


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     =			logging.get_logger(__name__)

__a     =			{
    """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     =			[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Tuple:
   UpperCAmelCase							=     {}
   with open(lowerCAmelCase_  ,						"""r"""   ) as file:
      for line_number, line in enumerate(lowerCAmelCase_   ):
         UpperCAmelCase							=     line.strip()
         if line:
            UpperCAmelCase							=     line.split()
            UpperCAmelCase							=     line_number
            UpperCAmelCase							=     words[0]
            UpperCAmelCase							=     value
   return result
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Optional[int]:
   for attribute in key.split("""."""   ):
      UpperCAmelCase							=     getattr(lowerCAmelCase_  ,						lowerCAmelCase_   )

   UpperCAmelCase							=     None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(lowerCAmelCase_   ):
         UpperCAmelCase							=     PARAM_MAPPING[full_name.split("""."""   )[-1]]
         UpperCAmelCase							=     """param"""

   if weight_type is not None and weight_type != "param":
      UpperCAmelCase							=     getattr(lowerCAmelCase_  ,						lowerCAmelCase_   ).shape
   elif weight_type is not None and weight_type == "param":
      UpperCAmelCase							=     hf_pointer
      for attribute in hf_param_name.split("""."""   ):
         UpperCAmelCase							=     getattr(lowerCAmelCase_  ,						lowerCAmelCase_   )
      UpperCAmelCase							=     shape_pointer.shape

      # let's reduce dimension
      UpperCAmelCase							=     value[0]
   else:
      UpperCAmelCase							=     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":
      UpperCAmelCase							=     value
   elif weight_type == "weight_g":
      UpperCAmelCase							=     value
   elif weight_type == "weight_v":
      UpperCAmelCase							=     value
   elif weight_type == "bias":
      UpperCAmelCase							=     value
   elif weight_type == "param":
      for attribute in hf_param_name.split("""."""   ):
         UpperCAmelCase							=     getattr(lowerCAmelCase_  ,						lowerCAmelCase_   )
      UpperCAmelCase							=     value
   else:
      UpperCAmelCase							=     value

   logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""   )
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Optional[int]:
   UpperCAmelCase							=     None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(lowerCAmelCase_   ):
         UpperCAmelCase							=     PARAM_MAPPING[full_name.split("""."""   )[-1]]
         UpperCAmelCase							=     """param"""

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

   UpperCAmelCase							=     value if """lm_head""" in full_key else value[0]


__a     =			{
    """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   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None   )						->Optional[Any]:
   UpperCAmelCase							=     False
   for key, mapped_key in MAPPING.items():
      UpperCAmelCase							=     """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]:
         UpperCAmelCase							=     True
         if "*" in mapped_key:
            UpperCAmelCase							=     name.split(lowerCAmelCase_   )[0].split("""."""   )[-2]
            UpperCAmelCase							=     mapped_key.replace("""*"""  ,						lowerCAmelCase_   )
         if "weight_g" in name:
            UpperCAmelCase							=     """weight_g"""
         elif "weight_v" in name:
            UpperCAmelCase							=     """weight_v"""
         elif "bias" in name:
            UpperCAmelCase							=     """bias"""
         elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            UpperCAmelCase							=     """weight"""
         else:
            UpperCAmelCase							=     None
         if hf_dict is not None:
            rename_dict(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )
         else:
            set_recursively(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )
         return is_used
   return is_used
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Tuple:
   UpperCAmelCase							=     []
   UpperCAmelCase							=     fairseq_model.state_dict()

   UpperCAmelCase							=     hf_model.wavaveca.feature_extractor

   for name, value in fairseq_dict.items():
      UpperCAmelCase							=     False
      if "conv_layers" in name:
         load_conv_layer(
             lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						hf_model.config.feat_extract_norm == """group"""  ,						)
         UpperCAmelCase							=     True
      else:
         UpperCAmelCase							=     load_wavaveca_layer(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )
      if not is_used:
         unused_weights.append(lowerCAmelCase_   )

