


import functools
import operator

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


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{
    'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					str         =   'sew-d'

       def __init__(		self: Tuple					,   _SCREAMING_SNAKE_CASE: int=32					,   _SCREAMING_SNAKE_CASE: Tuple=768					,   _SCREAMING_SNAKE_CASE: Tuple=12					,   _SCREAMING_SNAKE_CASE: Dict=12					,   _SCREAMING_SNAKE_CASE: Any=3072					,   _SCREAMING_SNAKE_CASE: List[str]=2					,   _SCREAMING_SNAKE_CASE: Optional[int]=512					,   _SCREAMING_SNAKE_CASE: Tuple=256					,   _SCREAMING_SNAKE_CASE: Tuple=True					,   _SCREAMING_SNAKE_CASE: str=True					,   _SCREAMING_SNAKE_CASE: List[str]=("p2c", "c2p")					,   _SCREAMING_SNAKE_CASE: int="layer_norm"					,   _SCREAMING_SNAKE_CASE: List[str]="gelu_python"					,   _SCREAMING_SNAKE_CASE: Any=0.1					,   _SCREAMING_SNAKE_CASE: str=0.1					,   _SCREAMING_SNAKE_CASE: Optional[Any]=0.1					,   _SCREAMING_SNAKE_CASE: Tuple=0.0					,   _SCREAMING_SNAKE_CASE: int=0.1					,   _SCREAMING_SNAKE_CASE: List[Any]=0.02					,   _SCREAMING_SNAKE_CASE: Tuple=1e-7					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=1e-5					,   _SCREAMING_SNAKE_CASE: List[Any]="group"					,   _SCREAMING_SNAKE_CASE: Union[str, Any]="gelu"					,   _SCREAMING_SNAKE_CASE: Dict=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)					,   _SCREAMING_SNAKE_CASE: Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)					,   _SCREAMING_SNAKE_CASE: Any=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)					,   _SCREAMING_SNAKE_CASE: Optional[Any]=False					,   _SCREAMING_SNAKE_CASE: List[Any]=128					,   _SCREAMING_SNAKE_CASE: List[str]=16					,   _SCREAMING_SNAKE_CASE: str=True					,   _SCREAMING_SNAKE_CASE: Tuple=0.05					,   _SCREAMING_SNAKE_CASE: Tuple=10					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=2					,   _SCREAMING_SNAKE_CASE: List[str]=0.0					,   _SCREAMING_SNAKE_CASE: Dict=10					,   _SCREAMING_SNAKE_CASE: List[str]=0					,   _SCREAMING_SNAKE_CASE: List[Any]="mean"					,   _SCREAMING_SNAKE_CASE: Optional[Any]=False					,   _SCREAMING_SNAKE_CASE: Dict=False					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=256					,   _SCREAMING_SNAKE_CASE: List[Any]=0					,   _SCREAMING_SNAKE_CASE: Tuple=1					,   _SCREAMING_SNAKE_CASE: str=2					,   **_SCREAMING_SNAKE_CASE: Optional[int]					,   )       -> Dict:




              """simple docstring"""


              super().__init__(**UpperCAmelCase__					,   pad_token_id=UpperCAmelCase__					,   bos_token_id=UpperCAmelCase__					,   eos_token_id=UpperCAmelCase__		)
              UpperCamelCase_          =  hidden_size
              UpperCamelCase_          =  feat_extract_norm
              UpperCamelCase_          =  feat_extract_activation
              UpperCamelCase_          =  list(UpperCAmelCase__		)
              UpperCamelCase_          =  list(UpperCAmelCase__		)
              UpperCamelCase_          =  list(UpperCAmelCase__		)
              UpperCamelCase_          =  conv_bias
              UpperCamelCase_          =  num_conv_pos_embeddings
              UpperCamelCase_          =  num_conv_pos_embedding_groups
              UpperCamelCase_          =  len(self.conv_dim		)
              UpperCamelCase_          =  num_hidden_layers
              UpperCamelCase_          =  intermediate_size
              UpperCamelCase_          =  squeeze_factor
              UpperCamelCase_          =  max_position_embeddings
              UpperCamelCase_          =  position_buckets
              UpperCamelCase_          =  share_att_key
              UpperCamelCase_          =  relative_attention
              UpperCamelCase_          =  norm_rel_ebd
              UpperCamelCase_          =  list(UpperCAmelCase__		)
              UpperCamelCase_          =  hidden_act
              UpperCamelCase_          =  num_attention_heads
              UpperCamelCase_          =  hidden_dropout
              UpperCamelCase_          =  attention_dropout
              UpperCamelCase_          =  activation_dropout
              UpperCamelCase_          =  feat_proj_dropout
              UpperCamelCase_          =  final_dropout
              UpperCamelCase_          =  layer_norm_eps
              UpperCamelCase_          =  feature_layer_norm_eps
              UpperCamelCase_          =  initializer_range
              UpperCamelCase_          =  vocab_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)`,"
                         f'''but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)'''
                         f'''= {len(self.conv_stride		)}`, `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
              UpperCamelCase_          =  mask_feature_min_masks

              # ctc loss
              UpperCamelCase_          =  ctc_loss_reduction
              UpperCamelCase_          =  ctc_zero_infinity

              # sequence classification
              UpperCamelCase_          =  use_weighted_layer_sum
              UpperCamelCase_          =  classifier_proj_size



       @property
       def lowercase (		self: Union[str, Any]		)       -> str:




              """simple docstring"""


              return functools.reduce(operator.mul					,   self.conv_stride					,   1		)








import sys




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Union[str, Any]:
       UpperCamelCase_          =  len(UpperCamelCase_  )
       UpperCamelCase_          =  [[0 for x in range(UpperCamelCase_  )] for x in range(UpperCamelCase_  )]
       UpperCamelCase_          =  [[0 for x in range(UpperCamelCase_  )] for x in range(UpperCamelCase_  )]

       for chain_length in range(2					,		UpperCamelCase_  ):
              for a in range(1					,		n - chain_length + 1  ):
                     UpperCamelCase_          =  a + chain_length - 1

                     UpperCamelCase_          =  sys.maxsize
                     for c in range(UpperCamelCase_					,		UpperCamelCase_  ):
                            UpperCamelCase_          =  (
                                matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
                            )
                            if cost < matrix[a][b]:
                                   UpperCamelCase_          =  cost
                                   UpperCamelCase_          =  c
       return matrix, sol




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		List[str]:
       if i == j:
              print("A" + str(UpperCamelCase_  )					,		end=" "  )
       else:
              print("("					,		end=" "  )
              print_optiomal_solution(UpperCamelCase_					,		UpperCamelCase_					,		optimal_solution[i][j]  )
              print_optiomal_solution(UpperCamelCase_					,		optimal_solution[i][j] + 1					,		UpperCamelCase_  )
              print(")"					,		end=" "  )




def  lowerCAmelCase_ (							)     ->		Any:
       UpperCamelCase_          =  [30, 35, 15, 5, 10, 20, 25]
       UpperCamelCase_          =  len(UpperCamelCase_  )
       # Size of matrix created from above array will be
       # 30*35 35*15 15*5 5*10 10*20 20*25
       UpperCamelCase_       , UpperCamelCase_          =  matrix_chain_order(UpperCamelCase_  )

       print("No. of Operation required: " + str(matrix[1][n - 1]  )  )
       print_optiomal_solution(UpperCamelCase_					,		1					,		n - 1  )


if __name__ == "__main__":
     main()

