"""simple docstring"""





import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed

import numpy as np

import datasets

from .execute import check_correctness


lowerCAmelCase_     =	'''\
@misc{chen2021evaluating,
      title={Evaluating Large Language Models Trained on Code},
      author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \
and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \
and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \
and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \
and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \
and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \
and Mohammad Bavarian and Clemens Winter and Philippe Tillet \
and Felipe Petroski Such and Dave Cummings and Matthias Plappert \
and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \
and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \
and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \
and William Saunders and Christopher Hesse and Andrew N. Carr \
and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \
and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \
and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \
and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},
      year={2021},
      eprint={2107.03374},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
'''

lowerCAmelCase_     =	'''\
This metric implements the evaluation harness for the HumanEval problem solving dataset
described in the paper "Evaluating Large Language Models Trained on Code"
(https://arxiv.org/abs/2107.03374).
'''


lowerCAmelCase_     =	'''
Calculates how good are predictions given some references, using certain scores
Args:
    predictions: list of candidates to evaluate. Each candidates should be a list
        of strings with several code candidates to solve the problem.
    references: a list with a test for each prediction. Each test should evaluate the
        correctness of a code candidate.
    k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])
    num_workers: number of workers used to evaluate the canidate programs (Default: 4).
    timeout:
Returns:
    pass_at_k: dict with pass rates for each k
    results: dict with granular results of each unittest
Examples:
    >>> code_eval = datasets.load_metric("code_eval")
    >>> test_cases = ["assert add(2,3)==5"]
    >>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]
    >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])
    >>> print(pass_at_k)
    {\'pass@1\': 0.5, \'pass@2\': 1.0}
'''


lowerCAmelCase_     =	'''
################################################################################
                                  !!!WARNING!!!
################################################################################
The "code_eval" metric executes untrusted model-generated code in Python.
Although it is highly unlikely that model-generated code will do something
overtly malicious in response to this test suite, model-generated code may act
destructively due to a lack of model capability or alignment.
Users are strongly encouraged to sandbox this evaluation suite so that it
does not perform destructive actions on their host or network. For more
information on how OpenAI sandboxes its code, see the paper "Evaluating Large
Language Models Trained on Code" (https://arxiv.org/abs/2107.03374).

Once you have read this disclaimer and taken appropriate precautions,
set the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this
with:

>>> import os
>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"

################################################################################\
'''

lowerCAmelCase_     =	'''The MIT License

Copyright (c) OpenAI (https://openai.com)

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.'''




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,       _KWARGS_DESCRIPTION )
class  _snake_case		(    datasets.Metric ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											return datasets.MetricInfo(
											    # This is the description that will appear on the metrics page.
											    description=_DESCRIPTION      ,    citation=_CITATION      ,    inputs_description=_KWARGS_DESCRIPTION      ,    features=datasets.Features(
											        {
											            """predictions""": datasets.Sequence(datasets.Value("""string""")),
											            """references""": datasets.Value("""string"""),
											        })      ,    homepage="""https://github.com/openai/human-eval"""      ,    codebase_urls=["""https://github.com/openai/human-eval"""]      ,    reference_urls=["""https://github.com/openai/human-eval"""]      ,    license=_LICENSE      ,    )





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							Optional[int]      ,    _A							:							List[Any]      ,    _A							:							List[Any]=[1, 1_0, 1_0_0]      ,    _A							:							Union[str, Any]=4      ,    _A							:							Optional[int]=3.0):



											"""simple docstring"""







											if os.getenv("""HF_ALLOW_CODE_EVAL"""      ,    0) != "1":
																		raise ValueError(_WARNING)

											if os.name == "nt":
																		raise NotImplementedError("""This metric is currently not supported on Windows.""")

