


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 EfficientNetImageProcessor


class        __lowerCamelCase      (				unittest.TestCase ):


				"""simple docstring"""







				def __init__(				self     :    int	,							lowerCamelCase_     :    int	,							lowerCamelCase_     :    Any=13	,							lowerCamelCase_     :    Any=3	,							lowerCamelCase_     :    List[Any]=18	,							lowerCamelCase_     :    Union[str, Any]=30	,							lowerCamelCase_     :    str=400	,							lowerCamelCase_     :    Tuple=True	,							lowerCamelCase_     :    str=None	,							lowerCamelCase_     :    int=True	,							lowerCamelCase_     :    Tuple=[0.5, 0.5, 0.5]	,							lowerCamelCase_     :    Tuple=[0.5, 0.5, 0.5]	,							):
								_lowerCAmelCase  						=size if size is not None else {"""height""": 18, """width""": 18}
								_lowerCAmelCase  						=parent
								_lowerCAmelCase  						=batch_size
								_lowerCAmelCase  						=num_channels
								_lowerCAmelCase  						=image_size
								_lowerCAmelCase  						=min_resolution
								_lowerCAmelCase  						=max_resolution
								_lowerCAmelCase  						=do_resize
								_lowerCAmelCase  						=size
								_lowerCAmelCase  						=do_normalize
								_lowerCAmelCase  						=image_mean
								_lowerCAmelCase  						=image_std






				def        lowerCAmelCase__			(				self     :    Union[str, Any]					):
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								}


@require_torch
@require_vision
class        __lowerCamelCase      (				lowerCamelCase_				,		unittest.TestCase ):


				"""simple docstring"""







				a_:		List[Any]					      =	EfficientNetImageProcessor if is_vision_available() else None






				def        lowerCAmelCase__			(				self     :    Union[str, Any]					):
								_lowerCAmelCase  						=EfficientNetImageProcessorTester(self					)






				@property
				def        lowerCAmelCase__			(				self     :    Optional[int]					):
								return self.image_processor_tester.prepare_image_processor_dict()






				def        lowerCAmelCase__			(				self     :    List[Any]					):
								_lowerCAmelCase  						=self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(lowerCamelCase_	,							"""image_mean"""					)					)
								self.assertTrue(hasattr(lowerCamelCase_	,							"""image_std"""					)					)
								self.assertTrue(hasattr(lowerCamelCase_	,							"""do_normalize"""					)					)
								self.assertTrue(hasattr(lowerCamelCase_	,							"""do_resize"""					)					)
								self.assertTrue(hasattr(lowerCamelCase_	,							"""size"""					)					)






				def        lowerCAmelCase__			(				self     :    Optional[int]					):
								_lowerCAmelCase  						=self.image_processing_class.from_dict(self.image_processor_dict					)
								self.assertEqual(image_processor.size	,							{"""height""": 18, """width""": 18}					)

								_lowerCAmelCase  						=self.image_processing_class.from_dict(self.image_processor_dict	,							size=42					)
								self.assertEqual(image_processor.size	,							{"""height""": 42, """width""": 42}					)






				def        lowerCAmelCase__			(				self     :    Optional[int]					):
								# Initialize image_processing
								_lowerCAmelCase  						=self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								_lowerCAmelCase  						=prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCamelCase_					)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase_	,							Image.Image					)

								# Test not batched input
								_lowerCAmelCase  						=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.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )	,							)

								# Test batched
								_lowerCAmelCase  						=image_processing(lowerCamelCase_	,							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.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )	,							)






				def        lowerCAmelCase__			(				self     :    Any					):
								# Initialize image_processing
								_lowerCAmelCase  						=self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								_lowerCAmelCase  						=prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCamelCase_	,							numpify=lowerCamelCase_					)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase_	,							np.ndarray					)

								# Test not batched input
								_lowerCAmelCase  						=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.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )	,							)

								# Test batched
								_lowerCAmelCase  						=image_processing(lowerCamelCase_	,							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.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )	,							)






				def        lowerCAmelCase__			(				self     :    str					):
								# Initialize image_processing
								_lowerCAmelCase  						=self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								_lowerCAmelCase  						=prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCamelCase_	,							torchify=lowerCamelCase_					)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase_	,							torch.Tensor					)

								# Test not batched input
								_lowerCAmelCase  						=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.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )	,							)

								# Test batched
								_lowerCAmelCase  						=image_processing(lowerCamelCase_	,							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.size["""height"""],
								        self.image_processor_tester.size["""width"""],
								    )	,							)





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 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    bool     ):






				'''simple docstring'''






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

				return ma

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    List[int]     ):






				'''simple docstring'''






				_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 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    int     ):






				'''simple docstring'''






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

def 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int       ,     lowercase__     :    float = 1e-4     ):






				'''simple docstring'''






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

def 		snake_case_   (				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     ):






				'''simple docstring'''






				_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 		snake_case_   (				lowercase__     :    Callable[[list], Any]       ,     lowercase__     :    List[dict]     ):






				'''simple docstring'''






				_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 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor     ):






				'''simple docstring'''






				_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 		snake_case_   (				lowercase__     :    torch.Tensor       ,     lowercase__     :    torch.Tensor       ,     lowercase__     :    int = 0       ,     lowercase__     :    int = 0     ):






				'''simple docstring'''






				_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	:							str           =  TypeVar('''T''')

def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    Dict[Any, Union[dict, list, tuple, T]]       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				_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 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    T       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    dict       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    list       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

@overload
def 		snake_case_   (				lowercase__     :    Callable[[T], Any]       ,     lowercase__     :    tuple       ,     lowercase__     :    Type[T]     ):






				'''simple docstring'''






				...

def 		snake_case_   (				lowercase__     :    Optional[Any]       ,     lowercase__     :    str       ,     lowercase__     :    Optional[int]     ):






				'''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"""     )





__SCREAMING_SNAKE_CASE	:							Any           =  partial(tree_map, leaf_type=torch.Tensor)

