

from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
 from .features import FeatureType







@dataclass
class   __lowercase      :
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  None
 # Automatically constructed
 _UpperCamelCase			  =  "dict"
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  field(default="""Translation"""		,   init=_UpperCAmelCase		,   repr=_UpperCAmelCase )
 def __call__(							self					)					->List[Any]:



       '''simple docstring'''





       return pa.struct({lang: pa.string() for lang in sorted(self.languages					)}					)





 def 	UpperCamelCase__  (							self					)					->Union["FeatureType", Dict[str, "FeatureType"]]:



       '''simple docstring'''





       from .features import Value

       return {k: Value('''string'''					) for k in sorted(self.languages					)}







@dataclass
class   __lowercase      :
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  None
 # Automatically constructed
 _UpperCamelCase			  =  "dict"
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  field(default="""TranslationVariableLanguages"""		,   init=_UpperCAmelCase		,   repr=_UpperCAmelCase )
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					sorted(set(self.languages					)					) if self.languages else None
       __lowerCAmelCase					:						Optional[Any]					=					len(self.languages					) if self.languages else None
 def __call__(							self					)					->Dict:



       '''simple docstring'''





       return pa.struct({'''language''': pa.list_(pa.string()					), '''translation''': pa.list_(pa.string()					)}					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					set(self.languages					)
       if self.languages and set(A_					) - lang_set:
             raise ValueError(
                 f"""Some languages in example ({", ".join(sorted(set(A_					) - lang_set					)					)}) are not in valid set ({", ".join(A_					)})."""					)

       # Convert dictionary into tuples, splitting out cases where there are
       # multiple translations for a single language.
       __lowerCAmelCase					:						Optional[int]					=					[]
       for lang, text in translation_dict.items():
             if isinstance(A_      ,							A_					):
                   translation_tuples.append((lang, text)					)
             else:
                   translation_tuples.extend([(lang, el) for el in text]					)

        # Ensure translations are in ascending order by language code.
       __lowerCAmelCase,							__lowerCAmelCase					:						Any					=					zip(*sorted(A_					)					)

       return {"language": languages, "translation": translations}





 def 	UpperCamelCase__  (							self					)					->Union["FeatureType", Dict[str, "FeatureType"]]:



       '''simple docstring'''





       from .features import Sequence, Value

       return {
           "language": Sequence(Value('''string'''					)					),
           "translation": Sequence(Value('''string'''					)					),
       }





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()
_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "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",
}
_UpperCamelCase 			=		[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						List[str]					=					{}
      with open(lowercase__   ,	'''r'''						) as file:
            for line_number, line in enumerate(lowercase__						):
                  __lowerCAmelCase					:						Any					=					line.strip()
                  if line:
                        __lowerCAmelCase					:						Dict					=					line.split()
                        __lowerCAmelCase					:						str					=					line_number
                        __lowerCAmelCase					:						List[str]					=					words[0]
                        __lowerCAmelCase					:						Any					=					value
      return result







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      for attribute in key.split('''.'''						):
            __lowerCAmelCase					:						List[Any]					=					getattr(lowercase__   ,	lowercase__						)

      __lowerCAmelCase					:						Any					=					None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(lowercase__						):
                  __lowerCAmelCase					:						Tuple					=					PARAM_MAPPING[full_name.split('''.'''						)[-1]]
                  __lowerCAmelCase					:						List[Any]					=					'''param'''

      if weight_type is not None and weight_type != "param":
            __lowerCAmelCase					:						str					=					getattr(lowercase__   ,	lowercase__						).shape
      elif weight_type is not None and weight_type == "param":
            __lowerCAmelCase					:						Dict					=					hf_pointer
            for attribute in hf_param_name.split('''.'''						):
                  __lowerCAmelCase					:						Dict					=					getattr(lowercase__   ,	lowercase__						)
            __lowerCAmelCase					:						str					=					shape_pointer.shape

            # let's reduce dimension
            __lowerCAmelCase					:						Any					=					value[0]
      else:
            __lowerCAmelCase					:						str					=					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					:						Union[str, Any]					=					value
      elif weight_type == "weight_g":
            __lowerCAmelCase					:						List[str]					=					value
      elif weight_type == "weight_v":
            __lowerCAmelCase					:						int					=					value
      elif weight_type == "bias":
            __lowerCAmelCase					:						Union[str, Any]					=					value
      elif weight_type == "param":
            for attribute in hf_param_name.split('''.'''						):
                  __lowerCAmelCase					:						Dict					=					getattr(lowercase__   ,	lowercase__						)
            __lowerCAmelCase					:						Tuple					=					value
      else:
            __lowerCAmelCase					:						Any					=					value

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







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Any					=					None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(lowercase__						):
                  __lowerCAmelCase					:						str					=					PARAM_MAPPING[full_name.split('''.'''						)[-1]]
                  __lowerCAmelCase					:						int					=					'''param'''

      if weight_type is not None and weight_type != "param":
            __lowerCAmelCase					:						Tuple					=					'''.'''.join([key, weight_type]						)
      elif weight_type is not None and weight_type == "param":
            __lowerCAmelCase					:						List[str]					=					'''.'''.join([key, hf_param_name]						)
      else:
            __lowerCAmelCase					:						Optional[int]					=					key

