


import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
	import tensorflow as tf

	from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

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

UpperCAmelCase							 =					logging.get_logger(__name__)

class  __snake_case      (   enum.Enum):







							'''simple docstring'''






							UpperCamelCase__				:      List[Any]         =					0
							UpperCamelCase__				:      Tuple         =					1




@add_end_docstrings(_UpperCamelCase)
class  __snake_case      (   _UpperCamelCase):







							'''simple docstring'''






							UpperCamelCase__				:      Optional[int]         =					"""generated"""


							def __init__(							self  ,  *a_  ,  **a_		):
											super().__init__(*a_  ,  **a_		)

											self.check_model_type(
											    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
											    if self.framework == """tf"""
											    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING		)


							def _a      (							self  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  **a_  ,  ):
											a__				      =		{}
											if truncation is not None:
															a__				      =		truncation

											a__				      =		generate_kwargs

											a__				      =		{}
											if return_tensors is not None and return_type is None:
															a__				      =		ReturnType.TENSORS if return_tensors else ReturnType.TEXT
											if return_type is not None:
															a__				      =		return_type

											if clean_up_tokenization_spaces is not None:
															a__				      =		clean_up_tokenization_spaces

											if stop_sequence is not None:
															a__				      =		self.tokenizer.encode(a_  ,  add_special_tokens=a_		)
															if len(a_		) > 1:
																			warnings.warn(
																			    """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
																			    """ the stop sequence will be used as the stop sequence string in the interim."""		)
															a__				      =		stop_sequence_ids[0]

											return preprocess_params, forward_params, postprocess_params


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											return True


							def _a      (							self  ,  *a_  ,  a_		):
											a__				      =		self.model.config.prefix if self.model.config.prefix is not None else ""
											if isinstance(args[0]  ,  a_		):
															if self.tokenizer.pad_token_id is None:
																			raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input"""		)
															a__				      =		([prefix + arg for arg in args[0]],)
															a__				      =		True

											elif isinstance(args[0]  ,  a_		):
															a__				      =		(prefix + args[0],)
															a__				      =		False
											else:
															raise ValueError(
															    F''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''		)
											a__				      =		self.tokenizer(*a_  ,  padding=a_  ,  truncation=a_  ,  return_tensors=self.framework		)
											# This is produced by tokenizers but is an invalid generate kwargs
											if "token_type_ids" in inputs:
															del inputs["token_type_ids"]
											return inputs


							def __call__(							self  ,  *a_  ,  **a_		):
											a__				      =		super().__call__(*a_  ,  **a_		)
											if (
											    isinstance(args[0]  ,  a_		)
											    and all(isinstance(a_  ,  a_		) for el in args[0]		)
											    and all(len(a_		) == 1 for res in result		)
											):
															return [res[0] for res in result]
											return result


							def _a      (							self  ,  a_  ,  a_=TruncationStrategy.DO_NOT_TRUNCATE  ,  **a_		):
											a__				      =		self._parse_and_tokenize(a_  ,  truncation=a_  ,  **a_		)
											return inputs


							def _a      (							self  ,  a_  ,  **a_		):
											if self.framework == "pt":
															a__				      =		model_inputs["input_ids"].shape
											elif self.framework == "tf":
															a__				      =		tf.shape(model_inputs["""input_ids"""]		).numpy()

											a__				      =		generate_kwargs.get("""min_length"""  ,  self.model.config.min_length		)
											a__				      =		generate_kwargs.get("""max_length"""  ,  self.model.config.max_length		)
											self.check_inputs(a_  ,  generate_kwargs["""min_length"""]  ,  generate_kwargs["""max_length"""]		)
											a__				      =		self.model.generate(**a_  ,  **a_		)
											a__				      =		output_ids.shape[0]
											if self.framework == "pt":
															a__				      =		output_ids.reshape(a_  ,  out_b // in_b  ,  *output_ids.shape[1:]		)
											elif self.framework == "tf":
															a__				      =		tf.reshape(a_  ,  (in_b, out_b // in_b, *output_ids.shape[1:])		)
											return {"output_ids": output_ids}







							def _a      (							self  ,  a_  ,  a_=ReturnType.TEXT  ,  a_=False		):
											a__				      =		[]
											for output_ids in model_outputs["output_ids"][0]:
															if return_type == ReturnType.TENSORS:
																			a__				      =		{F'''{self.return_name}_token_ids''': output_ids}
															elif return_type == ReturnType.TEXT:
																			a__				      =		{
																			    F'''{self.return_name}_text''': self.tokenizer.decode(
																			        a_  ,  skip_special_tokens=a_  ,  clean_up_tokenization_spaces=a_  ,  )
																			}
															records.append(a_		)
											return records




