




import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

a_										=       logging.get_logger(__name__)




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ = True )    ->		str:

    """simple docstring"""


    __UpperCamelCase											=  {}
    for name in model.attn_processors.keys():
        __UpperCamelCase											=  name.endswith('''attn2.processor''' ) and not (
            name.startswith('''transformer_in''' ) or '''temp_attentions''' in name.split('''.''' )
        )
        __UpperCamelCase											=  model.config.cross_attention_dim if has_cross_attention else None
        if name.startswith('''mid_block''' ):
            __UpperCamelCase											=  model.config.block_out_channels[-1]
        elif name.startswith('''up_blocks''' ):
            __UpperCamelCase											=  int(name[len('''up_blocks.''' )] )
            __UpperCamelCase											=  list(reversed(model.config.block_out_channels ) )[block_id]
        elif name.startswith('''down_blocks''' ):
            __UpperCamelCase											=  int(name[len('''down_blocks.''' )] )
            __UpperCamelCase											=  model.config.block_out_channels[block_id]
        elif name.startswith('''transformer_in''' ):
            # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
            __UpperCamelCase											=  8 * model.config.attention_head_dim

        __UpperCamelCase											=  LoRAAttnProcessor(hidden_size=lowercase_   ,       cross_attention_dim=lowercase_ )
        __UpperCamelCase											=  lora_attn_procs[name].to(model.device )

        if mock_weights:
            # add 1 to weights to mock trained weights
            with torch.no_grad():
                lora_attn_procs[name].to_q_lora.up.weight += 1
                lora_attn_procs[name].to_k_lora.up.weight += 1
                lora_attn_procs[name].to_v_lora.up.weight += 1
                lora_attn_procs[name].to_out_lora.up.weight += 1

    return lora_attn_procs


@skip_mps
class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     unittest.TestCase				):

      """simple docstring"""

      lowerCAmelCase__     :	Dict      =   UNetaDConditionModel
      lowerCAmelCase__     :	Union[str, Any]      =   "sample"







      @property
      def  snake_case		(       self   :	Dict			):
          __UpperCamelCase											=  4
          __UpperCamelCase											=  4
          __UpperCamelCase											=  4
          __UpperCamelCase											=  (32, 32)

          __UpperCamelCase											=  floats_tensor((batch_size, num_channels, num_frames) + sizes			).to(snake_case			)
          __UpperCamelCase											=  torch.tensor([10]			).to(snake_case			)
          __UpperCamelCase											=  floats_tensor((batch_size, 4, 32)			).to(snake_case			)

          return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}







      @property
      def  snake_case		(       self   :	Union[str, Any]			):
          return (4, 4, 32, 32)







      @property
      def  snake_case		(       self   :	Optional[int]			):
          return (4, 4, 32, 32)







      def  snake_case		(       self   :	Optional[int]			):
          __UpperCamelCase											=  {
              '''block_out_channels''': (32, 64),
              '''down_block_types''': (
                  '''CrossAttnDownBlock3D''',
                  '''DownBlock3D''',
              ),
              '''up_block_types''': ('''UpBlock3D''', '''CrossAttnUpBlock3D'''),
              '''cross_attention_dim''': 32,
              '''attention_head_dim''': 8,
              '''out_channels''': 4,
              '''in_channels''': 4,
              '''layers_per_block''': 1,
              '''sample_size''': 32,
          }
          __UpperCamelCase											=  self.dummy_input
          return init_dict, inputs_dict







      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()	,    reason='''XFormers attention is only available with CUDA and `xformers` installed'''	,    )
      def  snake_case		(       self   :	Dict			):
          __UpperCamelCase						, __UpperCamelCase											=  self.prepare_init_args_and_inputs_for_common()
          __UpperCamelCase											=  self.model_class(**snake_case			)

          model.enable_xformers_memory_efficient_attention()

          assert (
              model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
              == "XFormersAttnProcessor"
          ), "xformers is not enabled"







      def  snake_case		(       self   :	int			):
          __UpperCamelCase						, __UpperCamelCase											=  self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase											=  32

