import functools
import operator

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


A   :     Tuple       =				logging.get_logger(__name__)

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


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  '''sew'''







  def __init__(							self       :				int     ,   __magic_name__       :				Union[str, Any]=32     ,   __magic_name__       :				Optional[int]=768     ,   __magic_name__       :				List[Any]=12     ,   __magic_name__       :				Optional[Any]=12     ,   __magic_name__       :				Dict=3_072     ,   __magic_name__       :				Dict=2     ,   __magic_name__       :				Any="gelu"     ,   __magic_name__       :				int=0.1     ,   __magic_name__       :				Optional[Any]=0.1     ,   __magic_name__       :				str=0.1     ,   __magic_name__       :				Optional[int]=0.0     ,   __magic_name__       :				List[str]=0.1     ,   __magic_name__       :				List[str]=0.1     ,   __magic_name__       :				Tuple=0.02     ,   __magic_name__       :				Tuple=1e-5     ,   __magic_name__       :				Union[str, Any]="group"     ,   __magic_name__       :				Optional[int]="gelu"     ,   __magic_name__       :				Tuple=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)     ,   __magic_name__       :				Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)     ,   __magic_name__       :				str=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)     ,   __magic_name__       :				List[Any]=False     ,   __magic_name__       :				List[str]=128     ,   __magic_name__       :				Dict=16     ,   __magic_name__       :				List[str]=True     ,   __magic_name__       :				Optional[Any]=0.05     ,   __magic_name__       :				List[str]=10     ,   __magic_name__       :				Tuple=2     ,   __magic_name__       :				int=0.0     ,   __magic_name__       :				Dict=10     ,   __magic_name__       :				Dict=0     ,   __magic_name__       :				str="mean"     ,   __magic_name__       :				Dict=False     ,   __magic_name__       :				Tuple=False     ,   __magic_name__       :				Any=256     ,   __magic_name__       :				Tuple=0     ,   __magic_name__       :				Any=1     ,   __magic_name__       :				Tuple=2     ,   **__magic_name__       :				List[str]     ,   )     ->		Tuple:
        super().__init__(**__magic_name__     ,   pad_token_id=__magic_name__     ,   bos_token_id=__magic_name__     ,   eos_token_id=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     hidden_size
        SCREAMING_SNAKE_CASE_      =     feat_extract_norm
        SCREAMING_SNAKE_CASE_      =     feat_extract_activation
        SCREAMING_SNAKE_CASE_      =     list(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     list(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     list(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     conv_bias
        SCREAMING_SNAKE_CASE_      =     num_conv_pos_embeddings
        SCREAMING_SNAKE_CASE_      =     num_conv_pos_embedding_groups
        SCREAMING_SNAKE_CASE_      =     len(self.conv_dim      )
        SCREAMING_SNAKE_CASE_      =     num_hidden_layers
        SCREAMING_SNAKE_CASE_      =     intermediate_size
        SCREAMING_SNAKE_CASE_      =     squeeze_factor
        SCREAMING_SNAKE_CASE_      =     hidden_act
        SCREAMING_SNAKE_CASE_      =     num_attention_heads
        SCREAMING_SNAKE_CASE_      =     hidden_dropout
        SCREAMING_SNAKE_CASE_      =     attention_dropout
        SCREAMING_SNAKE_CASE_      =     activation_dropout
        SCREAMING_SNAKE_CASE_      =     feat_proj_dropout
        SCREAMING_SNAKE_CASE_      =     final_dropout
        SCREAMING_SNAKE_CASE_      =     layerdrop
        SCREAMING_SNAKE_CASE_      =     layer_norm_eps
        SCREAMING_SNAKE_CASE_      =     initializer_range
        SCREAMING_SNAKE_CASE_      =     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
        SCREAMING_SNAKE_CASE_      =     apply_spec_augment
        SCREAMING_SNAKE_CASE_      =     mask_time_prob
        SCREAMING_SNAKE_CASE_      =     mask_time_length
        SCREAMING_SNAKE_CASE_      =     mask_time_min_masks
        SCREAMING_SNAKE_CASE_      =     mask_feature_prob
        SCREAMING_SNAKE_CASE_      =     mask_feature_length
        SCREAMING_SNAKE_CASE_      =     mask_feature_min_masks

        # ctc loss
        SCREAMING_SNAKE_CASE_      =     ctc_loss_reduction
        SCREAMING_SNAKE_CASE_      =     ctc_zero_infinity

        # sequence classification
        SCREAMING_SNAKE_CASE_      =     use_weighted_layer_sum
        SCREAMING_SNAKE_CASE_      =     classifier_proj_size







  @property
  def       __A   (							self       :				int      )     ->		Dict:
        return functools.reduce(operator.mul     ,   self.conv_stride     ,   1      )



def    a__       (						__UpperCamelCase ):
      if not head:
            return True
      # split the list to two parts
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     head.next, head
      while fast and fast.next:
            SCREAMING_SNAKE_CASE_      =     fast.next.next
            SCREAMING_SNAKE_CASE_      =     slow.next
      SCREAMING_SNAKE_CASE_      =     slow.next
      SCREAMING_SNAKE_CASE_      =     None  # Don't forget here! But forget still works!
      # reverse the second part
      SCREAMING_SNAKE_CASE_      =     None
      while second:
            SCREAMING_SNAKE_CASE_      =     second.next
            SCREAMING_SNAKE_CASE_      =     node
            SCREAMING_SNAKE_CASE_      =     second
            SCREAMING_SNAKE_CASE_      =     nxt
      # compare two parts
      # second part has the same or one less node
      while node:
            if node.val != head.val:
                  return False
            SCREAMING_SNAKE_CASE_      =     node.next
            SCREAMING_SNAKE_CASE_      =     head.next
      return True


def    a__       (						__UpperCamelCase ):
      if not head or not head.next:
            return True

      # 1. Get the midpoint (slow)
      SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     SCREAMING_SNAKE_CASE_      =     head
      while fast and fast.next:
            SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     fast.next.next, slow.next

      # 2. Push the second half into the stack
      SCREAMING_SNAKE_CASE_      =     [slow.val]
      while slow.next:
            SCREAMING_SNAKE_CASE_      =     slow.next
            stack.append(slow.val )

      # 3. Comparison
      while stack:
            if stack.pop() != cur.val:
                  return False
            SCREAMING_SNAKE_CASE_      =     cur.next

      return True





def    a__       (						__UpperCamelCase ):
      if not head or not head.next:
            return True
      SCREAMING_SNAKE_CASE_      =     {}
      SCREAMING_SNAKE_CASE_      =     0
      while head:
            if head.val in d:
                  d[head.val].append(__UpperCamelCase )
            else:
                  SCREAMING_SNAKE_CASE_      =     [pos]
            SCREAMING_SNAKE_CASE_      =     head.next
            pos += 1
      SCREAMING_SNAKE_CASE_      =     pos - 1
      SCREAMING_SNAKE_CASE_      =     0
      for v in d.values():
            if len(__UpperCamelCase ) % 2 != 0:
                  middle += 1
            else:
                  SCREAMING_SNAKE_CASE_      =     0
                  for i in range(0   ,					len(__UpperCamelCase ) ):
                        if v[i] + v[len(__UpperCamelCase ) - 1 - step] != checksum:
                              return False
                        step += 1
            if middle > 1:
                  return False
      return True


