




import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
a_   		=     logging.get_logger(__name__)

a_   		=     {
    "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": "lm_head",
    "mask_emb": "masked_spec_embed",
}
a_   		=     [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	Dict:

    """simple docstring"""


    for attribute in key.split('''.'''      ):
        __UpperCamelCase  		=						getattr(lowercase_					,     lowercase_      )

    if weight_type is not None:
        __UpperCamelCase  		=						getattr(lowercase_					,     lowercase_      ).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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Dict:

    """simple docstring"""


    __UpperCamelCase  		=						[]
    __UpperCamelCase  		=						fairseq_model.state_dict()

    __UpperCamelCase  		=						hf_model.feature_extractor

    # if encoder has different dim to decoder -> use proj_weight
    __UpperCamelCase  		=						None

    for name, value in fairseq_dict.items():
        __UpperCamelCase  		=						False
        if "conv_layers" in name:
            load_conv_layer(
                lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     hf_model.config.feat_extract_norm == '''group'''					,     )
            __UpperCamelCase  		=						True
        elif name.split('''.'''      )[0] == "proj":
            __UpperCamelCase  		=						fairseq_model.proj
            __UpperCamelCase  		=						True
        else:
            for key, mapped_key in MAPPING.items():
                if key in name or key.split('''w2v_model.'''      )[-1] == name.split('''.'''      )[0]:
                    __UpperCamelCase  		=						True
                    if "*" in mapped_key:
                        __UpperCamelCase  		=						name.split(lowercase_      )[0].split('''.'''      )[-2]
                        __UpperCamelCase  		=						mapped_key.replace('''*'''					,     lowercase_      )
                    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:
                        __UpperCamelCase  		=						'''weight'''
                    else:
                        __UpperCamelCase  		=						None
                    set_recursively(lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )
                continue
        if not is_used:
            unused_weights.append(lowercase_      )

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

    return proj_weight




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	Tuple:

    """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(lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	str:

    """simple docstring"""


    __UpperCamelCase		,      __UpperCamelCase  		=						emb.weight.shape
    __UpperCamelCase  		=						nn.Linear(lowercase_					,     lowercase_					,     bias=lowercase_      )
    __UpperCamelCase  		=						emb.weight.data
    return lin_layer




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Any:

    """simple docstring"""


    with open(lowercase_					,     '''r'''					,     encoding='''utf-8'''      ) as f:
        __UpperCamelCase  		=						f.readlines()
        __UpperCamelCase  		=						[line.split(''' '''      )[0] for line in lines]

    __UpperCamelCase  		=						len(lowercase_      )

    __UpperCamelCase  		=						{
        '''<s>''': 0,
        '''<pad>''': 1,
        '''</s>''': 2,
        '''<unk>''': 3,
    }

    vocab_dict.update(dict(zip(lowercase_					,     range(4					,     num_words + 4      )      )      )      )
    return vocab_dict




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     )       ->	Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase  		=						WavaVecaConfig.from_pretrained(lowercase_      )
    __UpperCamelCase  		=						SpeechaTextaConfig.from_pretrained(
        lowercase_					,     vocab_size=lowercase_					,     decoder_layers=lowercase_					,     do_stable_layer_norm=lowercase_      )

    __UpperCamelCase  		=						WavaVecaFeatureExtractor(
        feature_size=1					,     sampling_rate=1_60_00					,     padding_value=0					,     do_normalize=lowercase_					,     return_attention_mask=lowercase_					,     )

    __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]					,     arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''      )[:-1]      )}      )
    __UpperCamelCase  		=						model[0].eval()

    # set weights for wav2vec2 encoder
    __UpperCamelCase  		=						WavaVecaModel(lowercase_      )
    __UpperCamelCase  		=						recursively_load_weights_wavaveca(model.encoder					,     lowercase_      )

    __UpperCamelCase  		=						SpeechaTextaForCausalLM(lowercase_      )
    __UpperCamelCase		,      __UpperCamelCase  		=						hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()					,     strict=lowercase_      )

    # set output linear layer
    unexpected_keys.remove('''embed_out'''      )
    __UpperCamelCase  		=						nn.Parameter(model.decoder.embed_out.detach()      )