											with ThreadPoolExecutor(max_workers=_A) as executor:
																		_SCREAMING_SNAKE_CASE		:						Dict			  =    []
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    Counter()
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    0
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    defaultdict(_A)

																		for task_id, (candidates, test_case) in enumerate(zip(_A      ,    _A)):
																									for candidate in candidates:
																																_SCREAMING_SNAKE_CASE		:						List[str]			  =    candidate + """\n""" + test_case
																																_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    (test_program, timeout, task_id, completion_id[task_id])
																																_SCREAMING_SNAKE_CASE		:						List[str]			  =    executor.submit(_A      ,    *_A)
																																futures.append(_A)
																																completion_id[task_id] += 1
																																n_samples += 1

																		for future in as_completed(_A):
																									_SCREAMING_SNAKE_CASE		:						int			  =    future.result()
																									results[result["task_id"]].append((result["""completion_id"""], result))

											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[Any]			  =    [], []
											for result in results.values():
																		result.sort()
																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    [r[1]["""passed"""] for r in result]
																		total.append(len(_A))
																		correct.append(sum(_A))
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    np.array(_A)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    np.array(_A)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    k
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    {f"""pass@{k}""": estimate_pass_at_k(_A      ,    _A      ,    _A).mean() for k in ks if (total >= k).all()}

											return pass_at_k, results



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[str]:

							def estimator(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) -> float:
														if n - c < k:
																					return 1.0
														return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1     ,	n + 1  )  )

							if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    itertools.repeat(__SCREAMING_SNAKE_CASE     ,	len(__SCREAMING_SNAKE_CASE  )  )
							else:
														assert len(__SCREAMING_SNAKE_CASE  ) == len(__SCREAMING_SNAKE_CASE  )
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    iter(__SCREAMING_SNAKE_CASE  )

							return np.array([estimator(int(__SCREAMING_SNAKE_CASE  )     ,	int(__SCREAMING_SNAKE_CASE  )     ,	__SCREAMING_SNAKE_CASE  ) for n, c in zip(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )]  )



"""simple docstring"""





import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 perform Cross Validation,
# 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
#
########################################################################


lowerCAmelCase_     =	16
lowerCAmelCase_     =	32



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = 16  )->    str:
							_SCREAMING_SNAKE_CASE		:						int			  =    AutoTokenizer.from_pretrained("""bert-base-cased"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    DatasetDict(
							    {
							        """train""": dataset["""train"""].select(__SCREAMING_SNAKE_CASE  ),
							        """validation""": dataset["""train"""].select(__SCREAMING_SNAKE_CASE  ),
							        """test""": dataset["""validation"""],
							    }  )

							def tokenize_function(__SCREAMING_SNAKE_CASE  ):
														# max_length=None => use the model max length (it's actually the default)
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    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():
														_SCREAMING_SNAKE_CASE		:						str			  =    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
							_SCREAMING_SNAKE_CASE		:						Any			  =    tokenized_datasets.rename_column("""label"""     ,	"""labels"""  )

							def collate_fn(__SCREAMING_SNAKE_CASE  ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														_SCREAMING_SNAKE_CASE		:						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":
																					_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    16
														elif accelerator.mixed_precision != "no":
																					_SCREAMING_SNAKE_CASE		:						Any			  =    8
														else:
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    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.
							_SCREAMING_SNAKE_CASE		:						int			  =    DataLoader(
							    tokenized_datasets["""train"""]     ,	shuffle=__SCREAMING_SNAKE_CASE     ,	collate_fn=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    DataLoader(
							    tokenized_datasets["""validation"""]     ,	shuffle=__SCREAMING_SNAKE_CASE     ,	collate_fn=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						Dict			  =    DataLoader(
							    tokenized_datasets["""test"""]     ,	shuffle=__SCREAMING_SNAKE_CASE     ,	collate_fn=__SCREAMING_SNAKE_CASE     ,	batch_size=__SCREAMING_SNAKE_CASE  )

							return train_dataloader, eval_dataloader, test_dataloader



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Dict:
							# New Code #
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    []
							# Download the dataset
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    load_dataset("""glue"""     ,	"""mrpc"""  )
							# Create our splits
							_SCREAMING_SNAKE_CASE		:						Dict			  =    StratifiedKFold(n_splits=int(args.num_folds  )  )
							# Initialize accelerator
							_SCREAMING_SNAKE_CASE		:						Any			  =    Accelerator(cpu=args.cpu     ,	mixed_precision=args.mixed_precision  )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    config["""lr"""]
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    int(config["""num_epochs"""]  )
							_SCREAMING_SNAKE_CASE		:						int			  =    int(config["""seed"""]  )
							_SCREAMING_SNAKE_CASE		:						int			  =    int(config["""batch_size"""]  )

