

'''simple docstring'''




import argparse

import torch

from diffusers import UNetaDConditionModel




def 						UpperCamelCase       ( a		,						a		,						a		,						a=None		,						a=None		,						a=None )      ->	Union[str, Any]:






     '''simple docstring'''
     assert isinstance(_UpperCAmelCase		,						_UpperCAmelCase ), "Paths should be a list of dicts containing 'old' and 'new' keys."

     # Splits the attention layers into three variables.
     if attention_paths_to_split is not None:
          for path, path_map in attention_paths_to_split.items():
               __magic_name__						   =				old_checkpoint[path]
               __magic_name__						   =				old_tensor.shape[0] // 3

               __magic_name__						   =				(-1, channels) if len(old_tensor.shape ) == 3 else (-1)

               __magic_name__						   =				old_tensor.shape[0] // config['''num_head_channels'''] // 3

               __magic_name__						   =				old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
               __magic_name__   ,  __magic_name__   ,  __magic_name__						   =				old_tensor.split(channels // num_heads		,						dim=1 )

               __magic_name__						   =				query.reshape(_UpperCAmelCase )
               __magic_name__						   =				key.reshape(_UpperCAmelCase )
               __magic_name__						   =				value.reshape(_UpperCAmelCase )

     for path in paths:
          __magic_name__						   =				path['''new''']

          # These have already been assigned
          if attention_paths_to_split is not None and new_path in attention_paths_to_split:
               continue

          if additional_replacements is not None:
               for replacement in additional_replacements:
                    __magic_name__						   =				new_path.replace(replacement['''old''']		,						replacement['''new'''] )

        # proj_attn.weight has to be converted from conv 1D to linear
          __magic_name__						   =				old_checkpoint[path['''old''']]
          __magic_name__						   =				['''proj_attn.weight''']
          __magic_name__						   =				['''proj_out.weight''', '''proj_in.weight''']
          if any(k in new_path for k in names ):
               __magic_name__						   =				weight[:, :, 0]
          elif any(k in new_path for k in names_a ) and len(weight.shape ) > 2 and ".attentions." not in new_path:
               __magic_name__						   =				weight[:, :, 0]
          else:
               __magic_name__						   =				weight




def 						UpperCamelCase       ( a		,						a=0 )      ->	List[Any]:






     '''simple docstring'''
     __magic_name__						   =				[]
     for old_item in old_list:
          __magic_name__						   =				old_item

          #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
          #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

          #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
          #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

          #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

          mapping.append({'''old''': old_item, '''new''': new_item} )

     return mapping




def 						UpperCamelCase       ( a		,						a=1 )      ->	Any:






     '''simple docstring'''
     if n_shave_prefix_segments >= 0:
          return ".".join(path.split('''.''' )[n_shave_prefix_segments:] )
     else:
          return ".".join(path.split('''.''' )[:n_shave_prefix_segments] )




def 						UpperCamelCase       ( a		,						a=0 )      ->	int:






     '''simple docstring'''
     __magic_name__						   =				[]
     for old_item in old_list:
          mapping.append({'''old''': old_item, '''new''': old_item} )

     return mapping




def 						UpperCamelCase       ( a		,						a=0 )      ->	Optional[int]:






     '''simple docstring'''
     __magic_name__						   =				[]
     for old_item in old_list:
          __magic_name__						   =				old_item.replace('''in_layers.0'''		,						'''norm1''' )
          __magic_name__						   =				new_item.replace('''in_layers.2'''		,						'''conv1''' )

          __magic_name__						   =				new_item.replace('''out_layers.0'''		,						'''norm2''' )
          __magic_name__						   =				new_item.replace('''out_layers.3'''		,						'''conv2''' )

          __magic_name__						   =				new_item.replace('''emb_layers.1'''		,						'''time_emb_proj''' )
          __magic_name__						   =				new_item.replace('''skip_connection'''		,						'''conv_shortcut''' )

          __magic_name__						   =				shave_segments(_UpperCAmelCase		,						n_shave_prefix_segments=_UpperCAmelCase )

          if "temopral_conv" not in old_item:
               mapping.append({'''old''': old_item, '''new''': new_item} )

     return mapping




def 						UpperCamelCase       ( a		,						a		,						a=None		,						a=False )      ->	int:






