






'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import MobileNetVaImageProcessor





class 		SCREAMING_SNAKE_CASE__   (     unittest.TestCase    ):



		def __init__(    self:    Any				,   a:    str				,   a:    int=7				,   a:    Any=3				,   a:    Any=18				,   a:    str=30				,   a:    List[Any]=4_00				,   a:    int=True				,   a:    Any=None				,   a:    List[str]=True				,   a:    Optional[Any]=None				,   )  ->Any:






								'''simple docstring'''


								a_				     =   size if size is not None else {"shortest_edge": 20}
								a_				     =   crop_size if crop_size is not None else {"height": 18, "width": 18}
								a_				     =   parent
								a_				     =   batch_size
								a_				     =   num_channels
								a_				     =   image_size
								a_				     =   min_resolution
								a_				     =   max_resolution
								a_				     =   do_resize
								a_				     =   size
								a_				     =   do_center_crop
								a_				     =   crop_size




		def _lowerCAmelCase     (    self:    int)  ->List[Any]:






								'''simple docstring'''


								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								}





@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__   (     lowercase_		,		unittest.TestCase    ):
		_UpperCAmelCase         =MobileNetVaImageProcessor if is_vision_available() else None



		def _lowerCAmelCase     (    self:    Dict)  ->Union[str, Any]:






								'''simple docstring'''


								a_				     =   MobileNetVaImageProcessingTester(self)



		@property
		def _lowerCAmelCase     (    self:    List[str])  ->Optional[Any]:






								'''simple docstring'''


								return self.image_processor_tester.prepare_image_processor_dict()



		def _lowerCAmelCase     (    self:    Any)  ->str:






								'''simple docstring'''


								a_				     =   self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(a				,   "do_resize"))
								self.assertTrue(hasattr(a				,   "size"))
								self.assertTrue(hasattr(a				,   "do_center_crop"))
								self.assertTrue(hasattr(a				,   "center_crop"))



		def _lowerCAmelCase     (    self:    Any)  ->List[str]:






								'''simple docstring'''


								a_				     =   self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size				,   {"shortest_edge": 20})
								self.assertEqual(image_processor.crop_size				,   {"height": 18, "width": 18})

								a_				     =   self.image_processing_class.from_dict(self.image_processor_dict				,   size=42				,   crop_size=84)
								self.assertEqual(image_processor.size				,   {"shortest_edge": 42})
								self.assertEqual(image_processor.crop_size				,   {"height": 84, "width": 84})



		def _lowerCAmelCase     (    self:    Dict)  ->Tuple:






								'''simple docstring'''


								pass



		def _lowerCAmelCase     (    self:    Tuple)  ->Dict:






								'''simple docstring'''


								a_				     =   self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								a_				     =   prepare_image_inputs(self.image_processor_tester				,   equal_resolution=a)
								for image in image_inputs:
														self.assertIsInstance(a				,   Image.Image)

								# Test not batched input
								a_				     =   image_processing(image_inputs[0]				,   return_tensors="pt").pixel_values
								self.assertEqual(
								    encoded_images.shape				,   (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )				,   )

								# Test batched
								a_				     =   image_processing(a				,   return_tensors="pt").pixel_values
								self.assertEqual(
								    encoded_images.shape				,   (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )				,   )



		def _lowerCAmelCase     (    self:    Tuple)  ->Dict:






								'''simple docstring'''


								a_				     =   self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								a_				     =   prepare_image_inputs(self.image_processor_tester				,   equal_resolution=a				,   numpify=a)
								for image in image_inputs:
														self.assertIsInstance(a				,   np.ndarray)

								# Test not batched input
								a_				     =   image_processing(image_inputs[0]				,   return_tensors="pt").pixel_values
								self.assertEqual(
								    encoded_images.shape				,   (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )				,   )

								# Test batched
								a_				     =   image_processing(a				,   return_tensors="pt").pixel_values
								self.assertEqual(
								    encoded_images.shape				,   (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )				,   )




		def _lowerCAmelCase     (    self:    List[Any])  ->List[str]:






								'''simple docstring'''


								a_				     =   self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								a_				     =   prepare_image_inputs(self.image_processor_tester				,   equal_resolution=a				,   torchify=a)
								for image in image_inputs:
														self.assertIsInstance(a				,   torch.Tensor)

