




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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__UpperCamelCase   :			Tuple          = 16
__UpperCamelCase   :			Any          = 32





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase = 16       ) ->			Optional[int]:
	a									=				AutoTokenizer.from_pretrained("""bert-base-cased"""       )
	a									=				load_dataset("""glue"""				,     """mrpc"""       )

	def tokenize_function(__lowerCamelCase       ):
		# max_length=None => use the model max length (it's actually the default)
		a									=				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									=				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									=				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									=				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(
		    __lowerCamelCase				,     padding="""longest"""				,     max_length=__lowerCamelCase				,     pad_to_multiple_of=__lowerCamelCase				,     return_tensors="""pt"""				,     )

	# Instantiate dataloaders.
	a									=				DataLoader(
	    tokenized_datasets["""train"""]				,     shuffle=__lowerCamelCase				,     collate_fn=__lowerCamelCase				,     batch_size=__lowerCamelCase       )
	a									=				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

		__UpperCamelCase   :			Tuple          = mocked_dataloaders  # noqa: F811





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			List[Any]:
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,     __lowerCamelCase       ) == "1":
		a									=				2
	# New Code #
	a									=				int(args.gradient_accumulation_steps       )
	a									=				int(args.local_sgd_steps       )
	# Initialize accelerator
	a									=				Accelerator(
	    cpu=args.cpu				,     mixed_precision=args.mixed_precision				,     gradient_accumulation_steps=__lowerCamelCase       )
	if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
		raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"""       )
	# 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"""]       )

	a									=				evaluate.load("""glue"""				,     """mrpc"""       )

	set_seed(__lowerCamelCase       )
	a							,					a									=				get_dataloaders(__lowerCamelCase				,     __lowerCamelCase       )
	# 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=__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									=				model.to(accelerator.device       )

	# Instantiate optimizer
	a									=				AdamW(params=model.parameters()				,     lr=__lowerCamelCase       )

	# Instantiate scheduler
	a									=				get_linear_schedule_with_warmup(
	    optimizer=__lowerCamelCase				,     num_warmup_steps=100				,     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							,					a							,					a							,					a							,					a									=				accelerator.prepare(
	    __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       )

	# Now we train the model
	for epoch in range(__lowerCamelCase       ):
		model.train()
		with LocalSGD(
		    accelerator=__lowerCamelCase				,     model=__lowerCamelCase				,     local_sgd_steps=__lowerCamelCase				,     enabled=local_sgd_steps is not None       ) as local_sgd:
			for step, batch in enumerate(__lowerCamelCase       ):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device       )
				# New code #
				# We use the new `accumulate` context manager to perform gradient accumulation
				# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
				with accelerator.accumulate(__lowerCamelCase       ):
					a									=				model(**__lowerCamelCase       )
					a									=				output.loss
					accelerator.backward(__lowerCamelCase       )
					optimizer.step()
					lr_scheduler.step()
					optimizer.zero_grad()
					# LocalSGD-specific line
					local_sgd.step()

		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									=				model(**__lowerCamelCase       )
			a									=				outputs.logits.argmax(dim=-1       )
			a							,					a									=				accelerator.gather_for_metrics((predictions, batch["""labels"""])       )
			metric.add_batch(
			    predictions=__lowerCamelCase				,     references=__lowerCamelCase				,     )

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





def 						__A						(      ) ->			List[Any]:
	a									=				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."""				,     )
	# New Code #
	parser.add_argument(
	    """--gradient_accumulation_steps"""				,     type=__lowerCamelCase				,     default=1				,     help="""The number of minibatches to be ran before gradients are accumulated."""				,     )
	parser.add_argument(
	    """--local_sgd_steps"""				,     type=__lowerCamelCase				,     default=8				,     help="""Number of local SGD steps or None to disable local SGD"""       )
	parser.add_argument("""--cpu"""				,     action="""store_true"""				,     help="""If passed, will train on the CPU."""       )
	a									=				parser.parse_args()
	a									=				{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(__lowerCamelCase				,     __lowerCamelCase       )


if __name__ == "__main__":
		main()







import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING





def 						__A						(      __lowerCamelCase       ) ->			List[str]:
	return 1.0 / (1.0 + np.exp(-_outputs       ))





def 						__A						(      __lowerCamelCase       ) ->			List[str]:
	a									=				np.max(_outputs				,     axis=-1				,     keepdims=__lowerCamelCase       )
	a									=				np.exp(_outputs - maxes       )
	return shifted_exp / shifted_exp.sum(axis=-1				,     keepdims=__lowerCamelCase       )

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    '''sigmoid'''
							UpperCamelCase__   	=    '''softmax'''
							UpperCamelCase__   	=    '''none'''







