


# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_snake_case     :		Any						=				2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
_snake_case     :		Optional[int]						=				{
    # fairseq:
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.15},
    "wmt19-en-de": {"length_penalty": 1.0},
    "wmt19-de-en": {"length_penalty": 1.1},
    # allenai:
    "wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
    "wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
    "wmt16-en-de-12-1": {"length_penalty": 0.8},
    "wmt19-de-en-6-6-base": {"length_penalty": 0.6},
    "wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}

# this remaps the different models to their organization names
_snake_case     :		Optional[Any]						=				{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
			_snake_case     :		List[str]						=				"facebook"
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
			_snake_case     :		Optional[Any]						=				"allenai"







def 				lowerCAmelCase_     (					__lowerCamelCase	):
							# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
							# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
							__snake_case		:			Union[str, Any]							 =   dict((re.sub(R"@@$"							,	""							,	lowercase__	), v) if k.endswith("@@"	) else (re.sub(R"$"							,	"</w>"							,	lowercase__	), v) for k, v in d.items()	)
							__snake_case		:			Tuple							 =   '''<s> <pad> </s> <unk>'''.split()
							# restore the special tokens
							for k in keep_keys:
														del da[F'{k}</w>']
														__snake_case		:			int							 =   d[k]  # restore
							return da







def 				lowerCAmelCase_     (					__lowerCamelCase							,	__lowerCamelCase	):
							# prep
							assert os.path.exists(lowercase__	)
							os.makedirs(lowercase__							,	exist_ok=lowercase__	)
							print(F'Writing results to {pytorch_dump_folder_path}'	)

							# handle various types of models

							__snake_case		:			Optional[Any]							 =   basename(lowercase__	)
							__snake_case		:			Union[str, Any]							 =   dirname(lowercase__	)

							__snake_case		:			Tuple							 =   fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
							__snake_case		:			int							 =   cls.hub_models()
							__snake_case		:			str							 =   {'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
							__snake_case		:			Union[str, Any]							 =   '''.'''
							# note: since the model dump is old, fairseq has upgraded its model some
							# time later, and it does a whole lot of rewrites and splits on the saved
							# weights, therefore we can't use torch.load() directly on the model file.
							# see: upgrade_state_dict(state_dict) in fairseq_model.py
							print(F'using checkpoint {checkpoint_file}'	)
							__snake_case		:			Union[str, Any]							 =   hub_utils.from_pretrained(
							    lowercase__							,	lowercase__							,	lowercase__							,	archive_map=lowercase__							,	**lowercase__	)

							__snake_case		:			Dict							 =   vars(chkpt["args"]["model"]	)

							__snake_case		:			Union[str, Any]							 =   args['''source_lang''']
							__snake_case		:			List[Any]							 =   args['''target_lang''']

							__snake_case		:			int							 =   dirname(lowercase__	)
							__snake_case		:			int							 =   basename(lowercase__	)

							# dicts
							__snake_case		:			Optional[Any]							 =   os.path.join(lowercase__							,	F'dict.{src_lang}.txt'	)
							__snake_case		:			Optional[Any]							 =   os.path.join(lowercase__							,	F'dict.{tgt_lang}.txt'	)

							__snake_case		:			Union[str, Any]							 =   Dictionary.load(lowercase__	)
							__snake_case		:			Optional[int]							 =   rewrite_dict_keys(src_dict.indices	)
							__snake_case		:			str							 =   len(lowercase__	)
							__snake_case		:			Optional[Any]							 =   os.path.join(lowercase__							,	"vocab-src.json"	)
							print(F'Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records'	)
							with open(lowercase__							,	"w"							,	encoding="utf-8"	) as f:
														f.write(json.dumps(lowercase__							,	ensure_ascii=lowercase__							,	indent=lowercase__	)	)

							# detect whether this is a do_lower_case situation, which can be derived by checking whether we
							# have at least one uppercase letter in the source vocab
							__snake_case		:			List[Any]							 =   True
							for k in src_vocab.keys():
														if not k.islower():
																					__snake_case		:			Union[str, Any]							 =   False
																					break

							__snake_case		:			int							 =   Dictionary.load(lowercase__	)
							__snake_case		:			Optional[Any]							 =   rewrite_dict_keys(tgt_dict.indices	)
							__snake_case		:			Tuple							 =   len(lowercase__	)
							__snake_case		:			Union[str, Any]							 =   os.path.join(lowercase__							,	"vocab-tgt.json"	)
							print(F'Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records'	)
							with open(lowercase__							,	"w"							,	encoding="utf-8"	) as f:
														f.write(json.dumps(lowercase__							,	ensure_ascii=lowercase__							,	indent=lowercase__	)	)

							# merges_file (bpecodes)
							__snake_case		:			Optional[int]							 =   os.path.join(lowercase__							,	VOCAB_FILES_NAMES["merges_file"]	)
							for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
														__snake_case		:			Dict							 =   os.path.join(lowercase__							,	lowercase__	)
														if os.path.exists(lowercase__	):
																					break
							with open(lowercase__							,	encoding="utf-8"	) as fin:
														__snake_case		:			Dict							 =   fin.read()
							__snake_case		:			Tuple							 =   re.sub(R" \d+$"							,	""							,	lowercase__							,	0							,	re.M	)  # remove frequency number
							print(F'Generating {merges_file}'	)
							with open(lowercase__							,	"w"							,	encoding="utf-8"	) as fout:
														fout.write(lowercase__	)

