


'''simple docstring'''



import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


_lowercase    :    Optional[Any]			     =  logging.get_logger(__name__)


def    lowerCamelCase__   ( A     :    Any   ,     A     :    Dict   ,     A     :    str   ,     A     :    Optional[Any]=False  ):




						'''simple docstring'''






						try:
												import torch  # noqa: F401
						except ImportError:
												logger.error(
												    '''Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'''
												    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
												    ''' instructions.'''  )
												raise

						if not is_sharded:
												UpperCAmelCase         =			os.path.abspath(A  )
												logger.info(f"""Loading PyTorch weights from {pt_path}"""  )

												UpperCAmelCase         =			torch.load(A   ,     map_location='''cpu'''  )
												logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()  ):,} parameters."""  )

												UpperCAmelCase         =			convert_pytorch_state_dict_to_flax(A   ,     A  )
						else:
												# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
												UpperCAmelCase         =			convert_pytorch_sharded_state_dict_to_flax(A   ,     A  )
						return flax_state_dict


def    lowerCamelCase__   ( A     :    Tuple[str]   ,     A     :    np.ndarray   ,     A     :    Dict[str, jnp.ndarray]   ,     A     :    str   ,     ):




						'''simple docstring'''






						def is_key_or_prefix_key_in_dict(A     :    Tuple[str]  ) -> bool:
												return len(set(A  ) & {key, (model_prefix,) + key}  ) > 0

						# layer norm
						UpperCAmelCase         =			pt_tuple_key[:-1] + ('''scale''',)
						if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(A  ):
												return renamed_pt_tuple_key, pt_tensor

						# batch norm layer mean
						UpperCAmelCase         =			pt_tuple_key[:-1] + ('''mean''',)
						if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(A  ):
												return renamed_pt_tuple_key, pt_tensor

						# batch norm layer var
						UpperCAmelCase         =			pt_tuple_key[:-1] + ('''var''',)
						if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(A  ):
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						UpperCAmelCase         =			pt_tuple_key[:-1] + ('''embedding''',)
						if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(A  ):
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						UpperCAmelCase         =			pt_tuple_key[:-1] + ('''kernel''',)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(A  ):
												UpperCAmelCase         =			pt_tensor.transpose(2   ,     3   ,     1   ,     0  )
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						UpperCAmelCase         =			pt_tuple_key[:-1] + ('''kernel''',)
						if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(A  ):
												UpperCAmelCase         =			pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm weight
						UpperCAmelCase         =			pt_tuple_key[:-1] + ('''weight''',)
						if pt_tuple_key[-1] == "gamma":
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm bias
						UpperCAmelCase         =			pt_tuple_key[:-1] + ('''bias''',)
						if pt_tuple_key[-1] == "beta":
												return renamed_pt_tuple_key, pt_tensor

						# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
						UpperCAmelCase         =			None
						if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
												UpperCAmelCase         =			pt_tuple_key[-2] + '''_g'''
						elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
												UpperCAmelCase         =			pt_tuple_key[-2] + '''_v'''
						if name is not None:
												UpperCAmelCase         =			pt_tuple_key[:-3] + (name,)
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor


def    lowerCamelCase__   ( A     :    int   ,     A     :    int  ):




						'''simple docstring'''






						UpperCAmelCase         =			{k: v.numpy() for k, v in pt_state_dict.items()}

						UpperCAmelCase         =			flax_model.base_model_prefix

						# use params dict if the model contains batch norm layers
						if "params" in flax_model.params:
												UpperCAmelCase         =			flax_model.params['''params''']
						else:
												UpperCAmelCase         =			flax_model.params
						UpperCAmelCase         =			flatten_dict(A  )

						# add batch_stats keys,values to dict
						if "batch_stats" in flax_model.params:
												UpperCAmelCase         =			flatten_dict(flax_model.params['''batch_stats''']  )
												random_flax_state_dict.update(A  )

