





"""simple docstring"""







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


a__      :	Tuple       				=      logging.get_logger(__name__)

a__      :	Any       				=      {
    """speechbrain/m-ctc-t-large""": """https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json""",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}




class __magic_name__   (       __lowerCAmelCase		):
       UpperCamelCase    :						Optional[Any]      =	'''mctct'''
       def __init__(     self   ,  __magic_name__=8_0_6_5   ,  __magic_name__=1_5_3_6   ,  __magic_name__=3_6   ,  __magic_name__=6_1_4_4   ,  __magic_name__=4   ,  __magic_name__=3_8_4   ,  __magic_name__=9_2_0   ,  __magic_name__=1e-5   ,  __magic_name__=0.3   ,  __magic_name__="relu"   ,  __magic_name__=0.02   ,  __magic_name__=0.3   ,  __magic_name__=0.3   ,  __magic_name__=1   ,  __magic_name__=0   ,  __magic_name__=2   ,  __magic_name__=1   ,  __magic_name__=0.3   ,  __magic_name__=1   ,  __magic_name__=(7,)   ,  __magic_name__=(3,)   ,  __magic_name__=8_0   ,  __magic_name__=1   ,  __magic_name__=None   ,  __magic_name__="sum"   ,  __magic_name__=False   ,  **__magic_name__   ,  ):





         """simple docstring"""


         super().__init__(**_UpperCAmelCase   ,  pad_token_id=_UpperCAmelCase   ,  bos_token_id=_UpperCAmelCase   ,  eos_token_id=_UpperCAmelCase	)
         _lowerCAmelCase	      =					vocab_size
         _lowerCAmelCase	      =					hidden_size
         _lowerCAmelCase	      =					num_hidden_layers
         _lowerCAmelCase	      =					intermediate_size
         _lowerCAmelCase	      =					num_attention_heads
         _lowerCAmelCase	      =					attention_head_dim
         _lowerCAmelCase	      =					max_position_embeddings
         _lowerCAmelCase	      =					layer_norm_eps
         _lowerCAmelCase	      =					layerdrop
         _lowerCAmelCase	      =					hidden_act
         _lowerCAmelCase	      =					initializer_range
         _lowerCAmelCase	      =					hidden_dropout_prob
         _lowerCAmelCase	      =					attention_probs_dropout_prob
         _lowerCAmelCase	      =					pad_token_id
         _lowerCAmelCase	      =					bos_token_id
         _lowerCAmelCase	      =					eos_token_id
         _lowerCAmelCase	      =					conv_glu_dim
         _lowerCAmelCase	      =					conv_dropout
         _lowerCAmelCase	      =					num_conv_layers
         _lowerCAmelCase	      =					input_feat_per_channel
         _lowerCAmelCase	      =					input_channels
         _lowerCAmelCase	      =					conv_channels
         _lowerCAmelCase	      =					ctc_loss_reduction
         _lowerCAmelCase	      =					ctc_zero_infinity

         # prevents config testing fail with exporting to json
         _lowerCAmelCase	      =					list(_UpperCAmelCase	)
         _lowerCAmelCase	      =					list(_UpperCAmelCase	)

         if len(self.conv_kernel	) != self.num_conv_layers:
           raise ValueError(
               'Configuration for convolutional module is incorrect. '
               'It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '
               F'''but is `len(config.conv_kernel) = {len(self.conv_kernel	)}`, '''
               F'''`config.num_conv_layers = {self.num_conv_layers}`.'''	)









"""simple docstring"""

import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a							      =     logging.getLogger(__name__)





@dataclass
class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[float]     =      field(
     default=0.0     ,      metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}				)
 UpperCAmelCase       :	bool     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''Whether to SortishSamler or not.'''}				)
 UpperCAmelCase       :	bool     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}				)
 UpperCAmelCase       :	bool     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''whether to use adafactor'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(default=__lowerCAmelCase     ,      metadata={'''help''': '''Dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[float]     =      field(
     default=__lowerCAmelCase     ,      metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}				)
 UpperCAmelCase       :	Optional[str]     =      field(
     default='''linear'''     ,      metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()				)}'''}     ,      )

