
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

snake_case :		Union[str, Any]							      =  logging.getLogger()






def   lowerCAmelCase_						(					)				->		str:







	'''simple docstring'''


	__magic_name__		:		Tuple	   =     argparse.ArgumentParser()
	parser.add_argument("-f"   )
	__magic_name__		:		Any	   =     parser.parse_args()
	return args.f






def   lowerCAmelCase_						(					_snake_case      :  Any   )				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		str	   =     {}
	__magic_name__		:		List[Any]	   =     os.path.join(_snake_case      ,   "all_results.json"   )
	if os.path.exists(_snake_case   ):
		with open(_snake_case      ,   "r"   ) as f:
			__magic_name__		:		Optional[Any]	   =     json.load(_snake_case   )
	else:
		raise ValueError(F'''can\'t find {path}'''   )
	return results






def   lowerCAmelCase_						(					)				->		Optional[int]:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     torch.cuda.is_available() and torch_device == "cuda"
	return is_using_cuda and is_apex_available()


snake_case :		Dict							      =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class       _snake_case					(     snake_case				):







			@classmethod
			def 			SCREAMING_SNAKE_CASE    (				cls		):
				# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
				__magic_name__		:		Any	   =     tempfile.mkdtemp()
				__magic_name__		:		List[Any]	   =     os.path.join(cls.tmpdir						,   "default_config.yml"		)
				write_basic_config(save_location=cls.configPath		)
				__magic_name__		:		Any	   =     ["accelerate", "launch", "--config_file", cls.configPath]







			@classmethod
			def 			SCREAMING_SNAKE_CASE    (				cls		):
				shutil.rmtree(cls.tmpdir		)







			@mock.patch.dict(os.environ						,   {"WANDB_MODE": "offline"}		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Union[str, Any]	   =     self.get_auto_remove_tmp_dir()
				__magic_name__		:		str	   =     f'''
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

				if is_cuda_and_apex_available():
					testargs.append("--fp16"		)

				run_command(self._launch_args + testargs		)
				__magic_name__		:		Optional[Any]	   =     get_results(_a		)
				self.assertGreaterEqual(result["eval_accuracy"]						,   0.75		)
				self.assertTrue(os.path.exists(os.path.join(_a						,   "epoch_0"		)		)		)
				self.assertTrue(os.path.exists(os.path.join(_a						,   "glue_no_trainer"		)		)		)







			@mock.patch.dict(os.environ						,   {"WANDB_MODE": "offline"}		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     self.get_auto_remove_tmp_dir()
				__magic_name__		:		List[str]	   =     f'''
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

				if torch.cuda.device_count() > 1:
					# Skipping because there are not enough batches to train the model + would need a drop_last to work.
					return

				run_command(self._launch_args + testargs		)
				__magic_name__		:		Union[str, Any]	   =     get_results(_a		)
				self.assertLess(result["perplexity"]						,   100		)
				self.assertTrue(os.path.exists(os.path.join(_a						,   "epoch_0"		)		)		)
				self.assertTrue(os.path.exists(os.path.join(_a						,   "clm_no_trainer"		)		)		)







			@mock.patch.dict(os.environ						,   {"WANDB_MODE": "offline"}		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     self.get_auto_remove_tmp_dir()
				__magic_name__		:		Dict	   =     f'''
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

				run_command(self._launch_args + testargs		)
				__magic_name__		:		Tuple	   =     get_results(_a		)
				self.assertLess(result["perplexity"]						,   42		)
				self.assertTrue(os.path.exists(os.path.join(_a						,   "epoch_0"		)		)		)
				self.assertTrue(os.path.exists(os.path.join(_a						,   "mlm_no_trainer"		)		)		)







			@mock.patch.dict(os.environ						,   {"WANDB_MODE": "offline"}		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
				__magic_name__		:		Union[str, Any]	   =     7 if get_gpu_count() > 1 else 2