						UpperCAmelCase         =			{}

						UpperCAmelCase         =			(model_prefix not in flax_model_params) and (
						    model_prefix in {k.split('''.'''  )[0] for k in pt_state_dict.keys()}
						)
						UpperCAmelCase         =			(model_prefix in flax_model_params) and (
						    model_prefix not in {k.split('''.'''  )[0] for k in pt_state_dict.keys()}
						)

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												UpperCAmelCase         =			tuple(pt_key.split('''.'''  )  )

												# remove base model prefix if necessary
												UpperCAmelCase         =			pt_tuple_key[0] == model_prefix
												if load_model_with_head_into_base_model and has_base_model_prefix:
																		UpperCAmelCase         =			pt_tuple_key[1:]

												# Correctly rename weight parameters
												UpperCAmelCase       ,       UpperCAmelCase         =			rename_key_and_reshape_tensor(
												    A   ,     A   ,     A   ,     A  )

												# add model prefix if necessary
												UpperCAmelCase         =			(model_prefix,) + flax_key in random_flax_state_dict
												if load_base_model_into_model_with_head and require_base_model_prefix:
																		UpperCAmelCase         =			(model_prefix,) + flax_key

												if flax_key in random_flax_state_dict:
																		if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																								raise ValueError(
																								    f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
																								    f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""  )

        # add batch stats if the model contains batchnorm layers
												if "batch_stats" in flax_model.params:
																		if "mean" in flax_key[-1] or "var" in flax_key[-1]:
																								UpperCAmelCase         =			jnp.asarray(A  )
																								continue
																		# remove num_batches_tracked key
																		if "num_batches_tracked" in flax_key[-1]:
																								flax_state_dict.pop(A   ,     A  )
																								continue

																		# also add unexpected weight so that warning is thrown
																		UpperCAmelCase         =			jnp.asarray(A  )

												else:
																		# also add unexpected weight so that warning is thrown
																		UpperCAmelCase         =			jnp.asarray(A  )

						return unflatten_dict(A  )


def    lowerCamelCase__   ( A     :    Optional[int]   ,     A     :    str  ):




						'''simple docstring'''






						import torch

						# Load the index
						UpperCAmelCase         =			{}
						for shard_file in shard_filenames:
												# load using msgpack utils
												UpperCAmelCase         =			torch.load(A  )
												UpperCAmelCase         =			{k: v.numpy() for k, v in pt_state_dict.items()}

												UpperCAmelCase         =			flax_model.base_model_prefix

												# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
												if "batch_stats" in flax_model.params:
																		UpperCAmelCase         =			flax_model.params['''params''']

																		UpperCAmelCase         =			flatten_dict(A  )
																		random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']  )  )
												else:
																		UpperCAmelCase         =			flax_model.params
																		UpperCAmelCase         =			flatten_dict(A  )

												UpperCAmelCase         =			(model_prefix not in flax_model_params) and (
												    model_prefix in {k.split('''.'''  )[0] for k in pt_state_dict.keys()}
												)
												UpperCAmelCase         =			(model_prefix in flax_model_params) and (
												    model_prefix not in {k.split('''.'''  )[0] for k in pt_state_dict.keys()}
												)
												# Need to change some parameters name to match Flax names
												for pt_key, pt_tensor in pt_state_dict.items():
																		UpperCAmelCase         =			tuple(pt_key.split('''.'''  )  )

																		# remove base model prefix if necessary
																		UpperCAmelCase         =			pt_tuple_key[0] == model_prefix
																		if load_model_with_head_into_base_model and has_base_model_prefix:
																								UpperCAmelCase         =			pt_tuple_key[1:]

