




"""simple docstring"""




import functools
import operator

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


_lowerCamelCase     :		str								=       logging.get_logger(__name__)

_lowerCamelCase     :		Optional[Any]								=       {
    """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_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				'''sew-d'''


    def __init__(      self				:     Optional[int] ,			UpperCAmelCase__				:     Optional[Any]=32 ,			UpperCAmelCase__				:     List[Any]=768 ,			UpperCAmelCase__				:     Dict=12 ,			UpperCAmelCase__				:     List[Any]=12 ,			UpperCAmelCase__				:     Tuple=3_072 ,			UpperCAmelCase__				:     Tuple=2 ,			UpperCAmelCase__				:     int=512 ,			UpperCAmelCase__				:     Union[str, Any]=256 ,			UpperCAmelCase__				:     List[str]=True ,			UpperCAmelCase__				:     Optional[Any]=True ,			UpperCAmelCase__				:     Union[str, Any]=("p2c", "c2p") ,			UpperCAmelCase__				:     Optional[Any]="layer_norm" ,			UpperCAmelCase__				:     int="gelu_python" ,			UpperCAmelCase__				:     int=0.1 ,			UpperCAmelCase__				:     str=0.1 ,			UpperCAmelCase__				:     Any=0.1 ,			UpperCAmelCase__				:     Union[str, Any]=0.0 ,			UpperCAmelCase__				:     Tuple=0.1 ,			UpperCAmelCase__				:     Any=0.02 ,			UpperCAmelCase__				:     Optional[int]=1e-7 ,			UpperCAmelCase__				:     Tuple=1e-5 ,			UpperCAmelCase__				:     Any="group" ,			UpperCAmelCase__				:     str="gelu" ,			UpperCAmelCase__				:     int=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) ,			UpperCAmelCase__				:     Optional[Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,			UpperCAmelCase__				:     int=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,			UpperCAmelCase__				:     int=False ,			UpperCAmelCase__				:     str=128 ,			UpperCAmelCase__				:     Union[str, Any]=16 ,			UpperCAmelCase__				:     Any=True ,			UpperCAmelCase__				:     List[str]=0.05 ,			UpperCAmelCase__				:     Dict=10 ,			UpperCAmelCase__				:     Optional[int]=2 ,			UpperCAmelCase__				:     int=0.0 ,			UpperCAmelCase__				:     Optional[int]=10 ,			UpperCAmelCase__				:     List[Any]=0 ,			UpperCAmelCase__				:     List[Any]="mean" ,			UpperCAmelCase__				:     Union[str, Any]=False ,			UpperCAmelCase__				:     Tuple=False ,			UpperCAmelCase__				:     Tuple=256 ,			UpperCAmelCase__				:     Union[str, Any]=0 ,			UpperCAmelCase__				:     str=1 ,			UpperCAmelCase__				:     Any=2 ,			**UpperCAmelCase__				:     Tuple ,			)						->str:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__ ,			pad_token_id=UpperCAmelCase__ ,			bos_token_id=UpperCAmelCase__ ,			eos_token_id=UpperCAmelCase__)
           A__							=   hidden_size
           A__							=   feat_extract_norm
           A__							=   feat_extract_activation
           A__							=   list(UpperCAmelCase__)
           A__							=   list(UpperCAmelCase__)
           A__							=   list(UpperCAmelCase__)
           A__							=   conv_bias
           A__							=   num_conv_pos_embeddings
           A__							=   num_conv_pos_embedding_groups
           A__							=   len(self.conv_dim)
           A__							=   num_hidden_layers
           A__							=   intermediate_size
           A__							=   squeeze_factor
           A__							=   max_position_embeddings
           A__							=   position_buckets
           A__							=   share_att_key
           A__							=   relative_attention
           A__							=   norm_rel_ebd
           A__							=   list(UpperCAmelCase__)
           A__							=   hidden_act
           A__							=   num_attention_heads
           A__							=   hidden_dropout
           A__							=   attention_dropout
           A__							=   activation_dropout
           A__							=   feat_proj_dropout
           A__							=   final_dropout
           A__							=   layer_norm_eps
           A__							=   feature_layer_norm_eps
           A__							=   initializer_range
           A__							=   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
           A__							=   apply_spec_augment
           A__							=   mask_time_prob
           A__							=   mask_time_length
           A__							=   mask_time_min_masks
           A__							=   mask_feature_prob
           A__							=   mask_feature_length
           A__							=   mask_feature_min_masks

           # ctc loss
           A__							=   ctc_loss_reduction
           A__							=   ctc_zero_infinity

           # sequence classification
           A__							=   use_weighted_layer_sum
           A__							=   classifier_proj_size





    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     int)						->List[str]:





           '''simple docstring'''

