




'''simple docstring'''






import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


lowercase						:   Any      						=							{
    '''vit_b32''': {
        '''embed_dim''': 512,
        '''image_resolution''': 768,
        '''context_length''': 16,
        '''vocab_size''': 49408,
        '''vision_layers''': 12,
        '''vision_width''': 768,
        '''vision_patch_size''': 32,
        '''transformer_width''': 512,
        '''transformer_heads''': 8,
        '''transformer_layers''': 12,
    },
    '''vit_b16''': {
        '''embed_dim''': 512,
        '''image_resolution''': 768,
        '''context_length''': 16,
        '''vocab_size''': 49408,
        '''vision_layers''': 12,
        '''vision_width''': 768,
        '''vision_patch_size''': 16,
        '''transformer_width''': 512,
        '''transformer_heads''': 8,
        '''transformer_layers''': 12,
    },
    '''vit_l14''': {
        '''embed_dim''': 768,
        '''image_resolution''': 840,
        '''context_length''': 16,
        '''vocab_size''': 49408,
        '''vision_layers''': 24,
        '''vision_width''': 1024,
        '''vision_patch_size''': 14,
        '''transformer_width''': 768,
        '''transformer_heads''': 12,
        '''transformer_layers''': 12,
    },
}




def 		a_(			lowerCamelCase ,					lowerCamelCase="" ,					lowerCamelCase="/"   )					->str:






       '''simple docstring'''




       lowercase	:		List[Any]    =   []

       for k, v in params.items():
              lowercase	:		int    =   parent_key + sep + k if parent_key else k

              if isinstance(lowerCamelCase ,					collections.MutableMapping   ):
                     items.extend(flatten_nested_dict(lowerCamelCase ,					lowerCamelCase ,					sep=lowerCamelCase   ).items()   )
              else:
                     items.append((new_key, v)   )
       return dict(lowerCamelCase   )




def 		a_(			lowerCamelCase   )					->Optional[Any]:






       '''simple docstring'''




       return jax.tree_util.tree_map(lambda lowerCamelCase   : x.astype(jnp.floataa   ) if x.dtype == jnp.bfloataa else x ,					lowerCamelCase   )




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Dict:






       '''simple docstring'''




       lowercase,  lowercase,  lowercase	:		Optional[Any]    =   pt_attn_layer.in_proj_weight.chunk(3 ,					dim=0   )
       lowercase,  lowercase,  lowercase	:		Optional[int]    =   pt_attn_layer.in_proj_bias.chunk(3 ,					dim=0   )

       lowercase	:		Union[str, Any]    =   pt_attn_layer.out_proj.weight
       lowercase	:		Tuple    =   pt_attn_layer.out_proj.bias

       lowercase	:		int    =   q_proj
       lowercase	:		Optional[Any]    =   q_proj_bias

       lowercase	:		str    =   k_proj
       lowercase	:		List[Any]    =   k_proj_bias

       lowercase	:		Dict    =   v_proj
       lowercase	:		List[str]    =   v_proj_bias

       lowercase	:		Optional[int]    =   out_proj_weights
       lowercase	:		Tuple    =   out_proj_bias




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->List[str]:






       '''simple docstring'''




       copy_linear(hf_mlp.fca ,					pt_mlp.c_fc   )
       copy_linear(hf_mlp.fca ,					pt_mlp.c_proj   )




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Any:






       '''simple docstring'''




       lowercase	:		List[str]    =   pt_linear.weight
       lowercase	:		Tuple    =   pt_linear.bias




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->str:






       '''simple docstring'''




       copy_linear(hf_layer.layer_norma ,					pt_layer.ln_a   )
       copy_linear(hf_layer.layer_norma ,					pt_layer.ln_a   )

       # copy MLP
       copy_mlp(hf_layer.mlp ,					pt_layer.mlp   )

       # copy attn
       copy_attn_layer(hf_layer.self_attn ,					pt_layer.attn   )




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->List[Any]:






       '''simple docstring'''




       for hf_layer, pt_layer in zip(lowerCamelCase ,					lowerCamelCase   ):
              copy_layer(lowerCamelCase ,					lowerCamelCase   )




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Dict:






       '''simple docstring'''




       lowercase	:		List[str]    =   pt_model.token_embedding.weight
       lowercase	:		Optional[int]    =   pt_model.positional_embedding

       # copy layer norm
       copy_linear(hf_encoder.final_layer_norm ,					pt_model.ln_final   )

       # copy hidden layers
       copy_layers(hf_encoder.encoder.layers ,					pt_model.transformer.resblocks   )




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Tuple:






