
import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_a      :			str					=							16
_a      :			Union[str, Any]					=							32
def 				a__   (   a   :				Union[str, Any]       ,     a   :				Optional[int] = 16		):

  """simple docstring"""







  _snake_case     :						Tuple						=   AutoTokenizer.from_pretrained("bert-base-cased"		)
  _snake_case     :						Dict						=   load_dataset("glue"       ,     "mrpc"		)

  def tokenize_function(a   :				Union[str, Any]		):
    # max_length=None => use the model max length (it's actually the default)
    _snake_case     :						Any						=   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
  # starting with the main process first:
  with accelerator.main_process_first():
    _snake_case     :						Optional[int]						=   datasets.map(
        a__       ,     batched=a__       ,     remove_columns=["idx", "sentence1", "sentence2"]       ,     )

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

  def collate_fn(a   :				str		):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _snake_case     :						Dict						=   128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _snake_case     :						int						=   16
    elif accelerator.mixed_precision != "no":
      _snake_case     :						str						=   8
    else:
      _snake_case     :						Optional[int]						=   None

    return tokenizer.pad(
        a__       ,     padding="longest"       ,     max_length=a__       ,     pad_to_multiple_of=a__       ,     return_tensors="pt"       ,     )

  # Instantiate dataloaders.
  _snake_case     :						Union[str, Any]						=   DataLoader(
      tokenized_datasets["train"]       ,     shuffle=a__       ,     collate_fn=a__       ,     batch_size=a__		)
  _snake_case     :						int						=   DataLoader(
      tokenized_datasets["validation"]       ,     shuffle=a__       ,     collate_fn=a__       ,     batch_size=a__		)

  return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    _a      :			int					=							mocked_dataloaders  # noqa: F811
def 				a__   (   a   :				str       ,     a   :				Any		):

  """simple docstring"""







  if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,     a__		) == "1":
    _snake_case     :						Union[str, Any]						=   2
  # Initialize accelerator
  _snake_case     :						List[Any]						=   Accelerator(cpu=args.cpu       ,     mixed_precision=args.mixed_precision		)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _snake_case     :						Dict						=   config["lr"]
  _snake_case     :						str						=   int(config["num_epochs"]		)
  _snake_case     :						Optional[int]						=   int(config["seed"]		)
  _snake_case     :						Optional[int]						=   int(config["batch_size"]		)

  _snake_case     :						Tuple						=   evaluate.load("glue"       ,     "mrpc"		)

  # If the batch size is too big we use gradient accumulation
  _snake_case     :						int						=   1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    _snake_case     :						List[str]						=   batch_size // MAX_GPU_BATCH_SIZE
    _snake_case     :						Dict						=   MAX_GPU_BATCH_SIZE

  set_seed(a__		)
  _snake_case			,					_snake_case     :						Optional[Any]						=   get_dataloaders(a__       ,     a__		)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _snake_case     :						Union[str, Any]						=   AutoModelForSequenceClassification.from_pretrained("bert-base-cased"       ,     return_dict=a__		)

  # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
  # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
  # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
  _snake_case     :						int						=   model.to(accelerator.device		)

  # Instantiate optimizer
  _snake_case     :						Dict						=   AdamW(params=model.parameters()       ,     lr=a__		)

  # Instantiate scheduler
  _snake_case     :						Any						=   get_linear_schedule_with_warmup(
      optimizer=a__       ,     num_warmup_steps=100       ,     num_training_steps=(len(a__		) * num_epochs) // gradient_accumulation_steps       ,     )

  # 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.
  _snake_case			,					_snake_case			,					_snake_case			,					_snake_case			,					_snake_case     :						Optional[int]						=   accelerator.prepare(
      a__       ,     a__       ,     a__       ,     a__       ,     a__		)

  # Now we train the model
  for epoch in range(a__		):
    model.train()
    for step, batch in enumerate(a__		):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device		)
      _snake_case     :						Tuple						=   model(**a__		)
      _snake_case     :						int						=   outputs.loss
      _snake_case     :						Optional[int]						=   loss / gradient_accumulation_steps
      accelerator.backward(a__		)
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    _snake_case     :						List[Any]						=   0
    for step, batch in enumerate(a__		):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device		)
      with torch.no_grad():
        _snake_case     :						Optional[Any]						=   model(**a__		)
      _snake_case     :						Union[str, Any]						=   outputs.logits.argmax(dim=-1		)
      _snake_case			,					_snake_case     :						Optional[Any]						=   accelerator.gather((predictions, batch["labels"])		)
      # New Code #
      # First we check if it's a distributed system
      if accelerator.use_distributed:
        # Then see if we're on the last batch of our eval dataloader
        if step == len(a__		) - 1:
          # Last batch needs to be truncated on distributed systems as it contains additional samples
          _snake_case     :						str						=   predictions[: len(eval_dataloader.dataset		) - samples_seen]
          _snake_case     :						Any						=   references[: len(eval_dataloader.dataset		) - samples_seen]
        else:
          # Otherwise we add the number of samples seen
          samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
      metric.add_batch(
          predictions=a__       ,     references=a__       ,     )