     '''simple docstring'''
     __magic_name__						   =				{}
     __magic_name__						   =				list(checkpoint.keys() )

     __magic_name__						   =				'''model.diffusion_model.'''

     # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
     if sum(k.startswith('''model_ema''' ) for k in keys ) > 100 and extract_ema:
          print(F'''Checkpoint {path} has both EMA and non-EMA weights.''' )
          print(
              '''In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'''
              ''' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.''' )
          for key in keys:
               if key.startswith('''model.diffusion_model''' ):
                    __magic_name__						   =				'''model_ema.''' + ''''''.join(key.split('''.''' )[1:] )
                    __magic_name__						   =				checkpoint.pop(_UpperCAmelCase )
     else:
          if sum(k.startswith('''model_ema''' ) for k in keys ) > 100:
               print(
                   '''In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'''
                   ''' weights (usually better for inference), please make sure to add the `--extract_ema` flag.''' )

          for key in keys:
               __magic_name__						   =				checkpoint.pop(_UpperCAmelCase )

     __magic_name__						   =				{}

     __magic_name__						   =				unet_state_dict['''time_embed.0.weight''']
     __magic_name__						   =				unet_state_dict['''time_embed.0.bias''']
     __magic_name__						   =				unet_state_dict['''time_embed.2.weight''']
     __magic_name__						   =				unet_state_dict['''time_embed.2.bias''']

     if config["class_embed_type"] is None:
          # No parameters to port
          ...
     elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
          __magic_name__						   =				unet_state_dict['''label_emb.0.0.weight''']
          __magic_name__						   =				unet_state_dict['''label_emb.0.0.bias''']
          __magic_name__						   =				unet_state_dict['''label_emb.0.2.weight''']
          __magic_name__						   =				unet_state_dict['''label_emb.0.2.bias''']
     else:
          raise NotImplementedError(F'''Not implemented `class_embed_type`: {config["class_embed_type"]}''' )

     __magic_name__						   =				unet_state_dict['''input_blocks.0.0.weight''']
     __magic_name__						   =				unet_state_dict['''input_blocks.0.0.bias''']

     __magic_name__						   =				[v for v in unet_state_dict if v.startswith('''input_blocks.0.1''' )]
     __magic_name__						   =				renew_attention_paths(_UpperCAmelCase )
     __magic_name__						   =				{'''old''': '''input_blocks.0.1''', '''new''': '''transformer_in'''}
     assign_to_checkpoint(_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						additional_replacements=[meta_path]		,						config=_UpperCAmelCase )

     __magic_name__						   =				unet_state_dict['''out.0.weight''']
     __magic_name__						   =				unet_state_dict['''out.0.bias''']
     __magic_name__						   =				unet_state_dict['''out.2.weight''']
     __magic_name__						   =				unet_state_dict['''out.2.bias''']

     # Retrieves the keys for the input blocks only
     __magic_name__						   =				len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in unet_state_dict if '''input_blocks''' in layer} )
     __magic_name__						   =				{
         layer_id: [key for key in unet_state_dict if F'''input_blocks.{layer_id}''' in key]
         for layer_id in range(_UpperCAmelCase )
     }

     # Retrieves the keys for the middle blocks only
     __magic_name__						   =				len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in unet_state_dict if '''middle_block''' in layer} )
     __magic_name__						   =				{
         layer_id: [key for key in unet_state_dict if F'''middle_block.{layer_id}''' in key]
         for layer_id in range(_UpperCAmelCase )
     }

     # Retrieves the keys for the output blocks only
     __magic_name__						   =				len({'''.'''.join(layer.split('''.''' )[:2] ) for layer in unet_state_dict if '''output_blocks''' in layer} )
     __magic_name__						   =				{
         layer_id: [key for key in unet_state_dict if F'''output_blocks.{layer_id}''' in key]
         for layer_id in range(_UpperCAmelCase )
     }

     for i in range(1		,						_UpperCAmelCase ):
          __magic_name__						   =				(i - 1) // (config['''layers_per_block'''] + 1)
          __magic_name__						   =				(i - 1) % (config['''layers_per_block'''] + 1)