				__magic_name__		:		Optional[Any]	   =     self.get_auto_remove_tmp_dir()
				__magic_name__		:		Union[str, Any]	   =     f'''
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

				run_command(self._launch_args + testargs		)
				__magic_name__		:		List[Any]	   =     get_results(_a		)
				self.assertGreaterEqual(result["eval_accuracy"]						,   0.75		)
				self.assertLess(result["train_loss"]						,   0.5		)
				self.assertTrue(os.path.exists(os.path.join(_a						,   "epoch_0"		)		)		)
				self.assertTrue(os.path.exists(os.path.join(_a						,   "ner_no_trainer"		)		)		)







			@unittest.skip(reason="Fix me @muellerzr"		)
			@mock.patch.dict(os.environ						,   {"WANDB_MODE": "offline"}		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     self.get_auto_remove_tmp_dir()
				__magic_name__		:		List[Any]	   =     f'''
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

				run_command(self._launch_args + testargs		)
				__magic_name__		:		Optional[Any]	   =     get_results(_a		)
				# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
				self.assertGreaterEqual(result["eval_f1"]						,   28		)
				self.assertGreaterEqual(result["eval_exact"]						,   28		)
				self.assertTrue(os.path.exists(os.path.join(_a						,   "epoch_0"		)		)		)
				self.assertTrue(os.path.exists(os.path.join(_a						,   "qa_no_trainer"		)		)		)







			@mock.patch.dict(os.environ						,   {"WANDB_MODE": "offline"}		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[Any]	   =     self.get_auto_remove_tmp_dir()
				__magic_name__		:		str	   =     f'''
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        '''.split()

				run_command(self._launch_args + testargs		)
				__magic_name__		:		Tuple	   =     get_results(_a		)
				self.assertGreaterEqual(result["eval_accuracy"]						,   0.8		)
				self.assertTrue(os.path.exists(os.path.join(_a						,   "swag_no_trainer"		)		)		)







			@slow
			@mock.patch.dict(os.environ						,   {"WANDB_MODE": "offline"}		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		str	   =     self.get_auto_remove_tmp_dir()
				__magic_name__		:		Tuple	   =     f'''
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

				run_command(self._launch_args + testargs		)
				__magic_name__		:		List[Any]	   =     get_results(_a		)
				self.assertGreaterEqual(result["eval_rouge1"]						,   10		)
				self.assertGreaterEqual(result["eval_rouge2"]						,   2		)
				self.assertGreaterEqual(result["eval_rougeL"]						,   7		)
				self.assertGreaterEqual(result["eval_rougeLsum"]						,   7		)
				self.assertTrue(os.path.exists(os.path.join(_a						,   "epoch_0"		)		)		)
				self.assertTrue(os.path.exists(os.path.join(_a						,   "summarization_no_trainer"		)		)		)







			@slow
			@mock.patch.dict(os.environ						,   {"WANDB_MODE": "offline"}		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     self.get_auto_remove_tmp_dir()
				__magic_name__		:		List[str]	   =     f'''
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        '''.split()

				run_command(self._launch_args + testargs		)
				__magic_name__		:		Dict	   =     get_results(_a		)
				self.assertGreaterEqual(result["eval_bleu"]						,   30		)
				self.assertTrue(os.path.exists(os.path.join(_a						,   "epoch_0"		)		)		)
				self.assertTrue(os.path.exists(os.path.join(_a						,   "translation_no_trainer"		)		)		)







			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     logging.StreamHandler(sys.stdout		)
				logger.addHandler(_a		)

				__magic_name__		:		str	   =     self.get_auto_remove_tmp_dir()
				__magic_name__		:		int	   =     f'''
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        '''.split()

				run_command(self._launch_args + testargs		)
				__magic_name__		:		Union[str, Any]	   =     get_results(_a		)
				self.assertGreaterEqual(result["eval_overall_accuracy"]						,   0.10		)