       '''simple docstring'''




       lowercase	:		List[str]    =   pt_model.text_projection.data.T

       # copy text encoder
       copy_encoder(hf_model.text_model ,					lowerCamelCase   )




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->str:






       '''simple docstring'''




       lowercase	:		List[str]    =   pt_model.visual.proj.data.T

       # copy layer norms
       copy_linear(hf_model.vision_model.pre_layernorm ,					pt_model.visual.ln_pre   )
       copy_linear(hf_model.vision_model.post_layernorm ,					pt_model.visual.ln_post   )

       # copy embeds
       lowercase	:		Union[str, Any]    =   pt_model.visual.conva.weight.data
       lowercase	:		int    =   pt_model.visual.class_embedding
       lowercase	:		str    =   pt_model.visual.positional_embedding.data

       # copy encoder
       copy_layers(hf_model.vision_model.encoder.layers ,					pt_model.visual.transformer.resblocks   )




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->List[Any]:






       '''simple docstring'''




       lowercase	:		Dict    =   flatten_nested_dict(flax_params['''backbone''']['''merged_class_token''']   )

       lowercase	:		Union[str, Any]    =   torch.from_numpy(flax_class_token_params['''scale''']   )
       lowercase	:		List[str]    =   torch.from_numpy(flax_class_token_params['''bias''']   )
       lowercase	:		Tuple    =   nn.Parameter(lowerCamelCase   )
       lowercase	:		Union[str, Any]    =   nn.Parameter(lowerCamelCase   )




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->List[Any]:






       '''simple docstring'''




       lowercase	:		Optional[int]    =   hf_model.state_dict()
       lowercase	:		Dict    =   {}

       # Rename class prediction head flax params to pytorch HF
       lowercase	:		Dict    =   flatten_nested_dict(flax_params['''class_head''']   )

       for flax_key, v in flax_class_params.items():
              lowercase	:		List[str]    =   flax_key.replace('''/''' ,					'''.'''   )
              lowercase	:		Tuple    =   torch_key.replace('''.kernel''' ,					'''.weight'''   )
              lowercase	:		Dict    =   torch_key.replace('''Dense_0''' ,					'''dense0'''   )
              lowercase	:		Optional[int]    =   '''class_head.''' + torch_key

              if "weight" in torch_key and v.ndim == 2:
                     lowercase	:		Optional[int]    =   v.T

              lowercase	:		Optional[int]    =   nn.Parameter(torch.from_numpy(lowerCamelCase   )   )

       # Rename box prediction box flax params to pytorch HF
       lowercase	:		Optional[Any]    =   flatten_nested_dict(flax_params['''obj_box_head''']   )

       for flax_key, v in flax_box_params.items():
              lowercase	:		Dict    =   flax_key.replace('''/''' ,					'''.'''   )
              lowercase	:		Union[str, Any]    =   torch_key.replace('''.kernel''' ,					'''.weight'''   )
              lowercase	:		int    =   torch_key.replace('''_''' ,					''''''   ).lower()
              lowercase	:		Union[str, Any]    =   '''box_head.''' + torch_key

              if "weight" in torch_key and v.ndim == 2:
                     lowercase	:		Tuple    =   v.T

              lowercase	:		Tuple    =   nn.Parameter(torch.from_numpy(lowerCamelCase   )   )

       # Copy flax params to PyTorch params
       for name, param in new_params.items():
              if name in pt_params.keys():
                     pt_params[name].copy_(lowerCamelCase   )




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Dict:






       '''simple docstring'''




       for k, v in flax_attn_params.items():
              if k.startswith('''transformer'''   ):
                     lowercase	:		Optional[Any]    =   k.replace('''transformer.resblocks''' ,					'''text_model.encoder.layers'''   )
              else:
                     lowercase	:		Optional[Any]    =   k.replace('''visual.transformer.resblocks''' ,					'''vision_model.encoder.layers'''   )

              lowercase	:		Optional[Any]    =   torch_key.replace('''attn''' ,					'''self_attn'''   )
              lowercase	:		Optional[int]    =   torch_key.replace('''key''' ,					'''k_proj'''   )
              lowercase	:		Dict    =   torch_key.replace('''value''' ,					'''v_proj'''   )
              lowercase	:		List[Any]    =   torch_key.replace('''query''' ,					'''q_proj'''   )
              lowercase	:		Any    =   torch_key.replace('''out''' ,					'''out_proj'''   )

              if "bias" in torch_key and v.ndim == 2:
                     lowercase	:		str    =   v.shape[0] * v.shape[1]
                     lowercase	:		List[str]    =   v.reshape(lowerCamelCase   )

              if "weight" in torch_key and "out" in torch_key:
                     lowercase	:		Union[str, Any]    =   (v.shape[0] * v.shape[1], v.shape[2])
                     lowercase	:		Union[str, Any]    =   v.reshape(lowerCamelCase   ).T

              if "weight" in torch_key and "out" not in torch_key:
                     lowercase	:		Optional[int]    =   (v.shape[0], v.shape[1] * v.shape[2])
                     lowercase	:		List[Any]    =   v.reshape(lowerCamelCase   ).T

