





'''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    :			Dict		= logging.getLogger()


def        _a ():


  """simple docstring"""

  _UpperCamelCase							      =argparse.ArgumentParser()
  parser.add_argument('''-f''' )
  _UpperCamelCase							      =parser.parse_args()
  return args.f







class 		UpperCAmelCase (					lowercase_):


  """simple docstring"""







  def 	UpperCamelCase__   (					self     :       Dict	)  ->    None:
    _UpperCamelCase							      =logging.StreamHandler(sys.stdout	)
    logger.addHandler(A_	)


  def 	UpperCamelCase__   (					self     :       int				,						UpperCamelCase__     :       Tuple	)  ->    List[str]:
    _UpperCamelCase							      =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(A_				,						'''argv'''				,						A_	):
        _UpperCamelCase							      =run_glue_deebert.main()
        for value in result.values():
          self.assertGreaterEqual(A_				,						0.666	)




  @slow
  @require_torch_non_multi_gpu
  def 	UpperCamelCase__   (					self     :       List[str]	)  ->    Optional[int]:
    _UpperCamelCase							      ='''\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(A_	)

    _UpperCamelCase							      ='''\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(A_	)

    _UpperCamelCase							      ='''\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(A_	)













import functools
import operator

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


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'wavlm'

 def __init__(  self,		A_=32,		A_=768,		A_=12,		A_=12,		A_=3072,		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_="group",		A_="gelu",		A_=(512, 512, 512, 512, 512, 512, 512),		A_=(5, 2, 2, 2, 2, 2, 2),		A_=(10, 3, 3, 3, 3, 2, 2),		A_=False,		A_=128,		A_=16,		A_=320,		A_=800,		A_=False,		A_=True,		A_=0.05,		A_=10,		A_=2,		A_=0.0,		A_=10,		A_=320,		A_=2,		A_=0.1,		A_=100,		A_=256,		A_=256,		A_=0.1,		A_="mean",		A_=False,		A_=False,		A_=256,		A_=(512, 512, 512, 512, 1500),		A_=(5, 3, 3, 1, 1),		A_=(1, 2, 3, 1, 1),		A_=512,		A_=80,		A_=0,		A_=1,		A_=2,		A_=False,		A_=3,		A_=2,		A_=3,		A_=None,		**A_,		)	->       Union[str, Any]:
    super().__init__(**A_,		pad_token_id=A_,		bos_token_id=A_,		eos_token_id=A_		)
    UpperCAmelCase__						=hidden_size
    UpperCAmelCase__						=feat_extract_norm
    UpperCAmelCase__						=feat_extract_activation
    UpperCAmelCase__						=list(A_		)
    UpperCAmelCase__						=list(A_		)
    UpperCAmelCase__						=list(A_		)
    UpperCAmelCase__						=conv_bias
    UpperCAmelCase__						=num_buckets
    UpperCAmelCase__						=max_bucket_distance
    UpperCAmelCase__						=num_conv_pos_embeddings
    UpperCAmelCase__						=num_conv_pos_embedding_groups
    UpperCAmelCase__						=len(self.conv_dim		)
    UpperCAmelCase__						=num_hidden_layers
    UpperCAmelCase__						=intermediate_size
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=num_attention_heads
    UpperCAmelCase__						=hidden_dropout
    UpperCAmelCase__						=attention_dropout
    UpperCAmelCase__						=activation_dropout
    UpperCAmelCase__						=feat_proj_dropout
    UpperCAmelCase__						=final_dropout
    UpperCAmelCase__						=layerdrop
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=initializer_range
    UpperCAmelCase__						=num_ctc_classes
    UpperCAmelCase__						=vocab_size
    UpperCAmelCase__						=do_stable_layer_norm
    UpperCAmelCase__						=use_weighted_layer_sum
    UpperCAmelCase__						=classifier_proj_size

    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
    UpperCAmelCase__						=apply_spec_augment
    UpperCAmelCase__						=mask_time_prob
    UpperCAmelCase__						=mask_time_length
    UpperCAmelCase__						=mask_time_min_masks
    UpperCAmelCase__						=mask_feature_prob
    UpperCAmelCase__						=mask_feature_length

    # parameters for pretraining with codevector quantized representations
    UpperCAmelCase__						=num_codevectors_per_group
    UpperCAmelCase__						=num_codevector_groups
    UpperCAmelCase__						=contrastive_logits_temperature
    UpperCAmelCase__						=num_negatives
    UpperCAmelCase__						=codevector_dim
    UpperCAmelCase__						=proj_codevector_dim
    UpperCAmelCase__						=diversity_loss_weight

    # ctc loss
    UpperCAmelCase__						=ctc_loss_reduction
    UpperCAmelCase__						=ctc_zero_infinity

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

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

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






 @property
 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    return functools.reduce(operator.mul,		self.conv_stride,		1		)