          __UpperCamelCase											=  self.model_class(**snake_case			)
          model.to(snake_case			)
          model.eval()

          with torch.no_grad():
              __UpperCamelCase											=  model(**snake_case			)

              if isinstance(snake_case	,    snake_case			):
                  __UpperCamelCase											=  output.sample

          self.assertIsNotNone(snake_case			)
          __UpperCamelCase											=  inputs_dict['''sample'''].shape
          self.assertEqual(output.shape	,    snake_case	,    '''Input and output shapes do not match'''			)







      def  snake_case		(       self   :	Any			):
          __UpperCamelCase						, __UpperCamelCase											=  self.prepare_init_args_and_inputs_for_common()
          __UpperCamelCase											=  self.model_class(**snake_case			)
          model.to(snake_case			)
          model.eval()

          with torch.no_grad():
              # Warmup pass when using mps (see #372)
              if torch_device == "mps" and isinstance(snake_case	,    snake_case			):
                  model(**self.dummy_input			)

              __UpperCamelCase											=  model(**snake_case			)
              if isinstance(snake_case	,    snake_case			):
                  __UpperCamelCase											=  first.sample

              __UpperCamelCase											=  model(**snake_case			)
              if isinstance(snake_case	,    snake_case			):
                  __UpperCamelCase											=  second.sample

          __UpperCamelCase											=  first.cpu().numpy()
          __UpperCamelCase											=  second.cpu().numpy()
          __UpperCamelCase											=  out_a[~np.isnan(snake_case			)]
          __UpperCamelCase											=  out_a[~np.isnan(snake_case			)]
          __UpperCamelCase											=  np.amax(np.abs(out_a - out_a			)			)
          self.assertLessEqual(snake_case	,    1E-5			)







      def  snake_case		(       self   :	int			):
          __UpperCamelCase						, __UpperCamelCase											=  self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase											=  8

          __UpperCamelCase											=  self.model_class(**snake_case			)
          model.to(snake_case			)
          model.eval()

          model.set_attention_slice('''auto'''			)
          with torch.no_grad():
              __UpperCamelCase											=  model(**snake_case			)
          assert output is not None

          model.set_attention_slice('''max'''			)
          with torch.no_grad():
              __UpperCamelCase											=  model(**snake_case			)
          assert output is not None

          model.set_attention_slice(2			)
          with torch.no_grad():
              __UpperCamelCase											=  model(**snake_case			)
          assert output is not None







      def  snake_case		(       self   :	Optional[Any]			):
          __UpperCamelCase						, __UpperCamelCase											=  self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase											=  8

          __UpperCamelCase											=  self.model_class(**snake_case			)
          model.to(snake_case			)

          with torch.no_grad():
              __UpperCamelCase											=  model(**snake_case			).sample

          __UpperCamelCase											=  create_lora_layers(snake_case			)

          # make sure we can set a list of attention processors
          model.set_attn_processor(snake_case			)
          model.to(snake_case			)

          # test that attn processors can be set to itself
          model.set_attn_processor(model.attn_processors			)

          with torch.no_grad():
              __UpperCamelCase											=  model(**snake_case	,    cross_attention_kwargs={'''scale''': 0.0}			).sample
              __UpperCamelCase											=  model(**snake_case	,    cross_attention_kwargs={'''scale''': 0.5}			).sample
              __UpperCamelCase											=  model(**snake_case	,    cross_attention_kwargs={'''scale''': 0.5}			).sample

          assert (samplea - samplea).abs().max() < 3E-3
          assert (samplea - samplea).abs().max() < 3E-3

          # sample 2 and sample 3 should be different
          assert (samplea - samplea).abs().max() > 3E-3







      def  snake_case		(       self   :	Optional[Any]			):
          __UpperCamelCase						, __UpperCamelCase											=  self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase											=  8

          torch.manual_seed(0			)
          __UpperCamelCase											=  self.model_class(**snake_case			)
          model.to(snake_case			)

          with torch.no_grad():
              __UpperCamelCase											=  model(**snake_case			).sample

          __UpperCamelCase											=  create_lora_layers(snake_case			)
          model.set_attn_processor(snake_case			)

          with torch.no_grad():
              __UpperCamelCase											=  model(**snake_case	,    cross_attention_kwargs={'''scale''': 0.5}			).sample

