



'''simple docstring'''






import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

UpperCamelCase__:   Dict								=			logging.getLogger()


def  snake_case_     (					)      ->   Dict:
							UpperCAmelCase :       str 							=		argparse.ArgumentParser()
							parser.add_argument('''-f'''  )
							UpperCAmelCase :       List[Any] 							=		parser.parse_args()
							return args.f




class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	def      A	(						self			:     Optional[int]		)			->		None:
								UpperCAmelCase :       Any 							=		logging.StreamHandler(sys.stdout		)
								logger.addHandler(__snake_case		)







	def      A	(						self			:     str		,			__snake_case			:     Optional[int]		)			->		int:
								UpperCAmelCase :       Any 							=		get_gpu_count()

								if n_gpu > 1:
															pass
															# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
															# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
															# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
															# cmd = [sys.executable] + distributed_args + args
															# execute_subprocess_async(cmd, env=self.get_env())
															# XXX: test the results - need to save them first into .json file
								else:
															args.insert(0		,			'''run_glue_deebert.py'''		)
															with patch.object(__snake_case		,			'''argv'''		,			__snake_case		):
																						UpperCAmelCase :       int 							=		run_glue_deebert.main()
																						for value in result.values():
																													self.assertGreaterEqual(__snake_case		,			0.6_66		)







	@slow
	@require_torch_non_multi_gpu
	def      A	(						self			:     Tuple		)			->		int:
								UpperCAmelCase :       List[Any] 							=		'''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
								self.run_and_check(__snake_case		)

								UpperCAmelCase :       Union[str, Any] 							=		'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
								self.run_and_check(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
								self.run_and_check(__snake_case		)









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_video_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import VivitImageProcessor
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				def __init__(       self			:Any			,			lowerCAmelCase__			:Union[str, Any]			,			lowerCAmelCase__			:Tuple=7			,			lowerCAmelCase__			:List[Any]=3			,			lowerCAmelCase__			:Any=10			,			lowerCAmelCase__			:Optional[int]=18			,			lowerCAmelCase__			:Dict=30			,			lowerCAmelCase__			:Tuple=400			,			lowerCAmelCase__			:List[Any]=True			,			lowerCAmelCase__			:Tuple=None			,			lowerCAmelCase__			:str=True			,			lowerCAmelCase__			:List[str]=[0.5, 0.5, 0.5]			,			lowerCAmelCase__			:List[str]=[0.5, 0.5, 0.5]			,			lowerCAmelCase__			:Optional[Any]=None			,			)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Dict					=		size if size is not None else {'''shortest_edge''': 18}
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

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



				def 		__magic_name__(       self			:Tuple	)		->			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,
										    "crop_size": self.crop_size,
										}




@require_torch
@require_vision
class        _lowercase      (			A__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			List[str]						=		VivitImageProcessor if is_vision_available() else None


				def 		__magic_name__(       self			:List[str]	)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     str					=		VivitImageProcessingTester(self	)


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


				def 		__magic_name__(       self			:List[str]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Optional[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 		__magic_name__(       self			:Optional[Any]	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.image_processing_class.from_dict(self.image_processor_dict	)
										self.assertEqual(image_processor.size			,			{'''shortest_edge''': 18}	)
										self.assertEqual(image_processor.crop_size			,			{'''height''': 18, '''width''': 18}	)

										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.image_processing_class.from_dict(self.image_processor_dict			,			size=42			,			crop_size=84	)
										self.assertEqual(image_processor.size			,			{'''shortest_edge''': 42}	)
										self.assertEqual(image_processor.crop_size			,			{'''height''': 84, '''width''': 84}	)


				def 		__magic_name__(       self			:List[Any]	)		->			Union[str, Any]:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.image_processing_class(**self.image_processor_dict	)
										# create random PIL videos
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		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
										__SCREAMING_SNAKE_CASE						:     List[str]					=		image_processing(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
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		image_processing(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 		__magic_name__(       self			:str	)		->			int:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Any					=		self.image_processing_class(**self.image_processor_dict	)
										# create random numpy tensors
										__SCREAMING_SNAKE_CASE						:     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
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		image_processing(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
										__SCREAMING_SNAKE_CASE						:     Any					=		image_processing(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 		__magic_name__(       self			:Any	)		->			List[str]:
										# Initialize image_processing
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.image_processing_class(**self.image_processor_dict	)
										# create random PyTorch tensors
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		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
										__SCREAMING_SNAKE_CASE						:     Tuple					=		image_processing(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
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		image_processing(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'''],
										    )			,			)
