



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_						:     List[Any]	     =		logging.get_logger(__name__)

a_						:     Union[str, 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',
    '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_						:     Optional[int]	     =		[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		{}
      with open(UpperCAmelCase__   ,   "r" ) as file:
            for line_number, line in enumerate(UpperCAmelCase__ ):
                  lowerCamelCase								=		line.strip()
                  if line:
                        lowerCamelCase								=		line.split()
                        lowerCamelCase								=		line_number
                        lowerCamelCase								=		words[0]
                        lowerCamelCase								=		value
      return result

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      for attribute in key.split("." ):
            lowerCamelCase								=		getattr(UpperCAmelCase__   ,   UpperCAmelCase__ )

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

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

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

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

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



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

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

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


a_						:     Dict	     =		{
    '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(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__=None   ,   UpperCAmelCase__=None ):


      """simple docstring"""



      lowerCamelCase								=		False
      for key, mapped_key in MAPPING.items():
            lowerCamelCase								=		"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								=		True
                  if "*" in mapped_key:
                        lowerCamelCase								=		name.split(UpperCAmelCase__ )[0].split("." )[-2]
                        lowerCamelCase								=		mapped_key.replace("*"   ,   UpperCAmelCase__ )
                  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
                  if hf_dict is not None:
                        rename_dict(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )
                  else:
                        set_recursively(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )
                  return is_used
      return is_used

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		[]
      lowerCamelCase								=		fairseq_model.state_dict()

      lowerCamelCase								=		hf_model.wavaveca.feature_extractor

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

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

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """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:
                  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								=		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								=		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								=		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								=		value
                  logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
      else:
            unused_weights.append(UpperCAmelCase__ )

@torch.no_grad()
def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__=None   ,   UpperCAmelCase__=None   ,   UpperCAmelCase__=True   ,   UpperCAmelCase__=False ):


      """simple docstring"""



      if config_path is not None:
            lowerCamelCase								=		WavaVecaConfig.from_pretrained(UpperCAmelCase__ )
      else:
            lowerCamelCase								=		WavaVecaConfig()

      if is_seq_class:
            lowerCamelCase								=		read_txt_into_dict(UpperCAmelCase__ )
            lowerCamelCase								=		idalabel
            lowerCamelCase								=		WavaVecaForSequenceClassification(UpperCAmelCase__ )
            lowerCamelCase								=		WavaVecaFeatureExtractor(
                feature_size=1   ,   sampling_rate=16000   ,   padding_value=0   ,   do_normalize=UpperCAmelCase__   ,   return_attention_mask=UpperCAmelCase__   ,   )
            feature_extractor.save_pretrained(UpperCAmelCase__ )

      elif is_finetuned:
            if dict_path:
                  lowerCamelCase								=		Dictionary.load(UpperCAmelCase__ )

                  # 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(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__ )
                  lowerCamelCase								=		target_dict.indices

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

            lowerCamelCase								=		WavaVecaForCTC(UpperCAmelCase__ )
      else:
            lowerCamelCase								=		WavaVecaForPreTraining(UpperCAmelCase__ )

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

            lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   ,   task=UpperCAmelCase__ )

      lowerCamelCase								=		model[0].eval()

      recursively_load_weights(UpperCAmelCase__   ,   UpperCAmelCase__   ,   not is_finetuned )

      hf_wavavec.save_pretrained(UpperCAmelCase__ )


if __name__ == "__main__":
     a_						:     Dict	     =		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_						:     List[Any]	     =		parser.parse_args()

     a_						:     Tuple	     =		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,
     )




a_						:     List[str]	     =		'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

a_						:     Any	     =		[{'type': 'code', 'content': INSTALL_CONTENT}]
a_						:     str	     =		{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}