




from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin

@dataclass
class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      lowerCAmelCase__     :	torch.FloatTensor







class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      @register_to_config
      def __init__(       self   :	List[str]	,    snake_case   :	int = 16	,    snake_case   :	int = 88	,    snake_case   :	Optional[int] = None	,    snake_case   :	Optional[int] = None	,    snake_case   :	int = 1	,    snake_case   :	float = 0.0	,    snake_case   :	int = 32	,    snake_case   :	Optional[int] = None	,    snake_case   :	bool = False	,    snake_case   :	Optional[int] = None	,    snake_case   :	Optional[int] = None	,    snake_case   :	Optional[int] = None	,    snake_case   :	str = "geglu"	,    snake_case   :	Optional[int] = None	,    snake_case   :	bool = False	,    snake_case   :	bool = False	,    snake_case   :	bool = False	,    snake_case   :	str = "layer_norm"	,    snake_case   :	bool = True	,    ):
          super().__init__()
          __UpperCamelCase											=  use_linear_projection
          __UpperCamelCase											=  num_attention_heads
          __UpperCamelCase											=  attention_head_dim
          __UpperCamelCase											=  num_attention_heads * attention_head_dim

          # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
          # Define whether input is continuous or discrete depending on configuration
          __UpperCamelCase											=  (in_channels is not None) and (patch_size is None)
          __UpperCamelCase											=  num_vector_embeds is not None
          __UpperCamelCase											=  in_channels is not None and patch_size is not None

          if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
              __UpperCamelCase											=  (
                  F"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
                  ''' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'''
                  ''' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'''
                  ''' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'''
                  ''' would be very nice if you could open a Pull request for the `transformer/config.json` file'''
              )
              deprecate('''norm_type!=num_embeds_ada_norm'''	,    '''1.0.0'''	,    snake_case	,    standard_warn=snake_case			)
              __UpperCamelCase											=  '''ada_norm'''

          if self.is_input_continuous and self.is_input_vectorized:
              raise ValueError(
                  F"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
                  ''' sure that either `in_channels` or `num_vector_embeds` is None.'''			)
          elif self.is_input_vectorized and self.is_input_patches:
              raise ValueError(
                  F"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
                  ''' sure that either `num_vector_embeds` or `num_patches` is None.'''			)
          elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
              raise ValueError(
                  F"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
                  F" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."			)

          # 2. Define input layers
          if self.is_input_continuous:
              __UpperCamelCase											=  in_channels

              __UpperCamelCase											=  torch.nn.GroupNorm(num_groups=snake_case	,    num_channels=snake_case	,    eps=1E-6	,    affine=snake_case			)
              if use_linear_projection:
                  __UpperCamelCase											=  nn.Linear(snake_case	,    snake_case			)
              else:
                  __UpperCamelCase											=  nn.Convad(snake_case	,    snake_case	,    kernel_size=1	,    stride=1	,    padding=0			)
          elif self.is_input_vectorized:
              assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
              assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

              __UpperCamelCase											=  sample_size
              __UpperCamelCase											=  sample_size
              __UpperCamelCase											=  num_vector_embeds
              __UpperCamelCase											=  self.height * self.width

              __UpperCamelCase											=  ImagePositionalEmbeddings(
                  num_embed=snake_case	,    embed_dim=snake_case	,    height=self.height	,    width=self.width			)
          elif self.is_input_patches:
              assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

              __UpperCamelCase											=  sample_size
              __UpperCamelCase											=  sample_size

              __UpperCamelCase											=  patch_size
              __UpperCamelCase											=  PatchEmbed(
                  height=snake_case	,    width=snake_case	,    patch_size=snake_case	,    in_channels=snake_case	,    embed_dim=snake_case	,    )

          # 3. Define transformers blocks
          __UpperCamelCase											=  nn.ModuleList(
              [
                  BasicTransformerBlock(
                      snake_case	,    snake_case	,    snake_case	,    dropout=snake_case	,    cross_attention_dim=snake_case	,    activation_fn=snake_case	,    num_embeds_ada_norm=snake_case	,    attention_bias=snake_case	,    only_cross_attention=snake_case	,    upcast_attention=snake_case	,    norm_type=snake_case	,    norm_elementwise_affine=snake_case	,    )
                  for d in range(snake_case			)
              ]			)

          # 4. Define output layers
          __UpperCamelCase											=  in_channels if out_channels is None else out_channels
          if self.is_input_continuous:
              # TODO: should use out_channels for continuous projections
              if use_linear_projection:
                  __UpperCamelCase											=  nn.Linear(snake_case	,    snake_case			)
              else:
                  __UpperCamelCase											=  nn.Convad(snake_case	,    snake_case	,    kernel_size=1	,    stride=1	,    padding=0			)
          elif self.is_input_vectorized:
              __UpperCamelCase											=  nn.LayerNorm(snake_case			)
              __UpperCamelCase											=  nn.Linear(snake_case	,    self.num_vector_embeds - 1			)
          elif self.is_input_patches:
              __UpperCamelCase											=  nn.LayerNorm(snake_case	,    elementwise_affine=snake_case	,    eps=1E-6			)
              __UpperCamelCase											=  nn.Linear(snake_case	,    2 * inner_dim			)
              __UpperCamelCase											=  nn.Linear(snake_case	,    patch_size * patch_size * self.out_channels			)







