
"""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					  =						1_6
snake_case					  =						3_2

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = 1_6     ):
					SCREAMING_SNAKE_CASE           =      AutoTokenizer.from_pretrained('bert-base-cased'     )
					SCREAMING_SNAKE_CASE           =      load_dataset('glue',	'mrpc'     )

					def tokenize_function(SCREAMING_SNAKE_CASE_     ):
										# max_length=None => use the model max length (it's actually the default)
										SCREAMING_SNAKE_CASE           =      tokenizer(examples['sentence1'],	examples['sentence2'],	truncation=snake_case_,	max_length=snake_case_     )
										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():
										SCREAMING_SNAKE_CASE           =      datasets.map(
										    snake_case_,	batched=snake_case_,	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
					SCREAMING_SNAKE_CASE           =      tokenized_datasets.rename_column('label',	'labels'     )

					def collate_fn(SCREAMING_SNAKE_CASE_     ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										SCREAMING_SNAKE_CASE           =      1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															SCREAMING_SNAKE_CASE           =      1_6
										elif accelerator.mixed_precision != "no":
															SCREAMING_SNAKE_CASE           =      8
										else:
															SCREAMING_SNAKE_CASE           =      None

										return tokenizer.pad(
										    snake_case_,	padding='longest',	max_length=snake_case_,	pad_to_multiple_of=snake_case_,	return_tensors='pt',	)

					# Instantiate dataloaders.
					SCREAMING_SNAKE_CASE           =      DataLoader(
					    tokenized_datasets['train'],	shuffle=snake_case_,	collate_fn=snake_case_,	batch_size=snake_case_     )
					SCREAMING_SNAKE_CASE           =      DataLoader(
					    tokenized_datasets['validation'],	shuffle=snake_case_,	collate_fn=snake_case_,	batch_size=snake_case_     )

					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					  =						mocked_dataloaders  # noqa: F811

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):





					if os.environ.get('TESTING_MOCKED_DATALOADERS',	snake_case_     ) == "1":
										SCREAMING_SNAKE_CASE           =      2
					# Initialize accelerator
					SCREAMING_SNAKE_CASE           =      Accelerator(cpu=args.cpu,	mixed_precision=args.mixed_precision     )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					SCREAMING_SNAKE_CASE           =      config["lr"]
					SCREAMING_SNAKE_CASE           =      int(config['num_epochs']     )
					SCREAMING_SNAKE_CASE           =      int(config['seed']     )
					SCREAMING_SNAKE_CASE           =      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":
															SCREAMING_SNAKE_CASE           =      args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															SCREAMING_SNAKE_CASE           =      int(args.checkpointing_steps     )
										else:
															raise ValueError(
															    f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''     )
					else:
										SCREAMING_SNAKE_CASE           =      None

					set_seed(snake_case_     )

					SCREAMING_SNAKE_CASE           =      get_dataloaders(snake_case_,	snake_case_     )
					SCREAMING_SNAKE_CASE           =      evaluate.load('glue',	'mrpc'     )

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

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

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

					# Instantiate optimizer
					SCREAMING_SNAKE_CASE           =      AdamW(params=model.parameters(),	lr=snake_case_     )

					# Instantiate scheduler
					SCREAMING_SNAKE_CASE           =      get_linear_schedule_with_warmup(
					    optimizer=snake_case_,	num_warmup_steps=1_0_0,	num_training_steps=(len(snake_case_     ) * 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.
					SCREAMING_SNAKE_CASE           =      accelerator.prepare(
					    snake_case_,	snake_case_,	snake_case_,	snake_case_,	snake_case_     )

					# New Code #
					# We need to keep track of how many total steps we have iterated over
					SCREAMING_SNAKE_CASE           =      0
					# We also need to keep track of the stating epoch so files are named properly
					SCREAMING_SNAKE_CASE           =      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     )
															SCREAMING_SNAKE_CASE           =      os.path.basename(args.resume_from_checkpoint     )
										else:
															# Get the most recent checkpoint
															SCREAMING_SNAKE_CASE           =      [f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
															dirs.sort(key=os.path.getctime     )
															SCREAMING_SNAKE_CASE           =      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										SCREAMING_SNAKE_CASE           =      os.path.splitext(snake_case_     )[0]

