





import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A					:				int				=			logging.get_logger(__name__)

_A					:				Union[str, Any]				=			{
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Optional[int]				=	"""data2vec-audio"""



    def __init__(       self   ,				A_=32   ,				A_=7_68   ,				A_=12   ,				A_=12   ,				A_=30_72   ,				A_="gelu"   ,				A_=0.1   ,				A_=0.1   ,				A_=0.1   ,				A_=0.0   ,				A_=0.1   ,				A_=0.1   ,				A_=0.02   ,				A_=1E-5   ,				A_="gelu"   ,				A_=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)   ,				A_=(5, 2, 2, 2, 2, 2, 2)   ,				A_=(10, 3, 3, 3, 3, 2, 2)   ,				A_=False   ,				A_=16   ,				A_=19   ,				A_=5   ,				A_=0.05   ,				A_=10   ,				A_=2   ,				A_=0.0   ,				A_=10   ,				A_=0   ,				A_="sum"   ,				A_=False   ,				A_=False   ,				A_=2_56   ,				A_=(5_12, 5_12, 5_12, 5_12, 15_00)   ,				A_=(5, 3, 3, 1, 1)   ,				A_=(1, 2, 3, 1, 1)   ,				A_=5_12   ,				A_=0   ,				A_=1   ,				A_=2   ,				A_=False   ,				A_=3   ,				A_=2   ,				A_=3   ,				A_=None   ,				**A_   ,				):





        '''simple docstring'''

        super().__init__(**A_   ,				pad_token_id=A_   ,				bos_token_id=A_   ,				eos_token_id=A_     )
        SCREAMING_SNAKE_CASE__											=			hidden_size
        SCREAMING_SNAKE_CASE__											=			feat_extract_activation
        SCREAMING_SNAKE_CASE__											=			list(A_     )
        SCREAMING_SNAKE_CASE__											=			list(A_     )
        SCREAMING_SNAKE_CASE__											=			list(A_     )
        SCREAMING_SNAKE_CASE__											=			conv_bias
        SCREAMING_SNAKE_CASE__											=			num_conv_pos_embeddings
        SCREAMING_SNAKE_CASE__											=			num_conv_pos_embedding_groups
        SCREAMING_SNAKE_CASE__											=			conv_pos_kernel_size
        SCREAMING_SNAKE_CASE__											=			len(self.conv_dim     )
        SCREAMING_SNAKE_CASE__											=			num_hidden_layers
        SCREAMING_SNAKE_CASE__											=			intermediate_size
        SCREAMING_SNAKE_CASE__											=			hidden_act
        SCREAMING_SNAKE_CASE__											=			num_attention_heads
        SCREAMING_SNAKE_CASE__											=			hidden_dropout
        SCREAMING_SNAKE_CASE__											=			attention_dropout
        SCREAMING_SNAKE_CASE__											=			activation_dropout
        SCREAMING_SNAKE_CASE__											=			feat_proj_dropout
        SCREAMING_SNAKE_CASE__											=			final_dropout
        SCREAMING_SNAKE_CASE__											=			layerdrop
        SCREAMING_SNAKE_CASE__											=			layer_norm_eps
        SCREAMING_SNAKE_CASE__											=			initializer_range
        SCREAMING_SNAKE_CASE__											=			vocab_size
        SCREAMING_SNAKE_CASE__											=			use_weighted_layer_sum

        if (
            (len(self.conv_stride     ) != self.num_feat_extract_layers)
            or (len(self.conv_kernel     ) != self.num_feat_extract_layers)
            or (len(self.conv_dim     ) != self.num_feat_extract_layers)
        ):
            raise ValueError(
                '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                f''' {len(self.conv_dim     )}`, `len(config.conv_stride) = {len(self.conv_stride     )}`,'''
                f''' `len(config.conv_kernel) = {len(self.conv_kernel     )}`.'''     )

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        SCREAMING_SNAKE_CASE__											=			mask_time_prob
        SCREAMING_SNAKE_CASE__											=			mask_time_length
        SCREAMING_SNAKE_CASE__											=			mask_time_min_masks
        SCREAMING_SNAKE_CASE__											=			mask_feature_prob
        SCREAMING_SNAKE_CASE__											=			mask_feature_length
        SCREAMING_SNAKE_CASE__											=			mask_feature_min_masks

        # ctc loss
        SCREAMING_SNAKE_CASE__											=			ctc_loss_reduction
        SCREAMING_SNAKE_CASE__											=			ctc_zero_infinity

        # adapter
        SCREAMING_SNAKE_CASE__											=			add_adapter
        SCREAMING_SNAKE_CASE__											=			adapter_kernel_size
        SCREAMING_SNAKE_CASE__											=			adapter_stride
        SCREAMING_SNAKE_CASE__											=			num_adapter_layers
        SCREAMING_SNAKE_CASE__											=			output_hidden_size or hidden_size

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        SCREAMING_SNAKE_CASE__											=			classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        SCREAMING_SNAKE_CASE__											=			list(A_     )
        SCREAMING_SNAKE_CASE__											=			list(A_     )
        SCREAMING_SNAKE_CASE__											=			list(A_     )
        SCREAMING_SNAKE_CASE__											=			xvector_output_dim




    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return math.prod(self.conv_stride     )





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

def 	_lowerCAmelCase (    ):
      lowercase__  =	argparse.ArgumentParser()
      parser.add_argument('-f'						)
      lowercase__  =	parser.parse_args()
      return args.f



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   def       UpperCAmelCase		(    self	:      List[str])						->					None:




         """simple docstring"""

         lowercase__  =	logging.StreamHandler(sys.stdout)
         logger.addHandler(lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      Union[str, Any])						->					List[Any]:




         """simple docstring"""

         lowercase__  =	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):
                     lowercase__  =	run_glue_deebert.main()
                     for value in result.values():
                           self.assertGreaterEqual(lowerCAmelCase      ,					0.6_66)



   @slow
   @require_torch_non_multi_gpu
   def       UpperCAmelCase		(    self	:      Optional[Any])						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	'\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(lowerCAmelCase)

         lowercase__  =	'\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(lowerCAmelCase)

         lowercase__  =	'\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(lowerCAmelCase)
