


"""simple docstring"""







import math

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


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {
    '''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       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				str   ="data2vec-audio"

       def __init__(						self		,     snake_case__=32		,     snake_case__=768		,     snake_case__=12		,     snake_case__=12		,     snake_case__=3_072		,     snake_case__="gelu"		,     snake_case__=0.1		,     snake_case__=0.1		,     snake_case__=0.1		,     snake_case__=0.0		,     snake_case__=0.1		,     snake_case__=0.1		,     snake_case__=0.02		,     snake_case__=1e-5		,     snake_case__="gelu"		,     snake_case__=(512, 512, 512, 512, 512, 512, 512)		,     snake_case__=(5, 2, 2, 2, 2, 2, 2)		,     snake_case__=(10, 3, 3, 3, 3, 2, 2)		,     snake_case__=False		,     snake_case__=16		,     snake_case__=19		,     snake_case__=5		,     snake_case__=0.05		,     snake_case__=10		,     snake_case__=2		,     snake_case__=0.0		,     snake_case__=10		,     snake_case__=0		,     snake_case__="sum"		,     snake_case__=False		,     snake_case__=False		,     snake_case__=256		,     snake_case__=(512, 512, 512, 512, 1_500)		,     snake_case__=(5, 3, 3, 1, 1)		,     snake_case__=(1, 2, 3, 1, 1)		,     snake_case__=512		,     snake_case__=0		,     snake_case__=1		,     snake_case__=2		,     snake_case__=False		,     snake_case__=3		,     snake_case__=2		,     snake_case__=3		,     snake_case__=None		,     **snake_case__		,     ):






         """simple docstring"""





         super().__init__(**snake_case__		,     pad_token_id=snake_case__		,     bos_token_id=snake_case__		,     eos_token_id=snake_case__						)
         lowerCAmelCase     :     int								=							hidden_size
         lowerCAmelCase     :     Tuple								=							feat_extract_activation
         lowerCAmelCase     :     Any								=							list(snake_case__						)
         lowerCAmelCase     :     List[Any]								=							list(snake_case__						)
         lowerCAmelCase     :     Dict								=							list(snake_case__						)
         lowerCAmelCase     :     List[Any]								=							conv_bias
         lowerCAmelCase     :     Any								=							num_conv_pos_embeddings
         lowerCAmelCase     :     List[str]								=							num_conv_pos_embedding_groups
         lowerCAmelCase     :     List[str]								=							conv_pos_kernel_size
         lowerCAmelCase     :     Tuple								=							len(self.conv_dim						)
         lowerCAmelCase     :     Dict								=							num_hidden_layers
         lowerCAmelCase     :     List[Any]								=							intermediate_size
         lowerCAmelCase     :     Any								=							hidden_act
         lowerCAmelCase     :     Optional[int]								=							num_attention_heads
         lowerCAmelCase     :     Optional[int]								=							hidden_dropout
         lowerCAmelCase     :     Optional[int]								=							attention_dropout
         lowerCAmelCase     :     Tuple								=							activation_dropout
         lowerCAmelCase     :     Any								=							feat_proj_dropout
         lowerCAmelCase     :     List[str]								=							final_dropout
         lowerCAmelCase     :     Optional[Any]								=							layerdrop
         lowerCAmelCase     :     List[Any]								=							layer_norm_eps
         lowerCAmelCase     :     List[Any]								=							initializer_range
         lowerCAmelCase     :     Optional[Any]								=							vocab_size
         lowerCAmelCase     :     str								=							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
         lowerCAmelCase     :     int								=							mask_time_prob
         lowerCAmelCase     :     List[Any]								=							mask_time_length
         lowerCAmelCase     :     int								=							mask_time_min_masks
         lowerCAmelCase     :     Any								=							mask_feature_prob
         lowerCAmelCase     :     List[Any]								=							mask_feature_length
         lowerCAmelCase     :     Optional[Any]								=							mask_feature_min_masks

         # ctc loss
         lowerCAmelCase     :     Dict								=							ctc_loss_reduction
         lowerCAmelCase     :     Tuple								=							ctc_zero_infinity

         # adapter
         lowerCAmelCase     :     int								=							add_adapter
         lowerCAmelCase     :     Dict								=							adapter_kernel_size
         lowerCAmelCase     :     Tuple								=							adapter_stride
         lowerCAmelCase     :     Tuple								=							num_adapter_layers
         lowerCAmelCase     :     Optional[int]								=							output_hidden_size or hidden_size

         # SequenceClassification-specific parameter. Feel free to ignore for other classes.
         lowerCAmelCase     :     int								=							classifier_proj_size

         # XVector-specific parameters. Feel free to ignore for other classes.
         lowerCAmelCase     :     List[Any]								=							list(snake_case__						)
         lowerCAmelCase     :     Optional[int]								=							list(snake_case__						)
         lowerCAmelCase     :     List[str]								=							list(snake_case__						)
         lowerCAmelCase     :     Dict								=							xvector_output_dim







       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return math.prod(self.conv_stride						)









import os

import pytest
from attr import dataclass


lowerCamelCase											=       'us-east-1'  # defaults region




@dataclass
class A  :
      UpperCamelCase__							:  str
      UpperCamelCase__							:  Dict      ='arn:aws:iam::558105141721:role/sagemaker_execution_role'
      UpperCamelCase__							:  int      ={
          'task_name': 'mnli',
          'per_device_train_batch_size': 16,
          'per_device_eval_batch_size': 16,
          'do_train': True,
          'do_eval': True,
          'do_predict': True,
          'output_dir': '/opt/ml/model',
          'overwrite_output_dir': True,
          'max_steps': 500,
          'save_steps': 5500,
      }
      UpperCamelCase__							:  Optional[Any]      ={**hyperparameters, 'max_steps': 1000}






      @property
      def        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """simple docstring"""




             if self.framework == "pytorch":
                    return [
                        {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                        {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                        {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
                    ]
             else:
                    return [
                        {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                        {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                        {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
                    ]






      @property
      def        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """simple docstring"""




             return F'''{self.framework}-transfromers-test'''






      @property
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						str:
             """simple docstring"""




             return F'''./tests/sagemaker/scripts/{self.framework}'''






      @property
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						str:
             """simple docstring"""




             if self.framework == "pytorch":
                    return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
             else:
                    return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"




@pytest.fixture(scope='class'     )
def 	a_      (    SCREAMING_SNAKE_CASE__					:      Tuple     ):
       '''simple docstring'''





       _lowerCamelCase       :		List[Any]		=SageMakerTestEnvironment(framework=request.cls.framework     )