							_SCREAMING_SNAKE_CASE		:						List[str]			  =    evaluate.load("""glue"""     ,	"""mrpc"""  )

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

							set_seed(__SCREAMING_SNAKE_CASE  )

							# New Code #
							# Create our folds:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    kfold.split(np.zeros(datasets["""train"""].num_rows  )     ,	datasets["""train"""]["""label"""]  )
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    []
							# Iterate over them
							for i, (train_idxs, valid_idxs) in enumerate(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    get_fold_dataloaders(
														    __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	)
														# Instantiate the model (we build the model here so that the seed also control new weights initialization)
														_SCREAMING_SNAKE_CASE		:						Any			  =    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).
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    model.to(accelerator.device  )

														# Instantiate optimizer
														_SCREAMING_SNAKE_CASE		:						int			  =    AdamW(params=model.parameters()     ,	lr=__SCREAMING_SNAKE_CASE  )

														# Instantiate scheduler
														_SCREAMING_SNAKE_CASE		:						int			  =    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.
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, 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  )
																												_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model(**__SCREAMING_SNAKE_CASE  )
																												_SCREAMING_SNAKE_CASE		:						Dict			  =    outputs.loss
																												_SCREAMING_SNAKE_CASE		:						List[Any]			  =    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()
																					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():
																																			_SCREAMING_SNAKE_CASE		:						List[str]			  =    model(**__SCREAMING_SNAKE_CASE  )
																												_SCREAMING_SNAKE_CASE		:						str			  =    outputs.logits.argmax(dim=-1  )
																												_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						int			  =    accelerator.gather_for_metrics((predictions, batch["""labels"""])  )
																												metric.add_batch(
																												    predictions=__SCREAMING_SNAKE_CASE     ,	references=__SCREAMING_SNAKE_CASE     ,	)

																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    metric.compute()
																					# Use accelerator.print to print only on the main process.
																					accelerator.print(F"""epoch {epoch}:"""     ,	__SCREAMING_SNAKE_CASE  )

														# New Code #
														# We also run predictions on the test set at the very end
														_SCREAMING_SNAKE_CASE		:						str			  =    []
														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():
																												_SCREAMING_SNAKE_CASE		:						List[str]			  =    model(**__SCREAMING_SNAKE_CASE  )
																					_SCREAMING_SNAKE_CASE		:						List[Any]			  =    outputs.logits
																					_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[Any]			  =    accelerator.gather_for_metrics((predictions, batch["""labels"""])  )
																					fold_predictions.append(predictions.cpu()  )
																					if i == 0:
																												# We need all of the test predictions
																												test_references.append(references.cpu()  )
        # Use accelerator.print to print only on the main process.
														test_predictions.append(torch.cat(__SCREAMING_SNAKE_CASE     ,	dim=0  )  )
														# We now need to release all our memory and get rid of the current model, optimizer, etc
														accelerator.free_memory()
							# New Code #
							# Finally we check the accuracy of our folded results:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    torch.cat(__SCREAMING_SNAKE_CASE     ,	dim=0  )
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.stack(__SCREAMING_SNAKE_CASE     ,	dim=0  ).sum(dim=0  ).div(int(args.num_folds  )  ).argmax(dim=-1  )
							_SCREAMING_SNAKE_CASE		:						int			  =    metric.compute(predictions=__SCREAMING_SNAKE_CASE     ,	references=__SCREAMING_SNAKE_CASE  )
							accelerator.print("""Average test metrics from all folds:"""     ,	__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_()->    Optional[Any]:
							_SCREAMING_SNAKE_CASE		:						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."""  )
							# New Code #
							parser.add_argument("""--num_folds"""     ,	type=__SCREAMING_SNAKE_CASE     ,	default=3     ,	help="""The number of splits to perform across the dataset"""  )
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    parser.parse_args()
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
			main()


