import json
import logging
import os
import subprocess
from argparse import ArgumentParser


_SCREAMING_SNAKE_CASE:						int    	=       logging.getLogger(__name__)






def    UpperCAmelCase__(						):



			"""simple docstring"""





			_snake_case              =      ArgumentParser()
			_snake_case       , _snake_case              =      parser.parse_known_args()
			for arg in unknown:
						if arg.startswith(("-", "--")  ):
									parser.add_argument(arg.split("="  )[0]  )

			return parser.parse_args()






def    UpperCAmelCase__(						):



			"""simple docstring"""





			_snake_case              =      parse_args()
			_snake_case              =      8888
			_snake_case              =      int(os.environ["SM_NUM_GPUS"]  )
			_snake_case              =      json.loads(os.environ["SM_HOSTS"]  )
			_snake_case              =      len(lowerCamelCase_  )
			_snake_case              =      os.environ["SM_CURRENT_HOST"]
			_snake_case              =      hosts.index(lowerCamelCase_  )
			_snake_case              =      "INFO"

			if num_nodes > 1:
						_snake_case              =      f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]  )}'
			else:
						_snake_case              =      f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]  )}'
			try:
						subprocess.run(lowerCamelCase_							,	shell=lowerCamelCase_  )
			except Exception as e:
						logger.info(lowerCamelCase_  )


if __name__ == "__main__":
			main()


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"""





			_snake_case              =      []
			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
						_snake_case              =      [Image.fromarray(np.moveaxis(lowerCamelCase_							,	0							,	-1  )  ) for frame in video]

			if torchify:
						_snake_case              =      [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"

			_snake_case              =      []
			for i in range(image_processor_tester.batch_size  ):
						if equal_resolution:
									_snake_case              =      _snake_case              =      image_processor_tester.max_resolution
						else:
									_snake_case       , _snake_case              =      np.random.choice(
									    np.arange(image_processor_tester.min_resolution							,	image_processor_tester.max_resolution  )							,	2  )
									_snake_case              =      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    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=7  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=3  ,					__SCREAMING_SNAKE_CASE    :				List[str]=4  ,					__SCREAMING_SNAKE_CASE    :				Tuple=18  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=30  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=400  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=True  ,					__SCREAMING_SNAKE_CASE    :				Tuple=None  ,					__SCREAMING_SNAKE_CASE    :				int=True  ,					__SCREAMING_SNAKE_CASE    :				Any=[0.5, 0.5, 0.5]  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=[0.5, 0.5, 0.5]  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=True  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=None  ,					):






					"""simple docstring"""
					_snake_case              =      size if size is not None else {"shortest_edge": 18}
					_snake_case              =      crop_size if crop_size is not None else {"height": 18, "width": 18}

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







		def 				_a				(			self    :				List[Any]   ):






					"""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							(     _lowercase      ,     unittest.TestCase							):






		"""simple docstring"""







		A_						 =      TvltImageProcessor if is_vision_available() else None



		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					_snake_case              =      TvltImageProcessorTester(self   )



		@property
		def 				_a				(			self    :				List[Any]   ):






					"""simple docstring"""
					return self.image_processor_tester.prepare_image_processor_dict()



		def 				_a				(			self    :				Dict   ):






					"""simple docstring"""
					_snake_case              =      self.image_processing_class(**self.image_processor_dict   )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,					"image_mean"   )   )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,					"image_std"   )   )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,					"do_normalize"   )   )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,					"do_resize"   )   )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,					"do_center_crop"   )   )
					self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,					"size"   )   )



		def 				_a				(			self    :				Optional[Any]   ):






					"""simple docstring"""
					# Initialize image_processor
					_snake_case              =      self.image_processing_class(**self.image_processor_dict   )
					# create random PIL videos
					_snake_case              =      prepare_video_inputs(self.image_processor_tester  ,					equal_resolution=__SCREAMING_SNAKE_CASE   )
					for video in video_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
								self.assertIsInstance(video[0]  ,					Image.Image   )

					# Test not batched input
					_snake_case              =      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
					_snake_case              =      image_processor(__SCREAMING_SNAKE_CASE  ,					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 				_a				(			self    :				str   ):






					"""simple docstring"""
					# Initialize image_processor
					_snake_case              =      self.image_processing_class(**self.image_processor_dict   )
					# create random numpy tensors
					_snake_case              =      prepare_video_inputs(self.image_processor_tester  ,					equal_resolution=__SCREAMING_SNAKE_CASE  ,					numpify=__SCREAMING_SNAKE_CASE   )
					for video in video_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
								self.assertIsInstance(video[0]  ,					np.ndarray   )

					# Test not batched input
					_snake_case              =      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
					_snake_case              =      image_processor(__SCREAMING_SNAKE_CASE  ,					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 				_a				(			self    :				int   ):






					"""simple docstring"""
					# Initialize image_processor
					_snake_case              =      self.image_processing_class(**self.image_processor_dict   )
					# create random PyTorch tensors
					_snake_case              =      prepare_video_inputs(self.image_processor_tester  ,					equal_resolution=__SCREAMING_SNAKE_CASE  ,					torchify=__SCREAMING_SNAKE_CASE   )
					for video in video_inputs:
								self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
								self.assertIsInstance(video[0]  ,					torch.Tensor   )

					# Test not batched input
					_snake_case              =      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
					_snake_case              =      image_processor(__SCREAMING_SNAKE_CASE  ,					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"],
					    )  ,					)