			@mock.patch.dict(os.environ						,   {"WANDB_MODE": "offline"}		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[Any]	   =     self.get_auto_remove_tmp_dir()
				__magic_name__		:		Tuple	   =     f'''
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        '''.split()

				if is_cuda_and_apex_available():
					testargs.append("--fp16"		)

				run_command(self._launch_args + testargs		)
				__magic_name__		:		List[str]	   =     get_results(_a		)
				# The base model scores a 25%
				self.assertGreaterEqual(result["eval_accuracy"]						,   0.6		)
				self.assertTrue(os.path.exists(os.path.join(_a						,   "step_1"		)		)		)
				self.assertTrue(os.path.exists(os.path.join(_a						,   "image_classification_no_trainer"		)		)		)


import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
snake_case :		Optional[int]							      =  logging.getLogger(__name__)






def   lowerCAmelCase_						(					_snake_case      :  Dict      ,   _snake_case      :  Union[str, Any]   )				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		List[str]	   =     np.argmax(_snake_case      ,   axis=1   )
	return np.sum(outputs == labels   )






def   lowerCAmelCase_						(					_snake_case      :  Optional[Any]   )				->		Dict:







	'''simple docstring'''


	with open(_snake_case      ,   encoding="utf_8"   ) as f:
		__magic_name__		:		List[str]	   =     csv.reader(_snake_case   )
		__magic_name__		:		List[Any]	   =     []
		next(_snake_case   )  # skip the first line
		for line in tqdm(_snake_case   ):
			output.append((" ".join(line[1:5]   ), line[5], line[6], int(line[-1]   ) - 1)   )
	return output






def   lowerCAmelCase_						(					_snake_case      :  str      ,   _snake_case      :  Tuple      ,   _snake_case      :  Union[str, Any]      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Tuple      ,   _snake_case      :  Optional[int]   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     []
	for dataset in encoded_datasets:
		__magic_name__		:		Union[str, Any]	   =     len(_snake_case   )
		__magic_name__		:		Dict	   =     np.zeros((n_batch, 2, input_len)      ,   dtype=np.intaa   )
		__magic_name__		:		List[str]	   =     np.zeros((n_batch, 2)      ,   dtype=np.intaa   )
		__magic_name__		:		Optional[int]	   =     np.full((n_batch, 2, input_len)      ,   fill_value=-100      ,   dtype=np.intaa   )
		__magic_name__		:		int	   =     np.zeros((n_batch,)      ,   dtype=np.intaa   )
		for (
		    i,
		    (story, conta, conta, mc_label),
		) in enumerate(_snake_case   ):
			__magic_name__		:		Dict	   =     [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			__magic_name__		:		Dict	   =     [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			__magic_name__		:		str	   =     with_conta
			__magic_name__		:		Tuple	   =     with_conta
			__magic_name__		:		Union[str, Any]	   =     len(_snake_case   ) - 1
			__magic_name__		:		int	   =     len(_snake_case   ) - 1
			__magic_name__		:		Optional[Any]	   =     with_conta
			__magic_name__		:		Optional[Any]	   =     with_conta
			__magic_name__		:		Optional[int]	   =     mc_label
		__magic_name__		:		str	   =     (input_ids, mc_token_ids, lm_labels, mc_labels)
		tensor_datasets.append(tuple(torch.tensor(_snake_case   ) for t in all_inputs   )   )
	return tensor_datasets






def   lowerCAmelCase_						(					)				->		List[Any]:







	'''simple docstring'''