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








from __future__ import annotations

import queue


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Dict)						->Any:





           '''simple docstring'''

           A__							=   data
           A__							=   None
           A__							=   None







def SCREAMING_SNAKE_CASE    (	)       ->	TreeNode:



       """simple docstring"""

       print('''\n********Press N to stop entering at any point of time********\n'''						)
       A__							=   input('''Enter the value of the root node: '''						).strip().lower()
       A__							=   queue.Queue()
       A__							=   TreeNode(int(lowercase_						)						)
       q.put(lowercase_						)
       while not q.empty():
              A__							=   q.get()
              A__							=   f"""Enter the left node of {node_found.data}: """
              A__							=   input(lowercase_						).strip().lower() or '''n'''
              if check == "n":
                     return tree_node
              A__							=   TreeNode(int(lowercase_						)						)
              A__							=   left_node
              q.put(lowercase_						)
              A__							=   f"""Enter the right node of {node_found.data}: """
              A__							=   input(lowercase_						).strip().lower() or '''n'''
              if check == "n":
                     return tree_node
              A__							=   TreeNode(int(lowercase_						)						)
              A__							=   right_node
              q.put(lowercase_						)
       raise







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       print(node.data			,    end=''','''						)
       pre_order(node.left						)
       pre_order(node.right						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       in_order(node.left						)
       print(node.data			,    end=''','''						)
       in_order(node.right						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       post_order(node.left						)
       post_order(node.right						)
       print(node.data			,    end=''','''						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__							=   queue.Queue()
       q.put(lowercase_						)
       while not q.empty():
              A__							=   q.get()
              print(node_dequeued.data			,    end=''','''						)
              if node_dequeued.left:
                     q.put(node_dequeued.left						)
              if node_dequeued.right:
                     q.put(node_dequeued.right						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__							=   queue.Queue()
       q.put(lowercase_						)
       while not q.empty():
              A__							=   []
              while not q.empty():
                     A__							=   q.get()
                     print(node_dequeued.data			,    end=''','''						)
                     if node_dequeued.left:
                            list_.append(node_dequeued.left						)
                     if node_dequeued.right:
                            list_.append(node_dequeued.right						)
              print()
              for node in list_:
                     q.put(lowercase_						)







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__							=   []
       A__							=   node
       while n or stack:
              while n:  # start from root node, find its left child
                     print(n.data			,    end=''','''						)
                     stack.append(lowercase_						)
                     A__							=   n.left
              # end of while means current node doesn't have left child
              A__							=   stack.pop()
              # start to traverse its right child
              A__							=   n.right







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__							=   []
       A__							=   node
       while n or stack:
              while n:
                     stack.append(lowercase_						)
                     A__							=   n.left
              A__							=   stack.pop()
              print(n.data			,    end=''','''						)
              A__							=   n.right







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	None:



       """simple docstring"""

       if not isinstance(lowercase_			,    lowercase_						) or not node:
              return
       A__			,							A__							=   [], []
       A__							=   node
       stacka.append(lowercase_						)
       while stacka:  # to find the reversed order of post order, store it in stack2
              A__							=   stacka.pop()
              if n.left:
                     stacka.append(n.left						)
              if n.right:
                     stacka.append(n.right						)
              stacka.append(lowercase_						)
       while stacka:  # pop up from stack2 will be the post order
              print(stacka.pop().data			,    end=''','''						)







def SCREAMING_SNAKE_CASE    (	lowercase_ = ""			,    lowercase_=50			,    lowercase_="*"						)       ->	str:



       """simple docstring"""

       if not s:
              return "\n" + width * char
       A__			,							A__							=   divmod(width - len(lowercase_						) - 2			,    2						)
       return f"""{left * char} {s} {(left + extra) * char}"""


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(prompt("""Binary Tree Traversals"""))

  _lowerCamelCase     :		TreeNode								=       build_tree()
  print(prompt("""Pre Order Traversal"""))
  pre_order(node)
  print(prompt() + """\n""")

  print(prompt("""In Order Traversal"""))
  in_order(node)
  print(prompt() + """\n""")

  print(prompt("""Post Order Traversal"""))
  post_order(node)
  print(prompt() + """\n""")

  print(prompt("""Level Order Traversal"""))
  level_order(node)
  print(prompt() + """\n""")

  print(prompt("""Actual Level Order Traversal"""))
  level_order_actual(node)
  print("""*""" * 50 + """\n""")

  print(prompt("""Pre Order Traversal - Iteration Version"""))
  pre_order_iter(node)
  print(prompt() + """\n""")

  print(prompt("""In Order Traversal - Iteration Version"""))
  in_order_iter(node)
  print(prompt() + """\n""")

  print(prompt("""Post Order Traversal - Iteration Version"""))
  post_order_iter(node)
  print(prompt())