          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_attn_procs(snake_case			)
              self.assertTrue(os.path.isfile(os.path.join(snake_case	,    '''pytorch_lora_weights.bin'''			)			)			)
              torch.manual_seed(0			)
              __UpperCamelCase											=  self.model_class(**snake_case			)
              new_model.to(snake_case			)
              new_model.load_attn_procs(snake_case			)

          with torch.no_grad():
              __UpperCamelCase											=  new_model(**snake_case	,    cross_attention_kwargs={'''scale''': 0.5}			).sample

          assert (sample - new_sample).abs().max() < 1E-3

          # LoRA and no LoRA should NOT be the same
          assert (sample - old_sample).abs().max() > 1E-4







      def  snake_case		(       self   :	Dict			):
          __UpperCamelCase						, __UpperCamelCase											=  self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase											=  8

          torch.manual_seed(0			)
          __UpperCamelCase											=  self.model_class(**snake_case			)
          model.to(snake_case			)

          with torch.no_grad():
              __UpperCamelCase											=  model(**snake_case			).sample

          __UpperCamelCase											=  create_lora_layers(snake_case			)
          model.set_attn_processor(snake_case			)

          with torch.no_grad():
              __UpperCamelCase											=  model(**snake_case	,    cross_attention_kwargs={'''scale''': 0.5}			).sample

          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_attn_procs(snake_case	,    safe_serialization=snake_case			)
              self.assertTrue(os.path.isfile(os.path.join(snake_case	,    '''pytorch_lora_weights.safetensors'''			)			)			)
              torch.manual_seed(0			)
              __UpperCamelCase											=  self.model_class(**snake_case			)
              new_model.to(snake_case			)
              new_model.load_attn_procs(snake_case			)

          with torch.no_grad():
              __UpperCamelCase											=  new_model(**snake_case	,    cross_attention_kwargs={'''scale''': 0.5}			).sample

          assert (sample - new_sample).abs().max() < 3E-3

          # LoRA and no LoRA should NOT be the same
          assert (sample - old_sample).abs().max() > 1E-4







      def  snake_case		(       self   :	List[str]			):
          # enable deterministic behavior for gradient checkpointing
          __UpperCamelCase						, __UpperCamelCase											=  self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase											=  8

          torch.manual_seed(0			)
          __UpperCamelCase											=  self.model_class(**snake_case			)
          model.to(snake_case			)

          __UpperCamelCase											=  create_lora_layers(snake_case	,    mock_weights=snake_case			)
          model.set_attn_processor(snake_case			)
          # Saving as torch, properly reloads with directly filename
          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_attn_procs(snake_case			)
              self.assertTrue(os.path.isfile(os.path.join(snake_case	,    '''pytorch_lora_weights.bin'''			)			)			)
              torch.manual_seed(0			)
              __UpperCamelCase											=  self.model_class(**snake_case			)
              new_model.to(snake_case			)
              new_model.load_attn_procs(snake_case	,    weight_name='''pytorch_lora_weights.bin'''			)







      def  snake_case		(       self   :	int			):
          __UpperCamelCase						, __UpperCamelCase											=  self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase											=  8

          torch.manual_seed(0			)
          __UpperCamelCase											=  self.model_class(**snake_case			)
          model.to(snake_case			)

          __UpperCamelCase											=  create_lora_layers(snake_case	,    mock_weights=snake_case			)
          model.set_attn_processor(snake_case			)
          # Saving as torch, properly reloads with directly filename
          with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_attn_procs(snake_case			)
              self.assertTrue(os.path.isfile(os.path.join(snake_case	,    '''pytorch_lora_weights.bin'''			)			)			)
              torch.manual_seed(0			)
              __UpperCamelCase											=  self.model_class(**snake_case			)
              new_model.to(snake_case			)
              with self.assertRaises(snake_case			) as e:
                  new_model.load_attn_procs(snake_case	,    use_safetensors=snake_case			)
              self.assertIn('''Error no file named pytorch_lora_weights.safetensors'''	,    str(e.exception			)			)







      def  snake_case		(       self   :	int			):
          __UpperCamelCase						, __UpperCamelCase											=  self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase											=  8

          torch.manual_seed(0			)
          __UpperCamelCase											=  self.model_class(**snake_case			)
          model.to(snake_case			)

          with torch.no_grad():
              __UpperCamelCase											=  model(**snake_case			).sample

          __UpperCamelCase											=  create_lora_layers(snake_case			)
          model.set_attn_processor(snake_case			)

          with torch.no_grad():
              __UpperCamelCase											=  model(**snake_case	,    cross_attention_kwargs={'''scale''': 0.0}			).sample

          model.set_attn_processor(AttnProcessor()			)

          with torch.no_grad():
              __UpperCamelCase											=  model(**snake_case			).sample

          assert (sample - new_sample).abs().max() < 1E-4
          assert (sample - old_sample).abs().max() < 3E-3







