



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel

def        a						(      _UpperCAmelCase							:					Optional[Any] ,   _UpperCAmelCase							:					Tuple=1						):







		'''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        a						(      _UpperCAmelCase							:					int ,   _UpperCAmelCase							:					int=0						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	[]
		for old_item in old_list:
				__UpperCAmelCase    :     Optional[Any]													=	old_item.replace('''in_layers.0''' ,   '''norm1'''						)
				__UpperCAmelCase    :     List[str]													=	new_item.replace('''in_layers.2''' ,   '''conv1'''						)

				__UpperCAmelCase    :     Tuple													=	new_item.replace('''out_layers.0''' ,   '''norm2'''						)
				__UpperCAmelCase    :     Tuple													=	new_item.replace('''out_layers.3''' ,   '''conv2'''						)

				__UpperCAmelCase    :     Optional[Any]													=	new_item.replace('''emb_layers.1''' ,   '''time_emb_proj'''						)
				__UpperCAmelCase    :     Optional[Any]													=	new_item.replace('''skip_connection''' ,   '''conv_shortcut'''						)

				__UpperCAmelCase    :     Optional[int]													=	shave_segments(_UpperCAmelCase ,   n_shave_prefix_segments=_UpperCAmelCase						)

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

		return mapping

def        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					int=0						):







		'''simple docstring'''





		__UpperCAmelCase    :     str													=	[]
		for old_item in old_list:
				__UpperCAmelCase    :     Optional[int]													=	old_item

				__UpperCAmelCase    :     List[str]													=	new_item.replace('''norm.weight''' ,   '''group_norm.weight'''						)
				__UpperCAmelCase    :     Any													=	new_item.replace('''norm.bias''' ,   '''group_norm.bias'''						)

				__UpperCAmelCase    :     Tuple													=	new_item.replace('''proj_out.weight''' ,   '''proj_attn.weight'''						)
				__UpperCAmelCase    :     Optional[int]													=	new_item.replace('''proj_out.bias''' ,   '''proj_attn.bias'''						)

				__UpperCAmelCase    :     List[str]													=	shave_segments(_UpperCAmelCase ,   n_shave_prefix_segments=_UpperCAmelCase						)

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

		return mapping

def        a						(      _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					List[str]=None ,   _UpperCAmelCase							:					List[str]=None ,   _UpperCAmelCase							:					Tuple=None						):







		'''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    :     Tuple													=	old_checkpoint[path]
						__UpperCAmelCase    :     Optional[Any]													=	old_tensor.shape[0] // 3

						__UpperCAmelCase    :     Any													=	(-1, channels) if len(old_tensor.shape						) == 3 else (-1)

						__UpperCAmelCase    :     Optional[int]													=	old_tensor.shape[0] // config['''num_head_channels'''] // 3

						__UpperCAmelCase    :     Optional[int]													=	old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
						__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	old_tensor.split(channels // num_heads ,   dim=1						)

						__UpperCAmelCase    :     Union[str, Any]													=	query.reshape(_UpperCAmelCase						)
						__UpperCAmelCase    :     Dict													=	key.reshape(_UpperCAmelCase						)
						__UpperCAmelCase    :     Tuple													=	value.reshape(_UpperCAmelCase						)

		for path in paths:
				__UpperCAmelCase    :     str													=	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    :     List[str]													=	new_path.replace('''middle_block.0''' ,   '''mid_block.resnets.0'''						)
				__UpperCAmelCase    :     int													=	new_path.replace('''middle_block.1''' ,   '''mid_block.attentions.0'''						)
				__UpperCAmelCase    :     List[str]													=	new_path.replace('''middle_block.2''' ,   '''mid_block.resnets.1'''						)

				if additional_replacements is not None:
						for replacement in additional_replacements:
								__UpperCAmelCase    :     Union[str, Any]													=	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    :     str													=	old_checkpoint[path['''old''']][:, :, 0]
				else:
						__UpperCAmelCase    :     Any													=	old_checkpoint[path['''old''']]

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







		'''simple docstring'''





		__UpperCAmelCase    :     Tuple													=	{}

		__UpperCAmelCase    :     Union[str, Any]													=	checkpoint['''time_embed.0.weight''']
		__UpperCAmelCase    :     int													=	checkpoint['''time_embed.0.bias''']
		__UpperCAmelCase    :     List[Any]													=	checkpoint['''time_embed.2.weight''']
		__UpperCAmelCase    :     List[Any]													=	checkpoint['''time_embed.2.bias''']

		__UpperCAmelCase    :     Optional[Any]													=	checkpoint['''input_blocks.0.0.weight''']
		__UpperCAmelCase    :     Optional[Any]													=	checkpoint['''input_blocks.0.0.bias''']

		__UpperCAmelCase    :     str													=	checkpoint['''out.0.weight''']
		__UpperCAmelCase    :     List[str]													=	checkpoint['''out.0.bias''']
		__UpperCAmelCase    :     Any													=	checkpoint['''out.2.weight''']
		__UpperCAmelCase    :     Union[str, Any]													=	checkpoint['''out.2.bias''']

