





"""simple docstring"""
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 checkpointing 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
#
########################################################################

_snake_case	:						int					=					16
_snake_case	:						List[Any]					=					32






def    A__							(     UpperCamelCase   ,    UpperCamelCase = 16			):
							A						=    AutoTokenizer.from_pretrained("bert-base-cased"			)
							A						=    load_dataset("glue"   ,    "mrpc"			)

							def tokenize_function(UpperCamelCase			):
														# max_length=None => use the model max length (it's actually the default)
														A						=    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
							# starting with the main process first:
							with accelerator.main_process_first():
														A						=    datasets.map(
														    UpperCamelCase   ,    batched=UpperCamelCase   ,    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
							A						=    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.
														A						=    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":
																					A						=    16
														elif accelerator.mixed_precision != "no":
																					A						=    8
														else:
																					A						=    None

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

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

							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

		_snake_case	:						Any					=					mocked_dataloaders  # noqa: F811






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							# For testing only
							if os.environ.get("TESTING_MOCKED_DATALOADERS"   ,    UpperCamelCase			) == "1":
														A						=    2
							# Initialize accelerator
							A						=    Accelerator(cpu=args.cpu   ,    mixed_precision=args.mixed_precision			)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							A						=    config["lr"]
							A						=    int(config["num_epochs"]			)
							A						=    int(config["seed"]			)
							A						=    int(config["batch_size"]			)

							# New Code #
							# Parse out whether we are saving every epoch or after a certain number of batches
							if hasattr(args.checkpointing_steps   ,    "isdigit"			):
														if args.checkpointing_steps == "epoch":
																					A						=    args.checkpointing_steps
														elif args.checkpointing_steps.isdigit():
																					A						=    int(args.checkpointing_steps			)
														else:
																					raise ValueError(
																					    F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."			)
							else:
														A						=    None

							set_seed(UpperCamelCase			)

							A,      A						=    get_dataloaders(UpperCamelCase   ,    UpperCamelCase			)
							A						=    evaluate.load("glue"   ,    "mrpc"			)

							# If the batch size is too big we use gradient accumulation
							A						=    1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														A						=    batch_size // MAX_GPU_BATCH_SIZE
														A						=    MAX_GPU_BATCH_SIZE

							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							A						=    AutoModelForSequenceClassification.from_pretrained("bert-base-cased"   ,    return_dict=UpperCamelCase			)

							# 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).
							A						=    model.to(accelerator.device			)

							# Instantiate optimizer
							A						=    AdamW(params=model.parameters()   ,    lr=UpperCamelCase			)

							# Instantiate scheduler
							A						=    get_linear_schedule_with_warmup(
							    optimizer=UpperCamelCase   ,    num_warmup_steps=100   ,    num_training_steps=(len(UpperCamelCase			) * 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.
							A,      A,      A,      A,      A						=    accelerator.prepare(
							    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			)

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

							# We need to load the checkpoint back in before training here with `load_state`
							# The total number of epochs is adjusted based on where the state is being loaded from,
							# as we assume continuation of the same training script
							if args.resume_from_checkpoint:
														if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																					accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}"			)
																					accelerator.load_state(args.resume_from_checkpoint			)
																					A						=    os.path.basename(args.resume_from_checkpoint			)
														else:
																					# Get the most recent checkpoint
																					A						=    [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
																					dirs.sort(key=os.path.getctime			)
																					A						=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
														# Extract `epoch_{i}` or `step_{i}`
														A						=    os.path.splitext(UpperCamelCase			)[0]

														if "epoch" in training_difference:
																					A						=    int(training_difference.replace("epoch_"   ,    ""			)			) + 1
																					A						=    None
														else:
																					A						=    int(training_difference.replace("step_"   ,    ""			)			)
																					A						=    resume_step // len(UpperCamelCase			)
																					resume_step -= starting_epoch * len(UpperCamelCase			)

    # Now we train the model
							for epoch in range(UpperCamelCase   ,    UpperCamelCase			):
														model.train()
														# New Code #
														if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																					# We need to skip steps until we reach the resumed step
																					A						=    accelerator.skip_first_batches(UpperCamelCase   ,    UpperCamelCase			)
																					overall_step += resume_step
														else:
																					# After the first iteration though, we need to go back to the original dataloader
																					A						=    train_dataloader
														for step, batch in enumerate(UpperCamelCase			):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device			)
																					A						=    model(**UpperCamelCase			)
																					A						=    outputs.loss
																					A						=    loss / gradient_accumulation_steps
																					accelerator.backward(UpperCamelCase			)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()
																					# New Code #
																					overall_step += 1

																					# New Code #
																					# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																					# These are saved to folders named `step_{overall_step}`
																					# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																					# If mixed precision was used, will also save a "scalar.bin" file
																					if isinstance(UpperCamelCase   ,    UpperCamelCase			):
																												A						=    F"step_{overall_step}"
																												if overall_step % checkpointing_steps == 0:
																																			if args.output_dir is not None:
																																										A						=    os.path.join(args.output_dir   ,    UpperCamelCase			)
																																			accelerator.save_state(UpperCamelCase			)

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

														A						=    metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F"epoch {epoch}:"   ,    UpperCamelCase			)

