






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__					,     lowerCAmelCase__=10					,     lowerCAmelCase__=10					,     lowerCAmelCase__=False					,     lowerCAmelCase__=False	):


		'''simple docstring'''

		__A    =							[]
		for i in range(image_processor_tester.num_frames	):
				video.append(np.random.randint(255					,     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    =							[Image.fromarray(np.moveaxis(lowerCAmelCase__					,     0					,     -1	)	) for frame in video]

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

		return video



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__=False					,     lowerCAmelCase__=False					,     lowerCAmelCase__=False	):


		'''simple docstring'''

		assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

		return video_inputs





class        a__      (	unittest.TestCase		):






		def __init__(      self							, lowercase__							, lowercase__=7							, lowercase__=3							, lowercase__=4							, lowercase__=18							, lowercase__=30							, lowercase__=400							, lowercase__=True							, lowercase__=None							, lowercase__=True							, lowercase__=[0.5, 0.5, 0.5]							, lowercase__=[0.5, 0.5, 0.5]							, lowercase__=True							, lowercase__=None							, )				->				Optional[Any]:
				__A    =							size if size is not None else {"shortest_edge": 18}
				__A    =							crop_size if crop_size is not None else {"height": 18, "width": 18}

				__A    =							parent
				__A    =							batch_size
				__A    =							num_channels
				__A    =							num_frames
				__A    =							image_size
				__A    =							min_resolution
				__A    =							max_resolution
				__A    =							do_resize
				__A    =							size
				__A    =							do_normalize
				__A    =							image_mean
				__A    =							image_std
				__A    =							do_center_crop
				__A    =							crop_size






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				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__      (	lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       Tuple 					= TvltImageProcessor if is_vision_available() else None






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							TvltImageProcessorTester(self						)






		@property
		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				return self.image_processor_tester.prepare_image_processor_dict()






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				self.assertTrue(hasattr(lowercase__							, "image_mean"						)						)
				self.assertTrue(hasattr(lowercase__							, "image_std"						)						)
				self.assertTrue(hasattr(lowercase__							, "do_normalize"						)						)
				self.assertTrue(hasattr(lowercase__							, "do_resize"						)						)
				self.assertTrue(hasattr(lowercase__							, "do_center_crop"						)						)
				self.assertTrue(hasattr(lowercase__							, "size"						)						)






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

				# Test not batched input
				__A    =							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    =							image_processor(lowercase__							, 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						)				->				Any:
				# Initialize image_processor
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random numpy tensors
				__A    =							prepare_video_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, numpify=lowercase__						)
				for video in video_inputs:
						self.assertIsInstance(lowercase__							, lowercase__						)
						self.assertIsInstance(video[0]							, np.ndarray						)

				# Test not batched input
				__A    =							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    =							image_processor(lowercase__							, 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						)				->				Any:
				# Initialize image_processor
				__A    =							self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				__A    =							prepare_video_inputs(self.image_processor_tester							, equal_resolution=lowercase__							, torchify=lowercase__						)
				for video in video_inputs:
						self.assertIsInstance(lowercase__							, lowercase__						)
						self.assertIsInstance(video[0]							, torch.Tensor						)

				# Test not batched input
				__A    =							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    =							image_processor(lowercase__							, 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"],
				    )							, )



import torch

from accelerate import Accelerator, DistributedType







class 							lowercase						:




							def __enter__(							self):
												if self.enabled:
																	lowercase		      =		self.model.no_sync()
																	self.model_sync_obj.__enter__()

												return self




							def __exit__(							self  ,A__  ,A__  ,A__):
												if self.enabled:
																	# Average all models on exit
																	self._sync_and_avg_model_params()
																	self.model_sync_obj.__exit__(A__  ,A__  ,A__)




							def __init__(							self  ,A__  ,A__  ,A__  ,A__ = True):
												if accelerator.distributed_type not in [
												    DistributedType.NO,
												    DistributedType.MULTI_CPU,
												    DistributedType.MULTI_GPU,
												]:
																	raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)''')
												lowercase		      =		enabled and accelerator.distributed_type != DistributedType.NO
												lowercase		      =		0
												if self.enabled:
																	lowercase		      =		accelerator
																	lowercase		      =		model
																	lowercase		      =		local_sgd_steps




							def 					A__ (							self):
												self.num_steps += 1
												if not self.enabled:
																	return

												if self.num_steps % self.local_sgd_steps == 0:
																	self._sync_and_avg_model_params()





							def 					A__ (							self):
												self.accelerator.wait_for_everyone()
												with self.accelerator.autocast():
																	for param in self.model.parameters():
																						lowercase		      =		self.accelerator.reduce(param.data  ,reduction='''mean''')




