





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__=1 )  ->				List[str]:







			'''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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__=0 )  ->				int:







			'''simple docstring'''

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

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

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

						UpperCAmelCase   		=				shave_segments(_UpperCAmelCase			,			n_shave_prefix_segments=_UpperCAmelCase )

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

			return mapping





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__=0 )  ->				Any:







			'''simple docstring'''

			UpperCAmelCase   		=				[]
			for old_item in old_list:
						UpperCAmelCase   		=				old_item

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

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

						UpperCAmelCase   		=				shave_segments(_UpperCAmelCase			,			n_shave_prefix_segments=_UpperCAmelCase )

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

			return mapping





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__=None			,			UpperCamelCase__=None			,			UpperCamelCase__=None )  ->				List[str]:







			'''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():
									UpperCAmelCase   		=				old_checkpoint[path]
									UpperCAmelCase   		=				old_tensor.shape[0] // 3

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

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

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

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

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

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

						# Global renaming happens here
						UpperCAmelCase   		=				new_path.replace('''middle_block.0'''			,			'''mid_block.resnets.0''' )
						UpperCAmelCase   		=				new_path.replace('''middle_block.1'''			,			'''mid_block.attentions.0''' )
						UpperCAmelCase   		=				new_path.replace('''middle_block.2'''			,			'''mid_block.resnets.1''' )

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

        # proj_attn.weight has to be converted from conv 1D to linear
						if "proj_attn.weight" in new_path:
									UpperCAmelCase   		=				old_checkpoint[path['''old''']][:, :, 0]
						else:
									UpperCAmelCase   		=				old_checkpoint[path['''old''']]





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				int:







			'''simple docstring'''

			UpperCAmelCase   		=				{}

			UpperCAmelCase   		=				checkpoint['''time_embed.0.weight''']
			UpperCAmelCase   		=				checkpoint['''time_embed.0.bias''']
			UpperCAmelCase   		=				checkpoint['''time_embed.2.weight''']
			UpperCAmelCase   		=				checkpoint['''time_embed.2.bias''']

			UpperCAmelCase   		=				checkpoint['''input_blocks.0.0.weight''']
			UpperCAmelCase   		=				checkpoint['''input_blocks.0.0.bias''']

			UpperCAmelCase   		=				checkpoint['''out.0.weight''']
			UpperCAmelCase   		=				checkpoint['''out.0.bias''']
			UpperCAmelCase   		=				checkpoint['''out.2.weight''']
			UpperCAmelCase   		=				checkpoint['''out.2.bias''']

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

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

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

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

						UpperCAmelCase   		=				[key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
						UpperCAmelCase   		=				[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]

						if F"""input_blocks.{i}.0.op.weight""" in checkpoint:
									UpperCAmelCase   		=				checkpoint[
									    F"""input_blocks.{i}.0.op.weight"""
									]
									UpperCAmelCase   		=				checkpoint[
									    F"""input_blocks.{i}.0.op.bias"""
									]
									continue

						UpperCAmelCase   		=				renew_resnet_paths(_UpperCAmelCase )
						UpperCAmelCase   		=				{'''old''': F"""input_blocks.{i}.0""", '''new''': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
						UpperCAmelCase   		=				{'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
						assign_to_checkpoint(
						    _UpperCAmelCase			,			_UpperCAmelCase			,			_UpperCAmelCase			,			additional_replacements=[meta_path, resnet_op]			,			config=_UpperCAmelCase )

						if len(_UpperCAmelCase ):
									UpperCAmelCase   		=				renew_attention_paths(_UpperCAmelCase )
									UpperCAmelCase   		=				{
									    '''old''': F"""input_blocks.{i}.1""",
									    '''new''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
									}
									UpperCAmelCase   		=				{
									    F"""input_blocks.{i}.1.qkv.bias""": {
									        '''key''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
									        '''query''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
									        '''value''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
									    },
									    F"""input_blocks.{i}.1.qkv.weight""": {
									        '''key''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
									        '''query''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
									        '''value''': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
									    },
									}
									assign_to_checkpoint(
									    _UpperCAmelCase			,			_UpperCAmelCase			,			_UpperCAmelCase			,			additional_replacements=[meta_path]			,			attention_paths_to_split=_UpperCAmelCase			,			config=_UpperCAmelCase			,			)

