

"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_     :			Any	  =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_     :			Any	  =			{
    '''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''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
SCREAMING_SNAKE_CASE_     :			Dict	  =			[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def 	UpperCAmelCase__   (				A__	,    A__	,    A__	,    A__	,    A__	,    A__   )      ->      Dict:




   """simple docstring"""
   for attribute in key.split("."   ):
      if is_finetuned:
         if attribute in ["quantizer", "project_q", "project_hid"]:
            # those layers are only relevant for pretraining and should be dropped
            return

         if attribute == "ctc_proj":
            # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
            lowerCamelCase__						=			"lm_head"

      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 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      Tuple:




   """simple docstring"""
   lowerCamelCase__						=			[]
   lowerCamelCase__						=			fairseq_model.state_dict()

   lowerCamelCase__						=			hf_model.unispeech.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__						=			"unispeech." + 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__						=			True
               if "*" in mapped_key:
                  lowerCamelCase__						=			name.split(A__   )[0].split("."   )[-2]
                  lowerCamelCase__						=			mapped_key.replace("*"	,    A__   )
               if "weight_g" in name:
                  lowerCamelCase__						=			"weight_g"
               elif "weight_v" in name:
                  lowerCamelCase__						=			"weight_v"
               elif "bias" in name:
                  lowerCamelCase__						=			"bias"
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  lowerCamelCase__						=			"weight"
               else:
                  lowerCamelCase__						=			None
               set_recursively(A__	,    A__	,    A__	,    A__	,    A__	,    A__   )
            continue
      if not is_used:
         unused_weights.append(A__   )

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



def 	UpperCAmelCase__   (				A__	,    A__	,    A__	,    A__	,    A__   )      ->      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(A__   )



@torch.no_grad()
def 	UpperCAmelCase__   (				A__	,    A__	,    A__=None	,    A__=None	,    A__=True   )      ->      Tuple:




   """simple docstring"""
   if config_path is not None:
      lowerCamelCase__						=			UniSpeechConfig.from_pretrained(A__   )
   else:
      lowerCamelCase__						=			UniSpeechConfig()

   if is_finetuned:
      if dict_path:
         lowerCamelCase__						=			Dictionary.load_from_json(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.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__   )
         lowerCamelCase__						=			target_dict.indices

         # fairseq has the <pad> and <s> switched
         lowerCamelCase__						=			42
         lowerCamelCase__						=			43
         with open(A__	,    "w"	,    encoding="utf-8"   ) as vocab_handle:
            json.dump(A__	,    A__   )
         lowerCamelCase__						=			WavaVecaPhonemeCTCTokenizer(
             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__						=			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__	,    )
         lowerCamelCase__						=			WavaVecaProcessor(feature_extractor=A__	,    tokenizer=A__   )
         processor.save_pretrained(A__   )

      lowerCamelCase__						=			UniSpeechForCTC(A__   )
   else:
      lowerCamelCase__						=			UniSpeechForPreTraining(A__   )

   if is_finetuned:
      lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]	,    arg_overrides={"data": "/".join(dict_path.split("/"   )[:-1]   ), "w2v_path": checkpoint_path}   )
   else:
      lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

   lowerCamelCase__						=			model[0].eval()

   recursively_load_weights(A__	,    A__	,    A__   )

   hf_unispeech.save_pretrained(A__   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_     :			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(
         '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
     )
     SCREAMING_SNAKE_CASE_     :			List[str]	  =			parser.parse_args()
     convert_unispeech_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )







"""simple docstring"""




from __future__ import annotations

from typing import Any



def 	UpperCAmelCase__   (				A__   )      ->      None:




   """simple docstring"""
   create_state_space_tree(A__	,    []	,    0   )



def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      None:




   """simple docstring"""

   if index == len(A__   ):
      print(A__   )
      return

   create_state_space_tree(A__	,    A__	,    index + 1   )
   current_subsequence.append(sequence[index]   )
   create_state_space_tree(A__	,    A__	,    index + 1   )
   current_subsequence.pop()


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_     :			list[Any]	  =			[3, 1, 2, 4]
     generate_all_subsequences(seq)

     seq.clear()
     seq.extend(['''A''', '''B''', '''C'''])
     generate_all_subsequences(seq)