																		# Correctly rename weight parameters
																		UpperCAmelCase       ,       UpperCAmelCase         =			rename_key_and_reshape_tensor(
																		    A   ,     A   ,     A   ,     A  )
																		# add model prefix if necessary
																		UpperCAmelCase         =			(model_prefix,) + flax_key in random_flax_state_dict
																		if load_base_model_into_model_with_head and require_base_model_prefix:
																								UpperCAmelCase         =			(model_prefix,) + flax_key

																		if flax_key in random_flax_state_dict:
																								if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																														raise ValueError(
																														    f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
																														    f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""  )

            # add batch stats if the model contains batchnorm layers
																		if "batch_stats" in flax_model.params:
																								if "mean" in flax_key[-1]:
																														UpperCAmelCase         =			jnp.asarray(A  )
																														continue
																								if "var" in flax_key[-1]:
																														UpperCAmelCase         =			jnp.asarray(A  )
																														continue
																								# remove num_batches_tracked key
																								if "num_batches_tracked" in flax_key[-1]:
																														flax_state_dict.pop(A   ,     A  )
																														continue

																								# also add unexpected weight so that warning is thrown
																								UpperCAmelCase         =			jnp.asarray(A  )

																		else:
																								# also add unexpected weight so that warning is thrown
																								UpperCAmelCase         =			jnp.asarray(A  )
						return unflatten_dict(A  )


def    lowerCamelCase__   ( A     :    List[str]   ,     A     :    List[Any]  ):




						'''simple docstring'''






						UpperCAmelCase         =			os.path.abspath(A  )
						logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""  )

						# import correct flax class
						UpperCAmelCase         =			getattr(A   ,     '''Flax''' + model.__class__.__name__  )

						# load flax weight dict
						with open(A   ,     '''rb'''  ) as state_f:
												try:
																		UpperCAmelCase         =			from_bytes(A   ,     state_f.read()  )
												except UnpicklingError:
																		raise EnvironmentError(f"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """  )

						return load_flax_weights_in_pytorch_model(A   ,     A  )




def    lowerCamelCase__   ( A     :    Tuple   ,     A     :    List[Any]  ):




						'''simple docstring'''






						try:
												import torch  # noqa: F401
						except ImportError:
												logger.error(
												    '''Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'''
												    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
												    ''' instructions.'''  )
												raise

						# check if we have bf16 weights
						UpperCAmelCase         =			flatten_dict(jax.tree_util.tree_map(lambda A  : x.dtype == jnp.bfloataa   ,     A  )  ).values()
						if any(A  ):
												# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
												# and bf16 is not fully supported in PT yet.
												logger.warning(
												    '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
												    '''before loading those in PyTorch model.'''  )
												UpperCAmelCase         =			jax.tree_util.tree_map(
												    lambda A  : params.astype(np.floataa  ) if params.dtype == jnp.bfloataa else params   ,     A  )

						UpperCAmelCase         =			flatten_dict(A  )
						UpperCAmelCase         =			pt_model.state_dict()

						UpperCAmelCase         =			(pt_model.base_model_prefix in flax_state) and (
						    pt_model.base_model_prefix not in {k.split('''.'''  )[0] for k in pt_model_dict.keys()}
						)
						UpperCAmelCase         =			(pt_model.base_model_prefix not in flax_state) and (
						    pt_model.base_model_prefix in {k.split('''.'''  )[0] for k in pt_model_dict.keys()}
						)

						# keep track of unexpected & missing keys
						UpperCAmelCase         =			[]
						UpperCAmelCase         =			set(pt_model_dict.keys()  )

						for flax_key_tuple, flax_tensor in flax_state_dict.items():
												UpperCAmelCase         =			flax_key_tuple[0] == pt_model.base_model_prefix
												UpperCAmelCase         =			'''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple  ) in pt_model_dict

												# adapt flax_key to prepare for loading from/to base model only
												if load_model_with_head_into_base_model and has_base_model_prefix:
																		UpperCAmelCase         =			flax_key_tuple[1:]
												elif load_base_model_into_model_with_head and require_base_model_prefix:
																		UpperCAmelCase         =			(pt_model.base_model_prefix,) + flax_key_tuple