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

    # Now we train the model
					for epoch in range(snake_case_,	snake_case_     ):
										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
															SCREAMING_SNAKE_CASE           =      accelerator.skip_first_batches(snake_case_,	snake_case_     )
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															SCREAMING_SNAKE_CASE           =      train_dataloader
										for step, batch in enumerate(snake_case_     ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device     )
															SCREAMING_SNAKE_CASE           =      model(**snake_case_     )
															SCREAMING_SNAKE_CASE           =      outputs.loss
															SCREAMING_SNAKE_CASE           =      loss / gradient_accumulation_steps
															accelerator.backward(snake_case_     )
															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(snake_case_,	snake_case_     ):
																				SCREAMING_SNAKE_CASE           =      f'''step_{overall_step}'''
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														SCREAMING_SNAKE_CASE           =      os.path.join(args.output_dir,	snake_case_     )
																									accelerator.save_state(snake_case_     )

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

										SCREAMING_SNAKE_CASE           =      metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'''epoch {epoch}:''',	snake_case_     )

										# 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":
															SCREAMING_SNAKE_CASE           =      f'''epoch_{epoch}'''
															if args.output_dir is not None:
																				SCREAMING_SNAKE_CASE           =      os.path.join(args.output_dir,	snake_case_     )
															accelerator.save_state(snake_case_     )

def     UpperCamelCase_    (						):
					SCREAMING_SNAKE_CASE           =      argparse.ArgumentParser(description='Simple example of training script.'     )
					parser.add_argument(
					    '--mixed_precision',	type=snake_case_,	default=snake_case_,	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=snake_case_,	default=snake_case_,	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=snake_case_,	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=snake_case_,	default=snake_case_,	help='If the training should continue from a checkpoint folder.',	)
					SCREAMING_SNAKE_CASE           =      parser.parse_args()
					SCREAMING_SNAKE_CASE           =      {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
					training_function(snake_case_,	snake_case_     )


if __name__ == "__main__":
	main()








"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase					=     logging.get_logger(__name__)

UpperCamelCase					=     {
    """squeezebert/squeezebert-uncased""": (
        """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"""
    ),
    """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json""",
    """squeezebert/squeezebert-mnli-headless""": (
        """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"""
    ),
}







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
   UpperCamelCase__     	=							"""squeezebert"""





   def __init__(       self					, UpperCAmelCase_=30_522					, UpperCAmelCase_=768					, UpperCAmelCase_=12					, UpperCAmelCase_=12					, UpperCAmelCase_=3_072					, UpperCAmelCase_="gelu"					, UpperCAmelCase_=0.1					, UpperCAmelCase_=0.1					, UpperCAmelCase_=512					, UpperCAmelCase_=2					, UpperCAmelCase_=0.02					, UpperCAmelCase_=1E-1_2					, UpperCAmelCase_=0					, UpperCAmelCase_=768					, UpperCAmelCase_=4					, UpperCAmelCase_=4					, UpperCAmelCase_=4					, UpperCAmelCase_=1					, UpperCAmelCase_=4					, UpperCAmelCase_=4					, **UpperCAmelCase_					, )			->   int:




          '''simple docstring'''




          super().__init__(pad_token_id=UpperCAmelCase_					, **UpperCAmelCase_)

          lowercase__:							Union[str, Any]         =     vocab_size
          lowercase__:							int         =     hidden_size
          lowercase__:							int         =     num_hidden_layers
          lowercase__:							List[str]         =     num_attention_heads
          lowercase__:							Any         =     hidden_act
          lowercase__:							List[str]         =     intermediate_size
          lowercase__:							Any         =     hidden_dropout_prob
          lowercase__:							List[Any]         =     attention_probs_dropout_prob
          lowercase__:							Optional[int]         =     max_position_embeddings
          lowercase__:							List[Any]         =     type_vocab_size
          lowercase__:							Union[str, Any]         =     initializer_range
          lowercase__:							str         =     layer_norm_eps
          lowercase__:							Optional[Any]         =     embedding_size
          lowercase__:							Optional[int]         =     q_groups
          lowercase__:							List[str]         =     k_groups
          lowercase__:							Union[str, Any]         =     v_groups
          lowercase__:							str         =     post_attention_groups
          lowercase__:							Optional[int]         =     intermediate_groups
          lowercase__:							List[Any]         =     output_groups







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   @property
   def __lowercase							(       self)			->   Mapping[str, Mapping[int, str]]:




          '''simple docstring'''




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