              # Copy flax CLIP attn params to HF PyTorch params
              lowercase	:		List[str]    =   torch.from_numpy(lowerCamelCase   )
              hf_backbone.state_dict()[torch_key].copy_(lowerCamelCase   )




def 		a_(			lowerCamelCase   )					->Any:






       '''simple docstring'''




       lowercase	:		Union[str, Any]    =   {}
       lowercase	:		Union[str, Any]    =   []

       for k, v in params.items():
              if "attn." in k:
                     lowercase	:		Union[str, Any]    =   k[: k.rindex('''attn.'''   ) + 5]
                     if base in processed_attn_layers:
                            continue

                     processed_attn_layers.append(lowerCamelCase   )
                     lowercase	:		Dict    =   params[base + '''out.weight'''].shape[-1]
                     lowercase	:		Union[str, Any]    =   params[base + '''out.weight'''].reshape(lowerCamelCase ,					lowerCamelCase   ).T
                     lowercase	:		List[str]    =   params[base + '''out.bias''']
              else:
                     lowercase	:		Dict    =   v
       return new_params




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Dict:






       '''simple docstring'''




       lowercase	:		Tuple    =   CLIP(**lowerCamelCase   )
       torch_model.eval()
       lowercase	:		Any    =   torch_model.state_dict()

       lowercase	:		List[Any]    =   flatten_nested_dict(flax_params['''backbone''']['''clip''']   )
       lowercase	:		str    =   {}

       for flax_key, v in flax_clip_params.items():
              lowercase	:		int    =   flax_key.replace('''/''' ,					'''.'''   )
              lowercase	:		Optional[int]    =   torch_key.replace('''text.token_embedding.embedding''' ,					'''token_embedding.kernel'''   )

              if (
                  torch_key.startswith('''text.transformer'''   )
                  or torch_key.startswith('''text.text_projection'''   )
                  or torch_key.startswith('''text.ln_final'''   )
                  or torch_key.startswith('''text.positional_embedding'''   )
              ):
                     lowercase	:		int    =   torch_key[5:]

              lowercase	:		List[Any]    =   torch_key.replace('''text_projection.kernel''' ,					'''text_projection'''   )
              lowercase	:		Any    =   torch_key.replace('''visual.proj.kernel''' ,					'''visual.proj'''   )
              lowercase	:		Union[str, Any]    =   torch_key.replace('''.scale''' ,					'''.weight'''   )
              lowercase	:		Optional[Any]    =   torch_key.replace('''.kernel''' ,					'''.weight'''   )

              if "conv" in torch_key or "downsample.0.weight" in torch_key:
                     lowercase	:		List[str]    =   v.transpose(3 ,					2 ,					0 ,					1   )

              elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
                     # Fully connected layers are transposed, embeddings are not
                     lowercase	:		Union[str, Any]    =   v.T

              lowercase	:		str    =   v

       lowercase	:		Tuple    =   _convert_attn_layers(lowerCamelCase   )
       new_torch_params.update(lowerCamelCase   )
       lowercase	:		Union[str, Any]    =   {}

       # Copy flax CLIP backbone params to PyTorch params
       for name, param in new_torch_params.items():
              if name in torch_clip_params.keys():
                     lowercase	:		Any    =   torch.from_numpy(new_torch_params[name]   )
                     torch_clip_params[name].copy_(lowerCamelCase   )
              else:
                     lowercase	:		Optional[int]    =   param

       return torch_clip_params, torch_model, attn_params




@torch.no_grad()
def 		a_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase=None   )					->Tuple:






       '''simple docstring'''




       lowercase	:		List[str]    =   Repository(lowerCamelCase ,					clone_from=f"""google/{pytorch_dump_folder_path}"""   )
       repo.git_pull()

       if config_path is not None:
              lowercase	:		Any    =   OwlViTConfig.from_pretrained(lowerCamelCase   )
       else:
              lowercase	:		Optional[int]    =   OwlViTConfig()

       lowercase	:		Any    =   OwlViTModel(lowerCamelCase   ).eval()
       lowercase	:		str    =   OwlViTForObjectDetection(lowerCamelCase   ).eval()

       copy_text_model_and_projection(lowerCamelCase ,					lowerCamelCase   )
       copy_vision_model_and_projection(lowerCamelCase ,					lowerCamelCase   )
       lowercase	:		Any    =   pt_backbone.logit_scale
       copy_flax_attn_params(lowerCamelCase ,					lowerCamelCase   )

       lowercase	:		Optional[Any]    =   hf_backbone
       copy_class_merge_token(lowerCamelCase ,					lowerCamelCase   )
       copy_class_box_heads(lowerCamelCase ,					lowerCamelCase   )

