
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType





class       _snake_case					(     snake_case				):







			def __init__(				self						,   _a		):
				__magic_name__		:		Any	   =     data







			def __iter__(				self		):
				for element in self.data:
					yield element







def   lowerCAmelCase_						(					_snake_case      :  List[str]=True   )				->		Union[str, Any]:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     Accelerator(even_batches=_snake_case   )
	assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
	return accelerator






def   lowerCAmelCase_						(					_snake_case      :  Accelerator      ,   _snake_case      :  int      ,   _snake_case      :  int      ,   _snake_case      :  bool = False   )				->		str:







	'''simple docstring'''


	if iterable:
		__magic_name__		:		Union[str, Any]	   =     DummyIterableDataset(torch.as_tensor(range(_snake_case   )   )   )
	else:
		__magic_name__		:		Union[str, Any]	   =     TensorDataset(torch.as_tensor(range(_snake_case   )   )   )

	__magic_name__		:		Tuple	   =     DataLoader(_snake_case      ,   batch_size=_snake_case   )
	__magic_name__		:		Any	   =     accelerator.prepare(_snake_case   )

	return dl






def   lowerCAmelCase_						(					_snake_case      :  Accelerator      ,   _snake_case      :  int      ,   _snake_case      :  int      ,   _snake_case      :  List[int]      ,   _snake_case      :  List[int]      ,   )				->		List[Any]:







	'''simple docstring'''


	__magic_name__		:		str	   =     create_dataloader(accelerator=_snake_case      ,   dataset_size=_snake_case      ,   batch_size=_snake_case   )

	__magic_name__		:		int	   =     [len(batch[0]   ) for batch in dl]

	if accelerator.process_index == 0:
		assert batch_sizes == process_0_expected_batch_sizes
	elif accelerator.process_index == 1:
		assert batch_sizes == process_1_expected_batch_sizes






def   lowerCAmelCase_						(					)				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		str	   =     create_accelerator()

	# without padding, we would expect a different number of batches
	verify_dataloader_batch_sizes(
	    _snake_case      ,   dataset_size=3      ,   batch_size=1      ,   process_0_expected_batch_sizes=[1, 1]      ,   process_1_expected_batch_sizes=[1, 1]      ,   )

	# without padding, we would expect the same number of batches, but different sizes
	verify_dataloader_batch_sizes(
	    _snake_case      ,   dataset_size=7      ,   batch_size=2      ,   process_0_expected_batch_sizes=[2, 2]      ,   process_1_expected_batch_sizes=[2, 2]      ,   )






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







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     create_accelerator(even_batches=_snake_case   )

	verify_dataloader_batch_sizes(
	    _snake_case      ,   dataset_size=3      ,   batch_size=1      ,   process_0_expected_batch_sizes=[1, 1]      ,   process_1_expected_batch_sizes=[1]      ,   )

	verify_dataloader_batch_sizes(
	    _snake_case      ,   dataset_size=7      ,   batch_size=2      ,   process_0_expected_batch_sizes=[2, 2]      ,   process_1_expected_batch_sizes=[2, 1]      ,   )






def   lowerCAmelCase_						(					)				->		Tuple:







	'''simple docstring'''


	__magic_name__		:		Dict	   =     create_accelerator(even_batches=_snake_case   )

	__magic_name__		:		List[str]	   =     torch.nn.Linear(1      ,   1   )
	__magic_name__		:		Tuple	   =     accelerator.prepare(_snake_case   )

	__magic_name__		:		Dict	   =     create_dataloader(_snake_case      ,   dataset_size=3      ,   batch_size=1   )

	__magic_name__		:		str	   =     []
	with accelerator.join_uneven_inputs([ddp_model]   ):
		for batch_idx, batch in enumerate(_snake_case   ):
			__magic_name__		:		Union[str, Any]	   =     ddp_model(batch[0].float()   )
			__magic_name__		:		Tuple	   =     output.sum()
			loss.backward()
			batch_idxs.append(_snake_case   )

	accelerator.wait_for_everyone()

	if accelerator.process_index == 0:
		assert batch_idxs == [0, 1]
	elif accelerator.process_index == 1:
		assert batch_idxs == [0]






def   lowerCAmelCase_						(					_snake_case      :  str   )				->		str:







	'''simple docstring'''


	with warnings.catch_warnings(record=_snake_case   ) as w:
		with accelerator.join_uneven_inputs([Mock()]   ):
			pass

		assert issubclass(w[-1].category      ,   _snake_case   )
		assert "only supported for multi-GPU" in str(w[-1].message   )






def   lowerCAmelCase_						(					)				->		Optional[Any]:







	'''simple docstring'''


	__magic_name__		:		Any	   =     True
	__magic_name__		:		int	   =     False
	__magic_name__		:		str	   =     create_accelerator(even_batches=_snake_case   )
	__magic_name__		:		List[Any]	   =     torch.nn.Linear(1      ,   1   )
	__magic_name__		:		List[str]	   =     accelerator.prepare(_snake_case   )
	__magic_name__		:		Any	   =     create_dataloader(_snake_case      ,   dataset_size=3      ,   batch_size=1   )
	__magic_name__		:		Any	   =     create_dataloader(_snake_case      ,   dataset_size=3      ,   batch_size=1   )

	with accelerator.join_uneven_inputs([ddp_model]      ,   even_batches=_snake_case   ):
		__magic_name__		:		str	   =     train_dl.batch_sampler.even_batches
		__magic_name__		:		Any	   =     valid_dl.batch_sampler.even_batches

	assert train_dl_overridden_value == overridden_even_batches
	assert valid_dl_overridden_value == overridden_even_batches
	assert train_dl.batch_sampler.even_batches == default_even_batches
	assert valid_dl.batch_sampler.even_batches == default_even_batches






def   lowerCAmelCase_						(					)				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		Optional[Any]	   =     True
	__magic_name__		:		Any	   =     False
	__magic_name__		:		int	   =     create_accelerator(even_batches=_snake_case   )
	__magic_name__		:		Optional[int]	   =     torch.nn.Linear(1      ,   1   )
	__magic_name__		:		str	   =     accelerator.prepare(_snake_case   )
	create_dataloader(_snake_case      ,   dataset_size=3      ,   batch_size=1      ,   iterable=_snake_case   )
	__magic_name__		:		Dict	   =     create_dataloader(_snake_case      ,   dataset_size=3      ,   batch_size=1   )

	with warnings.catch_warnings():
		warnings.filterwarnings("ignore"   )
		try:
			with accelerator.join_uneven_inputs([ddp_model]      ,   even_batches=_snake_case   ):
				__magic_name__		:		List[str]	   =     batch_dl.batch_sampler.even_batches
		except AttributeError:
			# ensure attribute error is not raised when processing iterable dl
			raise AssertionError

	assert batch_dl_overridden_value == overridden_even_batches
	assert batch_dl.batch_sampler.even_batches == default_even_batches






def   lowerCAmelCase_						(					)				->		str:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     create_accelerator()
	__magic_name__		:		Tuple	   =     torch.nn.Linear(1      ,   1   )
	__magic_name__		:		Union[str, Any]	   =     accelerator.prepare(_snake_case   )
	create_dataloader(_snake_case      ,   dataset_size=3      ,   batch_size=1      ,   iterable=_snake_case   )

	with warnings.catch_warnings(record=_snake_case   ) as w:
		with accelerator.join_uneven_inputs([ddp_model]      ,   even_batches=_snake_case   ):
			pass

		assert issubclass(w[-1].category      ,   _snake_case   )
		assert "only supported for map-style datasets" in str(w[-1].message   )






def   lowerCAmelCase_						(					)				->		List[str]:







	'''simple docstring'''


	__magic_name__		:		str	   =     create_accelerator()

	accelerator.print("Test that even_batches variable ensures uniform batches across processes"   )
	test_default_ensures_even_batch_sizes()

	accelerator.print("Run tests with even_batches disabled"   )
	test_can_disable_even_batches()

	accelerator.print("Test joining uneven inputs"   )
	test_can_join_uneven_inputs()

	accelerator.print("Test overriding even_batches when joining uneven inputs"   )
	test_join_can_override_even_batches()

	accelerator.print("Test overriding even_batches for mixed dataloader types"   )
	test_join_can_override_for_mixed_type_dataloaders()

	accelerator.print("Test overriding even_batches raises a warning for iterable dataloaders"   )
	test_join_raises_warning_for_iterable_when_overriding_even_batches()

	accelerator.print("Test join with non DDP distributed raises warning"   )
	__magic_name__		:		Dict	   =     accelerator.state.distributed_type
	__magic_name__		:		int	   =     DistributedType.FSDP
	test_join_raises_warning_for_non_ddp_distributed(_snake_case   )
	__magic_name__		:		int	   =     original_state


if __name__ == "__main__":
				main()


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()
