


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
A   :					Tuple	=      logging.get_logger(__name__)

A   :					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",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
A   :					List[str]	=      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]

def    lowercase_	(	_A    :					List[str]		):

      """simple docstring"""




      lowerCamelCase__	:       List[str]					=						{}
      with open(_A					,      "r"		) as file:
            for line_number, line in enumerate(_A		):
                  lowerCamelCase__	:       int					=						line.strip()
                  if line:
                        lowerCamelCase__	:       Any					=						line.split()
                        lowerCamelCase__	:       Any					=						line_number
                        lowerCamelCase__	:       List[Any]					=						words[0]
                        lowerCamelCase__	:       int					=						value
      return result

def    lowercase_	(	_A    :					Tuple					,      _A    :					Optional[Any]					,      _A    :					Dict					,      _A    :					List[Any]					,      _A    :					Tuple		):

      """simple docstring"""




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

      lowerCamelCase__	:       str					=						None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(_A		):
                  lowerCamelCase__	:       int					=						PARAM_MAPPING[full_name.split("."		)[-1]]
                  lowerCamelCase__	:       Tuple					=						"param"

      if weight_type is not None and weight_type != "param":
            lowerCamelCase__	:       Union[str, Any]					=						getattr(_A					,      _A		).shape
      elif weight_type is not None and weight_type == "param":
            lowerCamelCase__	:       Tuple					=						hf_pointer
            for attribute in hf_param_name.split("."		):
                  lowerCamelCase__	:       Tuple					=						getattr(_A					,      _A		)
            lowerCamelCase__	:       List[str]					=						shape_pointer.shape

            # let's reduce dimension
            lowerCamelCase__	:       List[str]					=						value[0]
      else:
            lowerCamelCase__	:       int					=						hf_pointer.shape

      if hf_shape != value.shape:
            raise ValueError(
                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__	:       Dict					=						value
      elif weight_type == "weight_g":
            lowerCamelCase__	:       Union[str, Any]					=						value
      elif weight_type == "weight_v":
            lowerCamelCase__	:       Union[str, Any]					=						value
      elif weight_type == "bias":
            lowerCamelCase__	:       int					=						value
      elif weight_type == "param":
            for attribute in hf_param_name.split("."		):
                  lowerCamelCase__	:       Any					=						getattr(_A					,      _A		)
            lowerCamelCase__	:       int					=						value
      else:
            lowerCamelCase__	:       Dict					=						value

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

def    lowercase_	(	_A    :					List[Any]					,      _A    :					Any					,      _A    :					List[Any]					,      _A    :					List[Any]					,      _A    :					Tuple		):

      """simple docstring"""




      lowerCamelCase__	:       Union[str, Any]					=						None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(_A		):
                  lowerCamelCase__	:       List[Any]					=						PARAM_MAPPING[full_name.split("."		)[-1]]
                  lowerCamelCase__	:       int					=						"param"

      if weight_type is not None and weight_type != "param":
            lowerCamelCase__	:       Union[str, Any]					=						".".join([key, weight_type]		)
      elif weight_type is not None and weight_type == "param":
            lowerCamelCase__	:       Union[str, Any]					=						".".join([key, hf_param_name]		)
      else:
            lowerCamelCase__	:       str					=						key

      lowerCamelCase__	:       List[str]					=						value if "lm_head" in full_key else value[0]


A   :					int	=      {
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}

def    lowercase_	(	_A    :					Optional[int]					,      _A    :					Any					,      _A    :					Optional[Any]=None					,      _A    :					Tuple=None		):

      """simple docstring"""




      lowerCamelCase__	:       int					=						False
      for key, mapped_key in MAPPING.items():
            lowerCamelCase__	:       Dict					=						"wav2vec2." + 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]:
                  lowerCamelCase__	:       Tuple					=						True
                  if "*" in mapped_key:
                        lowerCamelCase__	:       Any					=						name.split(_A		)[0].split("."		)[-2]
                        lowerCamelCase__	:       Optional[int]					=						mapped_key.replace("*"					,      _A		)
                  if "weight_g" in name:
                        lowerCamelCase__	:       Union[str, Any]					=						"weight_g"
                  elif "weight_v" in name:
                        lowerCamelCase__	:       List[Any]					=						"weight_v"
                  elif "bias" in name:
                        lowerCamelCase__	:       List[Any]					=						"bias"
                  elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        lowerCamelCase__	:       Optional[Any]					=						"weight"
                  else:
                        lowerCamelCase__	:       Tuple					=						None
                  if hf_dict is not None:
                        rename_dict(_A					,      _A					,      _A					,      _A					,      _A		)
                  else:
                        set_recursively(_A					,      _A					,      _A					,      _A					,      _A		)
                  return is_used
      return is_used

def    lowercase_	(	_A    :					Dict					,      _A    :					int					,      _A    :					int		):