			UpperCAmelCase   		=				middle_blocks[0]
			UpperCAmelCase   		=				middle_blocks[1]
			UpperCAmelCase   		=				middle_blocks[2]

			UpperCAmelCase   		=				renew_resnet_paths(_UpperCAmelCase )
			assign_to_checkpoint(_UpperCAmelCase			,			_UpperCAmelCase			,			_UpperCAmelCase			,			config=_UpperCAmelCase )

			UpperCAmelCase   		=				renew_resnet_paths(_UpperCAmelCase )
			assign_to_checkpoint(_UpperCAmelCase			,			_UpperCAmelCase			,			_UpperCAmelCase			,			config=_UpperCAmelCase )

			UpperCAmelCase   		=				renew_attention_paths(_UpperCAmelCase )
			UpperCAmelCase   		=				{
			    '''middle_block.1.qkv.bias''': {
			        '''key''': '''mid_block.attentions.0.key.bias''',
			        '''query''': '''mid_block.attentions.0.query.bias''',
			        '''value''': '''mid_block.attentions.0.value.bias''',
			    },
			    '''middle_block.1.qkv.weight''': {
			        '''key''': '''mid_block.attentions.0.key.weight''',
			        '''query''': '''mid_block.attentions.0.query.weight''',
			        '''value''': '''mid_block.attentions.0.value.weight''',
			    },
			}
			assign_to_checkpoint(
			    _UpperCAmelCase			,			_UpperCAmelCase			,			_UpperCAmelCase			,			attention_paths_to_split=_UpperCAmelCase			,			config=_UpperCAmelCase )

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

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

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

									UpperCAmelCase   		=				renew_resnet_paths(_UpperCAmelCase )
									UpperCAmelCase   		=				renew_resnet_paths(_UpperCAmelCase )

									UpperCAmelCase   		=				{'''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 )

									if ["conv.weight", "conv.bias"] in output_block_list.values():
												UpperCAmelCase   		=				list(output_block_list.values() ).index(['''conv.weight''', '''conv.bias'''] )
												UpperCAmelCase   		=				checkpoint[
												    F"""output_blocks.{i}.{index}.conv.weight"""
												]
												UpperCAmelCase   		=				checkpoint[
												    F"""output_blocks.{i}.{index}.conv.bias"""
												]

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

									if len(_UpperCAmelCase ):
												UpperCAmelCase   		=				renew_attention_paths(_UpperCAmelCase )
												UpperCAmelCase   		=				{
												    '''old''': F"""output_blocks.{i}.1""",
												    '''new''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
												}
												UpperCAmelCase   		=				{
												    F"""output_blocks.{i}.1.qkv.bias""": {
												        '''key''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
												        '''query''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
												        '''value''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
												    },
												    F"""output_blocks.{i}.1.qkv.weight""": {
												        '''key''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
												        '''query''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
												        '''value''': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
												    },
												}
												assign_to_checkpoint(
												    _UpperCAmelCase			,			_UpperCAmelCase			,			_UpperCAmelCase			,			additional_replacements=[meta_path]			,			attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions ) else None			,			config=_UpperCAmelCase			,			)
						else:
									UpperCAmelCase   		=				renew_resnet_paths(_UpperCAmelCase			,			n_shave_prefix_segments=1 )
									for path in resnet_0_paths:
												UpperCAmelCase   		=				'''.'''.join(['''output_blocks''', str(_UpperCAmelCase ), path['''old''']] )
												UpperCAmelCase   		=				'''.'''.join(['''up_blocks''', str(_UpperCAmelCase ), '''resnets''', str(_UpperCAmelCase ), path['''new''']] )

												UpperCAmelCase   		=				checkpoint[old_path]

			return new_checkpoint


if __name__ == "__main__":
				__A				:    Dict       =   argparse.ArgumentParser()

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

				parser.add_argument(
				    "--config_file",
				    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.")