    _snake_case     :						List[str]						=   metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'epoch {epoch}:'       ,     a__		)
def 				a__   (   ):

  """simple docstring"""







  _snake_case     :						int						=   argparse.ArgumentParser(description="Simple example of training script."		)
  parser.add_argument(
      "--mixed_precision"       ,     type=a__       ,     default=a__       ,     choices=["no", "fp16", "bf16", "fp8"]       ,     help="Whether to use mixed precision. Choose"
      "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
      "and an Nvidia Ampere GPU."       ,     )
  parser.add_argument("--cpu"       ,     action="store_true"       ,     help="If passed, will train on the CPU."		)
  _snake_case     :						Tuple						=   parser.parse_args()
  _snake_case     :						Optional[Any]						=   {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(a__       ,     a__		)


if __name__ == "__main__":
    main()



"""simple docstring"""
import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def 				a__   (   a   :				List[str]       ,     a   :				Any		):

  """simple docstring"""







  if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
    # expert layer
    _snake_case     :						Any						=   flax_key_tuple[:-1] + ("weight",)
    _snake_case     :						str						=   torch.permute(a       ,     (0, 2, 1)		)
  elif flax_key_tuple[-1] == "kernel" and ".".join(a		):
    # linear layer
    _snake_case     :						Optional[int]						=   flax_key_tuple[:-1] + ("weight",)
    _snake_case     :						Any						=   flax_tensor.T
  elif flax_key_tuple[-1] in ["scale", "embedding"]:
    _snake_case     :						Union[str, Any]						=   flax_key_tuple[:-1] + ("weight",)

  return flax_key_tuple, flax_tensor
def 				a__   (   a   :				List[Any]       ,     a   :				Union[str, Any]       ,     a   :				List[str]		):

  """simple docstring"""







  if "metadata" in layer:
    _snake_case     :						Optional[int]						=   layer.split("metadata"		)
    _snake_case     :						Optional[int]						=   "".join(split_layer[0]		)[:-1]
    _snake_case     :						int						=   [tuple(("metadata" + split_layer[1]).split("/"		)		)]
  elif "kvstore" in layer:
    _snake_case     :						Any						=   layer.split("kvstore"		)
    _snake_case     :						str						=   "".join(split_layer[0]		)[:-1]
    _snake_case     :						Any						=   [tuple(("kvstore" + split_layer[1]).split("/"		)		)]

  else:
    _snake_case     :						List[Any]						=   layer.split("/"		)
    _snake_case     :						Tuple						=   "/".join(split_layer[:-1]		)
    _snake_case     :						int						=   (split_layer[-1],)

  if "kvstore/path" in layer:
    _snake_case     :						Optional[Any]						=   f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
  elif "kvstore/driver" in layer:
    _snake_case     :						Tuple						=   "file"
  else:
    _snake_case     :						Optional[int]						=   checkpoint_info[layer]

  return curr_real_layer_name, split_layer, content
def 				a__   (   a   :				List[Any]       ,     a   :				List[Any]		):

  """simple docstring"""







  _snake_case     :						Union[str, Any]						=   rename_keys(a		)
  _snake_case     :						int						=   {}
  for k, v in current_block.items():
    _snake_case     :						Optional[int]						=   v
  _snake_case     :						Optional[int]						=   new_current_block
  torch.save(a       ,     a		)
def 				a__   (   a   :				Dict       ,     a   :				Tuple       ,     a   :				List[str]       ,     a   :				int       ,     a   :				str = WEIGHTS_NAME		):

  """simple docstring"""







  _snake_case     :						Any						=   convert_file_size_to_int(a		)
  _snake_case     :						Tuple						=   []
  _snake_case     :						Optional[int]						=   {}
  _snake_case     :						Tuple						=   0
  _snake_case     :						Optional[Any]						=   0

  os.makedirs(a       ,     exist_ok=a		)
  with gfile.GFile(switch_checkpoint_path + "/checkpoint"       ,     "rb"		) as fp:
    _snake_case     :						Any						=   serialization.msgpack_restore(fp.read()		)["optimizer"]["target"]
    _snake_case     :						Optional[Any]						=   flatten_dict(a       ,     sep="/"		)

