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 the experiment tracking capability,
# 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  :      Optional[Any]            =    1_6
a  :      List[str]            =    3_2
def 		snake_case__	(		lowercase							,				lowercase = 16					):
   lowerCAmelCase_:							List[Any]       =	AutoTokenizer.from_pretrained("bert-base-cased"					)
   lowerCAmelCase_:							int       =	load_dataset("glue"							,				"mrpc"					)

   def tokenize_function(lowercase					):
      # max_length=None => use the model max length (it's actually the default)
      lowerCAmelCase_:							Tuple       =	tokenizer(examples["sentence1"]							,				examples["sentence2"]							,				truncation=lowercase							,				max_length=lowercase					)
      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():
      lowerCAmelCase_:							List[Any]       =	datasets.map(
          lowercase							,				batched=lowercase							,				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
   lowerCAmelCase_:							Optional[Any]       =	tokenized_datasets.rename_column("label"							,				"labels"					)

   def collate_fn(lowercase					):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      lowerCAmelCase_:							Optional[int]       =	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":
         lowerCAmelCase_:							Optional[int]       =	16
      elif accelerator.mixed_precision != "no":
         lowerCAmelCase_:							int       =	8
      else:
         lowerCAmelCase_:							List[str]       =	None

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

   # Instantiate dataloaders.
   lowerCAmelCase_:							Dict       =	DataLoader(
       tokenized_datasets["train"]							,				shuffle=lowercase							,				collate_fn=lowercase							,				batch_size=lowercase					)
   lowerCAmelCase_:							Optional[int]       =	DataLoader(
       tokenized_datasets["validation"]							,				shuffle=lowercase							,				collate_fn=lowercase							,				batch_size=lowercase					)

   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  :      Tuple            =    mocked_dataloaders  # noqa: F811
def 		snake_case__	(		lowercase							,				lowercase					):
   # For testing only
   if os.environ.get("TESTING_MOCKED_DATALOADERS"							,				lowercase					) == "1":
      lowerCAmelCase_:							List[str]       =	2
   # Initialize Accelerator

   # New Code #
   # We pass in "all" to `log_with` to grab all available trackers in the environment
   # Note: If using a custom `Tracker` class, should be passed in here such as:
   # >>> log_with = ["all", MyCustomTrackerClassInstance()]
   if args.with_tracking:
      lowerCAmelCase_:							Union[str, Any]       =	Accelerator(
          cpu=args.cpu							,				mixed_precision=args.mixed_precision							,				log_with="all"							,				project_dir=args.project_dir					)
   else:
      lowerCAmelCase_:							Dict       =	Accelerator(cpu=args.cpu							,				mixed_precision=args.mixed_precision					)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowerCAmelCase_:							Optional[int]       =	config["lr"]
   lowerCAmelCase_:							int       =	int(config["num_epochs"]					)
   lowerCAmelCase_:							Optional[Any]       =	int(config["seed"]					)
   lowerCAmelCase_:							List[Any]       =	int(config["batch_size"]					)
   set_seed(lowercase					)

   lowerCAmelCase_  ,	lowerCAmelCase_:							int       =	get_dataloaders(lowercase							,				lowercase					)
   lowerCAmelCase_:							Optional[Any]       =	evaluate.load("glue"							,				"mrpc"					)

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

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowerCAmelCase_:							Optional[int]       =	AutoModelForSequenceClassification.from_pretrained("bert-base-cased"							,				return_dict=lowercase					)

   # 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).
   lowerCAmelCase_:							List[Any]       =	model.to(accelerator.device					)

   # Instantiate optimizer
   lowerCAmelCase_:							Dict       =	AdamW(params=model.parameters()							,				lr=lowercase					)

   # Instantiate scheduler
   lowerCAmelCase_:							Optional[int]       =	get_linear_schedule_with_warmup(
       optimizer=lowercase							,				num_warmup_steps=100							,				num_training_steps=(len(lowercase					) * 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.
   lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Union[str, Any]       =	accelerator.prepare(
       lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase					)

   # New Code #
   # We need to initialize the trackers we use. Overall configurations can also be stored
   if args.with_tracking:
      lowerCAmelCase_:							Optional[Any]       =	os.path.split(lowercase					)[-1].split("."					)[0]
      accelerator.init_trackers(lowercase							,				lowercase					)

   # Now we train the model
   for epoch in range(lowercase					):
      model.train()
      # New Code #
      # For our tracking example, we will log the total loss of each epoch
      if args.with_tracking:
         lowerCAmelCase_:							Optional[int]       =	0
      for step, batch in enumerate(lowercase					):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device					)
         lowerCAmelCase_:							List[Any]       =	model(**lowercase					)
         lowerCAmelCase_:							Any       =	outputs.loss
         # New Code #
         if args.with_tracking:
            total_loss += loss.detach().float()
         lowerCAmelCase_:							str       =	loss / gradient_accumulation_steps
         accelerator.backward(lowercase					)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(lowercase					):
         # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
         batch.to(accelerator.device					)
         with torch.no_grad():
            lowerCAmelCase_:							Optional[Any]       =	model(**lowercase					)
         lowerCAmelCase_:							List[str]       =	outputs.logits.argmax(dim=-1					)
         lowerCAmelCase_  ,	lowerCAmelCase_:							Any       =	accelerator.gather_for_metrics((predictions, batch["labels"])					)
         metric.add_batch(
             predictions=lowercase							,				references=lowercase							,				)

      lowerCAmelCase_:							Any       =	metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''							,				lowercase					)

