"""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       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	bool						)  ->							torch.Tensor:
   # The first operation in a checkpoint can't be in-place, but it's
   # nice to have in-place addition during inference. Thus...
   if not inplace:
      __lowerCamelCase    		=					ma + ma
   else:
      ma += ma

   return ma



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	List[int]						)  ->							torch.Tensor:
   __lowerCamelCase    		=					-1 * len(lowercase_						)
   __lowerCamelCase    		=					list(range(len(tensor.shape[:zero_index]						)						)						)
   return tensor.permute(first_inds + [zero_index + i for i in inds]						)



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	int						)  ->							torch.Tensor:
   return t.reshape(t.shape[:-no_dims] + (-1,)						)



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	int      , lowercase_     :	float = 1e-4						)  ->							torch.Tensor:
   __lowerCamelCase    		=					mask.expand(*value.shape						)
   return torch.sum(mask * value      , dim=lowercase_						) / (eps + torch.sum(lowercase_      , dim=lowercase_						))



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.types.Number = 2.3_1_2_5      , lowercase_     :	torch.types.Number = 2_1.6_8_7_5      , lowercase_     :	int = 64						)  ->							torch.Tensor:
   __lowerCamelCase    		=					torch.linspace(lowercase_      , lowercase_      , no_bins - 1      , device=pts.device						)
   __lowerCamelCase    		=					torch.sqrt(torch.sum((pts.unsqueeze(-2						) - pts.unsqueeze(-3						)) ** 2      , dim=-1						)						)
   return torch.bucketize(lowercase_      , lowercase_						)



def       A__    (	lowercase_     :	Callable[[list], Any]      , lowercase_     :	List[dict]						)  ->							dict:
   __lowerCamelCase    		=					dicts[0]
   __lowerCamelCase    		=					{}
   for k, v in first.items():
      __lowerCamelCase    		=					[d[k] for d in dicts]
      if isinstance(lowercase_      , lowercase_						):
         __lowerCamelCase    		=					dict_multimap(lowercase_      , lowercase_						)
      else:
         __lowerCamelCase    		=					fn(lowercase_						)

   return new_dict



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor						)  ->							torch.Tensor:
   __lowerCamelCase    		=					v_bins.view(((1,) * len(x.shape						)) + (len(lowercase_						),)						)
   __lowerCamelCase    		=					x[..., None] - reshaped_bins
   __lowerCamelCase    		=					torch.argmin(torch.abs(lowercase_						)      , dim=-1						)
   return nn.functional.one_hot(lowercase_      , num_classes=len(lowercase_						)						).float()



def       A__    (	lowercase_     :	torch.Tensor      , lowercase_     :	torch.Tensor      , lowercase_     :	int = 0      , lowercase_     :	int = 0						)  ->							torch.Tensor:
   __lowerCamelCase    		=					[]
   for i, s in enumerate(data.shape[:no_batch_dims]						):
      __lowerCamelCase    		=					torch.arange(lowercase_						)
      __lowerCamelCase    		=					r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape						) - i - 1)))						)
      ranges.append(lowercase_						)

   __lowerCamelCase    		=					[slice(lowercase_						) for _ in range(len(data.shape						) - no_batch_dims						)]
   __lowerCamelCase    		=					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_						)]


_SCREAMING_SNAKE_CASE	:		Dict   =   TypeVar('''T''')



def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	Dict[Any, Union[dict, list, tuple, T]]      , lowercase_     :	Type[T]						)  ->							Dict[Any, Union[dict, list, tuple, Any]]:
   __lowerCamelCase    		=					{}
   for k, v in dic.items():
      if isinstance(lowercase_      , lowercase_						):
         __lowerCamelCase    		=					dict_map(lowercase_      , lowercase_      , lowercase_						)
      else:
         __lowerCamelCase    		=					tree_map(lowercase_      , lowercase_      , lowercase_						)

   return new_dict



@overload
def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	T      , lowercase_     :	Type[T]						)  ->							Any:
   ...



@overload
def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	dict      , lowercase_     :	Type[T]						)  ->							dict:
   ...



@overload
def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	list      , lowercase_     :	Type[T]						)  ->							list:
   ...