								# Test not batched input
								a_				     =   image_processing(image_inputs[0]				,   return_tensors="pt").pixel_values
								self.assertEqual(
								    encoded_images.shape				,   (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )				,   )

								# Test batched
								a_				     =   image_processing(a				,   return_tensors="pt").pixel_values
								self.assertEqual(
								    encoded_images.shape				,   (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["height"],
								        self.image_processor_tester.crop_size["width"],
								    )				,   )











'''simple docstring'''





from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						if not inplace:
												a_				     =   ma + ma
						else:
												ma += ma

						return ma



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   -1 * len(lowercase__				)
						a_				     =   list(range(len(tensor.shape[:zero_index]				)				)				)
						return tensor.permute(first_inds + [zero_index + i for i in inds]				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						return t.reshape(t.shape[:-no_dims] + (-1,)				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__ = 1e-4				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   mask.expand(*value.shape				)
						return torch.sum(mask * value	,dim=lowercase__				) / (eps + torch.sum(lowercase__	,dim=lowercase__				))



def 			__UpperCAmelCase				(lowercase__	,lowercase__ = 2.3125	,lowercase__ = 21.6875	,lowercase__ = 64				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   torch.linspace(lowercase__	,lowercase__	,no_bins - 1	,device=pts.device				)
						a_				     =   torch.sqrt(torch.sum((pts.unsqueeze(-2				) - pts.unsqueeze(-3				)) ** 2	,dim=-1				)				)
						return torch.bucketize(lowercase__	,lowercase__				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				dict:

						'''simple docstring'''


						a_				     =   dicts[0]
						a_				     =   {}
						for k, v in first.items():
												a_				     =   [d[k] for d in dicts]
												if isinstance(lowercase__	,lowercase__				):
																		a_				     =   dict_multimap(lowercase__	,lowercase__				)
												else:
																		a_				     =   fn(lowercase__				)

						return new_dict



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   v_bins.view(((1,) * len(x.shape				)) + (len(lowercase__				),)				)
						a_				     =   x[..., None] - reshaped_bins
						a_				     =   torch.argmin(torch.abs(lowercase__				)	,dim=-1				)
						return nn.functional.one_hot(lowercase__	,num_classes=len(lowercase__				)				).float()



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__ = 0	,lowercase__ = 0				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   []
						for i, s in enumerate(data.shape[:no_batch_dims]				):
												a_				     =   torch.arange(lowercase__				)
												a_				     =   r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape				) - i - 1)))				)
												ranges.append(lowercase__				)

						a_				     =   [slice(lowercase__				) for _ in range(len(data.shape				) - no_batch_dims				)]
						a_				     =   inds
						ranges.extend(lowercase__				)
						# Matt note: Editing this to get around the behaviour of using a list as an array index changing
						# in recent Numpy versions
						return data[tuple(lowercase__				)]


a_      = TypeVar('T')



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				Dict[Any, Union[dict, list, tuple, Any]]:

						'''simple docstring'''


						a_				     =   {}
						for k, v in dic.items():
												if isinstance(lowercase__	,lowercase__				):
																		a_				     =   dict_map(lowercase__	,lowercase__	,lowercase__				)
												else:
																		a_				     =   tree_map(lowercase__	,lowercase__	,lowercase__				)

						return new_dict



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				Any:

						'''simple docstring'''


						...



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				dict:

						'''simple docstring'''


						...



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				list:

						'''simple docstring'''


						...



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				tuple:

						'''simple docstring'''


						...



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				List[Any]:

						'''simple docstring'''


						if isinstance(lowercase__	,lowercase__				):
												return dict_map(lowercase__	,lowercase__	,lowercase__				)
						elif isinstance(lowercase__	,lowercase__				):
												return [tree_map(lowercase__	,lowercase__	,lowercase__				) for x in tree]
						elif isinstance(lowercase__	,lowercase__				):
												return tuple(tree_map(lowercase__	,lowercase__	,lowercase__				) for x in tree				)
						elif isinstance(lowercase__	,lowercase__				):
												return fn(lowercase__				)
						else:
												print(type(lowercase__				)				)
												raise ValueError("Not supported"				)
a_      = partial(tree_map, leaf_type=torch.Tensor)



