import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, 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',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    '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',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
UpperCamelCase__    							= [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]




def        lowerCAmelCase_			(					__A,  __A,  __A,  __A,  __A							)					->    Optional[Any]:







      '''simple docstring'''


      for attribute in key.split("."							):
            UpperCAmelCase__								=   getattr(__a,  __a							)

      if weight_type is not None:
            UpperCAmelCase__								=   getattr(__a,  __a							).shape
      else:
            UpperCAmelCase__								=   hf_pointer.shape

      if hf_shape != value.shape:
            raise ValueError(
                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_			(					__A,  __A							)					->    Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=   []
      UpperCAmelCase__								=   fairseq_model.state_dict()

      UpperCAmelCase__								=   hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            UpperCAmelCase__								=   False
            if "conv_layers" in name:
                  load_conv_layer(
                      __a,  __a,  __a,  __a,  hf_model.config.feat_extract_norm == "group",  )
                  UpperCAmelCase__								=   True
            else:
                  for key, mapped_key in MAPPING.items():
                        UpperCAmelCase__								=   'unispeech_sat.' + 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]:
                              if "layer_norm_for_extract" in name and (".".join(name.split("."							)[:-1]							) != key):
                                    # special case since naming is very similar
                                    continue
                              UpperCAmelCase__								=   True
                              if "*" in mapped_key:
                                    UpperCAmelCase__								=   name.split(__a							)[0].split("."							)[-2]
                                    UpperCAmelCase__								=   mapped_key.replace("*",  __a							)
                              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:
                                    # TODO: don't match quantizer.weight_proj
                                    UpperCAmelCase__								=   'weight'
                              else:
                                    UpperCAmelCase__								=   None
                              set_recursively(__a,  __a,  __a,  __a,  __a							)
                        continue
            if not is_used:
                  unused_weights.append(__a							)

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




def        lowerCAmelCase_			(					__A,  __A,  __A,  __A,  __A							)					->    int:







      '''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:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                        raise ValueError(
                            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:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                        raise ValueError(
                            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:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                        raise ValueError(
                            f"""{full_name} has size {value.shape}, but"""
                            f""" {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:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                        raise ValueError(
                            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(__a							)




@torch.no_grad()
def        lowerCAmelCase_			(					__A,  __A,  __A=None,  __A=None,  __A=True							)					->    str:







      '''simple docstring'''


      if config_path is not None:
            UpperCAmelCase__								=   UniSpeechSatConfig.from_pretrained(__a							)
      else:
            UpperCAmelCase__								=   UniSpeechSatConfig()

      UpperCAmelCase__								=   ''

      if is_finetuned:
            UpperCAmelCase__								=   UniSpeechSatForCTC(__a							)
      else:
            UpperCAmelCase__								=   UniSpeechSatForPreTraining(__a							)

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

      recursively_load_weights(__a,  __a							)

      hf_wavavec.save_pretrained(__a							)


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_sat_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )




import argparse
import gc
import json
import os

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


UpperCamelCase__    							= 1_6
UpperCamelCase__    							= 3_2




def        lowerCAmelCase_			(					__A							)					->    Union[str, Any]:







      '''simple docstring'''


      return int(x / 2**20							)





class 					A							:


   def __enter__(self					:						Dict		)						->    int:
         """simple docstring"""



         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         UpperCAmelCase__								=  torch.cuda.memory_allocated()
         return self





   def __exit__(self					:						List[str]	,  *__UpperCAmelCase					:						int		)						->    Optional[int]:
         """simple docstring"""



         gc.collect()
         torch.cuda.empty_cache()
         UpperCAmelCase__								=  torch.cuda.memory_allocated()
         UpperCAmelCase__								=  torch.cuda.max_memory_allocated()
         UpperCAmelCase__								=  bamb(self.end - self.begin		)
         UpperCAmelCase__								=  bamb(self.peak - self.begin		)
         # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")




def        lowerCAmelCase_			(					__A,  __A = 16,  __A = "bert-base-cased",  __A = 320,  __A = 160,  )					->    List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  AutoTokenizer.from_pretrained(__A							)
      UpperCAmelCase__								=  load_dataset(
          "glue",  "mrpc",  split={"train": f"""train[:{n_train}]""", "validation": f"""validation[:{n_val}]"""}							)

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

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

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

      def collate_fn(__A							):
            # 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(__A,  padding="max_length",  max_length=128,  return_tensors="pt"							)
            return tokenizer.pad(__A,  padding="longest",  return_tensors="pt"							)

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

      return train_dataloader, eval_dataloader




def        lowerCAmelCase_			(					__A,  __A							)					->    Dict:







      '''simple docstring'''


      UpperCAmelCase__								=  Accelerator()

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

      set_seed(__A							)
      UpperCAmelCase__		,				UpperCAmelCase__								=  get_dataloaders(__A,  __A,  __A,  args.n_train,  args.n_val							)

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

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

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

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

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

      # Now we train the model
      UpperCAmelCase__								=  {}
      for epoch in range(__A,  __A							):
            with TorchTracemalloc() as tracemalloc:
                  model.train()
                  for step, batch in enumerate(__A							):
                        UpperCAmelCase__								=  model(**__A							)
                        UpperCAmelCase__								=  outputs.loss
                        UpperCAmelCase__								=  loss / gradient_accumulation_steps
                        accelerator.backward(__A							)
                        if step % gradient_accumulation_steps == 0:
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()

                        overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
            accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin							)							)							)
            accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used							)							)
            accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked							)							)
            accelerator.print(
                "Total Peak Memory consumed during the train (max): {}".format(
                    tracemalloc.peaked + bamb(tracemalloc.begin							)							)							)
            UpperCAmelCase__								=  tracemalloc.peaked + bamb(tracemalloc.begin							)
            if args.peak_memory_upper_bound is not None:
                  assert (
                      train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
                  ), "Peak memory usage exceeded the upper bound"

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




def        lowerCAmelCase_			(					)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."							)
      parser.add_argument(
          "--model_name_or_path",  type=__A,  default="bert-base-cased",  help="Path to pretrained model or model identifier from huggingface.co/models.",  required=__A,  )
      parser.add_argument(
          "--output_dir",  type=__A,  default=".",  help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.",  )
      parser.add_argument(
          "--peak_memory_upper_bound",  type=__A,  default=__A,  help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.",  )
      parser.add_argument(
          "--n_train",  type=__A,  default=320,  help="Number of training examples to use.",  )
      parser.add_argument(
          "--n_val",  type=__A,  default=160,  help="Number of validation examples to use.",  )
      parser.add_argument(
          "--num_epochs",  type=__A,  default=1,  help="Number of train epochs.",  )
      UpperCAmelCase__								=  parser.parse_args()
      UpperCAmelCase__								=  {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
      training_function(__A,  __A							)


if __name__ == "__main__":
      main()



