
import sys
from random import randint






class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      key
													__UpperCamelCase  	=      freq



							def __str__(	self      )-> int:





													'''simple docstring'''






													return F"Node(key={self.key}, freq={self.freq})"





def 						A_			(				snake_case				: Optional[Any]			,   snake_case				: int			,   snake_case				: int			,   snake_case				: Any			,   snake_case				: Optional[Any]			,   snake_case				: Tuple			)   ->     Optional[Any]:



						'''simple docstring'''

						if i > j or i < 0 or j > len(a__			) - 1:
												return

						__UpperCamelCase  	=      root[i][j]
						if parent == -1:  # root does not have a parent
												print(f"{key[node]} is the root of the binary search tree."			)
						elif is_left:
												print(f"{key[node]} is the left child of key {parent}."			)
						else:
												print(f"{key[node]} is the right child of key {parent}."			)

						print_binary_search_tree(a__			,   a__			,   a__			,   node - 1			,   key[node]			,   a__			)
						print_binary_search_tree(a__			,   a__			,   node + 1			,   a__			,   key[node]			,   a__			)




def 						A_			(				snake_case				: Dict			)   ->     Optional[int]:



						'''simple docstring'''

						nodes.sort(key=lambda snake_case			: node.key			)

						__UpperCamelCase  	=      len(a__			)

						__UpperCamelCase  	=      [nodes[i].key for i in range(a__			)]
						__UpperCamelCase  	=      [nodes[i].freq for i in range(a__			)]

						# This 2D array stores the overall tree cost (which's as minimized as possible);
						# for a single key, cost is equal to frequency of the key.
						__UpperCamelCase  	=      [[freqs[i] if i == j else 0 for j in range(a__			)] for i in range(a__			)]
						# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
						# array
						__UpperCamelCase  	=      [[freqs[i] if i == j else 0 for j in range(a__			)] for i in range(a__			)]
						# stores tree roots that will be used later for constructing binary search tree
						__UpperCamelCase  	=      [[i if i == j else 0 for j in range(a__			)] for i in range(a__			)]

						for interval_length in range(2			,   n + 1			):
												for i in range(n - interval_length + 1			):
																		__UpperCamelCase  	=      i + interval_length - 1

																		__UpperCamelCase  	=      sys.maxsize  # set the value to "infinity"
																		__UpperCamelCase  	=      total[i][j - 1] + freqs[j]

																		# Apply Knuth's optimization
																		# Loop without optimization: for r in range(i, j + 1):
																		for r in range(root[i][j - 1]			,   root[i + 1][j] + 1			):  # r is a temporal root
																								__UpperCamelCase  	=      dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
																								__UpperCamelCase  	=      dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
																								__UpperCamelCase  	=      left + total[i][j] + right

																								if dp[i][j] > cost:
																														__UpperCamelCase  	=      cost
																														__UpperCamelCase  	=      r

						print('''Binary search tree nodes:'''			)
						for node in nodes:
												print(a__			)

						print(f"\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."			)
						print_binary_search_tree(a__			,   a__			,   0			,   n - 1			,   -1			,   a__			)




def 						A_			(				)   ->     List[Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      [Node(a__			,   randint(1			,   50			)			) for i in range(10			,   0			,   -1			)]
						find_optimal_binary_search_tree(a__			)


if __name__ == "__main__":
				main()






'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowerCAmelCase_      =   {
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}







def   lowerCAmelCase(  a__    :  List[Any]      , a__    :  List[str]      , a__    :  Optional[int]      , a__    :  Optional[Any]      , a__    :  List[Any]      ):







       '''simple docstring'''






       for attribute in key.split("."      ):
              lowerCamelCase__				   =      getattr(a__      , a__      )

       if weight_type is not None:
              lowerCamelCase__				   =      getattr(a__      , a__      ).shape
       else:
              lowerCamelCase__				   =      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":
              lowerCamelCase__				   =      value
       elif weight_type == "weight_g":
              lowerCamelCase__				   =      value
       elif weight_type == "weight_v":
              lowerCamelCase__				   =      value
       elif weight_type == "bias":
              lowerCamelCase__				   =      value
       else:
              lowerCamelCase__				   =      value

