



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()
a_						:     str     =		logging.get_logger(__name__)

a_						:     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',
}
a_						:     Optional[int]     =		[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      for attribute in key.split("." ):
            lowerCamelCase								=		getattr(lowerCamelCase_   ,   lowerCamelCase_ )

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

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

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		[]
      lowerCamelCase								=		fairseq_model.state_dict()

      lowerCamelCase								=		hf_model.feature_extractor

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

      for name, value in fairseq_dict.items():
            lowerCamelCase								=		False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   hf_model.config.feat_extract_norm == "group"   ,   )
                  lowerCamelCase								=		True
            elif name.split("." )[0] == "proj":
                  lowerCamelCase								=		fairseq_model.proj
                  lowerCamelCase								=		True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
                              lowerCamelCase								=		True
                              if "*" in mapped_key:
                                    lowerCamelCase								=		name.split(lowerCamelCase_ )[0].split("." )[-2]
                                    lowerCamelCase								=		mapped_key.replace("*"   ,   lowerCamelCase_ )
                              if "weight_g" in name:
                                    lowerCamelCase								=		"""weight_g"""
                              elif "weight_v" in name:
                                    lowerCamelCase								=		"""weight_v"""
                              elif "bias" in name:
                                    lowerCamelCase								=		"""bias"""
                              elif "weight" in name:
                                    lowerCamelCase								=		"""weight"""
                              else:
                                    lowerCamelCase								=		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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



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

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		emb.weight.shape
      lowerCamelCase								=		nn.Linear(lowerCamelCase_   ,   lowerCamelCase_   ,   bias=lowerCamelCase_ )
      lowerCamelCase								=		emb.weight.data
      return lin_layer

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



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

      lowerCamelCase								=		len(lowerCamelCase_ )

      lowerCamelCase								=		{
          """<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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   ):


      """simple docstring"""



      lowerCamelCase								=		WavaVecaConfig.from_pretrained(lowerCamelCase_ )
      lowerCamelCase								=		SpeechaTextaConfig.from_pretrained(
          lowerCamelCase_   ,   vocab_size=lowerCamelCase_   ,   decoder_layers=lowerCamelCase_   ,   do_stable_layer_norm=lowerCamelCase_ )

      lowerCamelCase								=		WavaVecaFeatureExtractor(
          feature_size=1   ,   sampling_rate=16000   ,   padding_value=0   ,   do_normalize=lowerCamelCase_   ,   return_attention_mask=lowerCamelCase_   ,   )

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

      # set weights for wav2vec2 encoder
      lowerCamelCase								=		WavaVecaModel(lowerCamelCase_ )
      lowerCamelCase								=		recursively_load_weights_wavaveca(model.encoder   ,   lowerCamelCase_ )

      lowerCamelCase								=		SpeechaTextaForCausalLM(lowerCamelCase_ )
      lowerCamelCase								=		hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()   ,   strict=lowerCamelCase_ )

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

      lowerCamelCase								=		SpeechEncoderDecoderModel(encoder=lowerCamelCase_   ,   decoder=lowerCamelCase_ )
      lowerCamelCase								=		False

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

      lowerCamelCase								=		create_vocab_dict(lowerCamelCase_ )

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

      lowerCamelCase								=		SpeechaTextaTokenizer(os.path.join(lowerCamelCase_   ,   "vocab.json" ) )
      tokenizer.save_pretrained(lowerCamelCase_ )

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

      lowerCamelCase								=		SpeechEncoderDecoderConfig.from_dict(lowerCamelCase_ )

      hf_wavavec.save_pretrained(lowerCamelCase_ )
      feature_extractor.save_pretrained(lowerCamelCase_ )


if __name__ == "__main__":
     a_						:     List[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_0_2_2_4, type=int, help='Vocab size of decoder')
     parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

     a_						:     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,
     )




import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


a_						:     Any	     =		1_6
a_						:     Any	     =		3_2

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ = 16   ,   UpperCAmelCase__ = "bert-base-cased" ):


      """simple docstring"""



      lowerCamelCase								=		AutoTokenizer.from_pretrained(UpperCAmelCase__ )
      lowerCamelCase								=		load_dataset("glue"   ,   "mrpc" )

      def tokenize_function(UpperCAmelCase__ ):
            # max_length=None => use the model max length (it's actually the default)
            lowerCamelCase								=		tokenizer(examples["sentence1"]   ,   examples["sentence2"]   ,   truncation=UpperCAmelCase__   ,   max_length=UpperCAmelCase__ )
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      lowerCamelCase								=		datasets.map(
          UpperCAmelCase__   ,   batched=UpperCAmelCase__   ,   remove_columns=["idx", "sentence1", "sentence2"]   ,   load_from_cache_file=UpperCAmelCase__ )

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      lowerCamelCase								=		tokenized_datasets.rename_column("label"   ,   "labels" )

      def collate_fn(UpperCAmelCase__ ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(UpperCAmelCase__   ,   padding="max_length"   ,   max_length=128   ,   return_tensors="pt" )
            return tokenizer.pad(UpperCAmelCase__   ,   padding="longest"   ,   return_tensors="pt" )

