


"""simple docstring"""
import functools
import operator

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


UpperCAmelCase				=						logging.get_logger(__name__)

UpperCAmelCase				=						{
    """facebook/wav2vec2-base-960h""": """https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json""",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}




class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					'''wav2vec2'''





   def __init__(							self      ,   SCREAMING_SNAKE_CASE=32      ,   SCREAMING_SNAKE_CASE=768      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=12      ,   SCREAMING_SNAKE_CASE=3072      ,   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=0.02      ,   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=0.05      ,   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="sum"      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE=256      ,   SCREAMING_SNAKE_CASE=(512, 512, 512, 512, 1500)      ,   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=False      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=None      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Tuple:
      super().__init__(**SCREAMING_SNAKE_CASE      ,   pad_token_id=SCREAMING_SNAKE_CASE      ,   bos_token_id=SCREAMING_SNAKE_CASE      ,   eos_token_id=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		hidden_size
      _lowerCamelCase	:					List[str]   =		feat_extract_norm
      _lowerCamelCase	:					Optional[Any]   =		feat_extract_activation
      _lowerCamelCase	:					Optional[int]   =		list(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[Any]   =		list(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[Any]   =		list(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[int]   =		conv_bias
      _lowerCamelCase	:					Union[str, Any]   =		num_conv_pos_embeddings
      _lowerCamelCase	:					Dict   =		num_conv_pos_embedding_groups
      _lowerCamelCase	:					Tuple   =		len(self.conv_dim)
      _lowerCamelCase	:					Any   =		num_hidden_layers
      _lowerCamelCase	:					Union[str, Any]   =		intermediate_size
      _lowerCamelCase	:					Optional[int]   =		hidden_act
      _lowerCamelCase	:					Tuple   =		num_attention_heads
      _lowerCamelCase	:					List[str]   =		hidden_dropout
      _lowerCamelCase	:					Tuple   =		attention_dropout
      _lowerCamelCase	:					Optional[int]   =		activation_dropout
      _lowerCamelCase	:					Optional[int]   =		feat_proj_dropout
      _lowerCamelCase	:					str   =		final_dropout
      _lowerCamelCase	:					List[str]   =		layerdrop
      _lowerCamelCase	:					Tuple   =		layer_norm_eps
      _lowerCamelCase	:					List[str]   =		initializer_range
      _lowerCamelCase	:					List[Any]   =		vocab_size
      _lowerCamelCase	:					str   =		do_stable_layer_norm
      _lowerCamelCase	:					str   =		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
      _lowerCamelCase	:					List[str]   =		apply_spec_augment
      _lowerCamelCase	:					List[str]   =		mask_time_prob
      _lowerCamelCase	:					List[Any]   =		mask_time_length
      _lowerCamelCase	:					Optional[int]   =		mask_time_min_masks
      _lowerCamelCase	:					Optional[Any]   =		mask_feature_prob
      _lowerCamelCase	:					Optional[int]   =		mask_feature_length
      _lowerCamelCase	:					Any   =		mask_feature_min_masks

      # parameters for pretraining with codevector quantized representations
      _lowerCamelCase	:					Optional[int]   =		num_codevectors_per_group
      _lowerCamelCase	:					Union[str, Any]   =		num_codevector_groups
      _lowerCamelCase	:					List[str]   =		contrastive_logits_temperature
      _lowerCamelCase	:					List[Any]   =		feat_quantizer_dropout
      _lowerCamelCase	:					List[Any]   =		num_negatives
      _lowerCamelCase	:					Optional[Any]   =		codevector_dim
      _lowerCamelCase	:					Union[str, Any]   =		proj_codevector_dim
      _lowerCamelCase	:					Optional[int]   =		diversity_loss_weight

      # ctc loss
      _lowerCamelCase	:					str   =		ctc_loss_reduction
      _lowerCamelCase	:					Any   =		ctc_zero_infinity