      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()	,    reason='''XFormers attention is only available with CUDA and `xformers` installed'''	,    )
      def  snake_case		(       self   :	Any			):
          # enable deterministic behavior for gradient checkpointing
          __UpperCamelCase						, __UpperCamelCase											=  self.prepare_init_args_and_inputs_for_common()

          __UpperCamelCase											=  4

          torch.manual_seed(0			)
          __UpperCamelCase											=  self.model_class(**snake_case			)
          model.to(snake_case			)
          __UpperCamelCase											=  create_lora_layers(snake_case			)
          model.set_attn_processor(snake_case			)

          # default
          with torch.no_grad():
              __UpperCamelCase											=  model(**snake_case			).sample

              model.enable_xformers_memory_efficient_attention()
              __UpperCamelCase											=  model(**snake_case			).sample

              model.disable_xformers_memory_efficient_attention()
              __UpperCamelCase											=  model(**snake_case			).sample

          assert (sample - on_sample).abs().max() < 1E-4
          assert (sample - off_sample).abs().max() < 1E-4







      def  snake_case		(       self   :	List[str]			):
          __UpperCamelCase						, __UpperCamelCase											=  self.prepare_init_args_and_inputs_for_common()
          __UpperCamelCase											=  32

          __UpperCamelCase											=  self.model_class(**snake_case			)
          model.to(snake_case			)
          model.eval()

          with torch.no_grad():
              __UpperCamelCase											=  model(**snake_case			)[0]

          model.enable_forward_chunking()
          with torch.no_grad():
              __UpperCamelCase											=  model(**snake_case			)[0]

          self.assertEqual(output.shape	,    output_a.shape	,    '''Shape doesn\'t match'''			)
          assert np.abs(output.cpu() - output_a.cpu()			).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.












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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################

a_										=       16
a_										=       32




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ = 16 )    ->		Tuple:

    """simple docstring"""


    __UpperCamelCase											=  AutoTokenizer.from_pretrained('''bert-base-cased''' )
    __UpperCamelCase											=  load_dataset('''glue'''   ,       '''mrpc''' )

    def tokenize_function(lowercase_ ):
        # max_length=None => use the model max length (it's actually the default)
        __UpperCamelCase											=  tokenizer(examples['''sentence1''']   ,       examples['''sentence2''']   ,       truncation=lowercase_   ,       max_length=lowercase_ )
        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(
            lowercase_   ,       batched=lowercase_   ,       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(lowercase_ ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __UpperCamelCase											=  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":
            __UpperCamelCase											=  16
        elif accelerator.mixed_precision != "no":
            __UpperCamelCase											=  8
        else:
            __UpperCamelCase											=  None

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

    # Instantiate dataloaders.
    __UpperCamelCase											=  DataLoader(
        tokenized_datasets['''train''']   ,       shuffle=lowercase_   ,       collate_fn=lowercase_   ,       batch_size=lowercase_ )
    __UpperCamelCase											=  DataLoader(
        tokenized_datasets['''validation''']   ,       shuffle=lowercase_   ,       collate_fn=lowercase_   ,       batch_size=lowercase_ )

    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

 a_										=       mocked_dataloaders  # noqa: F811




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Tuple:

    """simple docstring"""


    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,       lowercase_ ) == "1":
        __UpperCamelCase											=  2
    # Initialize accelerator
    __UpperCamelCase											=  Accelerator(cpu=args.cpu   ,       mixed_precision=args.mixed_precision )
    # 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'''] )

    # New Code #
    # Parse out whether we are saving every epoch or after a certain number of batches
    if hasattr(args.checkpointing_steps   ,       '''isdigit''' ):
        if args.checkpointing_steps == "epoch":
            __UpperCamelCase											=  args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            __UpperCamelCase											=  int(args.checkpointing_steps )
        else:
            raise ValueError(
                F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed." )
    else:
        __UpperCamelCase											=  None

    set_seed(lowercase_ )

    __UpperCamelCase						, __UpperCamelCase											=  get_dataloaders(lowercase_   ,       lowercase_ )
    __UpperCamelCase											=  evaluate.load('''glue'''   ,       '''mrpc''' )

