



'''simple docstring'''




import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


lowerCamelCase							:int       = 1_6
lowerCamelCase							:Union[str, Any]       = 3_2



def      a      (			lowerCamelCase__						,				lowerCamelCase__ = 16   ):
			'''simple docstring'''





			A_     :						Tuple     =					AutoTokenizer.from_pretrained("""bert-base-cased"""   )
			A_     :						List[Any]     =					load_dataset("""glue"""						,				"""mrpc"""   )

			def tokenize_function(lowerCamelCase__   ):
						# max_length=None => use the model max length (it's actually the default)
						A_     :						Dict     =					tokenizer(examples["""sentence1"""]						,				examples["""sentence2"""]						,				truncation=lowerCamelCase_						,				max_length=lowerCamelCase_   )
						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_     :						str     =					datasets.map(
						    lowerCamelCase_						,				batched=lowerCamelCase_						,				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_     :						Dict     =					tokenized_datasets.rename_column("""label"""						,				"""labels"""   )

			def collate_fn(lowerCamelCase__   ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						A_     :						str     =					1_28 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_     :						str     =					16
						elif accelerator.mixed_precision != "no":
									A_     :						List[Any]     =					8
						else:
									A_     :						Tuple     =					None

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

			# Instantiate dataloaders.
			A_     :						Any     =					DataLoader(
			    tokenized_datasets["""train"""]						,				shuffle=lowerCamelCase_						,				collate_fn=lowerCamelCase_						,				batch_size=lowerCamelCase_   )
			A_     :						Optional[int]     =					DataLoader(
			    tokenized_datasets["""validation"""]						,				shuffle=lowerCamelCase_						,				collate_fn=lowerCamelCase_						,				batch_size=lowerCamelCase_   )

			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

						lowerCamelCase							:int       = mocked_dataloaders  # noqa: F811



def      a      (			lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





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

			A_     :						Union[str, Any]     =					evaluate.load("""glue"""						,				"""mrpc"""   )

			# New Code #
			# We now can define an inner training loop function. It should take a batch size as the only parameter,
			# and build the dataloaders in there.
			# It also gets our decorator
			@find_executable_batch_size(starting_batch_size=lowerCamelCase_   )
			def inner_training_loop(lowerCamelCase__   ):
						# And now just move everything below under this function
						# We need to bring in the Accelerator object from earlier
						nonlocal accelerator
						# And reset all of its attributes that could hold onto any memory:
						accelerator.free_memory()

						# Then we can declare the model, optimizer, and everything else:
						set_seed(lowerCamelCase_   )

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

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

						# Instantiate optimizer
						A_     :						int     =					AdamW(params=model.parameters()						,				lr=lowerCamelCase_   )
						A_     :						Union[str, Any]     =					get_dataloaders(lowerCamelCase_						,				lowerCamelCase_   )

						# Instantiate scheduler
						A_     :						int     =					get_linear_schedule_with_warmup(
						    optimizer=lowerCamelCase_						,				num_warmup_steps=1_00						,				num_training_steps=(len(lowerCamelCase_   ) * num_epochs)						,				)

						# 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_     :						List[Any]     =					accelerator.prepare(
						    lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_						,				lowerCamelCase_   )

						# Now we train the model
						for epoch in range(lowerCamelCase_   ):
									model.train()
									for step, batch in enumerate(lowerCamelCase_   ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device   )
												A_     :						List[Any]     =					model(**lowerCamelCase_   )
												A_     :						Dict     =					outputs.loss
												accelerator.backward(lowerCamelCase_   )
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
			inner_training_loop()



def      a      (			):
			'''simple docstring'''





			A_     :						Tuple     =					argparse.ArgumentParser(description="""Simple example of training script."""   )
			parser.add_argument(
			    """--mixed_precision"""						,				type=lowerCamelCase_						,				default=lowerCamelCase_						,				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."""   )
			A_     :						Optional[Any]     =					parser.parse_args()
			A_     :						int     =					{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(lowerCamelCase_						,				lowerCamelCase_   )


if __name__ == "__main__":
						main()




'''simple docstring'''




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowerCamelCase							:Union[str, Any]       = logging.getLogger(__name__)







@dataclass
class      _lowerCAmelCase							:
	__SCREAMING_SNAKE_CASE       :		str		      = field(
	    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}			)
	__SCREAMING_SNAKE_CASE       :		Optional[str]		      = field(
	    default=__UpperCAmelCase				,      metadata={'help': 'Pretrained config name or path if not the same as model_name'}			)
	__SCREAMING_SNAKE_CASE       :		Optional[str]		      = field(
	    default=__UpperCAmelCase				,      metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}			)
	__SCREAMING_SNAKE_CASE       :		Optional[str]		      = field(
	    default=__UpperCAmelCase				,      metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}				,      )
	__SCREAMING_SNAKE_CASE       :		bool		      = field(default=__UpperCAmelCase				,      metadata={'help': 'Whether tp freeze the encoder.'}			)
	__SCREAMING_SNAKE_CASE       :		bool		      = field(default=__UpperCAmelCase				,      metadata={'help': 'Whether  to freeze the embeddings.'}			)







