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

lowerCAmelCase_ : 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",
}
lowerCAmelCase_ : List[Any]						  =      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]


def __a (	__lowerCamelCase			:						Any ,		__lowerCamelCase			:						Dict ,		__lowerCamelCase			:						Any ,		__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						Union[str, Any]				)	->							List[Any]:
      '''simple docstring'''


      for attribute in key.split("."				):
            lowercase_     =      getattr(__lowerCamelCase ,		__lowerCamelCase				)

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

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


def __a (	__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Tuple				)	->							List[str]:
      '''simple docstring'''


      lowercase_     =      []
      lowercase_     =      fairseq_model.state_dict()

      lowercase_     =      hf_model.feature_extractor

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

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

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

      return proj_weight


def __a (	__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						Any ,		__lowerCamelCase			:						List[str] ,		__lowerCamelCase			:						str ,		__lowerCamelCase			:						Union[str, Any]				)	->							List[Any]:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						Union[str, Any]				)	->							Dict:
      '''simple docstring'''


      lowercase_ ,							lowercase_     =      emb.weight.shape
      lowercase_     =      nn.Linear(__lowerCamelCase ,		__lowerCamelCase ,		bias=__lowerCamelCase				)
      lowercase_     =      emb.weight.data
      return lin_layer


def __a (	__lowerCamelCase			:						Optional[Any]				)	->							List[Any]:
      '''simple docstring'''


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

      lowercase_     =      len(__lowerCamelCase				)

      lowercase_     =      {
          "<s>": 0,
          "<pad>": 1,
          "</s>": 2,
          "<unk>": 3,
      }

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


@torch.no_grad()
def __a (	__lowerCamelCase			:						Optional[Any] ,		__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						Any ,		__lowerCamelCase			:						int ,		__lowerCamelCase			:						str ,		__lowerCamelCase			:						Optional[Any] ,		)	->							int:
      '''simple docstring'''


      lowercase_     =      WavaVecaConfig.from_pretrained(__lowerCamelCase				)
      lowercase_     =      SpeechaTextaConfig.from_pretrained(
          __lowerCamelCase ,		vocab_size=__lowerCamelCase ,		decoder_layers=__lowerCamelCase ,		do_stable_layer_norm=__lowerCamelCase				)

      lowercase_     =      WavaVecaFeatureExtractor(
          feature_size=1 ,		sampling_rate=16_000 ,		padding_value=0 ,		do_normalize=__lowerCamelCase ,		return_attention_mask=__lowerCamelCase ,		)

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

      # set weights for wav2vec2 encoder
      lowercase_     =      WavaVecaModel(__lowerCamelCase				)
      lowercase_     =      recursively_load_weights_wavaveca(model.encoder ,		__lowerCamelCase				)

      lowercase_     =      SpeechaTextaForCausalLM(__lowerCamelCase				)
      lowercase_ ,							lowercase_     =      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,		strict=__lowerCamelCase				)

      # set output linear layer
      unexpected_keys.remove("embed_out"				)
      lowercase_     =      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}'				)

      lowercase_     =      SpeechEncoderDecoderModel(encoder=__lowerCamelCase ,		decoder=__lowerCamelCase				)
      lowercase_     =      False

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

      lowercase_     =      create_vocab_dict(__lowerCamelCase				)

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

      lowercase_     =      SpeechaTextaTokenizer(os.path.join(__lowerCamelCase ,		"vocab.json"				)				)
      tokenizer.save_pretrained(__lowerCamelCase				)

      lowercase_     =      hf_wavavec.config.to_dict()
      lowercase_     =      tokenizer.pad_token_id
      lowercase_     =      tokenizer.bos_token_id
      lowercase_     =      tokenizer.eos_token_id
      lowercase_     =      "speech_to_text_2"
      lowercase_     =      "wav2vec2"

      lowercase_     =      SpeechEncoderDecoderConfig.from_dict(__lowerCamelCase				)

      hf_wavavec.save_pretrained(__lowerCamelCase				)
      feature_extractor.save_pretrained(__lowerCamelCase				)


if __name__ == "__main__":
       lowerCAmelCase_ : List[str]						  =      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=10_224, type=int, help="Vocab size of decoder")
       parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

       lowerCAmelCase_ : Optional[Any]						  =      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 io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


lowerCAmelCase_ : Union[str, Any]						  =      datasets.utils.logging.get_logger(__name__)





@dataclass
class 							lowercase						(						datasets.BuilderConfig ):
   lowerCamelCase_    					=None
   lowerCamelCase_    					="utf-8"
   lowerCamelCase_    					=None
   lowerCamelCase_    					=None
   lowerCamelCase_    					=True  # deprecated
   lowerCamelCase_    					=None  # deprecated
   lowerCamelCase_    					=1_0 << 2_0  # 10MB
   lowerCamelCase_    					=None





class 							lowercase						(						datasets.ArrowBasedBuilder ):
   lowerCamelCase_    					=JsonConfig


   def __UpperCAmelCase       (   self				:	int)      ->					List[str]:
         if self.config.block_size is not None:
               logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead")
               lowercase_     =      self.config.block_size
         if self.config.use_threads is not True:
               logger.warning(
                   "The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore.")
         if self.config.newlines_in_values is not None:
               raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported")
         return datasets.DatasetInfo(features=self.config.features)