		# Retrieves the keys for the input blocks only
		__UpperCAmelCase    :     Union[str, Any]													=	len({'''.'''.join(layer.split('''.'''						)[:2]						) for layer in checkpoint if '''input_blocks''' in layer}						)
		__UpperCAmelCase    :     Optional[Any]													=	{
		    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    :     Optional[int]													=	len({'''.'''.join(layer.split('''.'''						)[:2]						) for layer in checkpoint if '''middle_block''' in layer}						)
		__UpperCAmelCase    :     int													=	{
		    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    :     Optional[int]													=	len({'''.'''.join(layer.split('''.'''						)[:2]						) for layer in checkpoint if '''output_blocks''' in layer}						)
		__UpperCAmelCase    :     List[Any]													=	{
		    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    :     int													=	(i - 1) // (config['''num_res_blocks'''] + 1)
				__UpperCAmelCase    :     List[str]													=	(i - 1) % (config['''num_res_blocks'''] + 1)

				__UpperCAmelCase    :     int													=	[key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
				__UpperCAmelCase    :     str													=	[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    :     List[Any]													=	checkpoint[
						    f'input_blocks.{i}.0.op.weight'
						]
						__UpperCAmelCase    :     Optional[int]													=	checkpoint[
						    f'input_blocks.{i}.0.op.bias'
						]
						continue

				__UpperCAmelCase    :     str													=	renew_resnet_paths(_UpperCAmelCase						)
				__UpperCAmelCase    :     Any													=	{'''old''': f'input_blocks.{i}.0', '''new''': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
				__UpperCAmelCase    :     Any													=	{'''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    :     Optional[int]													=	renew_attention_paths(_UpperCAmelCase						)
						__UpperCAmelCase    :     Optional[Any]													=	{
						    '''old''': f'input_blocks.{i}.1',
						    '''new''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
						}
						__UpperCAmelCase    :     Dict													=	{
						    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    :     List[Any]													=	middle_blocks[0]
		__UpperCAmelCase    :     Optional[Any]													=	middle_blocks[1]
		__UpperCAmelCase    :     Union[str, Any]													=	middle_blocks[2]

		__UpperCAmelCase    :     str													=	renew_resnet_paths(_UpperCAmelCase						)
		assign_to_checkpoint(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   config=_UpperCAmelCase						)

		__UpperCAmelCase    :     Union[str, Any]													=	renew_resnet_paths(_UpperCAmelCase						)
		assign_to_checkpoint(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   config=_UpperCAmelCase						)

		__UpperCAmelCase    :     Tuple													=	renew_attention_paths(_UpperCAmelCase						)
		__UpperCAmelCase    :     str													=	{
		    '''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    :     Any													=	i // (config['''num_res_blocks'''] + 1)
				__UpperCAmelCase    :     Optional[int]													=	i % (config['''num_res_blocks'''] + 1)
				__UpperCAmelCase    :     Optional[Any]													=	[shave_segments(_UpperCAmelCase ,   2						) for name in output_blocks[i]]
				__UpperCAmelCase    :     int													=	{}

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

				if len(_UpperCAmelCase						) > 1:
						__UpperCAmelCase    :     List[str]													=	[key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
						__UpperCAmelCase    :     Optional[int]													=	[key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]

						__UpperCAmelCase    :     Tuple													=	renew_resnet_paths(_UpperCAmelCase						)
						__UpperCAmelCase    :     Optional[int]													=	renew_resnet_paths(_UpperCAmelCase						)

						__UpperCAmelCase    :     Dict													=	{'''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    :     int													=	list(output_block_list.values()						).index(['''conv.weight''', '''conv.bias''']						)
								__UpperCAmelCase    :     str													=	checkpoint[
								    f'output_blocks.{i}.{index}.conv.weight'
								]
								__UpperCAmelCase    :     List[Any]													=	checkpoint[
								    f'output_blocks.{i}.{index}.conv.bias'
								]

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

						if len(_UpperCAmelCase						):
								__UpperCAmelCase    :     Dict													=	renew_attention_paths(_UpperCAmelCase						)
								__UpperCAmelCase    :     Optional[int]													=	{
								    '''old''': f'output_blocks.{i}.1',
								    '''new''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
								}
								__UpperCAmelCase    :     Optional[int]													=	{
								    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    :     Optional[int]													=	renew_resnet_paths(_UpperCAmelCase ,   n_shave_prefix_segments=1						)
						for path in resnet_0_paths:
								__UpperCAmelCase    :     List[str]													=	'''.'''.join(['''output_blocks''', str(_UpperCAmelCase						), path['''old''']]						)
								__UpperCAmelCase    :     Dict													=	'''.'''.join(['''up_blocks''', str(_UpperCAmelCase						), '''resnets''', str(_UpperCAmelCase						), path['''new''']]						)

								__UpperCAmelCase    :     int													=	checkpoint[old_path]

		return new_checkpoint


if __name__ == "__main__":
				__A   			=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   			=parser.parse_args()

				__A   			=torch.load(args.checkpoint_path)

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

				__A   			=convert_ldm_checkpoint(checkpoint, config)

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

				__A   			=UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

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

								__A   			=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()





