





"""simple docstring"""






import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

lowerCAmelCase_			=							[
    # (stable-diffusion, HF Diffusers)
    ('''time_embed.0.weight''', '''time_embedding.linear_1.weight'''),
    ('''time_embed.0.bias''', '''time_embedding.linear_1.bias'''),
    ('''time_embed.2.weight''', '''time_embedding.linear_2.weight'''),
    ('''time_embed.2.bias''', '''time_embedding.linear_2.bias'''),
    ('''input_blocks.0.0.weight''', '''conv_in.weight'''),
    ('''input_blocks.0.0.bias''', '''conv_in.bias'''),
    ('''out.0.weight''', '''conv_norm_out.weight'''),
    ('''out.0.bias''', '''conv_norm_out.bias'''),
    ('''out.2.weight''', '''conv_out.weight'''),
    ('''out.2.bias''', '''conv_out.bias'''),
]

lowerCAmelCase_			=							[
    # (stable-diffusion, HF Diffusers)
    ('''in_layers.0''', '''norm1'''),
    ('''in_layers.2''', '''conv1'''),
    ('''out_layers.0''', '''norm2'''),
    ('''out_layers.3''', '''conv2'''),
    ('''emb_layers.1''', '''time_emb_proj'''),
    ('''skip_connection''', '''conv_shortcut'''),
]

lowerCAmelCase_			=							[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
    # loop over downblocks/upblocks

    for j in range(2):
        # loop over resnets/attentions for downblocks
        lowerCAmelCase_			=							F'''down_blocks.{i}.resnets.{j}.'''
        lowerCAmelCase_			=							F'''input_blocks.{3*i + j + 1}.0.'''
        unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

        if i < 3:
            # no attention layers in down_blocks.3
            lowerCAmelCase_			=							F'''down_blocks.{i}.attentions.{j}.'''
            lowerCAmelCase_			=							F'''input_blocks.{3*i + j + 1}.1.'''
            unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

    for j in range(3):
        # loop over resnets/attentions for upblocks
        lowerCAmelCase_			=							F'''up_blocks.{i}.resnets.{j}.'''
        lowerCAmelCase_			=							F'''output_blocks.{3*i + j}.0.'''
        unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

        if i > 0:
            # no attention layers in up_blocks.0
            lowerCAmelCase_			=							F'''up_blocks.{i}.attentions.{j}.'''
            lowerCAmelCase_			=							F'''output_blocks.{3*i + j}.1.'''
            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

    if i < 3:
        # no downsample in down_blocks.3
        lowerCAmelCase_			=							F'''down_blocks.{i}.downsamplers.0.conv.'''
        lowerCAmelCase_			=							F'''input_blocks.{3*(i+1)}.0.op.'''
        unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

        # no upsample in up_blocks.3
        lowerCAmelCase_			=							F'''up_blocks.{i}.upsamplers.0.'''
        lowerCAmelCase_			=							F'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCAmelCase_			=							'''mid_block.attentions.0.'''
lowerCAmelCase_			=							'''middle_block.1.'''
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
    lowerCAmelCase_			=							F'''mid_block.resnets.{j}.'''
    lowerCAmelCase_			=							F'''middle_block.{2*j}.'''
    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       List[str]:






       """simple docstring"""




       _UpperCAmelCase     =     {k: k for k in unet_state_dict.keys()}
       for sd_name, hf_name in unet_conversion_map:
              _UpperCAmelCase     =     sd_name
       for k, v in mapping.items():
              if "resnets" in k:
                     for sd_part, hf_part in unet_conversion_map_resnet:
                            _UpperCAmelCase     =     v.replace(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
                     _UpperCAmelCase     =     v
       for k, v in mapping.items():
              for sd_part, hf_part in unet_conversion_map_layer:
                     _UpperCAmelCase     =     v.replace(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
              _UpperCAmelCase     =     v
       _UpperCAmelCase     =     {v: unet_state_dict[k] for k, v in mapping.items()}
       return new_state_dict


