





"""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
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					      =   16
_UpperCamelCase					      =   32



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Accelerator     ,    SCREAMING_SNAKE_CASE							:				int = 16 ):


							'''simple docstring'''







							__lowerCamelCase   :   int       =AutoTokenizer.from_pretrained('''bert-base-cased''' )
							__lowerCamelCase   :   int       =load_dataset('''glue'''     ,    '''mrpc''' )

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

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

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

							# Instantiate dataloaders.
							__lowerCamelCase   :   Tuple       =DataLoader(
							    tokenized_datasets['''train''']     ,    shuffle=SCREAMING_SNAKE_CASE     ,    collate_fn=SCREAMING_SNAKE_CASE     ,    batch_size=SCREAMING_SNAKE_CASE )
							__lowerCamelCase   :   Union[str, Any]       =DataLoader(
							    tokenized_datasets['''validation''']     ,    shuffle=SCREAMING_SNAKE_CASE     ,    collate_fn=SCREAMING_SNAKE_CASE     ,    batch_size=SCREAMING_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

							_UpperCamelCase					      =   mocked_dataloaders  # noqa: F811



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Any     ,    SCREAMING_SNAKE_CASE							:				Optional[int] ):


							'''simple docstring'''







							if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,    SCREAMING_SNAKE_CASE ) == "1":
														__lowerCamelCase   :   List[str]       =2
							# New Code #
							__lowerCamelCase   :   Tuple       =int(args.gradient_accumulation_steps )
							__lowerCamelCase   :   Optional[Any]       =int(args.local_sgd_steps )
							# Initialize accelerator
							__lowerCamelCase   :   int       =Accelerator(
							    cpu=args.cpu     ,    mixed_precision=args.mixed_precision     ,    gradient_accumulation_steps=SCREAMING_SNAKE_CASE )
							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
							__lowerCamelCase   :   List[str]       =config['''lr''']
							__lowerCamelCase   :   Optional[Any]       =int(config['''num_epochs'''] )
							__lowerCamelCase   :   List[str]       =int(config['''seed'''] )
							__lowerCamelCase   :   str       =int(config['''batch_size'''] )

							__lowerCamelCase   :   Dict       =evaluate.load('''glue'''     ,    '''mrpc''' )

							set_seed(SCREAMING_SNAKE_CASE )
							__lowerCamelCase     ,    __lowerCamelCase   :   str       =get_dataloaders(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							__lowerCamelCase   :   List[Any]       =AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''     ,    return_dict=SCREAMING_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).
							__lowerCamelCase   :   List[str]       =model.to(accelerator.device )

							# Instantiate optimizer
							__lowerCamelCase   :   Any       =AdamW(params=model.parameters()     ,    lr=SCREAMING_SNAKE_CASE )

							# Instantiate scheduler
							__lowerCamelCase   :   Optional[int]       =get_linear_schedule_with_warmup(
							    optimizer=SCREAMING_SNAKE_CASE     ,    num_warmup_steps=100     ,    num_training_steps=(len(SCREAMING_SNAKE_CASE ) * 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.
							__lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase   :   str       =accelerator.prepare(
							    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

							# Now we train the model
							for epoch in range(SCREAMING_SNAKE_CASE ):
														model.train()
														with LocalSGD(
														    accelerator=SCREAMING_SNAKE_CASE     ,    model=SCREAMING_SNAKE_CASE     ,    local_sgd_steps=SCREAMING_SNAKE_CASE     ,    enabled=local_sgd_steps is not None ) as local_sgd:
																					for step, batch in enumerate(SCREAMING_SNAKE_CASE ):
																												# 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(SCREAMING_SNAKE_CASE ):
																																			__lowerCamelCase   :   Optional[Any]       =model(**SCREAMING_SNAKE_CASE )
																																			__lowerCamelCase   :   int       =output.loss
																																			accelerator.backward(SCREAMING_SNAKE_CASE )
																																			optimizer.step()
																																			lr_scheduler.step()
																																			optimizer.zero_grad()
																																			# LocalSGD-specific line
																																			local_sgd.step()

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

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



def 					lowerCAmelCase_      (   ):


							'''simple docstring'''







							__lowerCamelCase   :   List[str]       =argparse.ArgumentParser(description='''Simple example of training script.''' )
							parser.add_argument(
							    '''--mixed_precision'''     ,    type=SCREAMING_SNAKE_CASE     ,    default=SCREAMING_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.'''     ,    )
							# New Code #
							parser.add_argument(
							    '''--gradient_accumulation_steps'''     ,    type=SCREAMING_SNAKE_CASE     ,    default=1     ,    help='''The number of minibatches to be ran before gradients are accumulated.'''     ,    )
							parser.add_argument(
							    '''--local_sgd_steps'''     ,    type=SCREAMING_SNAKE_CASE     ,    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.''' )
							__lowerCamelCase   :   Optional[int]       =parser.parse_args()
							__lowerCamelCase   :   Dict       ={'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
							training_function(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
							main()











"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							int									=		"""philschmid/bart-large-cnn-samsum"""
				__snake_case :							Optional[int]									=		(
				    """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
				    """and returns a summary of the text."""
				)
				__snake_case :							Dict									=		"""summarizer"""
				__snake_case :							str									=		AutoTokenizer
				__snake_case :							List[str]									=		AutoModelForSeqaSeqLM

				__snake_case :							List[Any]									=		["""text"""]
				__snake_case :							Dict									=		["""text"""]




				def        __lowercase	(  self	:int    ,     __lowercase	:List[Any]      ):
											return self.pre_processor(__lowercase    ,     return_tensors='''pt'''    ,     truncation=__lowercase      )




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:Optional[int]      ):
											return self.model.generate(**__lowercase      )[0]




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:str      ):
											return self.pre_processor.decode(__lowercase    ,     skip_special_tokens=__lowercase    ,     clean_up_tokenization_spaces=__lowercase      )