@dataclass
class      _lowerCAmelCase							:
	__SCREAMING_SNAKE_CASE       :		str		      = field(
	    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}			)
	__SCREAMING_SNAKE_CASE       :		Optional[str]		      = field(
	    default='summarization'				,      metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}				,      )
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = field(
	    default=1_024				,      metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }				,      )
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = field(
	    default=128				,      metadata={
	        'help': (
	            'The maximum total sequence length for target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }				,      )
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = field(
	    default=142				,      metadata={
	        'help': (
	            'The maximum total sequence length for validation target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded. '
	            'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
	            'during ``evaluate`` and ``predict``.'
	        )
	    }				,      )
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = field(
	    default=142				,      metadata={
	        'help': (
	            'The maximum total sequence length for test target text after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }				,      )
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = field(default=-1				,      metadata={'help': '# training examples. -1 means use all.'}			)
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = field(default=-1				,      metadata={'help': '# validation examples. -1 means use all.'}			)
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = field(default=-1				,      metadata={'help': '# test examples. -1 means use all.'}			)
	__SCREAMING_SNAKE_CASE       :		Optional[str]		      = field(default=__UpperCAmelCase				,      metadata={'help': 'Source language id for translation.'}			)
	__SCREAMING_SNAKE_CASE       :		Optional[str]		      = field(default=__UpperCAmelCase				,      metadata={'help': 'Target language id for translation.'}			)
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = field(default=__UpperCAmelCase				,      metadata={'help': '# num_beams to use for evaluation.'}			)
	__SCREAMING_SNAKE_CASE       :		bool		      = field(
	    default=__UpperCAmelCase				,      metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}				,      )






def      a      (			lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
			'''simple docstring'''





			logger.info(f'***** {split} metrics *****'   )
			for key in sorted(metrics.keys()   ):
						logger.info(f'  {key} = {metrics[key]}'   )
			save_json(lowerCamelCase__						,				os.path.join(lowerCamelCase__						,				f'{split}_results.json'   )   )



def      a      (			):
			'''simple docstring'''





			A_     :						Union[str, Any]     =					HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)   )

			if len(sys.argv   ) == 2 and sys.argv[1].endswith(""".json"""   ):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						A_,   A_,   A_     :						str     =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
			else:
						A_,   A_,   A_     :						List[str]     =					parser.parse_args_into_dataclasses()

			check_output_dir(lowerCamelCase__   )

			# Setup logging
			logging.basicConfig(
			    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""						,				datefmt="""%m/%d/%Y %H:%M:%S"""						,				level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN						,				)
			logger.warning(
			    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""						,				training_args.local_rank						,				training_args.device						,				training_args.n_gpu						,				bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED   )						,				training_args.fpaa						,				)
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank   ):
						transformers.utils.logging.set_verbosity_info()
			logger.info("""Training/evaluation parameters %s"""						,				lowerCamelCase__   )

			# Set seed
			set_seed(training_args.seed   )

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			A_     :						List[Any]     =					AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,				cache_dir=model_args.cache_dir						,				)

			A_     :						int     =					("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
			for p in extra_model_params:
						if getattr(lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   ):
									assert hasattr(lowerCamelCase__						,				lowerCamelCase__   ), f'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
									setattr(lowerCamelCase__						,				lowerCamelCase__						,				getattr(lowerCamelCase__						,				lowerCamelCase__   )   )

			A_     :						Optional[Any]     =					AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path						,				cache_dir=model_args.cache_dir						,				)
			A_     :						Optional[Any]     =					AutoModelForSeqaSeqLM.from_pretrained(
			    model_args.model_name_or_path						,				from_tf=""".ckpt""" in model_args.model_name_or_path						,				config=lowerCamelCase__						,				cache_dir=model_args.cache_dir						,				)

			# use task specific params
			use_task_specific_params(lowerCamelCase__						,				data_args.task   )

			# set num_beams for evaluation
			if data_args.eval_beams is None:
						A_     :						int     =					model.config.num_beams

			# set decoder_start_token_id for MBart
			if model.config.decoder_start_token_id is None and isinstance(lowerCamelCase__						,				(MBartTokenizer, MBartTokenizerFast)   ):
						assert (
						    data_args.tgt_lang is not None and data_args.src_lang is not None
						), "mBart requires --tgt_lang and --src_lang"
						if isinstance(lowerCamelCase__						,				lowerCamelCase__   ):
									A_     :						Optional[Any]     =					tokenizer.lang_code_to_id[data_args.tgt_lang]
						else:
									A_     :						List[str]     =					tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

			if model_args.freeze_embeds:
						freeze_embeds(lowerCamelCase__   )
			if model_args.freeze_encoder:
						freeze_params(model.get_encoder()   )
						assert_all_frozen(model.get_encoder()   )