# ================#
# VAE Conversion #
# ================#

lowerCAmelCase_			=							[
    # (stable-diffusion, HF Diffusers)
    ('''nin_shortcut''', '''conv_shortcut'''),
    ('''norm_out''', '''conv_norm_out'''),
    ('''mid.attn_1.''', '''mid_block.attentions.0.'''),
]

for i in range(4):
    # down_blocks have two resnets
    for j in range(2):
        lowerCAmelCase_			=							F'''encoder.down_blocks.{i}.resnets.{j}.'''
        lowerCAmelCase_			=							F'''encoder.down.{i}.block.{j}.'''
        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

    if i < 3:
        lowerCAmelCase_			=							F'''down_blocks.{i}.downsamplers.0.'''
        lowerCAmelCase_			=							F'''down.{i}.downsample.'''
        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

        lowerCAmelCase_			=							F'''up_blocks.{i}.upsamplers.0.'''
        lowerCAmelCase_			=							F'''up.{3-i}.upsample.'''
        vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

    # up_blocks have three resnets
    # also, up blocks in hf are numbered in reverse from sd
    for j in range(3):
        lowerCAmelCase_			=							F'''decoder.up_blocks.{i}.resnets.{j}.'''
        lowerCAmelCase_			=							F'''decoder.up.{3-i}.block.{j}.'''
        vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
    lowerCAmelCase_			=							F'''mid_block.resnets.{i}.'''
    lowerCAmelCase_			=							F'''mid.block_{i+1}.'''
    vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowerCAmelCase_			=							[
    # (stable-diffusion, HF Diffusers)
    ('''norm.''', '''group_norm.'''),
    ('''q.''', '''query.'''),
    ('''k.''', '''key.'''),
    ('''v.''', '''value.'''),
    ('''proj_out.''', '''proj_attn.'''),
]

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       return w.reshape(*w.shape,1,1							)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       List[str]:






       """simple docstring"""




       _UpperCAmelCase     =     {k: k for k in vae_state_dict.keys()}
       for k, v in mapping.items():
              for sd_part, hf_part in vae_conversion_map:
                     _UpperCAmelCase     =     v.replace(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
              _UpperCAmelCase     =     v
       for k, v in mapping.items():
              if "attentions" in k:
                     for sd_part, hf_part in vae_conversion_map_attn:
                            _UpperCAmelCase     =     v.replace(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
                     _UpperCAmelCase     =     v
       _UpperCAmelCase     =     {v: vae_state_dict[k] for k, v in mapping.items()}
       _UpperCAmelCase     =     ['q', 'k', 'v', 'proj_out']
       for k, v in new_state_dict.items():
              for weight_name in weights_to_convert:
                     if F"""mid.attn_1.{weight_name}.weight""" in k:
                            print(F"""Reshaping {k} for SD format"""							)
                            _UpperCAmelCase     =     reshape_weight_for_sd(SCREAMING_SNAKE_CASE							)
       return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


lowerCAmelCase_			=							[
    # (stable-diffusion, HF Diffusers)
    ('''resblocks.''', '''text_model.encoder.layers.'''),
    ('''ln_1''', '''layer_norm1'''),
    ('''ln_2''', '''layer_norm2'''),
    ('''.c_fc.''', '''.fc1.'''),
    ('''.c_proj.''', '''.fc2.'''),
    ('''.attn''', '''.self_attn'''),
    ('''ln_final.''', '''transformer.text_model.final_layer_norm.'''),
    ('''token_embedding.weight''', '''transformer.text_model.embeddings.token_embedding.weight'''),
    ('''positional_embedding''', '''transformer.text_model.embeddings.position_embedding.weight'''),
]
lowerCAmelCase_			=							{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCAmelCase_			=							re.compile('''|'''.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowerCAmelCase_			=							{'''q''': 0, '''k''': 1, '''v''': 2}

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       Dict:






       """simple docstring"""




       _UpperCAmelCase     =     {}
       _UpperCAmelCase     =     {}
       _UpperCAmelCase     =     {}
       for k, v in text_enc_dict.items():
              if (
                  k.endswith('.self_attn.q_proj.weight'							)
                  or k.endswith('.self_attn.k_proj.weight'							)
                  or k.endswith('.self_attn.v_proj.weight'							)
              ):
                     _UpperCAmelCase     =     k[: -len('.q_proj.weight'							)]
                     _UpperCAmelCase     =     k[-len('q_proj.weight'							)]
                     if k_pre not in capture_qkv_weight:
                            _UpperCAmelCase     =     [None, None, None]
                     _UpperCAmelCase     =     v
                     continue

              if (
                  k.endswith('.self_attn.q_proj.bias'							)
                  or k.endswith('.self_attn.k_proj.bias'							)
                  or k.endswith('.self_attn.v_proj.bias'							)
              ):
                     _UpperCAmelCase     =     k[: -len('.q_proj.bias'							)]
                     _UpperCAmelCase     =     k[-len('q_proj.bias'							)]
                     if k_pre not in capture_qkv_bias:
                            _UpperCAmelCase     =     [None, None, None]
                     _UpperCAmelCase     =     v
                     continue