												# rename flax weights to PyTorch format
												if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(A  ) not in pt_model_dict:
																		# conv layer
																		UpperCAmelCase         =			flax_key_tuple[:-1] + ('''weight''',)
																		UpperCAmelCase         =			jnp.transpose(A   ,     (3, 2, 0, 1)  )
												elif flax_key_tuple[-1] == "kernel" and ".".join(A  ) not in pt_model_dict:
																		# linear layer
																		UpperCAmelCase         =			flax_key_tuple[:-1] + ('''weight''',)
																		UpperCAmelCase         =			flax_tensor.T
												elif flax_key_tuple[-1] in ["scale", "embedding"]:
																		UpperCAmelCase         =			flax_key_tuple[:-1] + ('''weight''',)

												# adding batch stats from flax batch norm to pt
												elif "mean" in flax_key_tuple[-1]:
																		UpperCAmelCase         =			flax_key_tuple[:-1] + ('''running_mean''',)
												elif "var" in flax_key_tuple[-1]:
																		UpperCAmelCase         =			flax_key_tuple[:-1] + ('''running_var''',)

												if "batch_stats" in flax_state:
																		UpperCAmelCase         =			'''.'''.join(flax_key_tuple[1:]  )  # Remove the params/batch_stats header
												else:
																		UpperCAmelCase         =			'''.'''.join(A  )

												# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
												UpperCAmelCase         =			{}
												# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
												for key in pt_model_dict:
																		UpperCAmelCase         =			key.split('''.'''  )
																		UpperCAmelCase         =			None
																		if key_components[-3::2] == ["parametrizations", "original0"]:
																								UpperCAmelCase         =			key_components[-2] + '''_g'''
																		elif key_components[-3::2] == ["parametrizations", "original1"]:
																								UpperCAmelCase         =			key_components[-2] + '''_v'''
																		if name is not None:
																								UpperCAmelCase         =			key_components[:-3] + [name]
																								UpperCAmelCase         =			'''.'''.join(A  )
																								UpperCAmelCase         =			key

												if flax_key in special_pt_names:
																		UpperCAmelCase         =			special_pt_names[flax_key]

												if flax_key in pt_model_dict:
																		if flax_tensor.shape != pt_model_dict[flax_key].shape:
																								raise ValueError(
																								    f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
																								    f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""  )
																		else:
																								# add weight to pytorch dict
																								UpperCAmelCase         =			np.asarray(A  ) if not isinstance(A   ,     np.ndarray  ) else flax_tensor
																								UpperCAmelCase         =			torch.from_numpy(A  )
																								# remove from missing keys
																								missing_keys.remove(A  )
												else:
																		# weight is not expected by PyTorch model
																		unexpected_keys.append(A  )

						pt_model.load_state_dict(A  )

						# re-transform missing_keys to list
						UpperCAmelCase         =			list(A  )

						if len(A  ) > 0:
												logger.warning(
												    '''Some weights of the Flax model were not used when initializing the PyTorch model'''
												    f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
												    f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
												    ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
												    f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
												    ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
												    ''' FlaxBertForSequenceClassification model).'''  )
						else:
												logger.warning(f"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""  )
						if len(A  ) > 0:
												logger.warning(
												    f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
												    f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
												    ''' use it for predictions and inference.'''  )
						else:
												logger.warning(
												    f"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
												    '''If your task is similar to the task the model of the checkpoint was trained on, '''
												    f"""you can already use {pt_model.__class__.__name__} for predictions without further training."""  )

						return pt_model









'''simple docstring'''



import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

_lowercase    :    Optional[Any]			     =  16
_lowercase    :    Dict			     =  32


def    lowerCamelCase__   ( A     :    Accelerator   ,     A     :    int = 16  ):




						'''simple docstring'''






						UpperCAmelCase         =			AutoTokenizer.from_pretrained('''bert-base-cased'''  )
						UpperCAmelCase         =			load_dataset('''glue'''   ,     '''mrpc'''  )

