


"""simple docstring"""


import functools
import operator

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


_snake_case          =							logging.get_logger(__name__)

_snake_case          =							{
    "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   _a      (      SCREAMING_SNAKE_CASE_ ):

    '''simple docstring'''





    a_    :				List[Any]             =    'sew-d'
    def __init__( self							:					str							,					SCREAMING_SNAKE_CASE__							:					Dict=32							,					SCREAMING_SNAKE_CASE__							:					Tuple=7_68							,					SCREAMING_SNAKE_CASE__							:					Any=12							,					SCREAMING_SNAKE_CASE__							:					str=12							,					SCREAMING_SNAKE_CASE__							:					List[str]=30_72							,					SCREAMING_SNAKE_CASE__							:					Tuple=2							,					SCREAMING_SNAKE_CASE__							:					Tuple=5_12							,					SCREAMING_SNAKE_CASE__							:					List[str]=2_56							,					SCREAMING_SNAKE_CASE__							:					List[str]=True							,					SCREAMING_SNAKE_CASE__							:					List[Any]=True							,					SCREAMING_SNAKE_CASE__							:					str=("p2c", "c2p")							,					SCREAMING_SNAKE_CASE__							:					Optional[int]="layer_norm"							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]="gelu_python"							,					SCREAMING_SNAKE_CASE__							:					Tuple=0.1							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=0.1							,					SCREAMING_SNAKE_CASE__							:					List[str]=0.1							,					SCREAMING_SNAKE_CASE__							:					str=0.0							,					SCREAMING_SNAKE_CASE__							:					Dict=0.1							,					SCREAMING_SNAKE_CASE__							:					Tuple=0.02							,					SCREAMING_SNAKE_CASE__							:					List[str]=1e-7							,					SCREAMING_SNAKE_CASE__							:					List[Any]=1e-5							,					SCREAMING_SNAKE_CASE__							:					str="group"							,					SCREAMING_SNAKE_CASE__							:					Tuple="gelu"							,					SCREAMING_SNAKE_CASE__							:					Tuple=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12)							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]=False							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=1_28							,					SCREAMING_SNAKE_CASE__							:					Dict=16							,					SCREAMING_SNAKE_CASE__							:					Dict=True							,					SCREAMING_SNAKE_CASE__							:					Tuple=0.05							,					SCREAMING_SNAKE_CASE__							:					Dict=10							,					SCREAMING_SNAKE_CASE__							:					Dict=2							,					SCREAMING_SNAKE_CASE__							:					Union[str, Any]=0.0							,					SCREAMING_SNAKE_CASE__							:					Any=10							,					SCREAMING_SNAKE_CASE__							:					Dict=0							,					SCREAMING_SNAKE_CASE__							:					Optional[int]="mean"							,					SCREAMING_SNAKE_CASE__							:					Any=False							,					SCREAMING_SNAKE_CASE__							:					int=False							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=2_56							,					SCREAMING_SNAKE_CASE__							:					Any=0							,					SCREAMING_SNAKE_CASE__							:					Optional[int]=1							,					SCREAMING_SNAKE_CASE__							:					Dict=2							,					**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__				)
          lowerCamelCase__           =		hidden_size
          lowerCamelCase__           =		feat_extract_norm
          lowerCamelCase__           =		feat_extract_activation
          lowerCamelCase__           =		list(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		list(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		list(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		conv_bias
          lowerCamelCase__           =		num_conv_pos_embeddings
          lowerCamelCase__           =		num_conv_pos_embedding_groups
          lowerCamelCase__           =		len(self.conv_dim				)
          lowerCamelCase__           =		num_hidden_layers
          lowerCamelCase__           =		intermediate_size
          lowerCamelCase__           =		squeeze_factor
          lowerCamelCase__           =		max_position_embeddings
          lowerCamelCase__           =		position_buckets
          lowerCamelCase__           =		share_att_key
          lowerCamelCase__           =		relative_attention
          lowerCamelCase__           =		norm_rel_ebd
          lowerCamelCase__           =		list(SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		hidden_act
          lowerCamelCase__           =		num_attention_heads
          lowerCamelCase__           =		hidden_dropout
          lowerCamelCase__           =		attention_dropout
          lowerCamelCase__           =		activation_dropout
          lowerCamelCase__           =		feat_proj_dropout
          lowerCamelCase__           =		final_dropout
          lowerCamelCase__           =		layer_norm_eps
          lowerCamelCase__           =		feature_layer_norm_eps
          lowerCamelCase__           =		initializer_range
          lowerCamelCase__           =		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
          lowerCamelCase__           =		apply_spec_augment
          lowerCamelCase__           =		mask_time_prob
          lowerCamelCase__           =		mask_time_length
          lowerCamelCase__           =		mask_time_min_masks
          lowerCamelCase__           =		mask_feature_prob
          lowerCamelCase__           =		mask_feature_length
          lowerCamelCase__           =		mask_feature_min_masks

          # ctc loss
          lowerCamelCase__           =		ctc_loss_reduction
          lowerCamelCase__           =		ctc_zero_infinity

          # sequence classification
          lowerCamelCase__           =		use_weighted_layer_sum
          lowerCamelCase__           =		classifier_proj_size


    @property
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          return functools.reduce(operator.mul							,					self.conv_stride							,					1				)







"""simple docstring"""


def   snake_case						(				_a:     list[list[float]]							)->		list[list[float]]:
      '''simple docstring'''


      lowerCamelCase__           =		[]
      for data in source_data:
            for i, el in enumerate(_a							):
                  if len(_a							) < i + 1:
                        data_lists.append([]							)
                  data_lists[i].append(float(_a							)							)
      return data_lists



def   snake_case						(				_a:     list[list[float]]		,   _a:     list[int]							)->		list[list[float]]:
      '''simple docstring'''


      lowerCamelCase__           =		[]
      for dlist, weight in zip(_a		,   _a							):
            lowerCamelCase__           =		min(_a							)
            lowerCamelCase__           =		max(_a							)

            lowerCamelCase__           =		[]
            # for weight 0 score is 1 - actual score
            if weight == 0:
                  for item in dlist:
                        try:
                              score.append(1 - ((item - mind) / (maxd - mind))							)
                        except ZeroDivisionError:
                              score.append(1							)

            elif weight == 1:
                  for item in dlist:
                        try:
                              score.append((item - mind) / (maxd - mind)							)
                        except ZeroDivisionError:
                              score.append(0							)

        # weight not 0 or 1
            else:
                  lowerCamelCase__           =		F'Invalid weight of {weight:f} provided'
                  raise ValueError(_a							)

            score_lists.append(_a							)

      return score_lists



def   snake_case						(				_a:     list[list[float]]							)->		list[float]:
      '''simple docstring'''


      lowerCamelCase__           =		[0 for i in range(len(score_lists[0]							)							)]

      for slist in score_lists:
            for j, ele in enumerate(_a							):
                  lowerCamelCase__           =		final_scores[j] + ele

      return final_scores






def   snake_case						(				_a:     list[list[float]]		,   _a:     list[int]							)->		list[list[float]]:
      '''simple docstring'''


      lowerCamelCase__           =		get_data(_a							)
      lowerCamelCase__           =		calculate_each_score(_a		,   _a							)
      lowerCamelCase__           =		generate_final_scores(_a							)

      # append scores to source data
      for i, ele in enumerate(_a							):
            source_data[i].append(_a							)

      return source_data