	__magic_name__		:		Any	   =     argparse.ArgumentParser()
	parser.add_argument("--model_name"      ,   type=_snake_case      ,   default="openai-gpt"      ,   help="pretrained model name"   )
	parser.add_argument("--do_train"      ,   action="store_true"      ,   help="Whether to run training."   )
	parser.add_argument("--do_eval"      ,   action="store_true"      ,   help="Whether to run eval on the dev set."   )
	parser.add_argument(
	    "--output_dir"      ,   default=_snake_case      ,   type=_snake_case      ,   required=_snake_case      ,   help="The output directory where the model predictions and checkpoints will be written."      ,   )
	parser.add_argument("--train_dataset"      ,   type=_snake_case      ,   default=""   )
	parser.add_argument("--eval_dataset"      ,   type=_snake_case      ,   default=""   )
	parser.add_argument("--seed"      ,   type=_snake_case      ,   default=42   )
	parser.add_argument("--num_train_epochs"      ,   type=_snake_case      ,   default=3   )
	parser.add_argument("--train_batch_size"      ,   type=_snake_case      ,   default=8   )
	parser.add_argument("--eval_batch_size"      ,   type=_snake_case      ,   default=16   )
	parser.add_argument("--adam_epsilon"      ,   default=1E-8      ,   type=_snake_case      ,   help="Epsilon for Adam optimizer."   )
	parser.add_argument("--max_grad_norm"      ,   type=_snake_case      ,   default=1   )
	parser.add_argument(
	    "--max_steps"      ,   default=-1      ,   type=_snake_case      ,   help=(
	        "If > 0: set total number of training                         steps to perform. Override num_train_epochs."
	    )      ,   )
	parser.add_argument(
	    "--gradient_accumulation_steps"      ,   type=_snake_case      ,   default=1      ,   help="Number of updates steps to accumulate before                        performing a backward/update pass."      ,   )
	parser.add_argument("--learning_rate"      ,   type=_snake_case      ,   default=6.25E-5   )
	parser.add_argument("--warmup_steps"      ,   default=0      ,   type=_snake_case      ,   help="Linear warmup over warmup_steps."   )
	parser.add_argument("--lr_schedule"      ,   type=_snake_case      ,   default="warmup_linear"   )
	parser.add_argument("--weight_decay"      ,   type=_snake_case      ,   default=0.01   )
	parser.add_argument("--lm_coef"      ,   type=_snake_case      ,   default=0.9   )
	parser.add_argument("--n_valid"      ,   type=_snake_case      ,   default=374   )

	parser.add_argument("--server_ip"      ,   type=_snake_case      ,   default=""      ,   help="Can be used for distant debugging."   )
	parser.add_argument("--server_port"      ,   type=_snake_case      ,   default=""      ,   help="Can be used for distant debugging."   )
	__magic_name__		:		List[Any]	   =     parser.parse_args()
	print(_snake_case   )

	if args.server_ip and args.server_port:
		# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
		import ptvsd

		print("Waiting for debugger attach"   )
		ptvsd.enable_attach(address=(args.server_ip, args.server_port)      ,   redirect_output=_snake_case   )
		ptvsd.wait_for_attach()

	random.seed(args.seed   )
	np.random.seed(args.seed   )
	torch.manual_seed(args.seed   )
	torch.cuda.manual_seed_all(args.seed   )

	__magic_name__		:		Dict	   =     torch.device("cuda" if torch.cuda.is_available() else "cpu"   )
	__magic_name__		:		Optional[int]	   =     torch.cuda.device_count()
	logger.info("device: {}, n_gpu {}".format(_snake_case      ,   _snake_case   )   )

	if not args.do_train and not args.do_eval:
		raise ValueError("At least one of `do_train` or `do_eval` must be True."   )

	if not os.path.exists(args.output_dir   ):
		os.makedirs(args.output_dir   )

	# Load tokenizer and model
	# This loading functions also add new tokens and embeddings called `special tokens`
	# These new embeddings will be fine-tuned on the RocStories dataset
	__magic_name__		:		List[Any]	   =     ["_start_", "_delimiter_", "_classify_"]
	__magic_name__		:		Optional[int]	   =     OpenAIGPTTokenizer.from_pretrained(args.model_name   )
	tokenizer.add_tokens(_snake_case   )
	__magic_name__		:		Optional[Any]	   =     tokenizer.convert_tokens_to_ids(_snake_case   )
	__magic_name__		:		List[str]	   =     OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name   )
	model.resize_token_embeddings(len(_snake_case   )   )
	model.to(_snake_case   )

