


'''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()
A__		:					Optional[Any]												=  logging.get_logger(__name__)

A__		:					List[Any]												=  {
    """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__					(	UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				Optional[int]      ,    UpperCAmelCase_    :				List[Any]      ,    UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				Optional[int]							)     ->		Any:
     for attribute in key.split('.'							):
          __lowerCamelCase   :	Optional[int]								=		getattr(UpperCAmelCase_      ,    UpperCAmelCase_							)

     if weight_type is not None:
          __lowerCamelCase   :	Optional[Any]								=		getattr(UpperCAmelCase_      ,    UpperCAmelCase_							).shape
     else:
          __lowerCamelCase   :	Optional[int]								=		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   :	int								=		value
     elif weight_type == "weight_g":
          __lowerCamelCase   :	Dict								=		value
     elif weight_type == "weight_v":
          __lowerCamelCase   :	Optional[Any]								=		value
     elif weight_type == "bias":
          __lowerCamelCase   :	Optional[Any]								=		value
     else:
          __lowerCamelCase   :	int								=		value

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



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Tuple      ,    UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				List[Any]							)     ->		str:
     __lowerCamelCase   :	Optional[int]								=		[]
     __lowerCamelCase   :	Any								=		fairseq_model.state_dict()

     __lowerCamelCase   :	Tuple								=		hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          __lowerCamelCase   :	Dict								=		False
          if "conv_layers" in name:
               load_conv_layer(
                   UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    hf_model.config.feat_extract_norm == 'group'      ,    )
               __lowerCamelCase   :	int								=		True
          else:
               for key, mapped_key in MAPPING.items():
                    __lowerCamelCase   :	str								=		'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   :	str								=		True
                         if "*" in mapped_key:
                              __lowerCamelCase   :	Dict								=		name.split(UpperCAmelCase_							)[0].split('.'							)[-2]
                              __lowerCamelCase   :	Any								=		mapped_key.replace('*'      ,    UpperCAmelCase_							)
                         if "weight_g" in name:
                              __lowerCamelCase   :	int								=		'weight_g'
                         elif "weight_v" in name:
                              __lowerCamelCase   :	Tuple								=		'weight_v'
                         elif "weight" in name:
                              __lowerCamelCase   :	int								=		'weight'
                         elif "bias" in name:
                              __lowerCamelCase   :	Optional[Any]								=		'bias'
                         else:
                              __lowerCamelCase   :	List[Any]								=		None
                         set_recursively(UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_							)
                    continue
          if not is_used:
               unused_weights.append(UpperCAmelCase_							)

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



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				Dict      ,    UpperCAmelCase_    :				List[str]      ,    UpperCAmelCase_    :				Any      ,    UpperCAmelCase_    :				List[Any]							)     ->		Any:
     __lowerCamelCase   :	List[str]								=		full_name.split('conv_layers.'							)[-1]
     __lowerCamelCase   :	str								=		name.split('.'							)
     __lowerCamelCase   :	Dict								=		int(items[0]							)
     __lowerCamelCase   :	Tuple								=		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   :	List[str]								=		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   :	Dict								=		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   :	Optional[Any]								=		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   :	str								=		value
               logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
     else:
          unused_weights.append(UpperCAmelCase_							)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int      ,    UpperCAmelCase_    :				List[str]							)     ->		List[str]:
     __lowerCamelCase   :	Optional[Any]								=		SEWConfig()
     if is_finetuned:
          __lowerCamelCase   :	str								=		model.wav_encoder.wav_model.cfg
     else:
          __lowerCamelCase   :	str								=		model.cfg

     __lowerCamelCase   :	Union[str, Any]								=		fs_config.conv_bias
     __lowerCamelCase   :	List[Any]								=		eval(fs_config.conv_feature_layers							)
     __lowerCamelCase   :	Tuple								=		[x[0] for x in conv_layers]
     __lowerCamelCase   :	Tuple								=		[x[1] for x in conv_layers]
     __lowerCamelCase   :	int								=		[x[2] for x in conv_layers]
     __lowerCamelCase   :	List[Any]								=		'gelu'
     __lowerCamelCase   :	Dict								=		'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
     __lowerCamelCase   :	int								=		0.0
     __lowerCamelCase   :	Dict								=		fs_config.activation_fn.name
     __lowerCamelCase   :	int								=		fs_config.encoder_embed_dim
     __lowerCamelCase   :	List[Any]								=		0.02
     __lowerCamelCase   :	Any								=		fs_config.encoder_ffn_embed_dim
     __lowerCamelCase   :	List[Any]								=		1e-5
     __lowerCamelCase   :	Dict								=		fs_config.encoder_layerdrop
     __lowerCamelCase   :	List[str]								=		fs_config.encoder_attention_heads
     __lowerCamelCase   :	Union[str, Any]								=		fs_config.conv_pos_groups
     __lowerCamelCase   :	Optional[int]								=		fs_config.conv_pos
     __lowerCamelCase   :	Optional[Any]								=		len(UpperCAmelCase_							)
     __lowerCamelCase   :	int								=		fs_config.encoder_layers
     __lowerCamelCase   :	str								=		fs_config.squeeze_factor

