





import warnings

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




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	['''image_processor''', '''tokenizer''']
 __SCREAMING_SNAKE_CASE							       =	'''CLIPImageProcessor'''
 __SCREAMING_SNAKE_CASE							       =	('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')



 def __init__(							self,__lowerCamelCase=None,__lowerCamelCase=None,**__lowerCamelCase ):
        A__											=						None
        if "feature_extractor" in kwargs:
               warnings.warn(
                   '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                   ''' instead.''',__lowerCamelCase,)
               A__											=						kwargs.pop('''feature_extractor''' )

        A__											=						image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
               raise ValueError('''You need to specify an `image_processor`.''' )
        if tokenizer is None:
               raise ValueError('''You need to specify a `tokenizer`.''' )

        super().__init__(__lowerCamelCase,__lowerCamelCase )



 def __call__(							self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,**__lowerCamelCase ):

        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:
               A__											=						self.tokenizer(__lowerCamelCase,return_tensors=__lowerCamelCase,**__lowerCamelCase )

        if images is not None:
               A__											=						self.image_processor(__lowerCamelCase,return_tensors=__lowerCamelCase,**__lowerCamelCase )

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



 def 				UpperCamelCase			(							self,*__lowerCamelCase,**__lowerCamelCase ):
        return self.tokenizer.batch_decode(*__lowerCamelCase,**__lowerCamelCase )



 def 				UpperCamelCase			(							self,*__lowerCamelCase,**__lowerCamelCase ):
        return self.tokenizer.decode(*__lowerCamelCase,**__lowerCamelCase )




 @property
 def 				UpperCamelCase			(							self ):
        A__											=						self.tokenizer.model_input_names
        A__											=						self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )












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)

a__:      List[Any]									=		logging.getLogger()


def 	UpperCamelCase__(       )->Union[str, Any]:
       A__											=						argparse.ArgumentParser()
       parser.add_argument('''-f'''  )
       A__											=						parser.parse_args()
       return args.f




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def 				UpperCamelCase			(							self ):
        A__											=						logging.StreamHandler(sys.stdout )
        logger.addHandler(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						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(__lowerCamelCase,'''argv''',__lowerCamelCase ):
                      A__											=						run_glue_deebert.main()
                      for value in result.values():
                             self.assertGreaterEqual(__lowerCamelCase,0.666 )




 @slow
 @require_torch_non_multi_gpu
 def 				UpperCamelCase			(							self ):
        A__											=						'''
            --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(__lowerCamelCase )

        A__											=						'''
            --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(__lowerCamelCase )

        A__											=						'''
            --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(__lowerCamelCase )