      """simple docstring"""




      lowerCamelCase__	:       List[Any]					=						[]
      lowerCamelCase__	:       Union[str, Any]					=						fairseq_model.state_dict()

      lowerCamelCase__	:       Optional[int]					=						hf_model.wavaveca.feature_extractor

      for name, value in fairseq_dict.items():
            lowerCamelCase__	:       Union[str, Any]					=						False
            if "conv_layers" in name:
                  load_conv_layer(
                      _A					,      _A					,      _A					,      _A					,      hf_model.config.feat_extract_norm == "group"					,      )
                  lowerCamelCase__	:       Optional[int]					=						True
            else:
                  lowerCamelCase__	:       Optional[int]					=						load_wavaveca_layer(_A					,      _A					,      _A		)
            if not is_used:
                  unused_weights.append(_A		)

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

def    lowercase_	(	_A    :					Union[str, Any]					,      _A    :					Dict					,      _A    :					Optional[Any]					,      _A    :					List[Any]					,      _A    :					Union[str, Any]		):

      """simple docstring"""




      lowerCamelCase__	:       int					=						full_name.split("conv_layers."		)[-1]
      lowerCamelCase__	:       Optional[Any]					=						name.split("."		)
      lowerCamelCase__	:       Any					=						int(items[0]		)
      lowerCamelCase__	:       int					=						int(items[1]		)

      if type_id == 0:
            if "bias" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                        raise ValueError(
                            F"{full_name} has size {value.shape}, but"
                            F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."		)
                  lowerCamelCase__	:       Tuple					=						value
                  logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                        raise ValueError(
                            F"{full_name} has size {value.shape}, but"
                            F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."		)
                  lowerCamelCase__	:       Tuple					=						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:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                        raise ValueError(
                            F"{full_name} has size {value.shape}, but"
                            F" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."		)
                  lowerCamelCase__	:       Union[str, Any]					=						value
                  logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                        raise ValueError(
                            F"{full_name} has size {value.shape}, but"
                            F" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."		)
                  lowerCamelCase__	:       Any					=						value
                  logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
      else:
            unused_weights.append(_A		)

@torch.no_grad()
def    lowercase_	(	_A    :					int					,      _A    :					Any					,      _A    :					Any=None					,      _A    :					List[Any]=None					,      _A    :					Tuple=True					,      _A    :					List[Any]=False		):

      """simple docstring"""




      if config_path is not None:
            lowerCamelCase__	:       Optional[Any]					=						WavaVecaConfig.from_pretrained(_A		)
      else:
            lowerCamelCase__	:       str					=						WavaVecaConfig()

      if is_seq_class:
            lowerCamelCase__	:       str					=						read_txt_into_dict(_A		)
            lowerCamelCase__	:       List[Any]					=						idalabel
            lowerCamelCase__	:       List[Any]					=						WavaVecaForSequenceClassification(_A		)
            lowerCamelCase__	:       Optional[int]					=						WavaVecaFeatureExtractor(
                feature_size=1					,      sampling_rate=16000					,      padding_value=0					,      do_normalize=_A					,      return_attention_mask=_A					,      )
            feature_extractor.save_pretrained(_A		)

      elif is_finetuned:
            if dict_path:
                  lowerCamelCase__	:       Dict					=						Dictionary.load(_A		)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  lowerCamelCase__	:       List[Any]					=						target_dict.pad_index
                  lowerCamelCase__	:       str					=						target_dict.bos_index
                  lowerCamelCase__	:       str					=						target_dict.eos_index
                  lowerCamelCase__	:       Tuple					=						len(target_dict.symbols		)
                  lowerCamelCase__	:       Optional[Any]					=						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		)
                  lowerCamelCase__	:       List[str]					=						target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  lowerCamelCase__	:       str					=						0
                  lowerCamelCase__	:       Dict					=						1
                  with open(_A					,      "w"					,      encoding="utf-8"		) as vocab_handle:
                        json.dump(_A					,      _A		)
                  lowerCamelCase__	:       Any					=						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__	:       Dict					=						True if config.feat_extract_norm == "layer" else False
                  lowerCamelCase__	:       List[Any]					=						WavaVecaFeatureExtractor(
                      feature_size=1					,      sampling_rate=16000					,      padding_value=0					,      do_normalize=_A					,      return_attention_mask=_A					,      )
                  lowerCamelCase__	:       Any					=						WavaVecaProcessor(feature_extractor=_A					,      tokenizer=_A		)
                  processor.save_pretrained(_A		)

            lowerCamelCase__	:       int					=						WavaVecaForCTC(_A		)
      else:
            lowerCamelCase__	:       Optional[Any]					=						WavaVecaForPreTraining(_A		)