   logger.warning(F"""Unused weights: {unused_weights}"""   )
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->List[str]:
   UpperCAmelCase							=     full_name.split("""conv_layers."""   )[-1]
   UpperCAmelCase							=     name.split("""."""   )
   UpperCAmelCase							=     int(items[0]   )
   UpperCAmelCase							=     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."""   )
         UpperCAmelCase							=     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."""   )
         UpperCAmelCase							=     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."""   )
         UpperCAmelCase							=     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."""   )
         UpperCAmelCase							=     value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
   else:
      unused_weights.append(lowerCAmelCase_   )
@torch.no_grad()
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=None  ,						lowerCAmelCase_=True  ,						lowerCAmelCase_=False   )						->str:
   if config_path is not None:
      UpperCAmelCase							=     WavaVecaConfig.from_pretrained(lowerCAmelCase_   )
   else:
      UpperCAmelCase							=     WavaVecaConfig()

   if is_seq_class:
      UpperCAmelCase							=     read_txt_into_dict(lowerCAmelCase_   )
      UpperCAmelCase							=     idalabel
      UpperCAmelCase							=     WavaVecaForSequenceClassification(lowerCAmelCase_   )
      UpperCAmelCase							=     WavaVecaFeatureExtractor(
          feature_size=1  ,						sampling_rate=1_6_0_0_0  ,						padding_value=0  ,						do_normalize=lowerCAmelCase_  ,						return_attention_mask=lowerCAmelCase_  ,						)
      feature_extractor.save_pretrained(lowerCAmelCase_   )

   elif is_finetuned:
      if dict_path:
         UpperCAmelCase							=     Dictionary.load(lowerCAmelCase_   )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         UpperCAmelCase							=     target_dict.pad_index
         UpperCAmelCase							=     target_dict.bos_index
         UpperCAmelCase							=     target_dict.eos_index
         UpperCAmelCase							=     len(target_dict.symbols   )
         UpperCAmelCase							=     os.path.join(lowerCAmelCase_  ,						"""vocab.json"""   )
         if not os.path.isdir(lowerCAmelCase_   ):
            logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowerCAmelCase_   )   )
            return
         os.makedirs(lowerCAmelCase_  ,						exist_ok=lowerCAmelCase_   )
         UpperCAmelCase							=     target_dict.indices

         # fairseq has the <pad> and <s> switched
         UpperCAmelCase							=     0
         UpperCAmelCase							=     1
         with open(lowerCAmelCase_  ,						"""w"""  ,						encoding="""utf-8"""   ) as vocab_handle:
            json.dump(lowerCAmelCase_  ,						lowerCAmelCase_   )
         UpperCAmelCase							=     WavaVecaCTCTokenizer(
             lowerCAmelCase_  ,						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=lowerCAmelCase_  ,						)
         UpperCAmelCase							=     True if config.feat_extract_norm == """layer""" else False
         UpperCAmelCase							=     WavaVecaFeatureExtractor(
             feature_size=1  ,						sampling_rate=1_6_0_0_0  ,						padding_value=0  ,						do_normalize=lowerCAmelCase_  ,						return_attention_mask=lowerCAmelCase_  ,						)
         UpperCAmelCase							=     WavaVecaProcessor(feature_extractor=lowerCAmelCase_  ,						tokenizer=lowerCAmelCase_   )
         processor.save_pretrained(lowerCAmelCase_   )

      UpperCAmelCase							=     WavaVecaForCTC(lowerCAmelCase_   )
   else:
      UpperCAmelCase							=     WavaVecaForPreTraining(lowerCAmelCase_   )

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

      UpperCAmelCase				,  UpperCAmelCase				,  UpperCAmelCase							=     fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  ,						task=lowerCAmelCase_   )

   UpperCAmelCase							=     model[0].eval()

   recursively_load_weights(lowerCAmelCase_  ,						lowerCAmelCase_  ,						not is_finetuned   )

   hf_wavavec.save_pretrained(lowerCAmelCase_   )


if __name__ == "__main__":
 __a     =			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     =			parser.parse_args()

 __a     =			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
def   _UpperCamelCase				(						lowerCAmelCase_   )						->None:
   create_state_space_tree(lowerCAmelCase_  ,						[]  ,						0  ,						[0 for i in range(len(lowerCAmelCase_   )   )]   )
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						)						->None:

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

   for i in range(len(lowerCAmelCase_   )   ):
      if not index_used[i]:
         current_sequence.append(sequence[i]   )
         UpperCAmelCase							=     True
         create_state_space_tree(lowerCAmelCase_  ,						lowerCAmelCase_  ,						index + 1  ,						lowerCAmelCase_   )
         current_sequence.pop()
         UpperCAmelCase							=     False
__a     =			[3, 1, 2, 4]
generate_all_permutations(sequence)

__a     =			["A", "B", "C"]







generate_all_permutations(sequence_a)