    # If the batch size is too big we use gradient accumulation
    __UpperCamelCase											=  1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        __UpperCamelCase											=  batch_size // MAX_GPU_BATCH_SIZE
        __UpperCamelCase											=  MAX_GPU_BATCH_SIZE

    # 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=lowercase_ )

    # 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=lowercase_ )

    # Instantiate scheduler
    __UpperCamelCase											=  get_linear_schedule_with_warmup(
        optimizer=lowercase_   ,       num_warmup_steps=1_00   ,       num_training_steps=(len(lowercase_ ) * num_epochs) // gradient_accumulation_steps   ,       )

    # 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						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase											=  accelerator.prepare(
        lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )

    # New Code #
    # We need to keep track of how many total steps we have iterated over
    __UpperCamelCase											=  0
    # We also need to keep track of the stating epoch so files are named properly
    __UpperCamelCase											=  0

    # We need to load the checkpoint back in before training here with `load_state`
    # The total number of epochs is adjusted based on where the state is being loaded from,
    # as we assume continuation of the same training script
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}" )
            accelerator.load_state(args.resume_from_checkpoint )
            __UpperCamelCase											=  os.path.basename(args.resume_from_checkpoint )
        else:
            # Get the most recent checkpoint
            __UpperCamelCase											=  [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
            dirs.sort(key=os.path.getctime )
            __UpperCamelCase											=  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        __UpperCamelCase											=  os.path.splitext(lowercase_ )[0]

        if "epoch" in training_difference:
            __UpperCamelCase											=  int(training_difference.replace('''epoch_'''   ,       '''''' ) ) + 1
            __UpperCamelCase											=  None
        else:
            __UpperCamelCase											=  int(training_difference.replace('''step_'''   ,       '''''' ) )
            __UpperCamelCase											=  resume_step // len(lowercase_ )
            resume_step -= starting_epoch * len(lowercase_ )

    # Now we train the model
    for epoch in range(lowercase_   ,       lowercase_ ):
        model.train()
        # New Code #
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We need to skip steps until we reach the resumed step
            __UpperCamelCase											=  accelerator.skip_first_batches(lowercase_   ,       lowercase_ )
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            __UpperCamelCase											=  train_dataloader
        for step, batch in enumerate(lowercase_ ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device )
            __UpperCamelCase											=  model(**lowercase_ )
            __UpperCamelCase											=  outputs.loss
            __UpperCamelCase											=  loss / gradient_accumulation_steps
            accelerator.backward(lowercase_ )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()
            # New Code #
            overall_step += 1

            # New Code #
            # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
            # These are saved to folders named `step_{overall_step}`
            # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
            # If mixed precision was used, will also save a "scalar.bin" file
            if isinstance(lowercase_   ,       lowercase_ ):
                __UpperCamelCase											=  F"step_{overall_step}"
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        __UpperCamelCase											=  os.path.join(args.output_dir   ,       lowercase_ )
                    accelerator.save_state(lowercase_ )

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

        __UpperCamelCase											=  metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F"epoch {epoch}:"   ,       lowercase_ )

        # New Code #
        # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
        # These are saved to folders named `epoch_{epoch}`
        # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
        # If mixed precision was used, will also save a "scalar.bin" file
        if checkpointing_steps == "epoch":
            __UpperCamelCase											=  F"epoch_{epoch}"
            if args.output_dir is not None:
                __UpperCamelCase											=  os.path.join(args.output_dir   ,       lowercase_ )
            accelerator.save_state(lowercase_ )




def    __SCREAMING_SNAKE_CASE				(					)    ->		int:

    """simple docstring"""


    __UpperCamelCase											=  argparse.ArgumentParser(description='''Simple example of training script.''' )
    parser.add_argument(
        '''--mixed_precision'''   ,       type=lowercase_   ,       default=lowercase_   ,       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.''' )
    parser.add_argument(
        '''--checkpointing_steps'''   ,       type=lowercase_   ,       default=lowercase_   ,       help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''   ,       )
    parser.add_argument(
        '''--output_dir'''   ,       type=lowercase_   ,       default='''.'''   ,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''   ,       )
    parser.add_argument(
        '''--resume_from_checkpoint'''   ,       type=lowercase_   ,       default=lowercase_   ,       help='''If the training should continue from a checkpoint folder.'''   ,       )
    __UpperCamelCase											=  parser.parse_args()
    __UpperCamelCase											=  {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(lowercase_   ,       lowercase_ )


if __name__ == "__main__":
 main()