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

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

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

						# Instantiate dataloaders.
						UpperCAmelCase         =			DataLoader(
						    tokenized_datasets['''train''']   ,     shuffle=A   ,     collate_fn=A   ,     batch_size=A  )
						UpperCAmelCase         =			DataLoader(
						    tokenized_datasets['''validation''']   ,     shuffle=A   ,     collate_fn=A   ,     batch_size=A  )

						return train_dataloader, eval_dataloader


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

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


def    lowerCamelCase__   ( A     :    Optional[Any]   ,     A     :    Tuple  ):




						'''simple docstring'''






						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,     A  ) == "1":
												UpperCAmelCase         =			2
						# Initialize Accelerator

						# New Code #
						# We pass in "all" to `log_with` to grab all available trackers in the environment
						# Note: If using a custom `Tracker` class, should be passed in here such as:
						# >>> log_with = ["all", MyCustomTrackerClassInstance()]
						if args.with_tracking:
												UpperCAmelCase         =			Accelerator(
												    cpu=args.cpu   ,     mixed_precision=args.mixed_precision   ,     log_with='''all'''   ,     project_dir=args.project_dir  )
						else:
												UpperCAmelCase         =			Accelerator(cpu=args.cpu   ,     mixed_precision=args.mixed_precision  )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						UpperCAmelCase         =			config['''lr''']
						UpperCAmelCase         =			int(config['''num_epochs''']  )
						UpperCAmelCase         =			int(config['''seed''']  )
						UpperCAmelCase         =			int(config['''batch_size''']  )
						set_seed(A  )

						UpperCAmelCase       ,       UpperCAmelCase         =			get_dataloaders(A   ,     A  )
						UpperCAmelCase         =			evaluate.load('''glue'''   ,     '''mrpc'''  )

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

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

						# 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         =			model.to(accelerator.device  )

						# Instantiate optimizer
						UpperCAmelCase         =			AdamW(params=model.parameters()   ,     lr=A  )

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

						# New Code #
						# We need to initialize the trackers we use. Overall configurations can also be stored
						if args.with_tracking:
												UpperCAmelCase         =			os.path.split(A  )[-1].split('''.'''  )[0]
												accelerator.init_trackers(A   ,     A  )

						# Now we train the model
						for epoch in range(A  ):
												model.train()
												# New Code #
												# For our tracking example, we will log the total loss of each epoch
												if args.with_tracking:
																		UpperCAmelCase         =			0
												for step, batch in enumerate(A  ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device  )
																		UpperCAmelCase         =			model(**A  )
																		UpperCAmelCase         =			outputs.loss
																		# New Code #
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		UpperCAmelCase         =			loss / gradient_accumulation_steps
																		accelerator.backward(A  )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

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

												UpperCAmelCase         =			metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"""epoch {epoch}:"""   ,     A  )

												# New Code #
												# To actually log, we call `Accelerator.log`
												# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        '''accuracy''': eval_metric['''accuracy'''],
																		        '''f1''': eval_metric['''f1'''],
																		        '''train_loss''': total_loss.item() / len(A  ),
																		        '''epoch''': epoch,
																		    }   ,     step=A   ,     )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
						if args.with_tracking:
												accelerator.end_training()


def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			argparse.ArgumentParser(description='''Simple example of training script.'''  )
						parser.add_argument(
						    '''--mixed_precision'''   ,     type=A   ,     default=A   ,     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(
						    '''--with_tracking'''   ,     action='''store_true'''   ,     help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''   ,     )
						parser.add_argument(
						    '''--project_dir'''   ,     type=A   ,     default='''logs'''   ,     help='''Location on where to store experiment tracking logs` and relevent project information'''   ,     )
						UpperCAmelCase         =			parser.parse_args()
						UpperCAmelCase         =			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(A   ,     A  )


if __name__ == "__main__":
	main()





