

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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


snake_case_        =			16
snake_case_        =			32

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Accelerator     ,					SCREAMING_SNAKE_CASE_		:   int = 16      ):



     '''simple docstring'''





     lowercase__   :       int				=	AutoTokenizer.from_pretrained('bert-base-cased'      )
     lowercase__   :       List[str]				=	load_dataset('glue'     ,					'mrpc'      )

     def tokenize_function(SCREAMING_SNAKE_CASE_		:   List[str]      ):
          # max_length=None => use the model max length (it's actually the default)
          lowercase__   :       Tuple				=	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():
          lowercase__   :       Optional[Any]				=	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
     lowercase__   :       Union[str, Any]				=	tokenized_datasets.rename_column('label'     ,					'labels'      )

     def collate_fn(SCREAMING_SNAKE_CASE_		:   Any      ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          lowercase__   :       Union[str, Any]				=	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__   :       int				=	8
          else:
               lowercase__   :       Tuple				=	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.
     lowercase__   :       List[Any]				=	DataLoader(
         tokenized_datasets['train']     ,					shuffle=SCREAMING_SNAKE_CASE_     ,					collate_fn=SCREAMING_SNAKE_CASE_     ,					batch_size=SCREAMING_SNAKE_CASE_      )
     lowercase__   :       int				=	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

       snake_case_        =			mocked_dataloaders  # noqa: F811

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Dict      ):



     '''simple docstring'''





     if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,					SCREAMING_SNAKE_CASE_      ) == "1":
          lowercase__   :       int				=	2
     # Initialize accelerator
     lowercase__   :       List[str]				=	Accelerator(cpu=args.cpu     ,					mixed_precision=args.mixed_precision      )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowercase__   :       Optional[int]				=	config['lr']
     lowercase__   :       List[Any]				=	int(config['num_epochs']      )
     lowercase__   :       List[str]				=	int(config['seed']      )
     lowercase__   :       List[Any]				=	int(config['batch_size']      )

     lowercase__   :       Dict				=	evaluate.load('glue'     ,					'mrpc'      )

     # If the batch size is too big we use gradient accumulation
     lowercase__   :       Union[str, Any]				=	1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          lowercase__   :       List[Any]				=	batch_size // MAX_GPU_BATCH_SIZE
          lowercase__   :       Optional[Any]				=	MAX_GPU_BATCH_SIZE

     set_seed(SCREAMING_SNAKE_CASE_      )
     lowercase__				,			lowercase__   :       Tuple				=	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)
     lowercase__   :       Tuple				=	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).
     lowercase__   :       Any				=	model.to(accelerator.device      )

     # Instantiate optimizer
     lowercase__   :       Optional[int]				=	AdamW(params=model.parameters()     ,					lr=SCREAMING_SNAKE_CASE_      )

     # Instantiate scheduler
     lowercase__   :       Any				=	get_linear_schedule_with_warmup(
         optimizer=SCREAMING_SNAKE_CASE_     ,					num_warmup_steps=100     ,					num_training_steps=(len(SCREAMING_SNAKE_CASE_      ) * 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__   :       Optional[Any]				=	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      )
               lowercase__   :       str				=	model(**SCREAMING_SNAKE_CASE_      )
               lowercase__   :       Optional[int]				=	outputs.loss
               lowercase__   :       str				=	loss / gradient_accumulation_steps
               accelerator.backward(SCREAMING_SNAKE_CASE_      )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          model.eval()
          lowercase__   :       Any				=	0
          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():
                    lowercase__   :       Dict				=	model(**SCREAMING_SNAKE_CASE_      )
               lowercase__   :       Any				=	outputs.logits.argmax(dim=-1      )
               lowercase__				,			lowercase__   :       str				=	accelerator.gather((predictions, batch['labels'])      )
               # New Code #
               # First we check if it's a distributed system
               if accelerator.use_distributed:
                    # Then see if we're on the last batch of our eval dataloader
                    if step == len(SCREAMING_SNAKE_CASE_      ) - 1:
                         # Last batch needs to be truncated on distributed systems as it contains additional samples
                         lowercase__   :       Tuple				=	predictions[: len(eval_dataloader.dataset      ) - samples_seen]
                         lowercase__   :       Dict				=	references[: len(eval_dataloader.dataset      ) - samples_seen]
                    else:
                         # Otherwise we add the number of samples seen
                         samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
               metric.add_batch(
                   predictions=SCREAMING_SNAKE_CASE_     ,					references=SCREAMING_SNAKE_CASE_     ,					)

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

def      snake_case__      (   ):



     '''simple docstring'''





     lowercase__   :       Optional[Any]				=	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.'     ,					)
     parser.add_argument('--cpu'     ,					action='store_true'     ,					help='If passed, will train on the CPU.'      )
     lowercase__   :       str				=	parser.parse_args()
     lowercase__   :       Optional[Any]				=	{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
       main()




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


snake_case_        =			False
snake_case_        =			True
snake_case_        =			False


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

       parser.add_argument(
           '''--repo_path''',
           default=None,
           type=str,
           required=True,
           help='''The config json file corresponding to the architecture.''',
       )

       parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

       snake_case_        =			parser.parse_args()

       snake_case_        =			{
           '''image_size''': '''sample_size''',
           '''num_res_blocks''': '''layers_per_block''',
           '''block_channels''': '''block_out_channels''',
           '''down_blocks''': '''down_block_types''',
           '''up_blocks''': '''up_block_types''',
           '''downscale_freq_shift''': '''freq_shift''',
           '''resnet_num_groups''': '''norm_num_groups''',
           '''resnet_act_fn''': '''act_fn''',
           '''resnet_eps''': '''norm_eps''',
           '''num_head_channels''': '''attention_head_dim''',
       }

       snake_case_        =			{
           '''time_steps''': '''time_proj''',
           '''mid''': '''mid_block''',
           '''downsample_blocks''': '''down_blocks''',
           '''upsample_blocks''': '''up_blocks''',
       }

       snake_case_        =			'''''' if has_file(args.repo_path, '''config.json''') else '''unet'''

       with open(os.path.join(args.repo_path, subfolder, '''config.json'''), '''r''', encoding='''utf-8''') as reader:
              snake_case_        =			reader.read()
              snake_case_        =			json.loads(text)

       if do_only_config:
              for key in config_parameters_to_change.keys():
                     config.pop(key, None)

       if has_file(args.repo_path, '''config.json'''):
              snake_case_        =			UNetaDModel(**config)
       else:
              snake_case_        =			UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
              snake_case_        =			class_name(**config)

       if do_only_config:
              model.save_config(os.path.join(args.repo_path, subfolder))

       snake_case_        =			dict(model.config)

       if do_only_renaming:
              for key, value in config_parameters_to_change.items():
                     if key in config:
                            snake_case_        =			config[key]
                            del config[key]

              snake_case_        =			[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
              snake_case_        =			[k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

       if do_only_weights:
              snake_case_        =			torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

              snake_case_        =			{}
              for param_key, param_value in state_dict.items():
                     if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
                            continue
                     snake_case_        =			False
                     for key, new_key in key_parameters_to_change.items():
                            if not has_changed and param_key.split('''.''')[0] == key:
                                   snake_case_        =			param_value
                                   snake_case_        =			True
                     if not has_changed:
                            snake_case_        =			param_value

              model.load_state_dict(new_state_dict)
              model.save_pretrained(os.path.join(args.repo_path, subfolder))