      __lowerCAmelCase					:						Union[str, Any]					=					value if '''lm_head''' in full_key else value[0]


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







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						List[str]					=					[]
      __lowerCAmelCase					:						Optional[Any]					=					fairseq_model.state_dict()

      __lowerCAmelCase					:						Tuple					=					hf_model.wavaveca.feature_extractor

      for name, value in fairseq_dict.items():
            __lowerCAmelCase					:						Any					=					False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	hf_model.config.feat_extract_norm == '''group'''   ,	)
                  __lowerCAmelCase					:						int					=					True
            else:
                  __lowerCAmelCase					:						Dict					=					load_wavaveca_layer(lowercase__   ,	lowercase__   ,	lowercase__						)
            if not is_used:
                  unused_weights.append(lowercase__						)

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







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      __lowerCAmelCase					:						Any					=					full_name.split('''conv_layers.'''						)[-1]
      __lowerCAmelCase					:						List[str]					=					name.split('''.'''						)
      __lowerCAmelCase					:						Any					=					int(items[0]						)
      __lowerCAmelCase					:						str					=					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					:						List[str]					=					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					:						List[str]					=					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					:						List[str]					=					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					:						Optional[int]					=					value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
      else:
            unused_weights.append(lowercase__						)







@torch.no_grad()
def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__=None   ,	lowercase__=None   ,	lowercase__=True   ,	lowercase__=False						):
      if config_path is not None:
            __lowerCAmelCase					:						Union[str, Any]					=					WavaVecaConfig.from_pretrained(lowercase__						)
      else:
            __lowerCAmelCase					:						Optional[int]					=					WavaVecaConfig()

      if is_seq_class:
            __lowerCAmelCase					:						Optional[Any]					=					read_txt_into_dict(lowercase__						)
            __lowerCAmelCase					:						int					=					idalabel
            __lowerCAmelCase					:						Optional[int]					=					WavaVecaForSequenceClassification(lowercase__						)
            __lowerCAmelCase					:						List[str]					=					WavaVecaFeatureExtractor(
                feature_size=1   ,	sampling_rate=1_6_0_0_0   ,	padding_value=0   ,	do_normalize=lowercase__   ,	return_attention_mask=lowercase__   ,	)
            feature_extractor.save_pretrained(lowercase__						)

      elif is_finetuned:
            if dict_path:
                  __lowerCAmelCase					:						List[str]					=					Dictionary.load(lowercase__						)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  __lowerCAmelCase					:						List[Any]					=					target_dict.pad_index
                  __lowerCAmelCase					:						List[Any]					=					target_dict.bos_index
                  __lowerCAmelCase					:						Optional[int]					=					target_dict.eos_index
                  __lowerCAmelCase					:						Any					=					len(target_dict.symbols						)
                  __lowerCAmelCase					:						Union[str, Any]					=					os.path.join(lowercase__   ,	'''vocab.json'''						)
                  if not os.path.isdir(lowercase__						):
                        logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowercase__						)						)
                        return
                  os.makedirs(lowercase__   ,	exist_ok=lowercase__						)
                  __lowerCAmelCase					:						Optional[int]					=					target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  __lowerCAmelCase					:						List[str]					=					0
                  __lowerCAmelCase					:						int					=					1
                  with open(lowercase__   ,	'''w'''   ,	encoding='''utf-8'''						) as vocab_handle:
                        json.dump(lowercase__   ,	lowercase__						)
                  __lowerCAmelCase					:						Dict					=					WavaVecaCTCTokenizer(
                      lowercase__   ,	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=lowercase__   ,	)
                  __lowerCAmelCase					:						List[str]					=					True if config.feat_extract_norm == '''layer''' else False
                  __lowerCAmelCase					:						List[str]					=					WavaVecaFeatureExtractor(
                      feature_size=1   ,	sampling_rate=1_6_0_0_0   ,	padding_value=0   ,	do_normalize=lowercase__   ,	return_attention_mask=lowercase__   ,	)
                  __lowerCAmelCase					:						List[Any]					=					WavaVecaProcessor(feature_extractor=lowercase__   ,	tokenizer=lowercase__						)
                  processor.save_pretrained(lowercase__						)

            __lowerCAmelCase					:						str					=					WavaVecaForCTC(lowercase__						)
      else:
            __lowerCAmelCase					:						Any					=					WavaVecaForPreTraining(lowercase__						)

      if is_finetuned or is_seq_class:
            __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						Optional[int]					=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]   ,	arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
      else:
            __lowerCAmelCase					:						Union[str, Any]					=					argparse.Namespace(task='''audio_pretraining'''						)
            __lowerCAmelCase					:						str					=					fairseq.tasks.setup_task(lowercase__						)

            __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						Dict					=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   ,	task=lowercase__						)

      __lowerCAmelCase					:						int					=					model[0].eval()

      recursively_load_weights(lowercase__   ,	lowercase__   ,	not is_finetuned						)

      hf_wavavec.save_pretrained(lowercase__						)


if __name__ == "__main__":
 _UpperCamelCase 			=		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",
 )
 _UpperCamelCase 			=		parser.parse_args()

 _UpperCamelCase 			=		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,
 )


