'''simple docstring'''
import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
lowercase__  :      str        =					logging.get_logger(__name__)

lowercase__  :      Optional[int]        =					{
    "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": "lm_head",
    "mask_emb": "masked_spec_embed",
}
lowercase__  :      int        =					[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  int							,    _UpperCamelCase  :  List[Any]					):


      '''simple docstring'''


      for attribute in key.split('''.'''					):
            UpperCAmelCase_            =  getattr(_UpperCamelCase							,    _UpperCamelCase					)

      if weight_type is not None:
            UpperCAmelCase_            =  getattr(_UpperCamelCase							,    _UpperCamelCase					).shape
      else:
            UpperCAmelCase_            =  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":
            UpperCAmelCase_            =  value
      elif weight_type == "weight_g":
            UpperCAmelCase_            =  value
      elif weight_type == "weight_v":
            UpperCAmelCase_            =  value
      elif weight_type == "bias":
            UpperCAmelCase_            =  value
      else:
            UpperCAmelCase_            =  value

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



def  __lowerCamelCase       (      _UpperCamelCase  :  List[Any]							,    _UpperCamelCase  :  Optional[Any]					):


      '''simple docstring'''


      UpperCAmelCase_            =  []
      UpperCAmelCase_            =  fairseq_model.state_dict()

      UpperCAmelCase_            =  hf_model.feature_extractor

      # if encoder has different dim to decoder -> use proj_weight
      UpperCAmelCase_            =  None

      for name, value in fairseq_dict.items():
            UpperCAmelCase_            =  False
            if "conv_layers" in name:
                  load_conv_layer(
                      _UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase							,    hf_model.config.feat_extract_norm == '''group'''							,    )
                  UpperCAmelCase_            =  True
            elif name.split('''.'''					)[0] == "proj":
                  UpperCAmelCase_            =  fairseq_model.proj
                  UpperCAmelCase_            =  True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split('''w2v_model.'''					)[-1] == name.split('''.'''					)[0]:
                              UpperCAmelCase_            =  True
                              if "*" in mapped_key:
                                    UpperCAmelCase_            =  name.split(_UpperCamelCase					)[0].split('''.'''					)[-2]
                                    UpperCAmelCase_            =  mapped_key.replace('''*'''							,    _UpperCamelCase					)
                              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:
                                    UpperCAmelCase_            =  '''weight'''
                              else:
                                    UpperCAmelCase_            =  None
                              set_recursively(_UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase					)
                        continue
            if not is_used:
                  unused_weights.append(_UpperCamelCase					)

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

      return proj_weight



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  int							,    _UpperCamelCase  :  List[Any]							,    _UpperCamelCase  :  int							,    _UpperCamelCase  :  List[Any]					):


      '''simple docstring'''


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



def  __lowerCamelCase       (      _UpperCamelCase  :  Any					):


      '''simple docstring'''


      UpperCAmelCase_      ,		UpperCAmelCase_            =  emb.weight.shape
      UpperCAmelCase_            =  nn.Linear(_UpperCamelCase							,    _UpperCamelCase							,    bias=_UpperCamelCase					)
      UpperCAmelCase_            =  emb.weight.data
      return lin_layer



def  __lowerCamelCase       (      _UpperCamelCase  :  Dict					):


      '''simple docstring'''


      with open(_UpperCamelCase							,    '''r'''							,    encoding='''utf-8'''					) as f:
            UpperCAmelCase_            =  f.readlines()
            UpperCAmelCase_            =  [line.split(''' '''					)[0] for line in lines]

      UpperCAmelCase_            =  len(_UpperCamelCase					)

      UpperCAmelCase_            =  {
          '''<s>''': 0,
          '''<pad>''': 1,
          '''</s>''': 2,
          '''<unk>''': 3,
      }

      vocab_dict.update(dict(zip(_UpperCamelCase							,    range(4							,    num_words + 4					)					)					)					)
      return vocab_dict