  _snake_case     :						Optional[Any]						=   {}
  for layer in checkpoint_info.keys():
    _snake_case			,					_snake_case			,					_snake_case     :						int						=   get_key_and_tensorstore_dict(
        a       ,     a       ,     a		)
    if curr_real_layer_name in all_layers:
      _snake_case     :						Dict						=   content
    else:
      _snake_case     :						Tuple						=   {split_layer[-1]: content}

  for key in all_layers.keys():
    # open tensorstore file
    _snake_case     :						List[str]						=   ts.open(unflatten_dict(all_layers[key]		)		).result().read().result()
    _snake_case     :						Dict						=   torch.tensor(a		)
    _snake_case     :						Dict						=   raw_weights.numel() * dtype_byte_size(raw_weights.dtype		)

    # use the renaming pattern from the small conversion scripts
    _snake_case			,					_snake_case     :						Optional[int]						=   rename_base_flax_keys(tuple(key.split("/"		)		)       ,     a		)
    _snake_case     :						Optional[Any]						=   "/".join(a		)

    # If this weight is going to tip up over the maximal size, we split.
    if current_block_size + weight_size > max_shard_size:
      _snake_case     :						Any						=   os.path.join(
          a       ,     weights_name.replace(".bin"       ,     f'-{len(a		)+1:05d}-of-???.bin'		)		)
      rename_and_save_block(a       ,     a		)
      sharded_state_dicts.append(current_block.keys()		)
      del current_block
      _snake_case     :						List[Any]						=   {}
      _snake_case     :						str						=   0

    _snake_case     :						List[str]						=   raw_weights.to(getattr(a       ,     a		)		)
    current_block_size += weight_size
    total_size += weight_size

  # Add the last block
  _snake_case     :						int						=   os.path.join(a       ,     weights_name.replace(".bin"       ,     f'-{len(a		)+1:05d}-of-???.bin'		)		)
  rename_and_save_block(a       ,     a		)
  sharded_state_dicts.append(current_block.keys()		)

  # If we only have one shard, we return it
  if len(a		) == 1:
    return {weights_name: sharded_state_dicts[0]}, None

  # Otherwise, let's build the index
  _snake_case     :						str						=   {}
  _snake_case     :						Any						=   {}
  for idx, shard in enumerate(a		):
    _snake_case     :						Optional[int]						=   weights_name.replace(
        ".bin"       ,     f'-{idx+1:05d}-of-{len(a		):05d}.bin'		)  # len(sharded_state_dicts):05d}
    _snake_case     :						Dict						=   os.path.join(a       ,     weights_name.replace(".bin"       ,     f'-{idx+1:05d}-of-???.bin'		)		)
    os.rename(a       ,     os.path.join(a       ,     a		)		)
    _snake_case     :						Dict						=   shard
    for key in shard:
      _snake_case     :						int						=   shard_file

    # Add the metadata
  _snake_case     :						List[Any]						=   {"total_size": total_size}
  _snake_case     :						Any						=   {"metadata": metadata, "weight_map": weight_map}

  with open(os.path.join(a       ,     a		)       ,     "w"       ,     encoding="utf-8"		) as f:
    _snake_case     :						Union[str, Any]						=   json.dumps(a       ,     indent=2       ,     sort_keys=a		) + "\n"
    f.write(a		)

  return metadata, index


if __name__ == "__main__":
    _a      :			Dict					=							argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--switch_t5x_checkpoint_path""",
        default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600""",
        type=str,
        required=False,
        help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
    )
    parser.add_argument("""--max_shard_size""", default="""10GB""", required=False, help="""Max shard size""")
    parser.add_argument("""--dtype""", default="""bfloat16""", type=str, required=False, help="""dtype of the saved model""")
    parser.add_argument(
        """--pytorch_dump_folder_path""",
        default="""/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted""",
        type=str,
        required=False,
        help="""Path to the output pytorch model.""",
    )
    _a      :			Optional[int]					=							parser.parse_args()
    shard_on_the_fly(
        args.switch_tax_checkpoint_path,
        args.pytorch_dump_folder_path,
        args.max_shard_size,
        args.dtype,
    )



def 				a__   (   ):

  """simple docstring"""







  from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

  _snake_case     :						List[str]						=   SwitchTransformersConfig.from_pretrained("google/switch-base-8"		)
  config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted"		)
  _snake_case     :						str						=   SwitchTransformersForConditionalGeneration.from_pretrained(
      "/home/arthur_huggingface_co/transformers/switch_converted"       ,     device_map="auto"		)

  _snake_case     :						List[Any]						=   TaTokenizer.from_pretrained("t5-small"		)
  _snake_case     :						Optional[Any]						=   "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

  _snake_case     :						Dict						=   tokenizer(a       ,     return_tensors="pt"		).input_ids
  _snake_case     :						List[Any]						=   model.generate(a       ,     decoder_start_token_id=0		)
  print(tokenizer.decode(out[0]		)		)

