





'''simple docstring'''


import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image




def lowercase_		(				_lowercase     ,    _lowercase=256			)       ->							int:
					'''simple docstring'''

					lowerCamelCase_   : Tuple       =						min(img.size			)

					if s < target_image_size:
										raise ValueError(F"""min dim for image {s} < {target_image_size}"""			)

					lowerCamelCase_   : Any       =						target_image_size / s
					lowerCamelCase_   : int       =						(round(r * img.size[1]			), round(r * img.size[0]			))
					lowerCamelCase_   : Union[str, Any]       =						TF.resize(_lowercase     ,    _lowercase     ,    interpolation=PIL.Image.LANCZOS			)
					lowerCamelCase_   : Union[str, Any]       =						TF.center_crop(_lowercase     ,    output_size=2 * [target_image_size]			)
					lowerCamelCase_   : Optional[Any]       =						torch.unsqueeze(T.ToTensor()(_lowercase			)     ,    0			)
					return img




def lowercase_		(				_lowercase			)       ->							Any:
					'''simple docstring'''

					lowerCamelCase_   : List[Any]       =						2.0 * x - 1.0
					return x




def lowercase_		(				_lowercase     ,    _lowercase=True     ,    _lowercase="RGB"			)       ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_   : Any       =						x.detach().cpu()
					if process:
										lowerCamelCase_   : Union[str, Any]       =						post_process_tensor(_lowercase			)
					lowerCamelCase_   : str       =						x.numpy()
					if process:
										lowerCamelCase_   : Union[str, Any]       =						(255 * x).astype(np.uinta			)
					lowerCamelCase_   : Tuple       =						Image.fromarray(_lowercase			)
					if not x.mode == mode:
										lowerCamelCase_   : int       =						x.convert(_lowercase			)
					return x




def lowercase_		(				_lowercase			)       ->							List[str]:
					'''simple docstring'''

					lowerCamelCase_   : Optional[Any]       =						torch.clamp(_lowercase     ,    -1.0     ,    1.0			)
					lowerCamelCase_   : Tuple       =						(x + 1.0) / 2.0
					lowerCamelCase_   : str       =						x.permute(1     ,    2     ,    0			)
					return x






def lowercase_		(				_lowercase			)       ->							Dict:
					'''simple docstring'''

					lowerCamelCase_   : List[Any]       =						post_process_tensor(x.squeeze()			)
					return x.permute(2     ,    0     ,    1			).unsqueeze(0			)




'''simple docstring'''


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

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__lowercase							:				Optional[Any]					=       logging.getLogger()




def lowercase_		(				)       ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_   : Any       =						argparse.ArgumentParser()
					parser.add_argument('''-f'''			)
					lowerCamelCase_   : Optional[int]       =						parser.parse_args()
					return args.f






class 		__lowercase			(					_lowercase      ):

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Dict       =						logging.StreamHandler(sys.stdout )
								logger.addHandler(A )

								lowerCamelCase_   : int       =						self.get_auto_remove_tmp_dir()
								lowerCamelCase_   : Dict       =						F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

								with patch.object(A     ,				'''argv'''     ,				A ):
													lowerCamelCase_   : Optional[Any]       =						run_glue_with_pabee.main()
													for value in result.values():
																		self.assertGreaterEqual(A     ,				0.75 )