      # Instantiate dataloaders.
      lowerCamelCase								=		DataLoader(
          tokenized_datasets["train"]   ,   shuffle=UpperCAmelCase__   ,   collate_fn=UpperCAmelCase__   ,   batch_size=UpperCAmelCase__ )
      lowerCamelCase								=		DataLoader(
          tokenized_datasets["validation"]   ,   shuffle=UpperCAmelCase__   ,   collate_fn=UpperCAmelCase__   ,   batch_size=UpperCAmelCase__ )

      return train_dataloader, eval_dataloader

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		Accelerator()

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCamelCase								=		config["lr"]
      lowerCamelCase								=		int(config["num_epochs"] )
      lowerCamelCase								=		int(config["seed"] )
      lowerCamelCase								=		int(config["batch_size"] )
      lowerCamelCase								=		args.model_name_or_path

      set_seed(UpperCAmelCase__ )
      lowerCamelCase			,							lowerCamelCase								=		get_dataloaders(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowerCamelCase								=		AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__   ,   return_dict=UpperCAmelCase__ )

      # Instantiate optimizer
      lowerCamelCase								=		(
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      lowerCamelCase								=		optimizer_cls(params=model.parameters()   ,   lr=UpperCAmelCase__ )

      if accelerator.state.deepspeed_plugin is not None:
            lowerCamelCase								=		accelerator.state.deepspeed_plugin.deepspeed_config[
                "gradient_accumulation_steps"
            ]
      else:
            lowerCamelCase								=		1
      lowerCamelCase								=		(len(UpperCAmelCase__ ) * num_epochs) // gradient_accumulation_steps

      # Instantiate scheduler
      if (
          accelerator.state.deepspeed_plugin is None
          or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
      ):
            lowerCamelCase								=		get_linear_schedule_with_warmup(
                optimizer=UpperCAmelCase__   ,   num_warmup_steps=0   ,   num_training_steps=UpperCAmelCase__   ,   )
      else:
            lowerCamelCase								=		DummyScheduler(UpperCAmelCase__   ,   total_num_steps=UpperCAmelCase__   ,   warmup_num_steps=0 )

      # Prepare everything
      # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
      # prepare method.
      lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		accelerator.prepare(
          UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ )

      # We need to keep track of how many total steps we have iterated over
      lowerCamelCase								=		0
      # We also need to keep track of the stating epoch so files are named properly
      lowerCamelCase								=		0

      # Now we train the model
      lowerCamelCase								=		evaluate.load("glue"   ,   "mrpc" )
      lowerCamelCase								=		0
      lowerCamelCase								=		{}
      for epoch in range(UpperCAmelCase__   ,   UpperCAmelCase__ ):
            model.train()
            for step, batch in enumerate(UpperCAmelCase__ ):
                  lowerCamelCase								=		model(**UpperCAmelCase__ )
                  lowerCamelCase								=		outputs.loss
                  lowerCamelCase								=		loss / gradient_accumulation_steps
                  accelerator.backward(UpperCAmelCase__ )
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1

            model.eval()
            lowerCamelCase								=		0
            for step, batch in enumerate(UpperCAmelCase__ ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device )
                  with torch.no_grad():
                        lowerCamelCase								=		model(**UpperCAmelCase__ )
                  lowerCamelCase								=		outputs.logits.argmax(dim=-1 )
                  # It is slightly faster to call this once, than multiple times
                  lowerCamelCase			,							lowerCamelCase								=		accelerator.gather(
                      (predictions, batch["labels"]) )  # If we are in a multiprocess environment, the last batch has duplicates
                  if accelerator.use_distributed:
                        if step == len(UpperCAmelCase__ ) - 1:
                              lowerCamelCase								=		predictions[: len(eval_dataloader.dataset ) - samples_seen]
                              lowerCamelCase								=		references[: len(eval_dataloader.dataset ) - samples_seen]
                        else:
                              samples_seen += references.shape[0]
                  metric.add_batch(
                      predictions=UpperCAmelCase__   ,   references=UpperCAmelCase__   ,   )

            lowerCamelCase								=		metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F"""epoch {epoch}:"""   ,   UpperCAmelCase__ )
            lowerCamelCase								=		eval_metric["accuracy"]

            if best_performance < eval_metric["accuracy"]:
                  lowerCamelCase								=		eval_metric["accuracy"]

      if args.performance_lower_bound is not None:
            assert (
                args.performance_lower_bound <= best_performance
            ), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
            with open(os.path.join(args.output_dir   ,   "all_results.json" )   ,   "w" ) as f:
                  json.dump(UpperCAmelCase__   ,   UpperCAmelCase__ )

def     __lowercase(	):


      """simple docstring"""



      lowerCamelCase								=		argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage." )
      parser.add_argument(
          "--model_name_or_path"   ,   type=UpperCAmelCase__   ,   default="bert-base-cased"   ,   help="Path to pretrained model or model identifier from huggingface.co/models."   ,   required=UpperCAmelCase__   ,   )
      parser.add_argument(
          "--output_dir"   ,   type=UpperCAmelCase__   ,   default="."   ,   help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."   ,   )
      parser.add_argument(
          "--performance_lower_bound"   ,   type=UpperCAmelCase__   ,   default=UpperCAmelCase__   ,   help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."   ,   )
      parser.add_argument(
          "--num_epochs"   ,   type=UpperCAmelCase__   ,   default=3   ,   help="Number of train epochs."   ,   )
      lowerCamelCase								=		parser.parse_args()
      lowerCamelCase								=		{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
      training_function(UpperCAmelCase__   ,   UpperCAmelCase__ )


if __name__ == "__main__":
     main()