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
#
########################################################################


_SCREAMING_SNAKE_CASE 		=      1_6
_SCREAMING_SNAKE_CASE 		=      3_2





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_ = 16       )		->	Union[str, Any]:







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

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

 def collate_fn(UpperCamelCase_       ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  UpperCamelCase			  =      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":
   UpperCamelCase			  =      16
  elif accelerator.mixed_precision != "no":
   UpperCamelCase			  =      8
  else:
   UpperCamelCase			  =      None

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

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

 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

     _SCREAMING_SNAKE_CASE 		=      mocked_dataloaders  # noqa: F811





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	str:







 '''simple docstring'''
 # For testing only
 if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,					UpperCamelCase_       ) == "1":
  UpperCamelCase			  =      2
 # New Code #
 UpperCamelCase			  =      int(args.gradient_accumulation_steps       )
 UpperCamelCase			  =      int(args.local_sgd_steps       )
 # Initialize accelerator
 UpperCamelCase			  =      Accelerator(
     cpu=args.cpu							,					mixed_precision=args.mixed_precision							,					gradient_accumulation_steps=UpperCamelCase_       )
 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
 UpperCamelCase			  =      config["lr"]
 UpperCamelCase			  =      int(config["""num_epochs"""]       )
 UpperCamelCase			  =      int(config["""seed"""]       )
 UpperCamelCase			  =      int(config["""batch_size"""]       )

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

 set_seed(UpperCamelCase_       )
 UpperCamelCase			  =      get_dataloaders(UpperCamelCase_							,					UpperCamelCase_       )
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 UpperCamelCase			  =      AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""							,					return_dict=UpperCamelCase_       )

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

 # Instantiate optimizer
 UpperCamelCase			  =      AdamW(params=model.parameters()							,					lr=UpperCamelCase_       )

 # Instantiate scheduler
 UpperCamelCase			  =      get_linear_schedule_with_warmup(
     optimizer=UpperCamelCase_							,					num_warmup_steps=100							,					num_training_steps=(len(UpperCamelCase_       ) * 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.
 UpperCamelCase			  =      accelerator.prepare(
     UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )

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

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

  UpperCamelCase			  =      metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(f"""epoch {epoch}:"""							,					UpperCamelCase_       )





def 			lowercase(	)		->	Dict:







 '''simple docstring'''
 UpperCamelCase			  =      argparse.ArgumentParser(description="""Simple example of training script."""       )
 parser.add_argument(
     """--mixed_precision"""							,					type=UpperCamelCase_							,					default=UpperCamelCase_							,					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=UpperCamelCase_							,					default=1							,					help="""The number of minibatches to be ran before gradients are accumulated."""							,					)
 parser.add_argument(
     """--local_sgd_steps"""							,					type=UpperCamelCase_							,					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."""       )
 UpperCamelCase			  =      parser.parse_args()
 UpperCamelCase			  =      {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
 training_function(UpperCamelCase_							,					UpperCamelCase_       )


if __name__ == "__main__":
     main()




'''simple docstring'''
from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image


lowerCAmelCase   : List[Any]             =				["""text""", """image""", """audio"""]




def 	_A  (       A							)  ->				Dict:
	lowercase						:		str   					=		[]

	for input_type in input_types:
		if input_type == "text":
			inputs.append("Text input"							)
		elif input_type == "image":
			inputs.append(
			    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"							)							) / "000000039769.png"							).resize((5_1_2, 5_1_2)							)							)
		elif input_type == "audio":
			inputs.append(torch.ones(3_0_0_0							)							)
		elif isinstance(A							,A							):
			inputs.append(create_inputs(A							)							)
		else:
			raise ValueError(F'''Invalid type requested: {input_type}'''							)

	return inputs




def 	_A  (       A							)  ->				str:
	lowercase						:		Tuple   					=		[]

	for output in outputs:
		if isinstance(A							,(str, AgentText)							):
			output_types.append("text"							)
		elif isinstance(A							,(Image.Image, AgentImage)							):
			output_types.append("image"							)
		elif isinstance(A							,(torch.Tensor, AgentAudio)							):
			output_types.append("audio"							)
		else:
			raise ValueError(F'''Invalid output: {output}'''							)

	return output_types






@is_tool_test
class        _UpperCamelCase	:


				'''simple docstring'''


				def 	a__		(     self							)    ->  Optional[Any]:
					self.assertTrue(hasattr(self.tool							,      "inputs"							)							)
					self.assertTrue(hasattr(self.tool							,      "outputs"							)							)

					lowercase						:		Optional[Any]   					=		self.tool.inputs
					for _input in inputs:
						if isinstance(_input							,      a_							):
							for __input in _input:
								self.assertTrue(__input in authorized_types							)
						else:
							self.assertTrue(_input in authorized_types							)

					lowercase						:		Any   					=		self.tool.outputs
					for _output in outputs:
						self.assertTrue(_output in authorized_types							)

				def 	a__		(     self							)    ->  Any:
					lowercase						:		Any   					=		create_inputs(self.tool.inputs							)
					lowercase						:		Tuple   					=		self.tool(*a_							)

					# There is a single output
					if len(self.tool.outputs							) == 1:
						lowercase						:		Any   					=		[outputs]

					self.assertListEqual(output_types(a_							)							,      self.tool.outputs							)

				def 	a__		(     self							)    ->  List[str]:
					self.assertTrue(hasattr(self.tool							,      "description"							)							)
					self.assertTrue(hasattr(self.tool							,      "default_checkpoint"							)							)
					self.assertTrue(self.tool.description.startswith("This is a tool that"							)							)

				def 	a__		(     self							)    ->  int:
					lowercase						:		str   					=		create_inputs(self.tool.inputs							)
					lowercase						:		str   					=		self.tool(*a_							)

					if not isinstance(a_							,      a_							):
						lowercase						:		Union[str, Any]   					=		[outputs]

					self.assertEqual(len(a_							)							,      len(self.tool.outputs							)							)

					for output, output_type in zip(a_							,      self.tool.outputs							):
						lowercase						:		List[str]   					=		AGENT_TYPE_MAPPING[output_type]
						self.assertTrue(isinstance(a_							,      a_							)							)


				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		int   					=		create_inputs(self.tool.inputs							)

					lowercase						:		str   					=		[]

					for _input, input_type in zip(a_							,      self.tool.inputs							):
						if isinstance(a_							,      a_							):
							_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input							) for _input_type in input_type]							)
						else:
							_inputs.append(AGENT_TYPE_MAPPING[input_type](_input							)							)

        # Should not raise an error
					lowercase						:		Optional[int]   					=		self.tool(*a_							)

					if not isinstance(a_							,      a_							):
						lowercase						:		str   					=		[outputs]

					self.assertEqual(len(a_							)							,      len(self.tool.outputs							)							)