				__A				:    Tuple       =   parser.parse_args()

				__A				:    List[Any]       =   torch.load(args.checkpoint_path)

				with open(args.config_file) as f:
								__A				:    str       =   json.loads(f.read())

				__A				:    str       =   convert_ldm_checkpoint(checkpoint, config)

				if "ldm" in config:
								del config["ldm"]

				__A				:    List[Any]       =   UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				try:
								__A				:    Tuple       =   DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
								__A				:    str       =   VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

								__A				:    Tuple       =   LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
								pipe.save_pretrained(args.dump_path)
				except:  # noqa: E722
								model.save_pretrained(args.dump_path)








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


__A   			=1_6
__A   			=3_2

def        a						(      _UpperCAmelCase							:					Accelerator ,   _UpperCAmelCase							:					int = 16						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	AutoTokenizer.from_pretrained('''bert-base-cased'''						)
		__UpperCAmelCase    :     Any													=	load_dataset('''glue''' ,   '''mrpc'''						)

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

		def collate_fn(_UpperCAmelCase							:					str						):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				__UpperCAmelCase    :     List[Any]													=	1_28 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						__UpperCAmelCase    :     Any													=	16
				elif accelerator.mixed_precision != "no":
						__UpperCAmelCase    :     Tuple													=	8
				else:
						__UpperCAmelCase    :     Optional[int]													=	None

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

		# Instantiate dataloaders.
		__UpperCAmelCase    :     Optional[Any]													=	DataLoader(
		    tokenized_datasets['''train'''] ,   shuffle=_UpperCAmelCase ,   collate_fn=_UpperCAmelCase ,   batch_size=_UpperCAmelCase						)
		__UpperCAmelCase    :     str													=	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

				__A   			=mocked_dataloaders  # noqa: F811

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					Any						):







		'''simple docstring'''





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

		__UpperCAmelCase    :     Optional[Any]													=	evaluate.load('''glue''' ,   '''mrpc'''						)

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

		set_seed(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	get_dataloaders(_UpperCAmelCase ,   _UpperCAmelCase						)
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__UpperCAmelCase    :     List[str]													=	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).
		__UpperCAmelCase    :     Tuple													=	model.to(accelerator.device						)

		# Instantiate optimizer
		__UpperCAmelCase    :     Any													=	AdamW(params=model.parameters() ,   lr=_UpperCAmelCase						)

		# Instantiate scheduler
		__UpperCAmelCase    :     str													=	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.
		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     List[str]													=	accelerator.prepare(
		    _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)

		# Now we train the model
		for epoch in range(_UpperCAmelCase						):
				model.train()
				for step, batch in enumerate(_UpperCAmelCase						):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device						)
						__UpperCAmelCase    :     int													=	model(**_UpperCAmelCase						)
						__UpperCAmelCase    :     str													=	outputs.loss
						__UpperCAmelCase    :     str													=	loss / gradient_accumulation_steps
						accelerator.backward(_UpperCAmelCase						)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				__UpperCAmelCase    :     Tuple													=	0
				for step, batch in enumerate(_UpperCAmelCase						):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device						)
						with torch.no_grad():
								__UpperCAmelCase    :     Dict													=	model(**_UpperCAmelCase						)
						__UpperCAmelCase    :     str													=	outputs.logits.argmax(dim=-1						)
						__UpperCAmelCase							,					__UpperCAmelCase    :     List[Any]													=	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(_UpperCAmelCase						) - 1:
										# Last batch needs to be truncated on distributed systems as it contains additional samples
										__UpperCAmelCase    :     Dict													=	predictions[: len(eval_dataloader.dataset						) - samples_seen]
										__UpperCAmelCase    :     List[str]													=	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=_UpperCAmelCase ,   references=_UpperCAmelCase ,   )

				__UpperCAmelCase    :     Tuple													=	metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'epoch {epoch}:' ,   _UpperCAmelCase						)

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     int													=	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.'''						)
		__UpperCAmelCase    :     int													=	parser.parse_args()
		__UpperCAmelCase    :     Union[str, Any]													=	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(_UpperCAmelCase ,   _UpperCAmelCase						)


if __name__ == "__main__":
				main()