														# New Code #
														# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
														# These are saved to folders named `epoch_{epoch}`
														# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
														# If mixed precision was used, will also save a "scalar.bin" file
														if checkpointing_steps == "epoch":
																					A						=    F"epoch_{epoch}"
																					if args.output_dir is not None:
																												A						=    os.path.join(args.output_dir   ,    UpperCamelCase			)
																					accelerator.save_state(UpperCamelCase			)






def    A__							(     ):
							A						=    argparse.ArgumentParser(description="Simple example of training script."			)
							parser.add_argument(
							    "--mixed_precision"   ,    type=UpperCamelCase   ,    default=UpperCamelCase   ,    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(
							    "--checkpointing_steps"   ,    type=UpperCamelCase   ,    default=UpperCamelCase   ,    help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."   ,    )
							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(
							    "--resume_from_checkpoint"   ,    type=UpperCamelCase   ,    default=UpperCamelCase   ,    help="If the training should continue from a checkpoint folder."   ,    )
							A						=    parser.parse_args()
							A						=    {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
							training_function(UpperCamelCase   ,    UpperCamelCase			)


if __name__ == "__main__":
		main()









"""simple docstring"""
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case	:						int					=					logging.get_logger(__name__)

_snake_case	:						Optional[int]					=					{}






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''fsmt_decoder'''


					def __init__(   self     :Optional[int]	,			__UpperCamelCase     :Tuple=0	,			__UpperCamelCase     :List[str]=0						):
												super().__init__()
												A						=    vocab_size
												A						=    bos_token_id







class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				'''fsmt'''
					UpperCamelCase						=				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


					def __init__(   self     :Dict	,			__UpperCamelCase     :List[str]=["en", "de"]	,			__UpperCamelCase     :List[str]=4_20_24	,			__UpperCamelCase     :Dict=4_20_24	,			__UpperCamelCase     :Tuple="relu"	,			__UpperCamelCase     :Optional[Any]=10_24	,			__UpperCamelCase     :Dict=2_00	,			__UpperCamelCase     :Optional[Any]=10_24	,			__UpperCamelCase     :Optional[Any]=40_96	,			__UpperCamelCase     :Union[str, Any]=12	,			__UpperCamelCase     :Dict=16	,			__UpperCamelCase     :Optional[Any]=0.0	,			__UpperCamelCase     :Dict=40_96	,			__UpperCamelCase     :Dict=12	,			__UpperCamelCase     :str=16	,			__UpperCamelCase     :Any=0.0	,			__UpperCamelCase     :str=0.0	,			__UpperCamelCase     :List[Any]=0.1	,			__UpperCamelCase     :List[Any]=0.0	,			__UpperCamelCase     :str=0.02	,			__UpperCamelCase     :List[str]=2	,			__UpperCamelCase     :Union[str, Any]=True	,			__UpperCamelCase     :Optional[Any]=True	,			__UpperCamelCase     :int=False	,			__UpperCamelCase     :List[str]=5	,			__UpperCamelCase     :Optional[int]=1.0	,			__UpperCamelCase     :List[str]=False	,			__UpperCamelCase     :Any=True	,			__UpperCamelCase     :List[str]=1	,			__UpperCamelCase     :int=0	,			__UpperCamelCase     :str=2	,			__UpperCamelCase     :Dict=2	,			**__UpperCamelCase     :Optional[int]	,			):
												A						=    langs
												A						=    src_vocab_size
												A						=    tgt_vocab_size
												A						=    d_model  # encoder_embed_dim and decoder_embed_dim

												A						=    encoder_ffn_dim
												A						=    A						=    encoder_layers
												A						=    encoder_attention_heads
												A						=    encoder_layerdrop
												A						=    decoder_layerdrop
												A						=    decoder_ffn_dim
												A						=    decoder_layers
												A						=    decoder_attention_heads
												A						=    max_position_embeddings
												A						=    init_std  # Normal(0, this parameter)
												A						=    activation_function

												A						=    DecoderConfig(vocab_size=__UpperCamelCase	,			bos_token_id=__UpperCamelCase						)
												if "decoder" in common_kwargs:
																			del common_kwargs["decoder"]

												A						=    scale_embedding  # scale factor will be sqrt(d_model) if True

												# 3 Types of Dropout
												A						=    attention_dropout
												A						=    activation_dropout
												A						=    dropout

												A						=    use_cache
												super().__init__(
												    pad_token_id=__UpperCamelCase	,			bos_token_id=__UpperCamelCase	,			eos_token_id=__UpperCamelCase	,			decoder_start_token_id=__UpperCamelCase	,			is_encoder_decoder=__UpperCamelCase	,			tie_word_embeddings=__UpperCamelCase	,			forced_eos_token_id=__UpperCamelCase	,			max_length=__UpperCamelCase	,			num_beams=__UpperCamelCase	,			length_penalty=__UpperCamelCase	,			early_stopping=__UpperCamelCase	,			**__UpperCamelCase	,			)


					def       lowerCamelCase			(   self     :List[str]						):
												A						=    copy.deepcopy(self.__dict__						)
												A						=    self.decoder.to_dict()
												A						=    self.__class__.model_type
												return output


