__				:					Union[str, Any]					=					batch_dl.batch_sampler.even_batches
				except AttributeError:
						# ensure attribute error is not raised when processing iterable dl
						raise AssertionError

		assert batch_dl_overridden_value == overridden_even_batches
		assert batch_dl.batch_sampler.even_batches == default_even_batches







def       __UpperCAmelCase						(  )							->     Union[str, Any]:
		UpperCamelCase__				:					Optional[Any]					=					create_accelerator()
		UpperCamelCase__				:					Tuple					=					torch.nn.Linear(1     ,      1)
		UpperCamelCase__				:					Optional[Any]					=					accelerator.prepare(lowerCamelCase_)
		create_dataloader(lowerCamelCase_     ,      dataset_size=3     ,      batch_size=1     ,      iterable=lowerCamelCase_)

		with warnings.catch_warnings(record=lowerCamelCase_) as w:
				with accelerator.join_uneven_inputs([ddp_model]     ,      even_batches=lowerCamelCase_):
						pass

				assert issubclass(w[-1].category     ,      lowerCamelCase_)
				assert "only supported for map-style datasets" in str(w[-1].message)







def       __UpperCAmelCase						(  )							->     Optional[Any]:
		UpperCamelCase__				:					int					=					create_accelerator()

		accelerator.print('Test that even_batches variable ensures uniform batches across processes')
		test_default_ensures_even_batch_sizes()

		accelerator.print('Run tests with even_batches disabled')
		test_can_disable_even_batches()

		accelerator.print('Test joining uneven inputs')
		test_can_join_uneven_inputs()

		accelerator.print('Test overriding even_batches when joining uneven inputs')
		test_join_can_override_even_batches()

		accelerator.print('Test overriding even_batches for mixed dataloader types')
		test_join_can_override_for_mixed_type_dataloaders()

		accelerator.print('Test overriding even_batches raises a warning for iterable dataloaders')
		test_join_raises_warning_for_iterable_when_overriding_even_batches()

		accelerator.print('Test join with non DDP distributed raises warning')
		UpperCamelCase__				:					Optional[Any]					=					accelerator.state.distributed_type
		UpperCamelCase__				:					List[Any]					=					DistributedType.FSDP
		test_join_raises_warning_for_non_ddp_distributed(lowerCamelCase_)
		UpperCamelCase__				:					Union[str, Any]					=					original_state


if __name__ == "__main__":
		main()












'''simple docstring'''




import numpy as np
from PIL import Image







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     np.ndarray:
		UpperCamelCase__				:					List[Any]					=					np.array(lowerCamelCase_)
		if arr.shape[0] != arr.shape[1]:
				raise ValueError('The input array is not a square matrix')
		UpperCamelCase__				:					Tuple					=					0
		UpperCamelCase__				:					int					=					0
		UpperCamelCase__				:					Optional[int]					=					0
		UpperCamelCase__				:					str					=					0

		# compute the shape of the output matrix
		UpperCamelCase__				:					int					=					(arr.shape[0] - size) // stride + 1
		# initialize the output matrix with zeros of shape maxpool_shape
		UpperCamelCase__				:					Dict					=					np.zeros((maxpool_shape, maxpool_shape))

		while i < arr.shape[0]:
				if i + size > arr.shape[0]:
						# if the end of the matrix is reached, break
						break
				while j < arr.shape[1]:
						# if the end of the matrix is reached, break
						if j + size > arr.shape[1]:
								break
						# compute the maximum of the pooling matrix
						UpperCamelCase__				:					Dict					=					np.max(arr[i : i + size, j : j + size])
						# shift the pooling matrix by stride of column pixels
						j += stride
						mat_j += 1

				# shift the pooling matrix by stride of row pixels
				i += stride
				mat_i += 1

				# reset the column index to 0
				UpperCamelCase__				:					List[Any]					=					0
				UpperCamelCase__				:					Optional[int]					=					0

		return updated_arr







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     np.ndarray:
		UpperCamelCase__				:					Tuple					=					np.array(lowerCamelCase_)
		if arr.shape[0] != arr.shape[1]:
				raise ValueError('The input array is not a square matrix')
		UpperCamelCase__				:					Optional[int]					=					0
		UpperCamelCase__				:					int					=					0
		UpperCamelCase__				:					List[str]					=					0
		UpperCamelCase__				:					List[Any]					=					0

		# compute the shape of the output matrix
		UpperCamelCase__				:					str					=					(arr.shape[0] - size) // stride + 1
		# initialize the output matrix with zeros of shape avgpool_shape
		UpperCamelCase__				:					Union[str, Any]					=					np.zeros((avgpool_shape, avgpool_shape))

		while i < arr.shape[0]:
				# if the end of the matrix is reached, break
				if i + size > arr.shape[0]:
						break
				while j < arr.shape[1]:
						# if the end of the matrix is reached, break
						if j + size > arr.shape[1]:
								break
						# compute the average of the pooling matrix
						UpperCamelCase__				:					List[Any]					=					int(np.average(arr[i : i + size, j : j + size]))
						# shift the pooling matrix by stride of column pixels
						j += stride
						mat_j += 1

				# shift the pooling matrix by stride of row pixels
				i += stride
				mat_i += 1
				# reset the column index to 0
				UpperCamelCase__				:					Union[str, Any]					=					0
				UpperCamelCase__				:					Optional[Any]					=					0

		return updated_arr


# Main Function
if __name__ == "__main__":
		from doctest import testmod

		testmod(name='avgpooling', verbose=True)

		# Loading the image
		lowerCAmelCase__          =						Image.open('path_to_image')

		# Converting the image to numpy array and maxpooling, displaying the result
		# Ensure that the image is a square matrix

		Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

		# Converting the image to numpy array and averagepooling, displaying the result
		# Ensure that the image is a square matrix

		Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()