			A_     :						Union[str, Any]     =					SeqaSeqDataset

			# Get datasets
			A_     :						Union[str, Any]     =					(
			    dataset_class(
			        lowerCamelCase__						,				type_path="""train"""						,				data_dir=data_args.data_dir						,				n_obs=data_args.n_train						,				max_target_length=data_args.max_target_length						,				max_source_length=data_args.max_source_length						,				prefix=model.config.prefix or """"""						,				)
			    if training_args.do_train
			    else None
			)
			A_     :						int     =					(
			    dataset_class(
			        lowerCamelCase__						,				type_path="""val"""						,				data_dir=data_args.data_dir						,				n_obs=data_args.n_val						,				max_target_length=data_args.val_max_target_length						,				max_source_length=data_args.max_source_length						,				prefix=model.config.prefix or """"""						,				)
			    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
			    else None
			)
			A_     :						Tuple     =					(
			    dataset_class(
			        lowerCamelCase__						,				type_path="""test"""						,				data_dir=data_args.data_dir						,				n_obs=data_args.n_test						,				max_target_length=data_args.test_max_target_length						,				max_source_length=data_args.max_source_length						,				prefix=model.config.prefix or """"""						,				)
			    if training_args.do_predict
			    else None
			)

			# Initialize our Trainer
			A_     :						Optional[Any]     =					(
			    build_compute_metrics_fn(data_args.task						,				lowerCamelCase__   ) if training_args.predict_with_generate else None
			)
			A_     :						List[str]     =					SeqaSeqTrainer(
			    model=lowerCamelCase__						,				args=lowerCamelCase__						,				data_args=lowerCamelCase__						,				train_dataset=lowerCamelCase__						,				eval_dataset=lowerCamelCase__						,				data_collator=SeqaSeqDataCollator(
			        lowerCamelCase__						,				lowerCamelCase__						,				model.config.decoder_start_token_id						,				training_args.tpu_num_cores   )						,				compute_metrics=lowerCamelCase__						,				tokenizer=lowerCamelCase__						,				)

			A_     :						str     =					{}
			# Training
			if training_args.do_train:
						logger.info("""*** Train ***"""   )

						A_     :						List[Any]     =					trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
						A_     :						Any     =					train_result.metrics
						A_     :						str     =					data_args.n_train

						trainer.save_model()  # this also saves the tokenizer

						if trainer.is_world_process_zero():
									handle_metrics("""train"""						,				lowerCamelCase__						,				training_args.output_dir   )
									all_metrics.update(lowerCamelCase__   )

									# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
									trainer.state.save_to_json(os.path.join(training_args.output_dir						,				"""trainer_state.json"""   )   )

									# For convenience, we also re-save the tokenizer to the same directory,
									# so that you can share your model easily on huggingface.co/models =)
									tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
			if training_args.do_eval:
						logger.info("""*** Evaluate ***"""   )

						A_     :						Tuple     =					trainer.evaluate(metric_key_prefix="""val"""   )
						A_     :						str     =					data_args.n_val
						A_     :						Optional[Any]     =					round(metrics["""val_loss"""]						,				4   )

						if trainer.is_world_process_zero():
									handle_metrics("""val"""						,				lowerCamelCase__						,				training_args.output_dir   )
									all_metrics.update(lowerCamelCase__   )

			if training_args.do_predict:
						logger.info("""*** Predict ***"""   )

						A_     :						Any     =					trainer.predict(test_dataset=lowerCamelCase__						,				metric_key_prefix="""test"""   )
						A_     :						int     =					test_output.metrics
						A_     :						Tuple     =					data_args.n_test

						if trainer.is_world_process_zero():
									A_     :						List[Any]     =					round(metrics["""test_loss"""]						,				4   )
									handle_metrics("""test"""						,				lowerCamelCase__						,				training_args.output_dir   )
									all_metrics.update(lowerCamelCase__   )

									if training_args.predict_with_generate:
												A_     :						List[Any]     =					tokenizer.batch_decode(
												    test_output.predictions						,				skip_special_tokens=lowerCamelCase__						,				clean_up_tokenization_spaces=lowerCamelCase__   )
												A_     :						Tuple     =					lmap(str.strip						,				lowerCamelCase__   )
												write_txt_file(lowerCamelCase__						,				os.path.join(training_args.output_dir						,				"""test_generations.txt"""   )   )

			if trainer.is_world_process_zero():
						save_json(lowerCamelCase__						,				os.path.join(training_args.output_dir						,				"""all_results.json"""   )   )

			return all_metrics



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			main()


if __name__ == "__main__":
						main()