'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:     Optional[Any]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     Dict  ={
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
SCREAMING_SNAKE_CASE_:     Tuple  =[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def 	lowerCAmelCase_				(					snake_case_       : Union[str, Any]					,    snake_case_       : Optional[Any]					,    snake_case_       : List[Any]					,    snake_case_       : Any					,    snake_case_       : Dict					,    snake_case_       : Tuple			)    ->      Dict:


     '''simple docstring'''




     for attribute in key.split("."			):
          if is_finetuned:
               if attribute in ["quantizer", "project_q", "project_hid"]:
                    # those layers are only relevant for pretraining and should be dropped
                    return

               if attribute == "ctc_proj":
                    # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                    UpperCAmelCase_           =   "lm_head"

          UpperCAmelCase_           =   getattr(snake_case_					,    snake_case_			)

     if weight_type is not None:
          UpperCAmelCase_           =   getattr(snake_case_					,    snake_case_			).shape
     else:
          UpperCAmelCase_           =   hf_pointer.shape

     assert hf_shape == value.shape, (
         f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
         f""" {value.shape} for {full_name}"""
     )

     if weight_type == "weight":
          UpperCAmelCase_           =   value
     elif weight_type == "weight_g":
          UpperCAmelCase_           =   value
     elif weight_type == "weight_v":
          UpperCAmelCase_           =   value
     elif weight_type == "bias":
          UpperCAmelCase_           =   value
     else:
          UpperCAmelCase_           =   value

     logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""			)





def 	lowerCAmelCase_				(					snake_case_       : Union[str, Any]					,    snake_case_       : List[Any]					,    snake_case_       : Optional[Any]			)    ->      List[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   []
     UpperCAmelCase_           =   fairseq_model.state_dict()

     UpperCAmelCase_           =   hf_model.unispeech.feature_extractor

     for name, value in fairseq_dict.items():
          UpperCAmelCase_           =   False
          if "conv_layers" in name:
               load_conv_layer(
                   snake_case_					,    snake_case_					,    snake_case_					,    snake_case_					,    hf_model.config.feat_extract_norm == "group"					,    )
               UpperCAmelCase_           =   True
          else:
               for key, mapped_key in MAPPING.items():
                    UpperCAmelCase_           =   "unispeech." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                    if key in name or key.split("w2v_model."			)[-1] == name.split("."			)[0]:
                         UpperCAmelCase_           =   True
                         if "*" in mapped_key:
                              UpperCAmelCase_           =   name.split(snake_case_			)[0].split("."			)[-2]
                              UpperCAmelCase_           =   mapped_key.replace("*"					,    snake_case_			)
                         if "weight_g" in name:
                              UpperCAmelCase_           =   "weight_g"
                         elif "weight_v" in name:
                              UpperCAmelCase_           =   "weight_v"
                         elif "bias" in name:
                              UpperCAmelCase_           =   "bias"
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              UpperCAmelCase_           =   "weight"
                         else:
                              UpperCAmelCase_           =   None
                         set_recursively(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_					,    snake_case_					,    snake_case_			)
                    continue
          if not is_used:
               unused_weights.append(snake_case_			)

     logger.warning(f"""Unused weights: {unused_weights}"""			)





def 	lowerCAmelCase_				(					snake_case_       : List[str]					,    snake_case_       : Union[str, Any]					,    snake_case_       : Any					,    snake_case_       : List[Any]					,    snake_case_       : Any			)    ->      List[str]:


     '''simple docstring'''




     UpperCAmelCase_           =   full_name.split("conv_layers."			)[-1]
     UpperCAmelCase_           =   name.split("."			)
     UpperCAmelCase_           =   int(items[0]			)
     UpperCAmelCase_           =   int(items[1]			)

     if type_id == 0:
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                   f"""{full_name} has size {value.shape}, but"""
                   f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
               )
               UpperCAmelCase_           =   value
               logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""			)
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                   f"""{full_name} has size {value.shape}, but"""
                   f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
               )
               UpperCAmelCase_           =   value
               logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""			)
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                   f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
                   " found."
               )
               UpperCAmelCase_           =   value
               logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""			)
          elif "weight" in name:
               assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                   f"""{full_name} has size {value.shape}, but"""
                   f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
               )
               UpperCAmelCase_           =   value
               logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""			)
     else:
          unused_weights.append(snake_case_			)





@torch.no_grad()
def 	lowerCAmelCase_				(					snake_case_       : Dict					,    snake_case_       : Dict					,    snake_case_       : List[str]=None					,    snake_case_       : int=None					,    snake_case_       : str=True			)    ->      Optional[int]:


     '''simple docstring'''




     if config_path is not None:
          UpperCAmelCase_           =   UniSpeechConfig.from_pretrained(snake_case_			)
     else:
          UpperCAmelCase_           =   UniSpeechConfig()

     if is_finetuned:
          if dict_path:
               UpperCAmelCase_           =   Dictionary.load_from_json(snake_case_			)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               UpperCAmelCase_           =   target_dict.pad_index
               UpperCAmelCase_           =   target_dict.bos_index
               UpperCAmelCase_           =   target_dict.eos_index
               UpperCAmelCase_           =   len(target_dict.symbols			)
               UpperCAmelCase_           =   os.path.join(snake_case_					,    "vocab.json"			)
               if not os.path.isdir(snake_case_			):
                    logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(snake_case_			)			)
                    return
               os.makedirs(snake_case_					,    exist_ok=snake_case_			)
               UpperCAmelCase_           =   target_dict.indices

               # fairseq has the <pad> and <s> switched
               UpperCAmelCase_           =   42
               UpperCAmelCase_           =   43
               with open(snake_case_					,    "w"					,    encoding="utf-8"			) as vocab_handle:
                    json.dump(snake_case_					,    snake_case_			)
               UpperCAmelCase_           =   WavaVecaPhonemeCTCTokenizer(
                   snake_case_					,    unk_token=target_dict.unk_word					,    pad_token=target_dict.pad_word					,    bos_token=target_dict.bos_word					,    eos_token=target_dict.eos_word					,    word_delimiter_token="|"					,    do_lower_case=snake_case_					,    )
               UpperCAmelCase_           =   True if config.feat_extract_norm == "layer" else False
               UpperCAmelCase_           =   WavaVecaFeatureExtractor(
                   feature_size=1					,    sampling_rate=1_60_00					,    padding_value=0					,    do_normalize=snake_case_					,    return_attention_mask=snake_case_					,    )
               UpperCAmelCase_           =   WavaVecaProcessor(feature_extractor=snake_case_					,    tokenizer=snake_case_			)
               processor.save_pretrained(snake_case_			)

          UpperCAmelCase_           =   UniSpeechForCTC(snake_case_			)
     else:
          UpperCAmelCase_           =   UniSpeechForPreTraining(snake_case_			)

     if is_finetuned:
          UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]					,    arg_overrides={"data": "/".join(dict_path.split("/"			)[:-1]			), "w2v_path": checkpoint_path}			)
     else:
          UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

     UpperCAmelCase_           =   model[0].eval()

     recursively_load_weights(snake_case_					,    snake_case_					,    snake_case_			)

     hf_unispeech.save_pretrained(snake_case_			)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     Any  =argparse.ArgumentParser()
    parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
    parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
    parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
    parser.add_argument(
        '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
    )
    SCREAMING_SNAKE_CASE_:     int  =parser.parse_args()
    convert_unispeech_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )





'''simple docstring'''





from __future__ import annotations

import queue




class        __A      :



    def __init__(self    :    Optional[Any]   ,			__a    :    str ):
         UpperCAmelCase_           =   data
         UpperCAmelCase_           =   None
         UpperCAmelCase_           =   None





def 	lowerCAmelCase_				(					)    ->      TreeNode:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''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 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''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 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''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 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




     if not isinstance(snake_case_					,    snake_case_			) or not node:
          return
     UpperCAmelCase_           =   queue.Queue()
     q.put(snake_case_			)
     while not q.empty():
          UpperCAmelCase_           =   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 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




     if not isinstance(snake_case_					,    snake_case_			) or not node:
          return
     UpperCAmelCase_           =   queue.Queue()
     q.put(snake_case_			)
     while not q.empty():
          UpperCAmelCase_           =   []
          while not q.empty():
               UpperCAmelCase_           =   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 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




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





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




     if not isinstance(snake_case_					,    snake_case_			) or not node:
          return
     UpperCAmelCase_    , UpperCAmelCase_           =   [], []
     UpperCAmelCase_           =   node
     stacka.append(snake_case_			)
     while stacka:  # to find the reversed order of post order, store it in stack2
          UpperCAmelCase_           =   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 	lowerCAmelCase_				(					snake_case_       : str = ""					,    snake_case_       : Any=50					,    snake_case_       : Union[str, Any]="*"			)    ->      str:


     '''simple docstring'''




     if not s:
          return "\n" + width * char
     UpperCAmelCase_    , UpperCAmelCase_           =   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'))

    SCREAMING_SNAKE_CASE_:     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())




