






'''simple docstring'''



import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      UpperCamelCase_             =      ComputeEnvironment.AMAZON_SAGEMAKER
      UpperCamelCase_             =      True
      UpperCamelCase_             =      '''ml.p3.2xlarge'''
      UpperCamelCase_             =      '''accelerate_sagemaker_execution_role'''
      UpperCamelCase_             =      '''hf-sm'''
      UpperCamelCase_             =      '''us-east-1'''
      UpperCamelCase_             =      1
      UpperCamelCase_             =      '''accelerate-sagemaker-1'''
      UpperCamelCase_             =      '''1.6'''
      UpperCamelCase_             =      '''4.4'''
      UpperCamelCase_             =      '''train.py'''
      UpperCamelCase_             =      [
          '''--model_name_or_path''',
          '''bert''',
          '''--do_train''',
          '''False''',
          '''--epochs''',
          '''3''',
          '''--learning_rate''',
          '''5e-5''',
          '''--max_steps''',
          '''50.5''',
      ]
      UpperCamelCase_             =      [
          '''--model_name_or_path''',
          '''bert''',
          '''--do_train''',
          '''--do_test''',
          '''False''',
          '''--do_predict''',
          '''--epochs''',
          '''3''',
          '''--learning_rate''',
          '''5e-5''',
          '''--max_steps''',
          '''50.5''',
      ]





class UpperCAmelCase_       (     unittest.TestCase					):




      """simple docstring"""




      def 					A__   (			self      :		int		)	->	Dict:


       '''simple docstring'''

       lowercase       : str						=_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args		)
       assert isinstance(converted_args['''model_name_or_path''']  ,   UpperCAmelCase		)
       assert isinstance(converted_args['''do_train''']  ,   UpperCAmelCase		)
       assert isinstance(converted_args['''epochs''']  ,   UpperCAmelCase		)
       assert isinstance(converted_args['''learning_rate''']  ,   UpperCAmelCase		)
       assert isinstance(converted_args['''max_steps''']  ,   UpperCAmelCase		)

       with pytest.raises(UpperCAmelCase		):
        _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args		)









'''simple docstring'''





import functools
import operator

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


UpperCamelCase_		:     Optional[Any]          =		logging.get_logger(__name__)

UpperCamelCase_		:     Any          =		{
    '''microsoft/unispeech-sat-base-100h-libri-ft''': (
        '''https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'''
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}

class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       str       							=		"""unispeech-sat"""






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=32   ,_SCREAMING_SNAKE_CASE=768   ,_SCREAMING_SNAKE_CASE=12   ,_SCREAMING_SNAKE_CASE=12   ,_SCREAMING_SNAKE_CASE=3_072   ,_SCREAMING_SNAKE_CASE="gelu"   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.0   ,_SCREAMING_SNAKE_CASE=0.0   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.0_2   ,_SCREAMING_SNAKE_CASE=1e-5   ,_SCREAMING_SNAKE_CASE="group"   ,_SCREAMING_SNAKE_CASE="gelu"   ,_SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 512, 512, 512)   ,_SCREAMING_SNAKE_CASE=(5, 2, 2, 2, 2, 2, 2)   ,_SCREAMING_SNAKE_CASE=(10, 3, 3, 3, 3, 2, 2)   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=128   ,_SCREAMING_SNAKE_CASE=16   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=0.0_5   ,_SCREAMING_SNAKE_CASE=10   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=0.0   ,_SCREAMING_SNAKE_CASE=10   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=320   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=100   ,_SCREAMING_SNAKE_CASE=256   ,_SCREAMING_SNAKE_CASE=256   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE="mean"   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE=256   ,_SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 1_500)   ,_SCREAMING_SNAKE_CASE=(5, 3, 3, 1, 1)   ,_SCREAMING_SNAKE_CASE=(1, 2, 3, 1, 1)   ,_SCREAMING_SNAKE_CASE=512   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=1   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=504   ,**_SCREAMING_SNAKE_CASE   ,)				->     Any:
          super().__init__(**_SCREAMING_SNAKE_CASE   ,pad_token_id=_SCREAMING_SNAKE_CASE   ,bos_token_id=_SCREAMING_SNAKE_CASE   ,eos_token_id=_SCREAMING_SNAKE_CASE       )
          _snake_case				= hidden_size
          _snake_case				= feat_extract_norm
          _snake_case				= feat_extract_activation
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= list(_SCREAMING_SNAKE_CASE       )
          _snake_case				= conv_bias
          _snake_case				= num_conv_pos_embeddings
          _snake_case				= num_conv_pos_embedding_groups
          _snake_case				= len(self.conv_dim       )
          _snake_case				= num_hidden_layers
          _snake_case				= intermediate_size
          _snake_case				= hidden_act
          _snake_case				= num_attention_heads
          _snake_case				= hidden_dropout
          _snake_case				= attention_dropout
          _snake_case				= activation_dropout
          _snake_case				= feat_proj_dropout
          _snake_case				= final_dropout
          _snake_case				= layerdrop
          _snake_case				= layer_norm_eps
          _snake_case				= initializer_range
          _snake_case				= vocab_size
          _snake_case				= num_clusters
          _snake_case				= do_stable_layer_norm
          _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
          _snake_case				= apply_spec_augment
          _snake_case				= mask_time_prob
          _snake_case				= mask_time_length
          _snake_case				= mask_time_min_masks
          _snake_case				= mask_feature_prob
          _snake_case				= mask_feature_length
          _snake_case				= mask_feature_min_masks

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

          # ctc loss
          _snake_case				= ctc_loss_reduction
          _snake_case				= ctc_zero_infinity

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

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






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


