

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  UpperCAmelCase_   (_lowerCAmelCase :		int     ,			_lowerCAmelCase :		Optional[Any]=10     ,			_lowerCAmelCase :		int=10     ,			_lowerCAmelCase :		Optional[int]=False     ,			_lowerCAmelCase :		Optional[Any]=False		):
			__UpperCamelCase    :    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
						__UpperCamelCase    :    str								=      [Image.fromarray(np.moveaxis(_lowerCAmelCase     ,			0     ,			-1		)		) for frame in video]

			if torchify:
						__UpperCamelCase    :    Tuple								=      [torch.from_numpy(_lowerCAmelCase		) for frame in video]

			return video







def  UpperCAmelCase_   (_lowerCAmelCase :		List[Any]     ,			_lowerCAmelCase :		Tuple=False     ,			_lowerCAmelCase :		Optional[Any]=False     ,			_lowerCAmelCase :		Dict=False		):
			assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

			return video_inputs
class 		SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):







						"""simple docstring"""




						def __init__(				self , __UpperCamelCase , __UpperCamelCase=7 , __UpperCamelCase=3 , __UpperCamelCase=4 , __UpperCamelCase=18 , __UpperCamelCase=30 , __UpperCamelCase=4_00 , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=True , __UpperCamelCase=[0.5, 0.5, 0.5] , __UpperCamelCase=[0.5, 0.5, 0.5] , __UpperCamelCase=True , __UpperCamelCase=None , )							->	Any:




									'''simple docstring'''


									__UpperCamelCase    :    Dict								=      size if size is not None else {"shortest_edge": 18}
									__UpperCamelCase    :    Dict								=      crop_size if crop_size is not None else {"height": 18, "width": 18}

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




						def __lowerCamelCase					(				self    )							->	Tuple:




									'''simple docstring'''


									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 		SCREAMING_SNAKE_CASE__					(		lowerCAmelCase__						,	unittest.TestCase   ):







						"""simple docstring"""




						lowercase  :				List[Any]											=							TvltImageProcessor if is_vision_available() else None

						def __lowerCamelCase					(				self    )							->	Any:




									'''simple docstring'''


									__UpperCamelCase    :    Dict								=      TvltImageProcessorTester(self    )

						@property
						def __lowerCamelCase					(				self    )							->	Tuple:




									'''simple docstring'''


									return self.image_processor_tester.prepare_image_processor_dict()

						def __lowerCamelCase					(				self    )							->	Any:




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, Any]								=      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_ , "do_center_crop"    )    )
									self.assertTrue(hasattr(lowerCAmelCase_ , "size"    )    )

						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									__UpperCamelCase    :    str								=      self.image_processing_class(**self.image_processor_dict    )
									# create random PIL videos
									__UpperCamelCase    :    Optional[int]								=      prepare_video_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase_    )
									for video in video_inputs:
												self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_    )
												self.assertIsInstance(video[0] , Image.Image    )

									# Test not batched input
									__UpperCamelCase    :    Union[str, 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
									__UpperCamelCase    :    Union[str, Any]								=      image_processor(lowerCAmelCase_ , 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 __lowerCamelCase					(				self    )							->	str:




									'''simple docstring'''


									__UpperCamelCase    :    Any								=      self.image_processing_class(**self.image_processor_dict    )
									# create random numpy tensors
									__UpperCamelCase    :    List[str]								=      prepare_video_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase_ , numpify=lowerCAmelCase_    )
									for video in video_inputs:
												self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_    )
												self.assertIsInstance(video[0] , np.ndarray    )

									# Test not batched input
									__UpperCamelCase    :    int								=      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
									__UpperCamelCase    :    Optional[Any]								=      image_processor(lowerCAmelCase_ , 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 __lowerCamelCase					(				self    )							->	str:




									'''simple docstring'''


									__UpperCamelCase    :    Dict								=      self.image_processing_class(**self.image_processor_dict    )
									# create random PyTorch tensors
									__UpperCamelCase    :    Any								=      prepare_video_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase_ , torchify=lowerCAmelCase_    )
									for video in video_inputs:
												self.assertIsInstance(lowerCAmelCase_ , lowerCAmelCase_    )
												self.assertIsInstance(video[0] , torch.Tensor    )

									# Test not batched input
									__UpperCamelCase    :    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
									__UpperCamelCase    :    List[str]								=      image_processor(lowerCAmelCase_ , 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"],
									    ) , )






lowercase     :       dict[str, float]        =       {
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.0009842073,
    "short-ton": 0.0011023122,
    "pound": 2.2046244202,
    "stone": 0.1574731728,
    "ounce": 35.273990723,
    "carrat": 5000,
    "atomic-mass-unit": 6.0_22_13_66_52E26,
}

lowercase     :       dict[str, float]        =       {
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1016.04608,
    "short-ton": 907.184,
    "pound": 0.453592,
    "stone": 6.35029,
    "ounce": 0.0283495,
    "carrat": 0.0002,
    "atomic-mass-unit": 1.6_60_54_01_99E-27,
}







def  UpperCAmelCase_   (_lowerCAmelCase :		str     ,			_lowerCAmelCase :		str     ,			_lowerCAmelCase :		float		):
			if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
						__UpperCamelCase    :    Dict								=      (
						    F'''Invalid \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
						    F'''Supported values are: {", ".join(_lowerCAmelCase		)}'''
						)
						raise ValueError(_lowerCAmelCase		)
			return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
					import doctest

					doctest.testmod()