'''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 logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


lowerCAmelCase_ : Dict						  =      logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__ )
class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=field(default=__lowerCamelCase ,				metadata={'help': 'Whether to use SortishSampler or not.'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'} )
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
               'to the `max_length` value of the model configuration.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': (
               'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
               'to the `num_beams` value of the model configuration.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=__lowerCamelCase ,				metadata={
           'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
       } ,				)


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Optional[int]:
         lowercase_     =      super().to_dict()
         for k, v in d.items():
               if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
                     lowercase_     =      v.to_dict()
         return d