							# model config
							__snake_case		:			str							 =   os.path.join(lowercase__							,	"config.json"	)

							# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
							# may have to modify the tokenizer if a different type is used by a future model
							assert args["bpe"] == "fastbpe", F'need to extend tokenizer to support bpe={args["bpe"]}'
							assert args["tokenizer"] == "moses", F'need to extend tokenizer to support bpe={args["tokenizer"]}'

							__snake_case		:			Dict							 =   {
							    '''architectures''': ['''FSMTForConditionalGeneration'''],
							    '''model_type''': '''fsmt''',
							    '''activation_dropout''': args['''activation_dropout'''],
							    '''activation_function''': '''relu''',
							    '''attention_dropout''': args['''attention_dropout'''],
							    '''d_model''': args['''decoder_embed_dim'''],
							    '''dropout''': args['''dropout'''],
							    '''init_std''': 0.0_2,
							    '''max_position_embeddings''': args['''max_source_positions'''],
							    '''num_hidden_layers''': args['''encoder_layers'''],
							    '''src_vocab_size''': src_vocab_size,
							    '''tgt_vocab_size''': tgt_vocab_size,
							    '''langs''': [src_lang, tgt_lang],
							    '''encoder_attention_heads''': args['''encoder_attention_heads'''],
							    '''encoder_ffn_dim''': args['''encoder_ffn_embed_dim'''],
							    '''encoder_layerdrop''': args['''encoder_layerdrop'''],
							    '''encoder_layers''': args['''encoder_layers'''],
							    '''decoder_attention_heads''': args['''decoder_attention_heads'''],
							    '''decoder_ffn_dim''': args['''decoder_ffn_embed_dim'''],
							    '''decoder_layerdrop''': args['''decoder_layerdrop'''],
							    '''decoder_layers''': args['''decoder_layers'''],
							    '''bos_token_id''': 0,
							    '''pad_token_id''': 1,
							    '''eos_token_id''': 2,
							    '''is_encoder_decoder''': True,
							    '''scale_embedding''': not args['''no_scale_embedding'''],
							    '''tie_word_embeddings''': args['''share_all_embeddings'''],
							}

							# good hparam defaults to start with
							__snake_case		:			int							 =   5
							__snake_case		:			Optional[int]							 =   False
							if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
														__snake_case		:			Optional[int]							 =   best_score_hparams[model_dir]['''length_penalty''']
							else:
														__snake_case		:			Optional[Any]							 =   1.0

							print(F'Generating {fsmt_model_config_file}'	)
							with open(lowercase__							,	"w"							,	encoding="utf-8"	) as f:
														f.write(json.dumps(lowercase__							,	ensure_ascii=lowercase__							,	indent=lowercase__	)	)

							# tokenizer config
							__snake_case		:			Tuple							 =   os.path.join(lowercase__							,	lowercase__	)

							__snake_case		:			int							 =   {
							    '''langs''': [src_lang, tgt_lang],
							    '''model_max_length''': 1_0_2_4,
							    '''do_lower_case''': do_lower_case,
							}

							print(F'Generating {fsmt_tokenizer_config_file}'	)
							with open(lowercase__							,	"w"							,	encoding="utf-8"	) as f:
														f.write(json.dumps(lowercase__							,	ensure_ascii=lowercase__							,	indent=lowercase__	)	)

							# model
							__snake_case		:			List[Any]							 =   chkpt['''models'''][0]
							__snake_case		:			List[Any]							 =   model.state_dict()

							# rename keys to start with 'model.'
							__snake_case		:			Any							 =   OrderedDict(("model." + k, v) for k, v in model_state_dict.items()	)

							# remove unneeded keys
							__snake_case		:			List[str]							 =   [
							    '''model.model''',
							    '''model.encoder.version''',
							    '''model.decoder.version''',
							    '''model.encoder_embed_tokens.weight''',
							    '''model.decoder_embed_tokens.weight''',
							    '''model.encoder.embed_positions._float_tensor''',
							    '''model.decoder.embed_positions._float_tensor''',
							]
							for k in ignore_keys:
														model_state_dict.pop(lowercase__							,	lowercase__	)

							__snake_case		:			Optional[Any]							 =   FSMTConfig.from_pretrained(lowercase__	)
							__snake_case		:			Any							 =   FSMTForConditionalGeneration(lowercase__	)

							# check that it loads ok
							model_new.load_state_dict(lowercase__							,	strict=lowercase__	)

							# save
							__snake_case		:			Dict							 =   os.path.join(lowercase__							,	lowercase__	)
							print(F'Generating {pytorch_weights_dump_path}'	)
							torch.save(lowercase__							,	lowercase__	)

							print("Conversion is done!"	)
							print("\nLast step is to upload the files to s3"	)
							print(F'cd {data_root}'	)
							print(F'transformers-cli upload {model_dir}'	)


if __name__ == "__main__":
			_snake_case     :		Optional[int]						=				argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--fsmt_checkpoint_path",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
			        " bpecodes, etc."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			_snake_case     :		int						=				parser.parse_args()
			convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)




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,
 )