    # layer norm is init to identity matrix so leaving it is fine
    logger.warning(F"The following keys are missing when loading the decoder weights: {missing_keys}"      )
    logger.warning(F"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"      )

    __UpperCamelCase  		=						SpeechEncoderDecoderModel(encoder=lowercase_					,     decoder=lowercase_      )
    __UpperCamelCase  		=						False

    # add projection layer
    __UpperCamelCase  		=						nn.Parameter(projection_layer.weight      )
    __UpperCamelCase  		=						nn.Parameter(projection_layer.bias      )

    __UpperCamelCase  		=						create_vocab_dict(lowercase_      )

    with open(os.path.join(lowercase_					,     '''vocab.json'''      )					,     '''w'''      ) as fp:
        json.dump(lowercase_					,     lowercase_      )

    __UpperCamelCase  		=						SpeechaTextaTokenizer(os.path.join(lowercase_					,     '''vocab.json'''      )      )
    tokenizer.save_pretrained(lowercase_      )

    __UpperCamelCase  		=						hf_wavavec.config.to_dict()
    __UpperCamelCase  		=						tokenizer.pad_token_id
    __UpperCamelCase  		=						tokenizer.bos_token_id
    __UpperCamelCase  		=						tokenizer.eos_token_id
    __UpperCamelCase  		=						'''speech_to_text_2'''
    __UpperCamelCase  		=						'''wav2vec2'''

    __UpperCamelCase  		=						SpeechEncoderDecoderConfig.from_dict(lowercase_      )

    hf_wavavec.save_pretrained(lowercase_      )
    feature_extractor.save_pretrained(lowercase_      )


if __name__ == "__main__":
 a_   		=     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(
     "--encoder_config_path",
     default="facebook/wav2vec2-large-lv60",
     type=str,
     help="Path to hf encoder wav2vec2 checkpoint config",
 )
 parser.add_argument(
     "--decoder_config_path",
     default="facebook/s2t-small-mustc-en-fr-st",
     type=str,
     help="Path to hf decoder s2t checkpoint config",
 )
 parser.add_argument("--vocab_size", default=1_0224, type=int, help="Vocab size of decoder")
 parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

 a_   		=     parser.parse_args()
 convert_wavaveca_checkpoint(
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.dict_path,
     encoder_config_path=args.encoder_config_path,
     decoder_config_path=args.decoder_config_path,
     vocab_size=args.vocab_size,
     num_decoder_layers=args.num_decoder_layers,
 )












from __future__ import annotations

import queue

class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: Any ,       snake_case					: Tuple				):
          __UpperCamelCase  		=						data
          __UpperCamelCase  		=						None
          __UpperCamelCase  		=						None







def 					__SCREAMING_SNAKE_CASE   (  )       ->	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(lowercase_      )      )
    q.put(lowercase_      )
    while not q.empty():
        __UpperCamelCase  		=						q.get()
        __UpperCamelCase  		=						F"Enter the left node of {node_found.data}: "
        __UpperCamelCase  		=						input(lowercase_      ).strip().lower() or '''n'''
        if check == "n":
            return tree_node
        __UpperCamelCase  		=						TreeNode(int(lowercase_      )      )
        __UpperCamelCase  		=						left_node
        q.put(lowercase_      )
        __UpperCamelCase  		=						F"Enter the right node of {node_found.data}: "
        __UpperCamelCase  		=						input(lowercase_      ).strip().lower() or '''n'''
        if check == "n":
            return tree_node
        __UpperCamelCase  		=						TreeNode(int(lowercase_      )      )
        __UpperCamelCase  		=						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
    __UpperCamelCase  		=						queue.Queue()
    q.put(lowercase_      )
    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 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    __UpperCamelCase  		=						queue.Queue()
    q.put(lowercase_      )
    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(lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) 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(lowercase_      )
            __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 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    __UpperCamelCase		,      __UpperCamelCase  		=						[], []
    __UpperCamelCase  		=						node
    stacka.append(lowercase_      )
    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(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
    __UpperCamelCase		,      __UpperCamelCase  		=						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"))

 a_   		=     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())