     # take care of any params that are overridden by the Wav2VecCtc model
     if is_finetuned:
          __lowerCamelCase   :	Union[str, Any]								=		model.cfg
          __lowerCamelCase   :	List[Any]								=		fs_config.final_dropout
          __lowerCamelCase   :	List[Any]								=		fs_config.layerdrop
     __lowerCamelCase   :	Union[str, Any]								=		fs_config.activation_dropout
     __lowerCamelCase   :	List[str]								=		fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
     __lowerCamelCase   :	Any								=		fs_config.attention_dropout
     __lowerCamelCase   :	Any								=		fs_config.dropout_input
     __lowerCamelCase   :	str								=		fs_config.dropout
     __lowerCamelCase   :	Union[str, Any]								=		fs_config.mask_channel_length
     __lowerCamelCase   :	str								=		fs_config.mask_channel_prob
     __lowerCamelCase   :	Optional[int]								=		fs_config.mask_length
     __lowerCamelCase   :	Tuple								=		fs_config.mask_prob

     __lowerCamelCase   :	Union[str, Any]								=		'Wav2Vec2FeatureExtractor'
     __lowerCamelCase   :	int								=		'Wav2Vec2CTCTokenizer'

     return config



@torch.no_grad()
def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Tuple      ,    UpperCAmelCase_    :				Optional[Any]      ,    UpperCAmelCase_    :				Dict=None      ,    UpperCAmelCase_    :				Dict=None      ,    UpperCAmelCase_    :				List[str]=True							)     ->		Any:

     if is_finetuned:
          __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase   :	List[str]								=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]      ,    arg_overrides={'data': '/'.join(dict_path.split('/'							)[:-1]							)}							)
     else:
          __lowerCamelCase				,       __lowerCamelCase				,       __lowerCamelCase   :	Tuple								=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

     if config_path is not None:
          __lowerCamelCase   :	Any								=		SEWConfig.from_pretrained(UpperCAmelCase_							)
     else:
          __lowerCamelCase   :	Optional[int]								=		convert_config(model[0]      ,    UpperCAmelCase_							)
     __lowerCamelCase   :	Tuple								=		model[0].eval()

     __lowerCamelCase   :	Optional[Any]								=		True if config.feat_extract_norm == 'layer' else False
     __lowerCamelCase   :	Tuple								=		WavaVecaFeatureExtractor(
         feature_size=1      ,    sampling_rate=1_60_00      ,    padding_value=0      ,    do_normalize=UpperCAmelCase_      ,    return_attention_mask=UpperCAmelCase_      ,    )

     if is_finetuned:
          if dict_path:
               __lowerCamelCase   :	str								=		Dictionary.load(UpperCAmelCase_							)

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

          __lowerCamelCase   :	Optional[Any]								=		SEWForCTC(UpperCAmelCase_							)
     else:
          __lowerCamelCase   :	Union[str, Any]								=		SEWModel(UpperCAmelCase_							)
          feature_extractor.save_pretrained(UpperCAmelCase_							)

     recursively_load_weights(UpperCAmelCase_      ,    UpperCAmelCase_      ,    UpperCAmelCase_							)

     hf_model.save_pretrained(UpperCAmelCase_							)


if __name__ == "__main__":
      A__		:					Tuple												=  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"""
      )
      A__		:					Any												=  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 unittest

from knapsack import knapsack as k





class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	int								=		0
         __lowerCamelCase   :	Union[str, Any]								=		[0]
         __lowerCamelCase   :	Any								=		[0]
         __lowerCamelCase   :	Union[str, Any]								=		len(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     0	)

         __lowerCamelCase   :	List[str]								=		[60]
         __lowerCamelCase   :	Union[str, Any]								=		[10]
         __lowerCamelCase   :	List[Any]								=		len(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     0	)







    def       lowercase_				(							self	)				->     List[str]:
         __lowerCamelCase   :	Optional[int]								=		3
         __lowerCamelCase   :	int								=		[1, 2, 3]
         __lowerCamelCase   :	str								=		[3, 2, 1]
         __lowerCamelCase   :	Union[str, Any]								=		len(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     5	)







    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	Optional[int]								=		50
         __lowerCamelCase   :	List[str]								=		[60, 1_00, 1_20]
         __lowerCamelCase   :	List[str]								=		[10, 20, 30]
         __lowerCamelCase   :	List[str]								=		len(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     2_20	)


if __name__ == "__main__":
      unittest.main()