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







def   lowerCAmelCase(  a__    :  Any      , a__    :  List[str]      , a__    :  str      ):







       '''simple docstring'''






       lowerCamelCase__				   =      []
       lowerCamelCase__				   =      fairseq_model.state_dict()

       lowerCamelCase__				   =      hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              lowerCamelCase__				   =      False
              if "conv_layers" in name:
                     load_conv_layer(
                         a__      , a__      , a__      , a__      , hf_model.config.feat_extract_norm == "group"      , )
                     lowerCamelCase__				   =      True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowerCamelCase__				   =      "sew_d." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

                            if key in name or key.split("w2v_model."      )[-1] == name.split("."      )[0]:
                                   lowerCamelCase__				   =      True
                                   if "*" in mapped_key:
                                          lowerCamelCase__				   =      name.split(a__      )[0].split("."      )[-2]
                                          if not layer_index.isnumeric():
                                                 continue
                                          lowerCamelCase__				   =      mapped_key.replace("*"      , a__      )
                                   if "weight_g" in name:
                                          lowerCamelCase__				   =      "weight_g"
                                   elif "weight_v" in name:
                                          lowerCamelCase__				   =      "weight_v"
                                   elif "weight" in name:
                                          lowerCamelCase__				   =      "weight"
                                   elif "bias" in name:
                                          lowerCamelCase__				   =      "bias"
                                   else:
                                          lowerCamelCase__				   =      None
                                   set_recursively(a__      , a__      , a__      , a__      , a__      )
                            continue
              if not is_used:
                     unused_weights.append(a__      )

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







def   lowerCAmelCase(  a__    :  Optional[Any]      , a__    :  Dict      , a__    :  List[Any]      , a__    :  Union[str, Any]      , a__    :  List[str]      ):







       '''simple docstring'''






       lowerCamelCase__				   =      full_name.split("conv_layers."      )[-1]
       lowerCamelCase__				   =      name.split("."      )
       lowerCamelCase__				   =      int(items[0]      )
       lowerCamelCase__				   =      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."""
                     )
                     lowerCamelCase__				   =      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."""
                     )
                     lowerCamelCase__				   =      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."
                     )
                     lowerCamelCase__				   =      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."""
                     )
                     lowerCamelCase__				   =      value
                     logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
       else:
              unused_weights.append(a__      )







def   lowerCAmelCase(  a__    :  Optional[int]      , a__    :  Tuple      ):







       '''simple docstring'''






       lowerCamelCase__				   =      SEWDConfig()
       if is_finetuned:
              lowerCamelCase__				   =      model.wav_encoder.wav_model.cfg
       else:
              lowerCamelCase__				   =      model.cfg

       lowerCamelCase__				   =      fs_config.conv_bias
       lowerCamelCase__				   =      eval(fs_config.conv_feature_layers      )
       lowerCamelCase__				   =      [x[0] for x in conv_layers]
       lowerCamelCase__				   =      [x[1] for x in conv_layers]
       lowerCamelCase__				   =      [x[2] for x in conv_layers]
       lowerCamelCase__				   =      "gelu"
       lowerCamelCase__				   =      "layer" if fs_config.extractor_mode == "layer_norm" else "group"
       lowerCamelCase__				   =      0.0
       lowerCamelCase__				   =      fs_config.activation_fn.name
       lowerCamelCase__				   =      fs_config.encoder_embed_dim
       lowerCamelCase__				   =      0.0_2
       lowerCamelCase__				   =      fs_config.encoder_ffn_embed_dim
       lowerCamelCase__				   =      1E-5
       lowerCamelCase__				   =      fs_config.encoder_layerdrop
       lowerCamelCase__				   =      fs_config.encoder_attention_heads
       lowerCamelCase__				   =      fs_config.conv_pos_groups
       lowerCamelCase__				   =      fs_config.conv_pos
       lowerCamelCase__				   =      len(a__      )
       lowerCamelCase__				   =      fs_config.encoder_layers
       lowerCamelCase__				   =      fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       lowerCamelCase__				   =      fs_config.max_position_embeddings
       lowerCamelCase__				   =      fs_config.position_buckets
       lowerCamelCase__				   =      fs_config.share_att_key
       lowerCamelCase__				   =      fs_config.relative_attention
       lowerCamelCase__				   =      fs_config.position_biased_input
       lowerCamelCase__				   =      tuple(fs_config.pos_att_type.split("|"      )      )
       lowerCamelCase__				   =      fs_config.norm_rel_ebd