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	int)      ->					Any:
         if not self.config.data_files:
               raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}')
         lowercase_     =      dl_manager.download_and_extract(self.config.data_files)
         if isinstance(__lowerCAmelCase   ,		(str, list, tuple)):
               lowercase_     =      data_files
               if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
                     lowercase_     =      [files]
               lowercase_     =      [dl_manager.iter_files(__lowerCAmelCase) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,		gen_kwargs={"files": files})]
         lowercase_     =      []
         for split_name, files in data_files.items():
               if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
                     lowercase_     =      [files]
               lowercase_     =      [dl_manager.iter_files(__lowerCAmelCase) for file in files]
               splits.append(datasets.SplitGenerator(name=__lowerCAmelCase   ,		gen_kwargs={"files": files}))
         return splits


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	pa.Table)      ->					pa.Table:
         if self.config.features is not None:
               # adding missing columns
               for column_name in set(self.config.features) - set(pa_table.column_names):
                     lowercase_     =      self.config.features.arrow_schema.field(__lowerCAmelCase).type
                     lowercase_     =      pa_table.append_column(__lowerCAmelCase   ,		pa.array([None] * len(__lowerCAmelCase)   ,		type=__lowerCAmelCase))
               # more expensive cast to support nested structures with keys in a different order
               # allows str <-> int/float or str to Audio for example
               lowercase_     =      table_cast(__lowerCAmelCase   ,		self.config.features.arrow_schema)
         return pa_table


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	List[Any])      ->					List[Any]:
         for file_idx, file in enumerate(itertools.chain.from_iterable(__lowerCAmelCase)):
               # If the file is one json object and if we need to look at the list of items in one specific field
               if self.config.field is not None:
                     with open(__lowerCAmelCase   ,		encoding=self.config.encoding   ,		errors=self.config.encoding_errors) as f:
                           lowercase_     =      json.load(__lowerCAmelCase)

                     # We keep only the field we are interested in
                     lowercase_     =      dataset[self.config.field]

                     # We accept two format: a list of dicts or a dict of lists
                     if isinstance(__lowerCAmelCase   ,		(list, tuple)):
                           lowercase_     =      set().union(*[row.keys() for row in dataset])
                           lowercase_     =      {col: [row.get(__lowerCAmelCase) for row in dataset] for col in keys}
                     else:
                           lowercase_     =      dataset
                     lowercase_     =      pa.Table.from_pydict(__lowerCAmelCase)
                     yield file_idx, self._cast_table(__lowerCAmelCase)

               # If the file has one json object per line
               else:
                     with open(__lowerCAmelCase   ,		"rb") as f:
                           lowercase_     =      0
                           # Use block_size equal to the chunk size divided by 32 to leverage multithreading
                           # Set a default minimum value of 16kB if the chunk size is really small
                           lowercase_     =      max(self.config.chunksize // 32   ,		16 << 10)
                           lowercase_     =      (
                               self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
                           )
                           while True:
                                 lowercase_     =      f.read(self.config.chunksize)
                                 if not batch:
                                       break
                                 # Finish current line
                                 try:
                                       batch += f.readline()
                                 except (AttributeError, io.UnsupportedOperation):
                                       batch += readline(__lowerCAmelCase)
                                 # PyArrow only accepts utf-8 encoded bytes
                                 if self.config.encoding != "utf-8":
                                       lowercase_     =      batch.decode(self.config.encoding   ,		errors=__lowerCAmelCase).encode("utf-8")
                                 try:
                                       while True:
                                             try:
                                                   lowercase_     =      paj.read_json(
                                                       io.BytesIO(__lowerCAmelCase)   ,		read_options=paj.ReadOptions(block_size=__lowerCAmelCase))
                                                   break
                                             except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                   if (
                                                       isinstance(__lowerCAmelCase   ,		pa.ArrowInvalid)
                                                       and "straddling" not in str(__lowerCAmelCase)
                                                       or block_size > len(__lowerCAmelCase)
                                                   ):
                                                         raise
                                                   else:
                                                         # Increase the block size in case it was too small.
                                                         # The block size will be reset for the next file.
                                                         logger.debug(
                                                             F'Batch of {len(__lowerCAmelCase)} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.')
                                                         block_size *= 2
                                 except pa.ArrowInvalid as e:
                                       try:
                                             with open(
                                                 __lowerCAmelCase   ,		encoding=self.config.encoding   ,		errors=self.config.encoding_errors) as f:
                                                   lowercase_     =      json.load(__lowerCAmelCase)
                                       except json.JSONDecodeError:
                                             logger.error(F'Failed to read file \'{file}\' with error {type(__lowerCAmelCase)}: {e}')
                                             raise e
                                       # If possible, parse the file as a list of json objects and exit the loop
                                       if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):  # list is the only sequence type supported in JSON
                                             try:
                                                   lowercase_     =      set().union(*[row.keys() for row in dataset])
                                                   lowercase_     =      {col: [row.get(__lowerCAmelCase) for row in dataset] for col in keys}
                                                   lowercase_     =      pa.Table.from_pydict(__lowerCAmelCase)
                                             except (pa.ArrowInvalid, AttributeError) as e:
                                                   logger.error(F'Failed to read file \'{file}\' with error {type(__lowerCAmelCase)}: {e}')
                                                   raise ValueError(F'Not able to read records in the JSON file at {file}.') from None
                                             yield file_idx, self._cast_table(__lowerCAmelCase)
                                             break
                                       else:
                                             logger.error(F'Failed to read file \'{file}\' with error {type(__lowerCAmelCase)}: {e}')
                                             raise ValueError(
                                                 F'Not able to read records in the JSON file at {file}. '
                                                 F'You should probably indicate the field of the JSON file containing your records. '
                                                 F'This JSON file contain the following fields: {str(list(dataset.keys()))}. '
                                                 F'Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ') from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                 yield (file_idx, batch_idx), self._cast_table(__lowerCAmelCase)
                                 batch_idx += 1