          __magic_name__						   =				[
              key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key and F'''input_blocks.{i}.0.op''' not in key
          ]
          __magic_name__						   =				[key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]
          __magic_name__						   =				[key for key in input_blocks[i] if F'''input_blocks.{i}.2''' in key]

          if F'''input_blocks.{i}.op.weight''' in unet_state_dict:
               __magic_name__						   =				unet_state_dict.pop(
                   F'''input_blocks.{i}.op.weight''' )
               __magic_name__						   =				unet_state_dict.pop(
                   F'''input_blocks.{i}.op.bias''' )

          __magic_name__						   =				renew_resnet_paths(_UpperCAmelCase )
          __magic_name__						   =				{'''old''': F'''input_blocks.{i}.0''', '''new''': F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
          assign_to_checkpoint(
              _UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						additional_replacements=[meta_path]		,						config=_UpperCAmelCase )

          __magic_name__						   =				[key for key in resnets if '''temopral_conv''' in key]
          __magic_name__						   =				renew_temp_conv_paths(_UpperCAmelCase )
          __magic_name__						   =				{
              '''old''': F'''input_blocks.{i}.0.temopral_conv''',
              '''new''': F'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
          }
          assign_to_checkpoint(
              _UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						additional_replacements=[meta_path]		,						config=_UpperCAmelCase )

          if len(_UpperCAmelCase ):
               __magic_name__						   =				renew_attention_paths(_UpperCAmelCase )
               __magic_name__						   =				{'''old''': F'''input_blocks.{i}.1''', '''new''': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
               assign_to_checkpoint(
                   _UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						additional_replacements=[meta_path]		,						config=_UpperCAmelCase )

          if len(_UpperCAmelCase ):
               __magic_name__						   =				renew_attention_paths(_UpperCAmelCase )
               __magic_name__						   =				{
                   '''old''': F'''input_blocks.{i}.2''',
                   '''new''': F'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
               }
               assign_to_checkpoint(
                   _UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						additional_replacements=[meta_path]		,						config=_UpperCAmelCase )

     __magic_name__						   =				middle_blocks[0]
     __magic_name__						   =				[key for key in resnet_a if '''temopral_conv''' in key]
     __magic_name__						   =				middle_blocks[1]
     __magic_name__						   =				middle_blocks[2]
     __magic_name__						   =				middle_blocks[3]
     __magic_name__						   =				[key for key in resnet_a if '''temopral_conv''' in key]

     __magic_name__						   =				renew_resnet_paths(_UpperCAmelCase )
     __magic_name__						   =				{'''old''': '''middle_block.0''', '''new''': '''mid_block.resnets.0'''}
     assign_to_checkpoint(
         _UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						config=_UpperCAmelCase		,						additional_replacements=[meta_path] )

     __magic_name__						   =				renew_temp_conv_paths(_UpperCAmelCase )
     __magic_name__						   =				{'''old''': '''middle_block.0.temopral_conv''', '''new''': '''mid_block.temp_convs.0'''}
     assign_to_checkpoint(
         _UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						config=_UpperCAmelCase		,						additional_replacements=[meta_path] )

     __magic_name__						   =				renew_resnet_paths(_UpperCAmelCase )
     __magic_name__						   =				{'''old''': '''middle_block.3''', '''new''': '''mid_block.resnets.1'''}
     assign_to_checkpoint(
         _UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						config=_UpperCAmelCase		,						additional_replacements=[meta_path] )

     __magic_name__						   =				renew_temp_conv_paths(_UpperCAmelCase )
     __magic_name__						   =				{'''old''': '''middle_block.3.temopral_conv''', '''new''': '''mid_block.temp_convs.1'''}
     assign_to_checkpoint(
         _UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						config=_UpperCAmelCase		,						additional_replacements=[meta_path] )

     __magic_name__						   =				renew_attention_paths(_UpperCAmelCase )
     __magic_name__						   =				{'''old''': '''middle_block.1''', '''new''': '''mid_block.attentions.0'''}
     assign_to_checkpoint(
         _UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						additional_replacements=[meta_path]		,						config=_UpperCAmelCase )