      # New Code #
      # To actually log, we call `Accelerator.log`
      # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
      if args.with_tracking:
         accelerator.log(
             {
                 "accuracy": eval_metric["accuracy"],
                 "f1": eval_metric["f1"],
                 "train_loss": total_loss.item() / len(lowercase					),
                 "epoch": epoch,
             }							,				step=lowercase							,				)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
   if args.with_tracking:
      accelerator.end_training()
def 		snake_case__	(		):
   lowerCAmelCase_:							int       =	argparse.ArgumentParser(description="Simple example of training script."					)
   parser.add_argument(
       "--mixed_precision"							,				type=lowercase							,				default=lowercase							,				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."					)
   parser.add_argument(
       "--with_tracking"							,				action="store_true"							,				help="Whether to load in all available experiment trackers from the environment and use them for logging."							,				)
   parser.add_argument(
       "--project_dir"							,				type=lowercase							,				default="logs"							,				help="Location on where to store experiment tracking logs` and relevent project information"							,				)
   lowerCAmelCase_:							Tuple       =	parser.parse_args()
   lowerCAmelCase_:							str       =	{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(lowercase							,				lowercase					)


if __name__ == "__main__":
 main()
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a  :      Tuple            =    logging.get_logger(__name__)

a  :      List[str]            =    {
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Any          =    'xlm'
    SCREAMING_SNAKE_CASE:				Optional[int]          =    {
        'hidden_size': 'emb_dim',
        'num_attention_heads': 'n_heads',
        'num_hidden_layers': 'n_layers',
        'n_words': 'vocab_size',  # For backward compatibility
    }




    def __init__(    self       ,						lowerCamelCase__=30_145       ,						lowerCamelCase__=2_048       ,						lowerCamelCase__=12       ,						lowerCamelCase__=16       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=True       ,						lowerCamelCase__=False       ,						lowerCamelCase__=False       ,						lowerCamelCase__=False       ,						lowerCamelCase__=1       ,						lowerCamelCase__=True       ,						lowerCamelCase__=512       ,						lowerCamelCase__=2_048**-0.5       ,						lowerCamelCase__=1E-12       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__=0       ,						lowerCamelCase__=1       ,						lowerCamelCase__=2       ,						lowerCamelCase__=3       ,						lowerCamelCase__=5       ,						lowerCamelCase__=True       ,						lowerCamelCase__="first"       ,						lowerCamelCase__=True       ,						lowerCamelCase__=None       ,						lowerCamelCase__=True       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=5       ,						lowerCamelCase__=5       ,						lowerCamelCase__=0       ,						lowerCamelCase__=0       ,						lowerCamelCase__=2       ,						lowerCamelCase__=0       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							Any       =	vocab_size
       lowerCAmelCase_:							int       =	emb_dim
       lowerCAmelCase_:							int       =	n_layers
       lowerCAmelCase_:							Optional[int]       =	n_heads
       lowerCAmelCase_:							Tuple       =	dropout
       lowerCAmelCase_:							Tuple       =	attention_dropout
       lowerCAmelCase_:							Tuple       =	gelu_activation
       lowerCAmelCase_:							Dict       =	sinusoidal_embeddings
       lowerCAmelCase_:							str       =	causal
       lowerCAmelCase_:							Tuple       =	asm
       lowerCAmelCase_:							Any       =	n_langs
       lowerCAmelCase_:							int       =	use_lang_emb
       lowerCAmelCase_:							Dict       =	layer_norm_eps
       lowerCAmelCase_:							Dict       =	bos_index
       lowerCAmelCase_:							Union[str, Any]       =	eos_index
       lowerCAmelCase_:							Optional[Any]       =	pad_index
       lowerCAmelCase_:							List[Any]       =	unk_index
       lowerCAmelCase_:							Dict       =	mask_index
       lowerCAmelCase_:							Any       =	is_encoder
       lowerCAmelCase_:							Dict       =	max_position_embeddings
       lowerCAmelCase_:							int       =	embed_init_std
       lowerCAmelCase_:							Optional[int]       =	init_std
       lowerCAmelCase_:							str       =	summary_type
       lowerCAmelCase_:							Union[str, Any]       =	summary_use_proj
       lowerCAmelCase_:							Dict       =	summary_activation
       lowerCAmelCase_:							List[Any]       =	summary_proj_to_labels
       lowerCAmelCase_:							List[str]       =	summary_first_dropout
       lowerCAmelCase_:							List[str]       =	start_n_top
       lowerCAmelCase_:							List[Any]       =	end_n_top
       lowerCAmelCase_:							Any       =	mask_token_id
       lowerCAmelCase_:							List[Any]       =	lang_id

       if "n_words" in kwargs:
          lowerCAmelCase_:							Union[str, Any]       =	kwargs["n_words"]

       super().__init__(pad_token_id=lowerCamelCase__       ,						bos_token_id=lowerCamelCase__       ,						**lowerCamelCase__ )





class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    @property
    def      _a    (    self ):
       if self.task == "multiple-choice":
          lowerCAmelCase_:							Optional[Any]       =	{0: "batch", 1: "choice", 2: "sequence"}
       else:
          lowerCAmelCase_:							Any       =	{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("token_type_ids", dynamic_axis),
           ] )