





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 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				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:
										__A     :						Optional[Any]          =    ma + ma
					else:
										ma += ma

					return ma







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				torch.Tensor:
					__A     :						List[Any]          =    -1 * len(a  )
					__A     :						List[str]          =    list(range(len(tensor.shape[:zero_index]  )  )  )
					return tensor.permute(first_inds + [zero_index + i for i in inds]  )







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				torch.Tensor:
					return t.reshape(t.shape[:-no_dims] + (-1,)  )







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a ,						a = 1e-4  )				->				torch.Tensor:
					__A     :						List[Any]          =    mask.expand(*value.shape  )
					return torch.sum(mask * value ,						dim=a  ) / (eps + torch.sum(a ,						dim=a  ))







def 			_SCREAMING_SNAKE_CASE    (       a ,						a = 2.3_125 ,						a = 21.6_875 ,						a = 64  )				->				torch.Tensor:
					__A     :						List[str]          =    torch.linspace(a ,						a ,						no_bins - 1 ,						device=pts.device  )
					__A     :						Tuple          =    torch.sqrt(torch.sum((pts.unsqueeze(-2  ) - pts.unsqueeze(-3  )) ** 2 ,						dim=-1  )  )
					return torch.bucketize(a ,						a  )







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				dict:
					__A     :						Optional[Any]          =    dicts[0]
					__A     :						Optional[Any]          =    {}
					for k, v in first.items():
										__A     :						Union[str, Any]          =    [d[k] for d in dicts]
										if isinstance(a ,						a  ):
															__A     :						Union[str, Any]          =    dict_multimap(a ,						a  )
										else:
															__A     :						Union[str, Any]          =    fn(a  )

					return new_dict







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				torch.Tensor:
					__A     :						Tuple          =    v_bins.view(((1,) * len(x.shape  )) + (len(a  ),)  )
					__A     :						Tuple          =    x[..., None] - reshaped_bins
					__A     :						Optional[int]          =    torch.argmin(torch.abs(a  ) ,						dim=-1  )
					return nn.functional.one_hot(a ,						num_classes=len(a  )  ).float()







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a = 0 ,						a = 0  )				->				torch.Tensor:
					__A     :						List[Union[slice, torch.Tensor]]          =    []
					for i, s in enumerate(data.shape[:no_batch_dims]  ):
										__A     :						Optional[Any]          =    torch.arange(a  )
										__A     :						List[str]          =    r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape  ) - i - 1)))  )
										ranges.append(a  )

					__A     :						List[Union[slice, torch.Tensor]]          =    [slice(a  ) for _ in range(len(data.shape  ) - no_batch_dims  )]
					__A     :						Optional[int]          =    inds
					ranges.extend(a  )
					# 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(a  )]


UpperCAmelCase						:       Optional[int]        =					TypeVar('''T''')







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				Dict[Any, Union[dict, list, tuple, Any]]:
					__A     :						Dict[Any, Union[dict, list, tuple, Any]]          =    {}
					for k, v in dic.items():
										if isinstance(a ,						a  ):
															__A     :						Optional[Any]          =    dict_map(a ,						a ,						a  )
										else:
															__A     :						Dict          =    tree_map(a ,						a ,						a  )

					return new_dict







@overload
def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				Any:
					...







@overload
def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				dict:
					...







@overload
def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				list:
					...







@overload
def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				tuple:
					...







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				List[str]:
					if isinstance(a ,						a  ):
										return dict_map(a ,						a ,						a  )
					elif isinstance(a ,						a  ):
										return [tree_map(a ,						a ,						a  ) for x in tree]
					elif isinstance(a ,						a  ):
										return tuple(tree_map(a ,						a ,						a  ) for x in tree  )
					elif isinstance(a ,						a  ):
										return fn(a  )
					else:
										print(type(a  )  )
										raise ValueError('Not supported'  )




UpperCAmelCase						:       Dict        =					partial(tree_map, leaf_type=torch.Tensor)