	# Load and encode the datasets
	def tokenize_and_encode(_snake_case      :  str   ):
		if isinstance(_snake_case      ,   _snake_case   ):
			return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_snake_case   )   )
		elif isinstance(_snake_case      ,   _snake_case   ):
			return obj
		return [tokenize_and_encode(_snake_case   ) for o in obj]

	logger.info("Encoding dataset..."   )
	__magic_name__		:		Optional[int]	   =     load_rocstories_dataset(args.train_dataset   )
	__magic_name__		:		str	   =     load_rocstories_dataset(args.eval_dataset   )
	__magic_name__		:		int	   =     (train_dataset, eval_dataset)
	__magic_name__		:		List[str]	   =     tokenize_and_encode(_snake_case   )

	# Compute the max input length for the Transformer
	__magic_name__		:		Optional[Any]	   =     model.config.n_positions // 2 - 2
	__magic_name__		:		Optional[int]	   =     max(
	    len(story[:max_length]   ) + max(len(conta[:max_length]   )      ,   len(conta[:max_length]   )   ) + 3
	    for dataset in encoded_datasets
	    for story, conta, conta, _ in dataset   )
	__magic_name__		:		List[str]	   =     min(_snake_case      ,   model.config.n_positions   )  # Max size of input for the pre-trained model

	# Prepare inputs tensors and dataloaders
	__magic_name__		:		List[Any]	   =     pre_process_datasets(_snake_case      ,   _snake_case      ,   _snake_case      ,   *_snake_case   )
	__magic_name__   ,    __magic_name__		:		Optional[int]	   =     tensor_datasets[0], tensor_datasets[1]

	__magic_name__		:		Tuple	   =     TensorDataset(*_snake_case   )
	__magic_name__		:		Union[str, Any]	   =     RandomSampler(_snake_case   )
	__magic_name__		:		Dict	   =     DataLoader(_snake_case      ,   sampler=_snake_case      ,   batch_size=args.train_batch_size   )

	__magic_name__		:		Any	   =     TensorDataset(*_snake_case   )
	__magic_name__		:		Optional[Any]	   =     SequentialSampler(_snake_case   )
	__magic_name__		:		int	   =     DataLoader(_snake_case      ,   sampler=_snake_case      ,   batch_size=args.eval_batch_size   )

	# Prepare optimizer
	if args.do_train:
		if args.max_steps > 0:
			__magic_name__		:		Tuple	   =     args.max_steps
			__magic_name__		:		List[str]	   =     args.max_steps // (len(_snake_case   ) // args.gradient_accumulation_steps) + 1
		else:
			__magic_name__		:		List[str]	   =     len(_snake_case   ) // args.gradient_accumulation_steps * args.num_train_epochs

		__magic_name__		:		str	   =     list(model.named_parameters()   )
		__magic_name__		:		Dict	   =     ["bias", "LayerNorm.bias", "LayerNorm.weight"]
		__magic_name__		:		str	   =     [
		    {
		        "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay   )],
		        "weight_decay": args.weight_decay,
		    },
		    {"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay   )], "weight_decay": 0.0},
		]
		__magic_name__		:		str	   =     AdamW(_snake_case      ,   lr=args.learning_rate      ,   eps=args.adam_epsilon   )
		__magic_name__		:		List[str]	   =     get_linear_schedule_with_warmup(
		    _snake_case      ,   num_warmup_steps=args.warmup_steps      ,   num_training_steps=_snake_case   )