       # Save HF model
       hf_model.save_pretrained(repo.local_dir   )

       # Initialize image processor
       lowercase	:		List[Any]    =   OwlViTImageProcessor(
           size=config.vision_config.image_size ,					crop_size=config.vision_config.image_size   )
       # Initialize tokenizer
       lowercase	:		str    =   CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32''' ,					pad_token='''!''' ,					model_max_length=16   )

       # Initialize processor
       lowercase	:		Tuple    =   OwlViTProcessor(image_processor=lowerCamelCase ,					tokenizer=lowerCamelCase   )
       image_processor.save_pretrained(repo.local_dir   )
       processor.save_pretrained(repo.local_dir   )

       repo.git_add()
       repo.git_commit('''Upload model and processor'''   )
       repo.git_push()


if __name__ == "__main__":
      lowercase						:   Optional[Any]      						=							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--owlvit_version''',
          default=None,
          type=str,
          required=True,
          help='''OWL-ViT model name [clip_b16, clip_b32, clip_l14].''',
      )
      parser.add_argument(
          '''--owlvit_checkpoint''', default=None, type=str, required=True, help='''Path to flax model checkpoint.'''
      )
      parser.add_argument('''--hf_config''', default=None, type=str, required=True, help='''Path to HF model config.''')
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default='''hf_model''', type=str, help='''Path to the output PyTorch model.'''
      )
      lowercase						:   Optional[int]      						=							parser.parse_args()

      # Initialize PyToch clip model
      lowercase						:   Any      						=							args.owlvit_version
      if model_name == "clip_b16":
            lowercase						:   Tuple      						=							CONFIGS['''vit_b16''']
      elif model_name == "clip_b32":
            lowercase						:   int      						=							CONFIGS['''vit_b32''']
      elif model_name == "clip_l14":
            lowercase						:   Any      						=							CONFIGS['''vit_l14''']

      # Load from checkpoint and convert params to float-32
      lowercase						:   List[str]      						=							checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['''optimizer''']['''target''']
      lowercase						:   str      						=							jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
      del variables

      # Convert CLIP backbone
      lowercase						,						lowercase						,						lowercase						:   Optional[int]      						=							convert_clip_backbone(flax_params, torch_config)

      convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)










'''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,
# 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
#
########################################################################

lowercase						:   Optional[int]      						=							16
lowercase						:   str      						=							32




def 		a_(			lowerCamelCase ,					lowerCamelCase = 16   )					->Union[str, Any]:






       '''simple docstring'''




       lowercase	:		List[Any]    =   AutoTokenizer.from_pretrained('''bert-base-cased'''   )
       lowercase	:		List[Any]    =   load_dataset('''glue''' ,					'''mrpc'''   )

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

       def collate_fn(lowerCamelCase   ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              lowercase	:		List[str]    =   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":
                     lowercase	:		Any    =   16
              elif accelerator.mixed_precision != "no":
                     lowercase	:		List[str]    =   8
              else:
                     lowercase	:		Optional[int]    =   None

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

       # Instantiate dataloaders.
       lowercase	:		Optional[Any]    =   DataLoader(
           tokenized_datasets['''train'''] ,					shuffle=lowerCamelCase ,					collate_fn=lowerCamelCase ,					batch_size=lowerCamelCase   )
       lowercase	:		Optional[Any]    =   DataLoader(
           tokenized_datasets['''validation'''] ,					shuffle=lowerCamelCase ,					collate_fn=lowerCamelCase ,					batch_size=lowerCamelCase   )

       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

      lowercase						:   str      						=							mocked_dataloaders  # noqa: F811




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Union[str, Any]:






       '''simple docstring'''




       if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,					lowerCamelCase   ) == "1":
              lowercase	:		Optional[int]    =   2
       # Initialize accelerator
       lowercase	:		Optional[Any]    =   Accelerator(cpu=args.cpu ,					mixed_precision=args.mixed_precision   )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowercase	:		int    =   config['''lr''']
       lowercase	:		int    =   int(config['''num_epochs''']   )
       lowercase	:		Tuple    =   int(config['''seed''']   )
       lowercase	:		Union[str, Any]    =   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":
                     lowercase	:		Any    =   args.checkpointing_steps
              elif args.checkpointing_steps.isdigit():
                     lowercase	:		List[Any]    =   int(args.checkpointing_steps   )
              else:
                     raise ValueError(
                         f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""   )
       else:
              lowercase	:		Any    =   None

       set_seed(lowerCamelCase   )

       lowercase,  lowercase	:		List[str]    =   get_dataloaders(lowerCamelCase ,					lowerCamelCase   )
       lowercase	:		List[Any]    =   evaluate.load('''glue''' ,					'''mrpc'''   )

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

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowercase	:		Dict    =   AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,					return_dict=lowerCamelCase   )

       # 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).
       lowercase	:		List[Any]    =   model.to(accelerator.device   )

       # Instantiate optimizer
       lowercase	:		List[Any]    =   AdamW(params=model.parameters() ,					lr=lowerCamelCase   )

       # Instantiate scheduler
       lowercase	:		Tuple    =   get_linear_schedule_with_warmup(
           optimizer=lowerCamelCase ,					num_warmup_steps=100 ,					num_training_steps=(len(lowerCamelCase   ) * 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.
       lowercase,  lowercase,  lowercase,  lowercase,  lowercase	:		Union[str, Any]    =   accelerator.prepare(
           lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )

       # New Code #
       # We need to keep track of how many total steps we have iterated over
       lowercase	:		str    =   0
       # We also need to keep track of the stating epoch so files are named properly
       lowercase	:		Optional[int]    =   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   )
                     lowercase	:		Optional[Any]    =   os.path.basename(args.resume_from_checkpoint   )
              else:
                     # Get the most recent checkpoint
                     lowercase	:		Optional[Any]    =   [f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
                     dirs.sort(key=os.path.getctime   )
                     lowercase	:		Any    =   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              lowercase	:		Any    =   os.path.splitext(lowerCamelCase   )[0]

              if "epoch" in training_difference:
                     lowercase	:		Union[str, Any]    =   int(training_difference.replace('''epoch_''' ,					''''''   )   ) + 1
                     lowercase	:		List[Any]    =   None
              else:
                     lowercase	:		int    =   int(training_difference.replace('''step_''' ,					''''''   )   )
                     lowercase	:		Optional[int]    =   resume_step // len(lowerCamelCase   )
                     resume_step -= starting_epoch * len(lowerCamelCase   )

    # Now we train the model
       for epoch in range(lowerCamelCase ,					lowerCamelCase   ):
              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
                     lowercase	:		Dict    =   accelerator.skip_first_batches(lowerCamelCase ,					lowerCamelCase   )
                     overall_step += resume_step
              else:
                     # After the first iteration though, we need to go back to the original dataloader
                     lowercase	:		Tuple    =   train_dataloader
              for step, batch in enumerate(lowerCamelCase   ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device   )
                     lowercase	:		Dict    =   model(**lowerCamelCase   )
                     lowercase	:		Any    =   outputs.loss
                     lowercase	:		Dict    =   loss / gradient_accumulation_steps
                     accelerator.backward(lowerCamelCase   )
                     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(lowerCamelCase ,					lowerCamelCase   ):
                            lowercase	:		str    =   f"""step_{overall_step}"""
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          lowercase	:		List[str]    =   os.path.join(args.output_dir ,					lowerCamelCase   )
                                   accelerator.save_state(lowerCamelCase   )

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

              lowercase	:		List[Any]    =   metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"""epoch {epoch}:""" ,					lowerCamelCase   )

              # 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":
                     lowercase	:		List[str]    =   f"""epoch_{epoch}"""
                     if args.output_dir is not None:
                            lowercase	:		Any    =   os.path.join(args.output_dir ,					lowerCamelCase   )
                     accelerator.save_state(lowerCamelCase   )




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






       '''simple docstring'''




       lowercase	:		Union[str, Any]    =   argparse.ArgumentParser(description='''Simple example of training script.'''   )
       parser.add_argument(
           '''--mixed_precision''' ,					type=lowerCamelCase ,					default=lowerCamelCase ,					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=lowerCamelCase ,					default=lowerCamelCase ,					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=lowerCamelCase ,					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=lowerCamelCase ,					default=lowerCamelCase ,					help='''If the training should continue from a checkpoint folder.''' ,					)
       lowercase	:		List[Any]    =   parser.parse_args()
       lowercase	:		Optional[Any]    =   {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(lowerCamelCase ,					lowerCamelCase   )


if __name__ == "__main__":
      main()