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


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import TvltImageProcessor







def 			_SCREAMING_SNAKE_CASE    (       a ,						a=10 ,						a=10 ,						a=False ,						a=False  )				->				Union[str, Any]:
					__A     :						str          =    []
					for i in range(image_processor_tester.num_frames  ):
										video.append(np.random.randint(2_55 ,						size=(image_processor_tester.num_channels, width, height) ,						dtype=np.uinta  )  )

					if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										__A     :						Union[str, Any]          =    [Image.fromarray(np.moveaxis(a ,						0 ,						-1  )  ) for frame in video]

					if torchify:
										__A     :						Dict          =    [torch.from_numpy(a  ) for frame in video]

					return video







def 			_SCREAMING_SNAKE_CASE    (       a ,						a=False ,						a=False ,						a=False  )				->				Union[str, Any]:
					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					__A     :						List[Any]          =    []
					for i in range(image_processor_tester.batch_size  ):
										if equal_resolution:
															__A     :						int          =    image_processor_tester.max_resolution
										else:
															__A   ,    __A     :						Tuple          =    np.random.choice(
															    np.arange(image_processor_tester.min_resolution ,						image_processor_tester.max_resolution  ) ,						2  )
															__A     :						Optional[int]          =    prepare_video(
															    image_processor_tester=a ,						width=a ,						height=a ,						numpify=a ,						torchify=a ,						)
										video_inputs.append(a  )

					return video_inputs





class 						_A( unittest.TestCase       ):


				"""simple docstring"""


				def __init__(      self					, _A					, _A=7					, _A=3					, _A=4					, _A=18					, _A=30					, _A=400					, _A=True					, _A=None					, _A=True					, _A=[0.5, 0.5, 0.5]					, _A=[0.5, 0.5, 0.5]					, _A=True					, _A=None					, ):
									__A     :						List[str]          =    size if size is not None else {'shortest_edge': 18}
									__A     :						Tuple          =    crop_size if crop_size is not None else {'height': 18, 'width': 18}

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





				def  UpperCAmelCase_		(      self					):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "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 						_A( snake_case__    ,			unittest.TestCase       ):


				"""simple docstring"""


				UpperCamelCase   :  Optional[Any]    =     TvltImageProcessor if is_vision_available() else None



				def  UpperCAmelCase_		(      self					):
									__A     :						str          =    TvltImageProcessorTester(self					)



				@property
				def  UpperCAmelCase_		(      self					):
									return self.image_processor_tester.prepare_image_processor_dict()



				def  UpperCAmelCase_		(      self					):
									__A     :						Union[str, Any]          =    self.image_processing_class(**self.image_processor_dict					)
									self.assertTrue(hasattr(_A					, 'image_mean'					)					)
									self.assertTrue(hasattr(_A					, 'image_std'					)					)
									self.assertTrue(hasattr(_A					, 'do_normalize'					)					)
									self.assertTrue(hasattr(_A					, 'do_resize'					)					)
									self.assertTrue(hasattr(_A					, 'do_center_crop'					)					)
									self.assertTrue(hasattr(_A					, 'size'					)					)



				def  UpperCAmelCase_		(      self					):
									# Initialize image_processor
									__A     :						List[str]          =    self.image_processing_class(**self.image_processor_dict					)
									# create random PIL videos
									__A     :						List[str]          =    prepare_video_inputs(self.image_processor_tester					, equal_resolution=_A					)
									for video in video_inputs:
														self.assertIsInstance(_A					, _A					)
														self.assertIsInstance(video[0]					, Image.Image					)

									# Test not batched input
									__A     :						str          =    image_processor(video_inputs[0]					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_videos.shape					, (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )					, )

									# Test batched
									__A     :						List[Any]          =    image_processor(_A					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_videos.shape					, (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )					, )



				def  UpperCAmelCase_		(      self					):
									# Initialize image_processor
									__A     :						Any          =    self.image_processing_class(**self.image_processor_dict					)
									# create random numpy tensors
									__A     :						Dict          =    prepare_video_inputs(self.image_processor_tester					, equal_resolution=_A					, numpify=_A					)
									for video in video_inputs:
														self.assertIsInstance(_A					, _A					)
														self.assertIsInstance(video[0]					, np.ndarray					)

									# Test not batched input
									__A     :						Dict          =    image_processor(video_inputs[0]					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_videos.shape					, (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )					, )

									# Test batched
									__A     :						Optional[int]          =    image_processor(_A					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_videos.shape					, (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )					, )





				def  UpperCAmelCase_		(      self					):
									# Initialize image_processor
									__A     :						Any          =    self.image_processing_class(**self.image_processor_dict					)
									# create random PyTorch tensors
									__A     :						Dict          =    prepare_video_inputs(self.image_processor_tester					, equal_resolution=_A					, torchify=_A					)
									for video in video_inputs:
														self.assertIsInstance(_A					, _A					)
														self.assertIsInstance(video[0]					, torch.Tensor					)

									# Test not batched input
									__A     :						Optional[Any]          =    image_processor(video_inputs[0]					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_videos.shape					, (
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )					, )

									# Test batched
									__A     :						int          =    image_processor(_A					, return_tensors='pt'					).pixel_values
									self.assertEqual(
									    encoded_videos.shape					, (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )					, )