@torch.no_grad()
def  __lowerCamelCase       (      _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  Dict							,    _UpperCamelCase  :  Any							,    _UpperCamelCase  :  List[Any]							,    _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  str							,    _UpperCamelCase  :  int							,    ):


      '''simple docstring'''


      UpperCAmelCase_            =  WavaVecaConfig.from_pretrained(_UpperCamelCase					)
      UpperCAmelCase_            =  SpeechaTextaConfig.from_pretrained(
          _UpperCamelCase							,    vocab_size=_UpperCamelCase							,    decoder_layers=_UpperCamelCase							,    do_stable_layer_norm=_UpperCamelCase					)

      UpperCAmelCase_            =  WavaVecaFeatureExtractor(
          feature_size=1							,    sampling_rate=1_6000							,    padding_value=0							,    do_normalize=_UpperCamelCase							,    return_attention_mask=_UpperCamelCase							,    )

      UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_            =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]							,    arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''					)[:-1]					)}					)
      UpperCAmelCase_            =  model[0].eval()

      # set weights for wav2vec2 encoder
      UpperCAmelCase_            =  WavaVecaModel(_UpperCamelCase					)
      UpperCAmelCase_            =  recursively_load_weights_wavaveca(model.encoder							,    _UpperCamelCase					)

      UpperCAmelCase_            =  SpeechaTextaForCausalLM(_UpperCamelCase					)
      UpperCAmelCase_      ,		UpperCAmelCase_            =  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()							,    strict=_UpperCamelCase					)

      # set output linear layer
      unexpected_keys.remove('''embed_out'''					)
      UpperCAmelCase_            =  nn.Parameter(model.decoder.embed_out.detach()					)

      # layer norm is init to identity matrix so leaving it is fine
      logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}"""					)
      logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""					)

      UpperCAmelCase_            =  SpeechEncoderDecoderModel(encoder=_UpperCamelCase							,    decoder=_UpperCamelCase					)
      UpperCAmelCase_            =  False

      # add projection layer
      UpperCAmelCase_            =  nn.Parameter(projection_layer.weight					)
      UpperCAmelCase_            =  nn.Parameter(projection_layer.bias					)

      UpperCAmelCase_            =  create_vocab_dict(_UpperCamelCase					)

      with open(os.path.join(_UpperCamelCase							,    '''vocab.json'''					)							,    '''w'''					) as fp:
            json.dump(_UpperCamelCase							,    _UpperCamelCase					)

      UpperCAmelCase_            =  SpeechaTextaTokenizer(os.path.join(_UpperCamelCase							,    '''vocab.json'''					)					)
      tokenizer.save_pretrained(_UpperCamelCase					)

      UpperCAmelCase_            =  hf_wavavec.config.to_dict()
      UpperCAmelCase_            =  tokenizer.pad_token_id
      UpperCAmelCase_            =  tokenizer.bos_token_id
      UpperCAmelCase_            =  tokenizer.eos_token_id
      UpperCAmelCase_            =  '''speech_to_text_2'''
      UpperCAmelCase_            =  '''wav2vec2'''

      UpperCAmelCase_            =  SpeechEncoderDecoderConfig.from_dict(_UpperCamelCase					)

      hf_wavavec.save_pretrained(_UpperCamelCase					)
      feature_extractor.save_pretrained(_UpperCamelCase					)


if __name__ == "__main__":
      lowercase__  :      Any        =					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(
          "--encoder_config_path",
          default="facebook/wav2vec2-large-lv60",
          type=str,
          help="Path to hf encoder wav2vec2 checkpoint config",
      )
      parser.add_argument(
          "--decoder_config_path",
          default="facebook/s2t-small-mustc-en-fr-st",
          type=str,
          help="Path to hf decoder s2t checkpoint config",
      )
      parser.add_argument("--vocab_size", default=1_0224, type=int, help="Vocab size of decoder")
      parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

      lowercase__  :      List[str]        =					parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.dict_path,
          encoder_config_path=args.encoder_config_path,
          decoder_config_path=args.decoder_config_path,
          vocab_size=args.vocab_size,
          num_decoder_layers=args.num_decoder_layers,
      )