@overload
def       A__    (	lowercase_     :	Callable[[T], Any]      , lowercase_     :	tuple      , lowercase_     :	Type[T]						)  ->							tuple:
   ...



def       A__    (	lowercase_     :	Optional[Any]      , lowercase_     :	Optional[int]      , lowercase_     :	List[str]						)  ->							Any:
   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'						)


_SCREAMING_SNAKE_CASE	:		Union[str, Any]   =   partial(tree_map, leaf_type=torch.Tensor)








'''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 DeiTImageProcessor






class 					lowerCAmelCase__  (    unittest.TestCase	):






					def __init__(  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE=7							,				__SCREAMING_SNAKE_CASE=3							,				__SCREAMING_SNAKE_CASE=18							,				__SCREAMING_SNAKE_CASE=30							,				__SCREAMING_SNAKE_CASE=4_00							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]							,				__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]							,				):


											"""simple docstring"""





											lowercase_      :	Union[str, Any]						=			size if size is not None else {'''height''': 20, '''width''': 20}
											lowercase_      :	List[Any]						=			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

											lowercase_      :	List[str]						=			parent
											lowercase_      :	Optional[Any]						=			batch_size
											lowercase_      :	int						=			num_channels
											lowercase_      :	List[str]						=			image_size
											lowercase_      :	Optional[Any]						=			min_resolution
											lowercase_      :	Tuple						=			max_resolution
											lowercase_      :	Union[str, Any]						=			do_resize
											lowercase_      :	List[str]						=			size
											lowercase_      :	Tuple						=			do_center_crop
											lowercase_      :	Dict						=			crop_size
											lowercase_      :	int						=			do_normalize
											lowercase_      :	Optional[int]						=			image_mean
											lowercase_      :	List[str]						=			image_std






					def       _snake_case       (  self						):


											"""simple docstring"""





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







@require_torch
@require_vision
class 					lowerCAmelCase__  (    lowerCamelCase_       ,		unittest.TestCase	):
					lowerCAmelCase_	  =	DeiTImageProcessor if is_vision_available() else None
					lowerCAmelCase_	  =	True






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			DeiTImageProcessingTester(self						)






					@property
					def       _snake_case       (  self						):


											"""simple docstring"""





											return self.image_processor_tester.prepare_image_processor_dict()






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	str						=			self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''do_resize'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''size'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''do_center_crop'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''center_crop'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''do_normalize'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''image_mean'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''image_std'''						)						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			self.image_processing_class.from_dict(self.image_processor_dict						)
											self.assertEqual(image_processor.size							,				{'''height''': 20, '''width''': 20}						)
											self.assertEqual(image_processor.crop_size							,				{'''height''': 18, '''width''': 18}						)

											lowercase_      :	List[str]						=			self.image_processing_class.from_dict(self.image_processor_dict							,				size=42							,				crop_size=84						)
											self.assertEqual(image_processor.size							,				{'''height''': 42, '''width''': 42}						)
											self.assertEqual(image_processor.crop_size							,				{'''height''': 84, '''width''': 84}						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											pass






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											lowercase_      :	Optional[int]						=			prepare_image_inputs(self.image_processor_tester							,				equal_resolution=__SCREAMING_SNAKE_CASE						)
											for image in image_inputs:
																	self.assertIsInstance(__SCREAMING_SNAKE_CASE							,				Image.Image						)

											# Test not batched input
											lowercase_      :	List[Any]						=			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
											lowercase_      :	List[str]						=			image_processing(__SCREAMING_SNAKE_CASE							,				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       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[Any]						=			self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											lowercase_      :	Tuple						=			prepare_image_inputs(self.image_processor_tester							,				equal_resolution=__SCREAMING_SNAKE_CASE							,				numpify=__SCREAMING_SNAKE_CASE						)
											for image in image_inputs:
																	self.assertIsInstance(__SCREAMING_SNAKE_CASE							,				np.ndarray						)

											# Test not batched input
											lowercase_      :	Dict						=			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
											lowercase_      :	Union[str, Any]						=			image_processing(__SCREAMING_SNAKE_CASE							,				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       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											lowercase_      :	List[Any]						=			prepare_image_inputs(self.image_processor_tester							,				equal_resolution=__SCREAMING_SNAKE_CASE							,				torchify=__SCREAMING_SNAKE_CASE						)
											for image in image_inputs:
																	self.assertIsInstance(__SCREAMING_SNAKE_CASE							,				torch.Tensor						)

											# Test not batched input
											lowercase_      :	List[str]						=			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
											lowercase_      :	Optional[int]						=			image_processing(__SCREAMING_SNAKE_CASE							,				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'''],
											    )							,				)



