






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()
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',
    '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',
}
UpperCamelCase__ =			[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """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
            lowercase_	:    List[str]           =     """lm_head"""

      lowercase_	:    Tuple           =     getattr(SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__							)

   if weight_type is not None:
      lowercase_	:    Optional[Any]           =     getattr(SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__							).shape
   else:
      lowercase_	:    Union[str, Any]           =     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":
      lowercase_	:    List[str]           =     value
   elif weight_type == "weight_g":
      lowercase_	:    Optional[Any]           =     value
   elif weight_type == "weight_v":
      lowercase_	:    List[str]           =     value
   elif weight_type == "bias":
      lowercase_	:    Optional[int]           =     value
   else:
      lowercase_	:    str           =     value

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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Tuple           =     []
   lowercase_	:    Optional[Any]           =     fairseq_model.state_dict()

   lowercase_	:    str           =     hf_model.unispeech.feature_extractor

   for name, value in fairseq_dict.items():
      lowercase_	:    Tuple           =     False
      if "conv_layers" in name:
         load_conv_layer(
             SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		hf_model.config.feat_extract_norm == "group"						,		)
         lowercase_	:    Optional[Any]           =     True
      else:
         for key, mapped_key in MAPPING.items():
            lowercase_	:    Dict           =     """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]:
               lowercase_	:    Dict           =     True
               if "*" in mapped_key:
                  lowercase_	:    Optional[Any]           =     name.split(SCREAMING_SNAKE_CASE__							)[0].split("."							)[-2]
                  lowercase_	:    Union[str, Any]           =     mapped_key.replace("*"						,		SCREAMING_SNAKE_CASE__							)
               if "weight_g" in name:
                  lowercase_	:    Optional[int]           =     """weight_g"""
               elif "weight_v" in name:
                  lowercase_	:    Tuple           =     """weight_v"""
               elif "bias" in name:
                  lowercase_	:    Any           =     """bias"""
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  lowercase_	:    str           =     """weight"""
               else:
                  lowercase_	:    List[str]           =     None
               set_recursively(SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__							)
            continue
      if not is_used:
         unused_weights.append(SCREAMING_SNAKE_CASE__							)

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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Any           =     full_name.split("conv_layers."							)[-1]
   lowercase_	:    int           =     name.split("."							)
   lowercase_	:    List[Any]           =     int(items[0]							)
   lowercase_	:    Any           =     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."""
         )
         lowercase_	:    Any           =     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."""
         )
         lowercase_	:    int           =     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."
         )
         lowercase_	:    str           =     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."""
         )
         lowercase_	:    int           =     value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
   else:
      unused_weights.append(SCREAMING_SNAKE_CASE__							)




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=None						,		_UpperCamelCase=None						,		_UpperCamelCase=True							):


   """simple docstring"""




   if config_path is not None:
      lowercase_	:    Union[str, Any]           =     UniSpeechConfig.from_pretrained(SCREAMING_SNAKE_CASE__							)
   else:
      lowercase_	:    Optional[int]           =     UniSpeechConfig()

   if is_finetuned:
      if dict_path:
         lowercase_	:    Optional[int]           =     Dictionary.load_from_json(SCREAMING_SNAKE_CASE__							)

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

         # fairseq has the <pad> and <s> switched
         lowercase_	:    int           =     42
         lowercase_	:    Optional[int]           =     43
         with open(SCREAMING_SNAKE_CASE__						,		"w"						,		encoding="utf-8"							) as vocab_handle:
            json.dump(SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__							)
         lowercase_	:    int           =     WavaVecaPhonemeCTCTokenizer(
             SCREAMING_SNAKE_CASE__						,		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=SCREAMING_SNAKE_CASE__						,		)
         lowercase_	:    str           =     True if config.feat_extract_norm == """layer""" else False
         lowercase_	:    Union[str, Any]           =     WavaVecaFeatureExtractor(
             feature_size=1						,		sampling_rate=1_6000						,		padding_value=0						,		do_normalize=SCREAMING_SNAKE_CASE__						,		return_attention_mask=SCREAMING_SNAKE_CASE__						,		)
         lowercase_	:    Union[str, Any]           =     WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__						,		tokenizer=SCREAMING_SNAKE_CASE__							)
         processor.save_pretrained(SCREAMING_SNAKE_CASE__							)

      lowercase_	:    List[str]           =     UniSpeechForCTC(SCREAMING_SNAKE_CASE__							)
   else:
      lowercase_	:    Dict           =     UniSpeechForPreTraining(SCREAMING_SNAKE_CASE__							)

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

   lowercase_	:    Any           =     model[0].eval()

   recursively_load_weights(SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__							)

   hf_unispeech.save_pretrained(SCREAMING_SNAKE_CASE__							)


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'
 )
 UpperCamelCase__ =			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'''
import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase__ =			namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   return (abs(source - target							) / target) < 0.01





@pytest.mark.integration
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     _TestCommandArgs(dataset=_UpperCamelCase						,		all_configs=_UpperCamelCase						,		save_infos=_UpperCamelCase							)
   lowercase_	:    int           =     TestCommand(*_UpperCamelCase							)
   test_command.run()
   lowercase_	:    List[str]           =     os.path.join(_UpperCamelCase						,		"README.md"							)
   assert os.path.exists(_UpperCamelCase							)
   lowercase_	:    Any           =     DatasetInfosDict.from_directory(_UpperCamelCase							)
   lowercase_	:    Optional[int]           =     DatasetInfosDict(
       {
           "default": DatasetInfo(
               features=Features(
                   {
                       "tokens": Sequence(Value("string"							)							),
                       "ner_tags": Sequence(
                           ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]							)							),
                       "langs": Sequence(Value("string"							)							),
                       "spans": Sequence(Value("string"							)							),
                   }							)						,		splits=[
                   {
                       "name": "train",
                       "num_bytes": 235_1563,
                       "num_examples": 1_0000,
                   },
                   {
                       "name": "validation",
                       "num_bytes": 23_8418,
                       "num_examples": 1000,
                   },
               ]						,		download_size=394_0680						,		dataset_size=258_9981						,		)
       }							)
   assert dataset_infos.keys() == expected_dataset_infos.keys()
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      lowercase_    ,		lowercase_	:    Optional[int]           =     getattr(dataset_infos["default"]						,		_UpperCamelCase							), getattr(expected_dataset_infos["default"]						,		_UpperCamelCase							)
      if key == "num_bytes":
         assert is_apercent_close(_UpperCamelCase						,		_UpperCamelCase							)
      elif key == "splits":
         assert list(_UpperCamelCase							) == list(_UpperCamelCase							)
         for split in result:
            assert result[split].name == expected[split].name
            assert result[split].num_examples == expected[split].num_examples
            assert is_apercent_close(result[split].num_bytes						,		expected[split].num_bytes							)
      else:
         result == expected