'''simple docstring'''
import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowercase__  :      Tuple        =					pytest.mark.integration



@pytest.mark.parametrize('''path'''							,    ['''paws''', '''csv''']					)
def  __lowerCamelCase       (      _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      inspect_dataset(_UpperCamelCase							,    _UpperCamelCase					)
      UpperCAmelCase_            =  path + '''.py'''
      assert script_name in os.listdir(_UpperCamelCase					)
      assert "__pycache__" not in os.listdir(_UpperCamelCase					)



@pytest.mark.filterwarnings('''ignore:inspect_metric is deprecated:FutureWarning'''					)
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''					)
@pytest.mark.parametrize('''path'''							,    ['''accuracy''']					)
def  __lowerCamelCase       (      _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  Optional[Any]					):


      '''simple docstring'''


      inspect_metric(_UpperCamelCase							,    _UpperCamelCase					)
      UpperCAmelCase_            =  path + '''.py'''
      assert script_name in os.listdir(_UpperCamelCase					)
      assert "__pycache__" not in os.listdir(_UpperCamelCase					)



@pytest.mark.parametrize(
    '''path, config_name, expected_splits'''							,    [
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  Any							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_            =  get_dataset_config_info(_UpperCamelCase							,    config_name=_UpperCamelCase					)
      assert info.config_name == config_name
      assert list(info.splits.keys()					) == expected_splits



@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''							,    [
        ('''paws''', None, ValueError),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  Union[str, Any]					):


      '''simple docstring'''


      with pytest.raises(_UpperCamelCase					):
            get_dataset_config_info(_UpperCamelCase							,    config_name=_UpperCamelCase					)



@pytest.mark.parametrize(
    '''path, expected'''							,    [
        ('''squad''', '''plain_text'''),
        ('''acronym_identification''', '''default'''),
        ('''lhoestq/squad''', '''plain_text'''),
        ('''lhoestq/test''', '''default'''),
        ('''lhoestq/demo1''', '''lhoestq--demo1'''),
        ('''dalle-mini/wit''', '''dalle-mini--wit'''),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Optional[Any]					):


      '''simple docstring'''


      UpperCAmelCase_            =  get_dataset_config_names(_UpperCamelCase					)
      assert expected in config_names



@pytest.mark.parametrize(
    '''path, expected_configs, expected_splits_in_first_config'''							,    [
        ('''squad''', ['''plain_text'''], ['''train''', '''validation''']),
        ('''dalle-mini/wit''', ['''dalle-mini--wit'''], ['''train''']),
        ('''paws''', ['''labeled_final''', '''labeled_swap''', '''unlabeled_final'''], ['''train''', '''test''', '''validation''']),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      UpperCAmelCase_            =  get_dataset_infos(_UpperCamelCase					)
      assert list(infos.keys()					) == expected_configs
      UpperCAmelCase_            =  expected_configs[0]
      assert expected_config in infos
      UpperCAmelCase_            =  infos[expected_config]
      assert info.config_name == expected_config
      assert list(info.splits.keys()					) == expected_splits_in_first_config



@pytest.mark.parametrize(
    '''path, expected_config, expected_splits'''							,    [
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  int							,    _UpperCamelCase  :  str					):


      '''simple docstring'''


      UpperCAmelCase_            =  get_dataset_infos(_UpperCamelCase					)
      assert expected_config in infos
      UpperCAmelCase_            =  infos[expected_config]
      assert info.config_name == expected_config
      assert list(info.splits.keys()					) == expected_splits





@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''							,    [
        ('''paws''', None, ValueError),
    ]							,    )
def  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  Optional[int]					):


      '''simple docstring'''


      with pytest.raises(_UpperCamelCase					):
            get_dataset_split_names(_UpperCamelCase							,    config_name=_UpperCamelCase					)