              _UpperCAmelCase     =     textenc_pattern.sub(lambda SCREAMING_SNAKE_CASE							: protected[re.escape(m.group(0							)							)],SCREAMING_SNAKE_CASE							)
              _UpperCAmelCase     =     v

       for k_pre, tensors in capture_qkv_weight.items():
              if None in tensors:
                     raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'							)
              _UpperCAmelCase     =     textenc_pattern.sub(lambda SCREAMING_SNAKE_CASE							: protected[re.escape(m.group(0							)							)],SCREAMING_SNAKE_CASE							)
              _UpperCAmelCase     =     torch.cat(SCREAMING_SNAKE_CASE							)

       for k_pre, tensors in capture_qkv_bias.items():
              if None in tensors:
                     raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'							)
              _UpperCAmelCase     =     textenc_pattern.sub(lambda SCREAMING_SNAKE_CASE							: protected[re.escape(m.group(0							)							)],SCREAMING_SNAKE_CASE							)
              _UpperCAmelCase     =     torch.cat(SCREAMING_SNAKE_CASE							)

       return new_state_dict

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       Tuple:






       """simple docstring"""




       return text_enc_dict


if __name__ == "__main__":
    lowerCAmelCase_			=							argparse.ArgumentParser()

    parser.add_argument('''--model_path''', default=None, type=str, required=True, help='''Path to the model to convert.''')
    parser.add_argument('''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output model.''')
    parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
    parser.add_argument(
        '''--use_safetensors''', action='''store_true''', help='''Save weights use safetensors, default is ckpt.'''
    )

    lowerCAmelCase_			=							parser.parse_args()

    assert args.model_path is not None, "Must provide a model path!"

    assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

    # Path for safetensors
    lowerCAmelCase_			=							osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.safetensors''')
    lowerCAmelCase_			=							osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.safetensors''')
    lowerCAmelCase_			=							osp.join(args.model_path, '''text_encoder''', '''model.safetensors''')

    # Load models from safetensors if it exists, if it doesn't pytorch
    if osp.exists(unet_path):
        lowerCAmelCase_			=							load_file(unet_path, device='''cpu''')
    else:
        lowerCAmelCase_			=							osp.join(args.model_path, '''unet''', '''diffusion_pytorch_model.bin''')
        lowerCAmelCase_			=							torch.load(unet_path, map_location='''cpu''')

    if osp.exists(vae_path):
        lowerCAmelCase_			=							load_file(vae_path, device='''cpu''')
    else:
        lowerCAmelCase_			=							osp.join(args.model_path, '''vae''', '''diffusion_pytorch_model.bin''')
        lowerCAmelCase_			=							torch.load(vae_path, map_location='''cpu''')

    if osp.exists(text_enc_path):
        lowerCAmelCase_			=							load_file(text_enc_path, device='''cpu''')
    else:
        lowerCAmelCase_			=							osp.join(args.model_path, '''text_encoder''', '''pytorch_model.bin''')
        lowerCAmelCase_			=							torch.load(text_enc_path, map_location='''cpu''')

    # Convert the UNet model
    lowerCAmelCase_			=							convert_unet_state_dict(unet_state_dict)
    lowerCAmelCase_			=							{'''model.diffusion_model.''' + k: v for k, v in unet_state_dict.items()}

    # Convert the VAE model
    lowerCAmelCase_			=							convert_vae_state_dict(vae_state_dict)
    lowerCAmelCase_			=							{'''first_stage_model.''' + k: v for k, v in vae_state_dict.items()}

    # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
    lowerCAmelCase_			=							'''text_model.encoder.layers.22.layer_norm2.bias''' in text_enc_dict

    if is_vaa_model:
        # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
        lowerCAmelCase_			=							{'''transformer.''' + k: v for k, v in text_enc_dict.items()}
        lowerCAmelCase_			=							convert_text_enc_state_dict_vaa(text_enc_dict)
        lowerCAmelCase_			=							{'''cond_stage_model.model.''' + k: v for k, v in text_enc_dict.items()}
    else:
        lowerCAmelCase_			=							convert_text_enc_state_dict(text_enc_dict)
        lowerCAmelCase_			=							{'''cond_stage_model.transformer.''' + k: v for k, v in text_enc_dict.items()}