     __magic_name__						   =				renew_attention_paths(_UpperCAmelCase )
     __magic_name__						   =				{'''old''': '''middle_block.2''', '''new''': '''mid_block.temp_attentions.0'''}
     assign_to_checkpoint(
         _UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						additional_replacements=[meta_path]		,						config=_UpperCAmelCase )

     for i in range(_UpperCAmelCase ):
          __magic_name__						   =				i // (config['''layers_per_block'''] + 1)
          __magic_name__						   =				i % (config['''layers_per_block'''] + 1)
          __magic_name__						   =				[shave_segments(_UpperCAmelCase		,						2 ) for name in output_blocks[i]]
          __magic_name__						   =				{}

          for layer in output_block_layers:
               __magic_name__   ,  __magic_name__						   =				layer.split('''.''' )[0], shave_segments(_UpperCAmelCase		,						1 )
               if layer_id in output_block_list:
                    output_block_list[layer_id].append(_UpperCAmelCase )
               else:
                    __magic_name__						   =				[layer_name]

          if len(_UpperCAmelCase ) > 1:
               __magic_name__						   =				[key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
               __magic_name__						   =				[key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]
               __magic_name__						   =				[key for key in output_blocks[i] if F'''output_blocks.{i}.2''' in key]

               __magic_name__						   =				renew_resnet_paths(_UpperCAmelCase )
               __magic_name__						   =				renew_resnet_paths(_UpperCAmelCase )

               __magic_name__						   =				{'''old''': F'''output_blocks.{i}.0''', '''new''': F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
               assign_to_checkpoint(
                   _UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						additional_replacements=[meta_path]		,						config=_UpperCAmelCase )

               __magic_name__						   =				[key for key in resnets if '''temopral_conv''' in key]
               __magic_name__						   =				renew_temp_conv_paths(_UpperCAmelCase )
               __magic_name__						   =				{
                   '''old''': F'''output_blocks.{i}.0.temopral_conv''',
                   '''new''': F'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
               }
               assign_to_checkpoint(
                   _UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						additional_replacements=[meta_path]		,						config=_UpperCAmelCase )

               __magic_name__						   =				{k: sorted(_UpperCAmelCase ) for k, v in output_block_list.items()}
               if ["conv.bias", "conv.weight"] in output_block_list.values():
                    __magic_name__						   =				list(output_block_list.values() ).index(['''conv.bias''', '''conv.weight'''] )
                    __magic_name__						   =				unet_state_dict[
                        F'''output_blocks.{i}.{index}.conv.weight'''
                    ]
                    __magic_name__						   =				unet_state_dict[
                        F'''output_blocks.{i}.{index}.conv.bias'''
                    ]

                    # Clear attentions as they have been attributed above.
                    if len(_UpperCAmelCase ) == 2:
                         __magic_name__						   =				[]

               if len(_UpperCAmelCase ):
                    __magic_name__						   =				renew_attention_paths(_UpperCAmelCase )
                    __magic_name__						   =				{
                        '''old''': F'''output_blocks.{i}.1''',
                        '''new''': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
                    }
                    assign_to_checkpoint(
                        _UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						additional_replacements=[meta_path]		,						config=_UpperCAmelCase )

               if len(_UpperCAmelCase ):
                    __magic_name__						   =				renew_attention_paths(_UpperCAmelCase )
                    __magic_name__						   =				{
                        '''old''': F'''output_blocks.{i}.2''',
                        '''new''': F'''up_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
                    }
                    assign_to_checkpoint(
                        _UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						additional_replacements=[meta_path]		,						config=_UpperCAmelCase )
          else:
               __magic_name__						   =				renew_resnet_paths(_UpperCAmelCase		,						n_shave_prefix_segments=1 )
               for path in resnet_0_paths:
                    __magic_name__						   =				'''.'''.join(['''output_blocks''', str(_UpperCAmelCase ), path['''old''']] )
                    __magic_name__						   =				'''.'''.join(['''up_blocks''', str(_UpperCAmelCase ), '''resnets''', str(_UpperCAmelCase ), path['''new''']] )
                    __magic_name__						   =				unet_state_dict[old_path]