      def  snake_case		(       self   :	Any	,    snake_case   :	torch.Tensor	,    snake_case   :	Optional[torch.Tensor] = None	,    snake_case   :	Optional[torch.LongTensor] = None	,    snake_case   :	Optional[torch.LongTensor] = None	,    snake_case   :	Dict[str, Any] = None	,    snake_case   :	Optional[torch.Tensor] = None	,    snake_case   :	Optional[torch.Tensor] = None	,    snake_case   :	bool = True	,    ):
          # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
          #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
          #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
          # expects mask of shape:
          #   [batch, key_tokens]
          # adds singleton query_tokens dimension:
          #   [batch,                    1, key_tokens]
          # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
          #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
          #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
          if attention_mask is not None and attention_mask.ndim == 2:
              # assume that mask is expressed as:
              #   (1 = keep,      0 = discard)
              # convert mask into a bias that can be added to attention scores:
              #       (keep = +0,     discard = -10000.0)
              __UpperCamelCase											=  (1 - attention_mask.to(hidden_states.dtype			)) * -1_00_00.0
              __UpperCamelCase											=  attention_mask.unsqueeze(1			)

          # convert encoder_attention_mask to a bias the same way we do for attention_mask
          if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
              __UpperCamelCase											=  (1 - encoder_attention_mask.to(hidden_states.dtype			)) * -1_00_00.0
              __UpperCamelCase											=  encoder_attention_mask.unsqueeze(1			)

          # 1. Input
          if self.is_input_continuous:
              __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase											=  hidden_states.shape
              __UpperCamelCase											=  hidden_states

              __UpperCamelCase											=  self.norm(snake_case			)
              if not self.use_linear_projection:
                  __UpperCamelCase											=  self.proj_in(snake_case			)
                  __UpperCamelCase											=  hidden_states.shape[1]
                  __UpperCamelCase											=  hidden_states.permute(0	,    2	,    3	,    1			).reshape(snake_case	,    height * width	,    snake_case			)
              else:
                  __UpperCamelCase											=  hidden_states.shape[1]
                  __UpperCamelCase											=  hidden_states.permute(0	,    2	,    3	,    1			).reshape(snake_case	,    height * width	,    snake_case			)
                  __UpperCamelCase											=  self.proj_in(snake_case			)
          elif self.is_input_vectorized:
              __UpperCamelCase											=  self.latent_image_embedding(snake_case			)
          elif self.is_input_patches:
              __UpperCamelCase											=  self.pos_embed(snake_case			)

          # 2. Blocks
          for block in self.transformer_blocks:
              __UpperCamelCase											=  block(
                  snake_case	,    attention_mask=snake_case	,    encoder_hidden_states=snake_case	,    encoder_attention_mask=snake_case	,    timestep=snake_case	,    cross_attention_kwargs=snake_case	,    class_labels=snake_case	,    )

          # 3. Output
          if self.is_input_continuous:
              if not self.use_linear_projection:
                  __UpperCamelCase											=  hidden_states.reshape(snake_case	,    snake_case	,    snake_case	,    snake_case			).permute(0	,    3	,    1	,    2			).contiguous()
                  __UpperCamelCase											=  self.proj_out(snake_case			)
              else:
                  __UpperCamelCase											=  self.proj_out(snake_case			)
                  __UpperCamelCase											=  hidden_states.reshape(snake_case	,    snake_case	,    snake_case	,    snake_case			).permute(0	,    3	,    1	,    2			).contiguous()

              __UpperCamelCase											=  hidden_states + residual
          elif self.is_input_vectorized:
              __UpperCamelCase											=  self.norm_out(snake_case			)
              __UpperCamelCase											=  self.out(snake_case			)
              # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
              __UpperCamelCase											=  logits.permute(0	,    2	,    1			)

              # log(p(x_0))
              __UpperCamelCase											=  F.log_softmax(logits.double()	,    dim=1			).float()
          elif self.is_input_patches:
              # TODO: cleanup!
              __UpperCamelCase											=  self.transformer_blocks[0].norma.emb(
                  snake_case	,    snake_case	,    hidden_dtype=hidden_states.dtype			)
              __UpperCamelCase						, __UpperCamelCase											=  self.proj_out_a(F.silu(snake_case			)			).chunk(2	,    dim=1			)
              __UpperCamelCase											=  self.norm_out(snake_case			) * (1 + scale[:, None]) + shift[:, None]
              __UpperCamelCase											=  self.proj_out_a(snake_case			)

              # unpatchify
              __UpperCamelCase											=  __UpperCamelCase											=  int(hidden_states.shape[1] ** 0.5			)
              __UpperCamelCase											=  hidden_states.reshape(
                  shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)			)
              __UpperCamelCase											=  torch.einsum('''nhwpqc->nchpwq'''	,    snake_case			)
              __UpperCamelCase											=  hidden_states.reshape(
                  shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)			)

          if not return_dict:
              return (output,)

          return TransformeraDModelOutput(sample=snake_case			)












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()