    # Put together new checkpoint
    lowerCAmelCase_			=							{**unet_state_dict, **vae_state_dict, **text_enc_dict}
    if args.half:
        lowerCAmelCase_			=							{k: v.half() for k, v in state_dict.items()}

    if args.use_safetensors:
        save_file(state_dict, args.checkpoint_path)
    else:
        lowerCAmelCase_			=							{'''state_dict''': state_dict}
        torch.save(state_dict, args.checkpoint_path)













"""simple docstring"""






import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform 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
#
########################################################################


lowerCAmelCase_			=							16
lowerCAmelCase_			=							32

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE = 16							)		->       int:






       """simple docstring"""




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

       def tokenize_function(SCREAMING_SNAKE_CASE							):
              # max_length=None => use the model max length (it's actually the default)
              _UpperCAmelCase     =     tokenizer(examples['sentence1'],examples['sentence2'],truncation=SCREAMING_SNAKE_CASE,max_length=SCREAMING_SNAKE_CASE							)
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              _UpperCAmelCase     =     datasets.map(
                  SCREAMING_SNAKE_CASE,batched=SCREAMING_SNAKE_CASE,remove_columns=['idx', 'sentence1', 'sentence2'],)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       _UpperCAmelCase     =     tokenized_datasets.rename_column('label','labels'							)

       def collate_fn(SCREAMING_SNAKE_CASE							):
              # 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(
                  SCREAMING_SNAKE_CASE,padding='longest',max_length=SCREAMING_SNAKE_CASE,pad_to_multiple_of=SCREAMING_SNAKE_CASE,return_tensors='pt',)

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

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    lowerCAmelCase_			=							mocked_dataloaders  # noqa: F811

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Union[str, Any]:






       """simple docstring"""




       if os.environ.get('TESTING_MOCKED_DATALOADERS',SCREAMING_SNAKE_CASE							) == "1":
              _UpperCAmelCase     =     2
       # New Code #
       _UpperCAmelCase     =     int(args.gradient_accumulation_steps							)
       # Initialize accelerator
       _UpperCAmelCase     =     Accelerator(
           cpu=args.cpu,mixed_precision=args.mixed_precision,gradient_accumulation_steps=SCREAMING_SNAKE_CASE							)
       if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
              raise NotImplementedError(
                  'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'							)
       # 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(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase    ,							_UpperCAmelCase     =     get_dataloaders(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       _UpperCAmelCase     =     AutoModelForSequenceClassification.from_pretrained('bert-base-cased',return_dict=SCREAMING_SNAKE_CASE							)

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       _UpperCAmelCase     =     model.to(accelerator.device							)

       # Instantiate optimizer
       _UpperCAmelCase     =     AdamW(params=model.parameters(),lr=SCREAMING_SNAKE_CASE							)

       # Instantiate scheduler
       _UpperCAmelCase     =     get_linear_schedule_with_warmup(
           optimizer=SCREAMING_SNAKE_CASE,num_warmup_steps=100,num_training_steps=(len(SCREAMING_SNAKE_CASE							) * num_epochs),)

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       _UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase     =     accelerator.prepare(
           SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

       # Now we train the model
       for epoch in range(SCREAMING_SNAKE_CASE							):
              model.train()
              for step, batch in enumerate(SCREAMING_SNAKE_CASE							):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device							)
                     # New code #
                     # We use the new `accumulate` context manager to perform gradient accumulation
                     # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                     with accelerator.accumulate(SCREAMING_SNAKE_CASE							):
                            _UpperCAmelCase     =     model(**SCREAMING_SNAKE_CASE							)
                            _UpperCAmelCase     =     output.loss
                            accelerator.backward(SCREAMING_SNAKE_CASE							)
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

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

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

def       __lowerCamelCase    (       )		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     argparse.ArgumentParser(description='Simple example of training script.'							)
       parser.add_argument(
           '--mixed_precision',type=SCREAMING_SNAKE_CASE,default=SCREAMING_SNAKE_CASE,choices=['no', 'fp16', 'bf16', 'fp8'],help='Whether to use mixed precision. Choose'
           'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
           'and an Nvidia Ampere GPU.',)
       # New Code #
       parser.add_argument(
           '--gradient_accumulation_steps',type=SCREAMING_SNAKE_CASE,default=1,help='The number of minibatches to be ran before gradients are accumulated.',)
       parser.add_argument('--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(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
    main()