               __magic_name__						   =				[l for l in output_block_layers if '''temopral_conv''' in l]
               for path in temopral_conv_paths:
                    __magic_name__						   =				path.split('''temopral_conv.''' )[-1]
                    __magic_name__						   =				'''.'''.join(['''output_blocks''', str(_UpperCAmelCase ), str(_UpperCAmelCase ), '''temopral_conv''', pruned_path] )
                    __magic_name__						   =				'''.'''.join(['''up_blocks''', str(_UpperCAmelCase ), '''temp_convs''', str(_UpperCAmelCase ), pruned_path] )
                    __magic_name__						   =				unet_state_dict[old_path]

     return new_checkpoint


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

      parser.add_argument(
          "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
      )
      parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
      _lowerCAmelCase    			=						parser.parse_args()

      _lowerCAmelCase    			=						torch.load(args.checkpoint_path, map_location="cpu")
      _lowerCAmelCase    			=						UNetaDConditionModel()

      _lowerCAmelCase    			=						convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

      _lowerCAmelCase    			=						set(unet.state_dict().keys()) - set(converted_ckpt.keys())
      _lowerCAmelCase    			=						set(converted_ckpt.keys()) - set(unet.state_dict().keys())

      assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

      # load state_dict
      unet.load_state_dict(converted_ckpt)

      unet.save_pretrained(args.dump_path)

      # -- finish converting the unet --













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

UpperCAmelCase__  :	Union[str, Any]     =16
UpperCAmelCase__  :	List[Any]     =32





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase = 16 )  ->     Dict:
				lowerCamelCase		       =AutoTokenizer.from_pretrained("""bert-base-cased""" )
				lowerCamelCase		       =load_dataset("""glue"""	,					"""mrpc""" )

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

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

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

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

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

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

				return train_dataloader, eval_dataloader


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

							UpperCAmelCase__  :	Union[str, Any]     =mocked_dataloaders  # noqa: F811





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

				set_seed(_UpperCAmelCase )

				lowerCamelCase       ,     lowerCamelCase		       =get_dataloaders(_UpperCAmelCase	,					_UpperCAmelCase )
				lowerCamelCase		       =evaluate.load("""glue"""	,					"""mrpc""" )

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

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

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

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

				# Instantiate scheduler
				lowerCamelCase		       =get_linear_schedule_with_warmup(
				    optimizer=_UpperCAmelCase	,					num_warmup_steps=1_00	,					num_training_steps=(len(_UpperCAmelCase ) * 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.
				lowerCamelCase       ,     lowerCamelCase       ,     lowerCamelCase       ,     lowerCamelCase       ,     lowerCamelCase		       =accelerator.prepare(
				    _UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase )

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

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

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

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

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

								# 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":
												lowerCamelCase		       =F"""epoch_{epoch}"""
												if args.output_dir is not None:
																lowerCamelCase		       =os.path.join(args.output_dir	,					_UpperCAmelCase )
												accelerator.save_state(_UpperCAmelCase )





def    _lowercase    (  )  ->     str:
				lowerCamelCase		       =argparse.ArgumentParser(description="""Simple example of training script.""" )
				parser.add_argument(
				    """--mixed_precision"""	,					type=_UpperCAmelCase	,					default=_UpperCAmelCase	,					choices=["""no""", """fp16""", """bf16""", """fp8"""]	,					help="""Whether to use mixed precision. Choose"""
				    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
				    """and an Nvidia Ampere GPU."""	,					)
				parser.add_argument("""--cpu"""	,					action="""store_true"""	,					help="""If passed, will train on the CPU.""" )
				parser.add_argument(
				    """--checkpointing_steps"""	,					type=_UpperCAmelCase	,					default=_UpperCAmelCase	,					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=_UpperCAmelCase	,					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=_UpperCAmelCase	,					default=_UpperCAmelCase	,					help="""If the training should continue from a checkpoint folder."""	,					)
				lowerCamelCase		       =parser.parse_args()
				lowerCamelCase		       ={"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(_UpperCAmelCase	,					_UpperCAmelCase )


if __name__ == "__main__":
							main()