	if args.do_train:
		__magic_name__   ,    __magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     0, 0, None
		model.train()
		for _ in trange(int(args.num_train_epochs   )      ,   desc="Epoch"   ):
			__magic_name__		:		List[str]	   =     0
			__magic_name__		:		Tuple	   =     0
			__magic_name__		:		Dict	   =     tqdm(_snake_case      ,   desc="Training"   )
			for step, batch in enumerate(_snake_case   ):
				__magic_name__		:		Optional[Any]	   =     tuple(t.to(_snake_case   ) for t in batch   )
				__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		Dict	   =     batch
				__magic_name__		:		Optional[Any]	   =     model(_snake_case      ,   mc_token_ids=_snake_case      ,   lm_labels=_snake_case      ,   mc_labels=_snake_case   )
				__magic_name__		:		Optional[Any]	   =     args.lm_coef * losses[0] + losses[1]
				loss.backward()
				optimizer.step()
				scheduler.step()
				optimizer.zero_grad()
				tr_loss += loss.item()
				__magic_name__		:		List[str]	   =     (
				    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
				)
				nb_tr_steps += 1
				__magic_name__		:		int	   =     "Training loss: {:.2e} lr: {:.2e}".format(_snake_case      ,   scheduler.get_lr()[0]   )

    # Save a trained model
	if args.do_train:
		# Save a trained model, configuration and tokenizer
		__magic_name__		:		Dict	   =     model.module if hasattr(_snake_case      ,   "module"   ) else model  # Only save the model itself

		# If we save using the predefined names, we can load using `from_pretrained`
		__magic_name__		:		List[Any]	   =     os.path.join(args.output_dir      ,   _snake_case   )
		__magic_name__		:		Dict	   =     os.path.join(args.output_dir      ,   _snake_case   )

		torch.save(model_to_save.state_dict()      ,   _snake_case   )
		model_to_save.config.to_json_file(_snake_case   )
		tokenizer.save_vocabulary(args.output_dir   )

		# Load a trained model and vocabulary that you have fine-tuned
		__magic_name__		:		Dict	   =     OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir   )
		__magic_name__		:		Optional[int]	   =     OpenAIGPTTokenizer.from_pretrained(args.output_dir   )
		model.to(_snake_case   )

	if args.do_eval:
		model.eval()
		__magic_name__   ,    __magic_name__		:		Any	   =     0, 0
		__magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     0, 0
		for batch in tqdm(_snake_case      ,   desc="Evaluating"   ):
			__magic_name__		:		int	   =     tuple(t.to(_snake_case   ) for t in batch   )
			__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     batch
			with torch.no_grad():
				__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__		:		Dict	   =     model(
				    _snake_case      ,   mc_token_ids=_snake_case      ,   lm_labels=_snake_case      ,   mc_labels=_snake_case   )

			__magic_name__		:		Tuple	   =     mc_logits.detach().cpu().numpy()
			__magic_name__		:		Any	   =     mc_labels.to("cpu"   ).numpy()
			__magic_name__		:		str	   =     accuracy(_snake_case      ,   _snake_case   )

			eval_loss += mc_loss.mean().item()
			eval_accuracy += tmp_eval_accuracy

			nb_eval_examples += input_ids.size(0   )
			nb_eval_steps += 1

		__magic_name__		:		Tuple	   =     eval_loss / nb_eval_steps
		__magic_name__		:		List[Any]	   =     eval_accuracy / nb_eval_examples
		__magic_name__		:		int	   =     tr_loss / nb_tr_steps if args.do_train else None
		__magic_name__		:		Any	   =     {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}

		__magic_name__		:		int	   =     os.path.join(args.output_dir      ,   "eval_results.txt"   )
		with open(_snake_case      ,   "w"   ) as writer:
			logger.info("***** Eval results *****"   )
			for key in sorted(result.keys()   ):
				logger.info("  %s = %s"      ,   _snake_case      ,   str(result[key]   )   )
				writer.write("%s = %s\n" % (key, str(result[key]   ))   )


if __name__ == "__main__":
				main()