@add_end_docstrings(
    __magic_name__ ,			r'''
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `"default"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `"sigmoid"`: Applies the sigmoid function on the output.
            - `"softmax"`: Applies the softmax function on the output.
            - `"none"`: Does not apply any function on the output.
    ''' ,			)
class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    False
							UpperCamelCase__   	=    ClassificationFunction.NONE
							def __init__(		self		:List[str]						, **__magic_name__		:List[Any]							):







								'''simple docstring'''







								super().__init__(**__magic_name__							)

								self.check_model_type(
								    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
								    if self.framework == """tf"""
								    else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING							)
							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:int=None						, __magic_name__		:Any=None						, __magic_name__		:Union[str, Any]=""						, **__magic_name__		:Tuple							):







								'''simple docstring'''







								a									=				tokenizer_kwargs

								a									=				{}
								if hasattr(self.model.config						, """return_all_scores"""							) and return_all_scores is None:
									a									=				self.model.config.return_all_scores

								if isinstance(__magic_name__						, __magic_name__							) or top_k is None:
									a									=				top_k
									a									=				False
								elif return_all_scores is not None:
									warnings.warn(
									    """`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"""
									    """ `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`."""						, __magic_name__						, )
									if return_all_scores:
										a									=				None
									else:
										a									=				1

								if isinstance(__magic_name__						, __magic_name__							):
									a									=				ClassificationFunction[function_to_apply.upper()]

								if function_to_apply is not None:
									a									=				function_to_apply
								return preprocess_params, {}, postprocess_params
							def __call__(		self		:Dict						, *__magic_name__		:Optional[int]						, **__magic_name__		:Optional[Any]							):







								'''simple docstring'''







								a									=				super().__call__(*__magic_name__						, **__magic_name__							)
								# TODO try and retrieve it in a nicer way from _sanitize_parameters.
								a									=				"""top_k""" not in kwargs
								if isinstance(args[0]						, __magic_name__							) and _legacy:
									# This pipeline is odd, and return a list when single item is run
									return [result]
								else:
									return result
							def  lowerCamelCase__				(		self		:Union[str, Any]						, __magic_name__		:Optional[Any]						, **__magic_name__		:Optional[Any]							):







								'''simple docstring'''







								a									=				self.framework
								if isinstance(__magic_name__						, __magic_name__							):
									return self.tokenizer(**__magic_name__						, return_tensors=__magic_name__						, **__magic_name__							)
								elif isinstance(__magic_name__						, __magic_name__							) and len(__magic_name__							) == 1 and isinstance(inputs[0]						, __magic_name__							) and len(inputs[0]							) == 2:
									# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
									return self.tokenizer(
									    text=inputs[0][0]						, text_pair=inputs[0][1]						, return_tensors=__magic_name__						, **__magic_name__							)
								elif isinstance(__magic_name__						, __magic_name__							):
									# This is likely an invalid usage of the pipeline attempting to pass text pairs.
									raise ValueError(
									    """The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"""
									    """ dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair."""							)
								return self.tokenizer(__magic_name__						, return_tensors=__magic_name__						, **__magic_name__							)
							def  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:Tuple							):







								'''simple docstring'''







								return self.model(**__magic_name__							)





							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:Union[str, Any]						, __magic_name__		:int=None						, __magic_name__		:Union[str, Any]=1						, __magic_name__		:Tuple=True							):







								'''simple docstring'''







								if function_to_apply is None:
									if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
										a									=				ClassificationFunction.SIGMOID
									elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
										a									=				ClassificationFunction.SOFTMAX
									elif hasattr(self.model.config						, """function_to_apply"""							) and function_to_apply is None:
										a									=				self.model.config.function_to_apply
									else:
										a									=				ClassificationFunction.NONE

								a									=				model_outputs["""logits"""][0]
								a									=				outputs.numpy()

								if function_to_apply == ClassificationFunction.SIGMOID:
									a									=				sigmoid(__magic_name__							)
								elif function_to_apply == ClassificationFunction.SOFTMAX:
									a									=				softmax(__magic_name__							)
								elif function_to_apply == ClassificationFunction.NONE:
									a									=				outputs
								else:
									raise ValueError(F'Unrecognized `function_to_apply` argument: {function_to_apply}'							)

								if top_k == 1 and _legacy:
									return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

								a									=				[
								    {"""label""": self.model.config.idalabel[i], """score""": score.item()} for i, score in enumerate(__magic_name__							)
								]
								if not _legacy:
									dict_scores.sort(key=lambda __magic_name__							: x["score"]						, reverse=__magic_name__							)
									if top_k is not None:
										a									=				dict_scores[:top_k]
								return dict_scores

