





"""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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

SCREAMING_SNAKE_CASE__					=   {
    "post_extract_proj": "feature_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.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 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[int]							,						SCREAMING_SNAKE_CASE     :						Any							,						SCREAMING_SNAKE_CASE     :						Any							,						SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]			):







      '''simple docstring'''




      for attribute in key.split("""."""			):
            lowerCAmelCase  						=       getattr(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)

      if weight_type is not None:
            lowerCAmelCase  						=       getattr(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			).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 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						Dict							,						SCREAMING_SNAKE_CASE     :						List[str]			):







      '''simple docstring'''




      lowerCAmelCase  						=       []
      lowerCAmelCase  						=       fairseq_model.state_dict()

      lowerCAmelCase  						=       hf_model.sew.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(
                      SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						hf_model.config.feat_extract_norm == """group"""							,						)
                  lowerCAmelCase  						=       True
            else:
                  for key, mapped_key in MAPPING.items():
                        lowerCAmelCase  						=       """sew.""" + 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(SCREAMING_SNAKE_CASE			)[0].split("""."""			)[-2]
                                    lowerCAmelCase  						=       mapped_key.replace("""*"""							,						SCREAMING_SNAKE_CASE			)
                              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(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)
                        continue
            if not is_used:
                  unused_weights.append(SCREAMING_SNAKE_CASE			)

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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Tuple							,						SCREAMING_SNAKE_CASE     :						Dict							,						SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]							,						SCREAMING_SNAKE_CASE     :						Dict			):







      '''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(SCREAMING_SNAKE_CASE			)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Any							,						SCREAMING_SNAKE_CASE     :						List[Any]			):







      '''simple docstring'''




      lowerCAmelCase  						=       SEWConfig()
      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  						=       0.02
      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(SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       fs_config.encoder_layers
      lowerCAmelCase  						=       fs_config.squeeze_factor

      # 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 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[Any]							,						SCREAMING_SNAKE_CASE     :						Optional[Any]							,						SCREAMING_SNAKE_CASE     :						Any=None							,						SCREAMING_SNAKE_CASE     :						Any=None							,						SCREAMING_SNAKE_CASE     :						int=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  						=       SEWConfig.from_pretrained(SCREAMING_SNAKE_CASE			)
      else:
            lowerCAmelCase  						=       convert_config(model[0]							,						SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       model[0].eval()

      lowerCAmelCase  						=       True if config.feat_extract_norm == """layer""" else False
      lowerCAmelCase  						=       WavaVecaFeatureExtractor(
          feature_size=1							,						sampling_rate=1_60_00							,						padding_value=0							,						do_normalize=SCREAMING_SNAKE_CASE							,						return_attention_mask=SCREAMING_SNAKE_CASE							,						)

      if is_finetuned:
            if dict_path:
                  lowerCAmelCase  						=       Dictionary.load(SCREAMING_SNAKE_CASE			)

                  # 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(SCREAMING_SNAKE_CASE							,						"""vocab.json"""			)
                  if not os.path.isdir(SCREAMING_SNAKE_CASE			):
                        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(SCREAMING_SNAKE_CASE			)			)
                        return
                  os.makedirs(SCREAMING_SNAKE_CASE							,						exist_ok=SCREAMING_SNAKE_CASE			)
                  with open(SCREAMING_SNAKE_CASE							,						"""w"""							,						encoding="""utf-8"""			) as vocab_handle:
                        json.dump(target_dict.indices							,						SCREAMING_SNAKE_CASE			)
                  lowerCAmelCase  						=       WavaVecaCTCTokenizer(
                      SCREAMING_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=SCREAMING_SNAKE_CASE							,						)
                  lowerCAmelCase  						=       WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE							,						tokenizer=SCREAMING_SNAKE_CASE			)
                  processor.save_pretrained(SCREAMING_SNAKE_CASE			)

            lowerCAmelCase  						=       SEWForCTC(SCREAMING_SNAKE_CASE			)
      else:
            lowerCAmelCase  						=       SEWModel(SCREAMING_SNAKE_CASE			)
            feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE			)

      recursively_load_weights(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)

      hf_model.save_pretrained(SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__					=   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"
       )
       SCREAMING_SNAKE_CASE__					=   parser.parse_args()
       convert_sew_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
       )







'''simple docstring'''




import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier

def 	UpperCamelCase_							(					_UpperCAmelCase  :      dict    )    ->    tuple:


       """simple docstring"""







       return (data["data"], data["target"])

def 	UpperCamelCase_							(					_UpperCAmelCase  :      np.ndarray			,	_UpperCAmelCase  :      np.ndarray    )    ->    XGBClassifier:


       """simple docstring"""







       _UpperCAmelCase     :  Any							      =    XGBClassifier()
       classifier.fit(_UpperCAmelCase			,	_UpperCAmelCase    )
       return classifier

def 	UpperCamelCase_							(					)    ->    None:


       """simple docstring"""







       _UpperCAmelCase     :  List[str]							      =    load_iris()
       _UpperCAmelCase      ,  _UpperCAmelCase     :  Dict							      =    data_handling(_UpperCAmelCase    )
       _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase     :  str							      =    train_test_split(
           _UpperCAmelCase			,	_UpperCAmelCase			,	test_size=0.2_5    )

       _UpperCAmelCase     :  Optional[Any]							      =    iris["target_names"]

       # Create an XGBoost Classifier from the training data
       _UpperCAmelCase     :  Tuple							      =    xgboost(_UpperCAmelCase			,	_UpperCAmelCase    )

       # Display the confusion matrix of the classifier with both training and test sets
       ConfusionMatrixDisplay.from_estimator(
           _UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase			,	display_labels=_UpperCAmelCase			,	cmap="Blues"			,	normalize="true"			,	)
       plt.title("Normalized Confusion Matrix - IRIS Dataset"    )
       plt.show()


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)
  main()