       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
              lowerCamelCase__				   =      model.cfg
              lowerCamelCase__				   =      fs_config.final_dropout
              lowerCamelCase__				   =      fs_config.layerdrop
       lowerCamelCase__				   =      fs_config.activation_dropout
       lowerCamelCase__				   =      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       lowerCamelCase__				   =      fs_config.attention_dropout
       lowerCamelCase__				   =      fs_config.dropout_input
       lowerCamelCase__				   =      fs_config.dropout
       lowerCamelCase__				   =      fs_config.mask_channel_length
       lowerCamelCase__				   =      fs_config.mask_channel_prob
       lowerCamelCase__				   =      fs_config.mask_length
       lowerCamelCase__				   =      fs_config.mask_prob

       lowerCamelCase__				   =      "Wav2Vec2FeatureExtractor"
       lowerCamelCase__				   =      "Wav2Vec2CTCTokenizer"

       return config







@torch.no_grad()
def   lowerCAmelCase(  a__    :  List[Any]      , a__    :  int      , a__    :  Any=None      , a__    :  Any=None      , a__    :  Any=True      ):







       '''simple docstring'''






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

       if config_path is not None:
              lowerCamelCase__				   =      SEWDConfig.from_pretrained(a__      )
       else:
              lowerCamelCase__				   =      convert_config(model[0]      , a__      )
       lowerCamelCase__				   =      model[0].eval()

       lowerCamelCase__				   =      True if config.feat_extract_norm == "layer" else False
       lowerCamelCase__				   =      WavaVecaFeatureExtractor(
           feature_size=1      , sampling_rate=1_6000      , padding_value=0      , do_normalize=a__      , return_attention_mask=a__      , )

       if is_finetuned:
              if dict_path:
                     lowerCamelCase__				   =      Dictionary.load(a__      )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     lowerCamelCase__				   =      target_dict.pad_index
                     lowerCamelCase__				   =      target_dict.bos_index
                     lowerCamelCase__				   =      target_dict.pad_index
                     lowerCamelCase__				   =      target_dict.bos_index
                     lowerCamelCase__				   =      target_dict.eos_index
                     lowerCamelCase__				   =      len(target_dict.symbols      )
                     lowerCamelCase__				   =      os.path.join(a__      , "vocab.json"      )
                     if not os.path.isdir(a__      ):
                            logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(a__      )      )
                            return
                     os.makedirs(a__      , exist_ok=a__      )
                     with open(a__      , "w"      , encoding="utf-8"      ) as vocab_handle:
                            json.dump(target_dict.indices      , a__      )
                     lowerCamelCase__				   =      WavaVecaCTCTokenizer(
                         a__      , 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=a__      , )
                     lowerCamelCase__				   =      WavaVecaProcessor(feature_extractor=a__      , tokenizer=a__      )
                     processor.save_pretrained(a__      )

              lowerCamelCase__				   =      SEWDForCTC(a__      )
       else:
              lowerCamelCase__				   =      SEWDModel(a__      )
              feature_extractor.save_pretrained(a__      )

       recursively_load_weights(a__      , a__      , a__      )

       hf_model.save_pretrained(a__      )


if __name__ == "__main__":
    lowerCAmelCase_      =   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(
        "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
    )
    lowerCAmelCase_      =   parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )

