


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 					_a							(				_lowerCAmelCase):
	_a  :  Dict          =							['''image_processor''', '''tokenizer''']
	_a  :  Optional[Any]          =							'''AutoImageProcessor'''
	_a  :  Any          =							'''AutoTokenizer'''




	def __init__(					self						:		Tuple		,					_SCREAMING_SNAKE_CASE						:		List[Any]		,					_SCREAMING_SNAKE_CASE						:		str		)->       Optional[Any]:
			super().__init__(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__   :			List[Any]         =					self.image_processor




	def __call__(					self						:		Union[str, Any]		,					_SCREAMING_SNAKE_CASE						:		int=None		,					_SCREAMING_SNAKE_CASE						:		List[Any]=None		,					_SCREAMING_SNAKE_CASE						:		List[str]=None		,					**_SCREAMING_SNAKE_CASE						:		Any		)->       List[str]:




			if text is None and images is None:
					raise ValueError('''You have to specify either text or images. Both cannot be none.'''		)

			if text is not None:
					lowerCAmelCase__   :			int         =					self.tokenizer(_SCREAMING_SNAKE_CASE		,					return_tensors=_SCREAMING_SNAKE_CASE		,					**_SCREAMING_SNAKE_CASE		)

			if images is not None:
					lowerCAmelCase__   :			Any         =					self.image_processor(_SCREAMING_SNAKE_CASE		,					return_tensors=_SCREAMING_SNAKE_CASE		,					**_SCREAMING_SNAKE_CASE		)

			if text is not None and images is not None:
					lowerCAmelCase__   :			Any         =					image_features.pixel_values
					return encoding
			elif text is not None:
					return encoding
			else:
					return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE		)		,					tensor_type=_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__(					self						:		List[Any]		,					*_SCREAMING_SNAKE_CASE						:		Optional[int]		,					**_SCREAMING_SNAKE_CASE						:		Union[str, Any]		)->       Union[str, Any]:
			return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE		,					**_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__(					self						:		Union[str, Any]		,					*_SCREAMING_SNAKE_CASE						:		Dict		,					**_SCREAMING_SNAKE_CASE						:		str		)->       Dict:
			return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE		,					**_SCREAMING_SNAKE_CASE		)




	@property
	def 							UpperCAmelCase__(					self						:		Optional[Any]		)->       str:
			return ["input_ids", "attention_mask", "pixel_values"]











'''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)

lowerCAmelCase					:     Optional[int]												=logging.getLogger()
def 							UpperCAmelCase_  (						):
   lowercase_			:Union[str, Any]    			= argparse.ArgumentParser()
   parser.add_argument("-f" )
   lowercase_			:Any    			= parser.parse_args()
   return args.f


class       a_   (				_lowerCAmelCase							):





     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:List[Any]    			= logging.StreamHandler(sys.stdout						)
        logger.addHandler(lowercase						)





     def 							lowercase__      ( self		:					str	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:Dict    			= 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(lowercase	,      "argv"	,      lowercase						):
              lowercase_			:int    			= run_glue_deebert.main()
              for value in result.values():
                 self.assertGreaterEqual(lowercase	,      0.6_66						)







     @slow
     @require_torch_non_multi_gpu
     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        lowercase_			:List[Any]    			= "\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            ".split()
        self.run_and_check(lowercase						)

        lowercase_			:List[str]    			= "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
        self.run_and_check(lowercase						)

        lowercase_			:Any    			= "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
        self.run_and_check(lowercase						)