@add_end_docstrings(_UpperCamelCase)
class  __snake_case      (   _UpperCamelCase):







							'''simple docstring'''






							UpperCamelCase__				:      str         =					"""summary"""


							def __call__(							self  ,  *a_  ,  **a_		):
											return super().__call__(*a_  ,  **a_		)







							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											if max_length < min_length:
															logger.warning(F'''Your min_length={min_length} must be inferior than your max_length={max_length}.'''		)

											if input_length < max_length:
															logger.warning(
															    F'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''
															    """a summarization task, where outputs shorter than the input are typically wanted, you might """
															    F'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'''		)




@add_end_docstrings(_UpperCamelCase)
class  __snake_case      (   _UpperCamelCase):







							'''simple docstring'''






							UpperCamelCase__				:      int         =					"""translation"""


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											if input_length > 0.9 * max_length:
															logger.warning(
															    F'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''
															    """increasing your max_length manually, e.g. translator('...', max_length=400)"""		)
											return True


							def _a      (							self  ,  *a_  ,  a_=TruncationStrategy.DO_NOT_TRUNCATE  ,  a_=None  ,  a_=None		):
											if getattr(self.tokenizer  ,  """_build_translation_inputs"""  ,  a_		):
															return self.tokenizer._build_translation_inputs(
															    *a_  ,  return_tensors=self.framework  ,  truncation=a_  ,  src_lang=a_  ,  tgt_lang=a_		)
											else:
															return super()._parse_and_tokenize(*a_  ,  truncation=a_		)


							def _a      (							self  ,  a_=None  ,  a_=None  ,  **a_		):
											a__				      =		super()._sanitize_parameters(**a_		)
											if src_lang is not None:
															a__				      =		src_lang
											if tgt_lang is not None:
															a__				      =		tgt_lang
											if src_lang is None and tgt_lang is None:
															# Backward compatibility, direct arguments use is preferred.
															a__				      =		kwargs.get("""task"""  ,  self.task		)
															a__				      =		task.split("""_"""		)
															if task and len(a_		) == 4:
																			# translation, XX, to YY
																			a__				      =		items[1]
																			a__				      =		items[3]
											return preprocess_params, forward_params, postprocess_params







							def __call__(							self  ,  *a_  ,  **a_		):
											return super().__call__(*a_  ,  **a_		)






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 			A_      (	__a     :				Accelerator ,		__a     :				int = 16		):

				"""simple docstring"""
				a__				      =		AutoTokenizer.from_pretrained("""bert-base-cased"""		)
				a__				      =		load_dataset("""glue""" ,		"""mrpc"""		)

				def tokenize_function(__a     :				int		):
								# max_length=None => use the model max length (it's actually the default)
								a__				      =		tokenizer(examples["""sentence1"""] ,		examples["""sentence2"""] ,		truncation=__a ,		max_length=__a		)
								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(
								    __a ,		batched=__a ,		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(__a     :				Optional[int]		):
								# 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(
								    __a ,		padding="""longest""" ,		max_length=__a ,		pad_to_multiple_of=__a ,		return_tensors="""pt""" ,		)

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

				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 			A_      (	__a     :				List[Any] ,		__a     :				Tuple		):

				"""simple docstring"""
				# For testing only
				if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,		__a		) == "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=__a		)
				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(__a		)
				a__      ,			a__				      =		get_dataloaders(__a ,		__a		)
				# 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=__a		)

				# 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=__a		)

				# Instantiate scheduler
				a__				      =		get_linear_schedule_with_warmup(
				    optimizer=__a ,		num_warmup_steps=100 ,		num_training_steps=(len(__a		) * 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(
				    __a ,		__a ,		__a ,		__a ,		__a		)

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

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

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

def 			A_      (	):

				"""simple docstring"""
				a__				      =		argparse.ArgumentParser(description="""Simple example of training script."""		)
				parser.add_argument(
				    """--mixed_precision""" ,		type=__a ,		default=__a ,		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=__a ,		default=1 ,		help="""The number of minibatches to be ran before gradients are accumulated.""" ,		)
				parser.add_argument(
				    """--local_sgd_steps""" ,		type=__a ,		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(__a ,		__a		)


if __name__ == "__main__":
	main()