      if is_finetuned or is_seq_class:
            lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__	:       Dict					=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]					,      arg_overrides={"data": "/".join(dict_path.split("/"		)[:-1]		)}		)
      else:
            lowerCamelCase__	:       Dict					=						argparse.Namespace(task="audio_pretraining"		)
            lowerCamelCase__	:       Any					=						fairseq.tasks.setup_task(_A		)

            lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__	:       str					=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,      task=_A		)

      lowerCamelCase__	:       List[str]					=						model[0].eval()

      recursively_load_weights(_A					,      _A					,      not is_finetuned		)

      hf_wavavec.save_pretrained(_A		)


if __name__ == "__main__":
    A   :					List[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"
    )
    parser.add_argument(
        "--is_seq_class",
        action="store_true",
        help="Whether the model to convert is a fine-tuned sequence classification model or not",
    )
    A   :					Optional[Any]	=      parser.parse_args()

    A   :					Any	=      not args.not_finetuned and not args.is_seq_class
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.dict_path,
        is_finetuned,
        args.is_seq_class,
    )






from __future__ import annotations

import time

import numpy as np

A   :					Dict	=      [8, 5, 9, 7]
A   :					Optional[Any]	=      [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
A   :					Any	=      [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    str      ,   __lowerCamelCase							:    list[int]      ,   __lowerCamelCase							:    list[list[int]]      ,   __lowerCamelCase							:    list[list[int]]      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						claim_vector
             lowerCamelCase__	:       str					=						allocated_resources_table
             lowerCamelCase__	:       int					=						maximum_claim_table





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             return [
                 sum(p_item[i] for p_item in self.__allocated_resources_table )
                 for i in range(len(self.__allocated_resources_table[0] ) )
             ]





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             return np.array(self.__claim_vector ) - np.array(
                 self.__processes_resource_summation() )





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             return [
                 list(np.array(self.__maximum_claim_table[i] ) - np.array(__lowerCamelCase ) )
                 for i, allocated_resource in enumerate(self.__allocated_resources_table )
             ]





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             return {self.__need().index(__lowerCamelCase ): i for i in self.__need()}





       def 		lowerCAmelCase  (     self							:    List[str]      ,   **__lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						self.__need()
             lowerCamelCase__	:       str					=						self.__allocated_resources_table
             lowerCamelCase__	:       List[Any]					=						self.__available_resources()
             lowerCamelCase__	:       str					=						self.__need_index_manager()
             for kw, val in kwargs.items():
                   if kw and val is True:
                         self.__pretty_data()
             print("_" * 50 + "\n" )
             while need_list:
                   lowerCamelCase__	:       int					=						False
                   for each_need in need_list:
                         lowerCamelCase__	:       Dict					=						True
                         for index, need in enumerate(__lowerCamelCase ):
                               if need > available_resources[index]:
                                     lowerCamelCase__	:       str					=						False
                                     break
                         if execution:
                               lowerCamelCase__	:       Tuple					=						True
                               # get the original index of the process from ind_ctrl db
                               for original_need_index, need_clone in need_index_manager.items():
                                     if each_need == need_clone:
                                           lowerCamelCase__	:       Any					=						original_need_index
                               print(f"Process {process_number + 1} is executing." )
                               # remove the process run from stack
                               need_list.remove(__lowerCamelCase )
                               # update available/freed resources stack
                               lowerCamelCase__	:       Union[str, Any]					=						np.array(__lowerCamelCase ) + np.array(
                                   alloc_resources_table[process_number] )
                               print(
                                   "Updated available resource stack for processes: "
                                   + " ".join([str(__lowerCamelCase ) for x in available_resources] ) )
                               break
                   if safe:
                         print("The process is in a safe state.\n" )
                   else:
                         print("System in unsafe state. Aborting...\n" )
                         break





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             print(" " * 9 + "Allocated Resource Table" )
             for item in self.__allocated_resources_table:
                   print(
                       f"P{self.__allocated_resources_table.index(__lowerCamelCase ) + 1}"
                       + " ".join(f"{it:>8}" for it in item )
                       + "\n" )
             print(" " * 9 + "System Resource Table" )
             for item in self.__maximum_claim_table:
                   print(
                       f"P{self.__maximum_claim_table.index(__lowerCamelCase ) + 1}"
                       + " ".join(f"{it:>8}" for it in item )
                       + "\n" )
             print(
                 "Current Usage by Active Processes: "
                 + " ".join(str(__lowerCamelCase ) for x in self.__claim_vector ) )
             print(
                 "Initial Available Resources:       "
                 + " ".join(str(__lowerCamelCase ) for x in self.__available_resources() ) )
             time.sleep(1 )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