      # adapter
      _lowerCamelCase	:					str   =		add_adapter
      _lowerCamelCase	:					List[Any]   =		adapter_kernel_size
      _lowerCamelCase	:					List[Any]   =		adapter_stride
      _lowerCamelCase	:					List[str]   =		num_adapter_layers
      _lowerCamelCase	:					int   =		output_hidden_size or hidden_size
      _lowerCamelCase	:					Union[str, Any]   =		adapter_attn_dim

      # SequenceClassification-specific parameter. Feel free to ignore for other classes.
      _lowerCamelCase	:					int   =		classifier_proj_size

      # XVector-specific parameters. Feel free to ignore for other classes.
      _lowerCamelCase	:					Optional[Any]   =		list(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					int   =		list(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		list(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Tuple   =		xvector_output_dim





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





"""simple docstring"""
from __future__ import annotations

import queue




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					int   =		data
      _lowerCamelCase	:					List[str]   =		None
      _lowerCamelCase	:					Any   =		None







def 				_snake_case      (   ):




   """simple docstring"""







   print("""\n********Press N to stop entering at any point of time********\n"""   )
   _lowerCamelCase	:					Optional[int]   =		input("""Enter the value of the root node: """   ).strip().lower()
   _lowerCamelCase	:					queue.Queue   =		queue.Queue()
   _lowerCamelCase	:					Optional[int]   =		TreeNode(int(__snake_case   )   )
   q.put(__snake_case   )
   while not q.empty():
      _lowerCamelCase	:					Tuple   =		q.get()
      _lowerCamelCase	:					Any   =		F'Enter the left node of {node_found.data}: '
      _lowerCamelCase	:					Union[str, Any]   =		input(__snake_case   ).strip().lower() or """n"""
      if check == "n":
         return tree_node
      _lowerCamelCase	:					Dict   =		TreeNode(int(__snake_case   )   )
      _lowerCamelCase	:					List[str]   =		left_node
      q.put(__snake_case   )
      _lowerCamelCase	:					Optional[int]   =		F'Enter the right node of {node_found.data}: '
      _lowerCamelCase	:					Optional[Any]   =		input(__snake_case   ).strip().lower() or """n"""
      if check == "n":
         return tree_node
      _lowerCamelCase	:					List[Any]   =		TreeNode(int(__snake_case   )   )
      _lowerCamelCase	:					List[Any]   =		right_node
      q.put(__snake_case   )
   raise

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase	:					queue.Queue   =		queue.Queue()
   q.put(__snake_case   )
   while not q.empty():
      _lowerCamelCase	:					Any   =		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 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase	:					queue.Queue   =		queue.Queue()
   q.put(__snake_case   )
   while not q.empty():
      _lowerCamelCase	:					Optional[Any]   =		[]
      while not q.empty():
         _lowerCamelCase	:					Dict   =		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(__snake_case   )

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase	:					list[TreeNode]   =		[]
   _lowerCamelCase	:					Optional[int]   =		node
   while n or stack:
      while n:  # start from root node, find its left child
         print(n.data						, end=""","""   )
         stack.append(__snake_case   )
         _lowerCamelCase	:					Tuple   =		n.left
      # end of while means current node doesn't have left child
      _lowerCamelCase	:					Optional[Any]   =		stack.pop()
      # start to traverse its right child
      _lowerCamelCase	:					Dict   =		n.right

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase	:					list[TreeNode]   =		[]
   _lowerCamelCase	:					int   =		node
   while n or stack:
      while n:
         stack.append(__snake_case   )
         _lowerCamelCase	:					Any   =		n.left
      _lowerCamelCase	:					Optional[Any]   =		stack.pop()
      print(n.data						, end=""","""   )
      _lowerCamelCase	:					List[Any]   =		n.right

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      str = ""						, __snake_case							:      Any=50						, __snake_case							:      List[str]="*"   ):




   """simple docstring"""







   if not s:
      return "\n" + width * char
   _lowerCamelCase   ,    _lowerCamelCase	:					Optional[int]   =		divmod(width - len(__snake_case   ) - 2						, 2   )
   return F'{left * char} {s} {(left + extra) * char}'


if __name__ == "__main__":
 import doctest

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

 UpperCAmelCase				=						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())

