



'''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 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


a   :			Tuple					  =    logging.get_logger(__name__)



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Optional[Any]:
							UpperCAmelCase     :		int							     =		b.T
							UpperCAmelCase     :		List[str]							     =		np.sum(np.square(_lowercase		)	,				axis=1		)
							UpperCAmelCase     :		List[str]							     =		np.sum(np.square(_lowercase		)	,				axis=0		)
							UpperCAmelCase     :		Union[str, Any]							     =		np.matmul(_lowercase	,				_lowercase		)
							UpperCAmelCase     :		str							     =		aa[:, None] - 2 * ab + ba[None, :]
							return d



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Tuple:
							UpperCAmelCase     :		Optional[Any]							     =		x.reshape(-1	,				3		)
							UpperCAmelCase     :		List[Any]							     =		squared_euclidean_distance(_lowercase	,				_lowercase		)
							return np.argmin(_lowercase	,				axis=1		)


class 			UpperCamelCase_ (							__magic_name__			):
	lowercase          = ['pixel_values']





	def __init__(  self		,		A = None		,		A = True		,		A = None		,		A = PILImageResampling.BILINEAR		,		A = True		,		A = True		,		**A		,		)			->			None:
								super().__init__(**A    )
								UpperCAmelCase     :		Union[str, Any]							     =		size if size is not None else {"""height""": 256, """width""": 256}
								UpperCAmelCase     :		int							     =		get_size_dict(A    )
								UpperCAmelCase     :		Optional[int]							     =		np.array(A    ) if clusters is not None else None
								UpperCAmelCase     :		str							     =		do_resize
								UpperCAmelCase     :		str							     =		size
								UpperCAmelCase     :		str							     =		resample
								UpperCAmelCase     :		Optional[int]							     =		do_normalize
								UpperCAmelCase     :		List[Any]							     =		do_color_quantize





	def  _lowercase(  self		,		A		,		A		,		A = PILImageResampling.BILINEAR		,		A = None		,		**A		,		)			->			np.ndarray:
								UpperCAmelCase     :		int							     =		get_size_dict(A    )
								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(
								    A		,		size=(size["""height"""], size["""width"""])		,		resample=A		,		data_format=A		,		**A    )





	def  _lowercase(  self		,		A		,		A = None		,		)			->			np.ndarray:
								UpperCAmelCase     :		str							     =		rescale(image=A		,		scale=1 / 1_2_7.5		,		data_format=A    )
								UpperCAmelCase     :		List[Any]							     =		image - 1
								return image






	def  _lowercase(  self		,		A		,		A = None		,		A = None		,		A = None		,		A = None		,		A = None		,		A = None		,		A = None		,		A = ChannelDimension.FIRST		,		**A		,		)			->			PIL.Image.Image:
								UpperCAmelCase     :		Optional[Any]							     =		do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase     :		Optional[int]							     =		size if size is not None else self.size
								UpperCAmelCase     :		int							     =		get_size_dict(A    )
								UpperCAmelCase     :		int							     =		resample if resample is not None else self.resample
								UpperCAmelCase     :		Optional[Any]							     =		do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase     :		Optional[int]							     =		do_color_quantize if do_color_quantize is not None else self.do_color_quantize
								UpperCAmelCase     :		Dict							     =		clusters if clusters is not None else self.clusters
								UpperCAmelCase     :		Dict							     =		np.array(A    )

								UpperCAmelCase     :		Union[str, Any]							     =		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_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.
								UpperCAmelCase     :		Any							     =		[to_numpy_array(A    ) for image in images]

								if do_resize:
															UpperCAmelCase     :		Optional[int]							     =		[self.resize(image=A		,		size=A		,		resample=A    ) for image in images]

								if do_normalize:
															UpperCAmelCase     :		List[Any]							     =		[self.normalize(image=A    ) for image in images]

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

															# flatten to (batch_size, height*width)
															UpperCAmelCase     :		List[Any]							     =		images.shape[0]
															UpperCAmelCase     :		Optional[Any]							     =		images.reshape(A		,		-1    )

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

								UpperCAmelCase     :		Tuple							     =		{"""input_ids""": images}
								return BatchFeature(data=A		,		tensor_type=A    )






'''simple docstring'''




import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory



def       __lowerCamelCase  (					)      ->			Any:
							raise RuntimeError("""CUDA out of memory."""		)

class 			UpperCamelCase_ (							nn.Module			):





	def __init__(  self    )			->			Any:
								super().__init__()
								UpperCAmelCase     :		Tuple							     =		nn.Linear(3		,		4    )
								UpperCAmelCase     :		Tuple							     =		nn.BatchNormad(4    )
								UpperCAmelCase     :		int							     =		nn.Linear(4		,		5    )






	def  _lowercase(  self		,		A    )			->			Any:
								return self.lineara(self.batchnorm(self.lineara(A    )    )    )




class 			UpperCamelCase_ (							unittest.TestCase			):





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Optional[int]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A    ):
															nonlocal batch_sizes
															batch_sizes.append(A    )
															if batch_size != 8:
																						raise_fake_out_of_memory()

								mock_training_loop_function()
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		Optional[Any]							     =		[]

								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A    ):
															nonlocal batch_sizes
															batch_sizes.append(A    )
															if batch_size != 8:
																						raise_fake_out_of_memory()
															return batch_size, arga

								UpperCAmelCase     ,     UpperCAmelCase     :		Optional[int]							     =		mock_training_loop_function("""hello"""    )
								self.assertListEqual(A		,		[128, 64, 32, 16, 8]    )
								self.assertListEqual([bs, arga]		,		[8, """hello"""]    )





	def  _lowercase(  self    )			->			Any:
								@find_executable_batch_size(starting_batch_size=0    )
								def mock_training_loop_function(A    ):
															pass

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""No executable batch size found, reached zero."""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			Optional[int]:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															if batch_size > 0:
																						raise_fake_out_of_memory()
															pass

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""No executable batch size found, reached zero."""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			Optional[Any]:
								@find_executable_batch_size(starting_batch_size=128    )
								def mock_training_loop_function(A		,		A		,		A    ):
															if batch_size != 8:
																						raise raise_fake_out_of_memory()

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function(128		,		"""hello"""		,		"""world"""    )
															self.assertIn("""Batch size was passed into `f`"""		,		cm.exception.args[0]    )
															self.assertIn("""`f(arg1='hello', arg2='world')"""		,		cm.exception.args[0]    )





	def  _lowercase(  self    )			->			int:
								@find_executable_batch_size(starting_batch_size=16    )
								def mock_training_loop_function(A    ):
															raise ValueError("""Oops, we had an error!"""    )

								with self.assertRaises(A    ) as cm:
															mock_training_loop_function()
															self.assertIn("""Oops, we had an error!"""		,		cm.exception.args[0]    )






	@require_cuda
	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		torch.cuda.memory_allocated()
								UpperCAmelCase     :		List[str]							     =		ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()		,		A    )
								UpperCAmelCase     :		Tuple							     =		release_memory(A    )
								self.assertEqual(torch.cuda.memory_allocated()		,		A    )

