

'''simple docstring'''


import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


lowerCAmelCase__										= get_logger(__name__)


lowerCAmelCase__										= Path(__file__).parent / "model_card_template.md"
lowerCAmelCase__										= uuida().hex
lowerCAmelCase__										= os.getenv("HF_HUB_OFFLINE", "").upper() in ENV_VARS_TRUE_VALUES
lowerCAmelCase__										= os.getenv("DISABLE_TELEMETRY", "").upper() in ENV_VARS_TRUE_VALUES
lowerCAmelCase__										= HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase = None  )    ->    str:
	UpperCAmelCase_		:      Optional[int]       	=					f"""diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"""
	if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
		return ua + "; telemetry/off"
	if is_torch_available():
		ua += f"""; torch/{_torch_version}"""
	if is_flax_available():
		ua += f"""; jax/{_jax_version}"""
		ua += f"""; flax/{_flax_version}"""
	if is_onnx_available():
		ua += f"""; onnxruntime/{_onnxruntime_version}"""
	# CI will set this value to True
	if os.environ.get('DIFFUSERS_IS_CI'   ,''  ).upper() in ENV_VARS_TRUE_VALUES:
		ua += "; is_ci/true"
	if isinstance(UpperCamelCase   ,UpperCamelCase  ):
		ua += "; " + "; ".join(f"""{k}/{v}""" for k, v in user_agent.items()  )
	elif isinstance(UpperCamelCase   ,UpperCamelCase  ):
		ua += "; " + user_agent
	return ua







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase = None   ,UpperCamelCase = None  )    ->    Dict:
	if token is None:
		UpperCAmelCase_		:      Optional[Any]       	=					HfFolder.get_token()
	if organization is None:
		UpperCAmelCase_		:      str       	=					whoami(UpperCamelCase  )['name']
		return f"""{username}/{model_id}"""
	else:
		return f"""{organization}/{model_id}"""







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Union[str, Any]:
	if not is_jinja_available():
		raise ValueError(
		    'Modelcard rendering is based on Jinja templates.'
		    ' Please make sure to have `jinja` installed before using `create_model_card`.'
		    ' To install it, please run `pip install Jinja2`.'  )

	if hasattr(UpperCamelCase   ,'local_rank'  ) and args.local_rank not in [-1, 0]:
		return

	UpperCAmelCase_		:      List[Any]       	=					args.hub_token if hasattr(UpperCamelCase   ,'hub_token'  ) else None
	UpperCAmelCase_		:      Dict       	=					get_full_repo_name(UpperCamelCase   ,token=UpperCamelCase  )

	UpperCAmelCase_		:      Optional[int]       	=					ModelCard.from_template(
	    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
	        language='en'   ,license='apache-2.0'   ,library_name='diffusers'   ,tags=[]   ,datasets=args.dataset_name   ,metrics=[]   ,)   ,template_path=UpperCamelCase   ,model_name=UpperCamelCase   ,repo_name=UpperCamelCase   ,dataset_name=args.dataset_name if hasattr(UpperCamelCase   ,'dataset_name'  ) else None   ,learning_rate=args.learning_rate   ,train_batch_size=args.train_batch_size   ,eval_batch_size=args.eval_batch_size   ,gradient_accumulation_steps=(
	        args.gradient_accumulation_steps if hasattr(UpperCamelCase   ,'gradient_accumulation_steps'  ) else None
	    )   ,adam_betaa=args.adam_betaa if hasattr(UpperCamelCase   ,'adam_beta1'  ) else None   ,adam_betaa=args.adam_betaa if hasattr(UpperCamelCase   ,'adam_beta2'  ) else None   ,adam_weight_decay=args.adam_weight_decay if hasattr(UpperCamelCase   ,'adam_weight_decay'  ) else None   ,adam_epsilon=args.adam_epsilon if hasattr(UpperCamelCase   ,'adam_epsilon'  ) else None   ,lr_scheduler=args.lr_scheduler if hasattr(UpperCamelCase   ,'lr_scheduler'  ) else None   ,lr_warmup_steps=args.lr_warmup_steps if hasattr(UpperCamelCase   ,'lr_warmup_steps'  ) else None   ,ema_inv_gamma=args.ema_inv_gamma if hasattr(UpperCamelCase   ,'ema_inv_gamma'  ) else None   ,ema_power=args.ema_power if hasattr(UpperCamelCase   ,'ema_power'  ) else None   ,ema_max_decay=args.ema_max_decay if hasattr(UpperCamelCase   ,'ema_max_decay'  ) else None   ,mixed_precision=args.mixed_precision   ,)

	UpperCAmelCase_		:      int       	=					os.path.join(args.output_dir   ,'README.md'  )
	model_card.save(UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase = None  )    ->    Any:
	if resolved_file is None or commit_hash is not None:
		return commit_hash
	UpperCAmelCase_		:      List[str]       	=					str(Path(UpperCamelCase  ).as_posix()  )
	UpperCAmelCase_		:      List[str]       	=					re.search(r'snapshots/([^/]+)/'   ,UpperCamelCase  )
	if search is None:
		return None
	UpperCAmelCase_		:      Tuple       	=					search.groups()[0]
	return commit_hash if REGEX_COMMIT_HASH.match(UpperCamelCase  ) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
lowerCAmelCase__										= os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
lowerCAmelCase__										= os.path.join(hf_cache_home, "diffusers")







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase = None   ,UpperCamelCase = None  )    ->    None:
	if new_cache_dir is None:
		UpperCAmelCase_		:      List[str]       	=					DIFFUSERS_CACHE
	if old_cache_dir is None:
		UpperCAmelCase_		:      str       	=					old_diffusers_cache

	UpperCAmelCase_		:      Any       	=					Path(UpperCamelCase  ).expanduser()
	UpperCAmelCase_		:      List[Any]       	=					Path(UpperCamelCase  ).expanduser()
	for old_blob_path in old_cache_dir.glob('**/blobs/*'  ):
		if old_blob_path.is_file() and not old_blob_path.is_symlink():
			UpperCAmelCase_		:      str       	=					new_cache_dir / old_blob_path.relative_to(UpperCamelCase  )
			new_blob_path.parent.mkdir(parents=UpperCamelCase   ,exist_ok=UpperCamelCase  )
			os.replace(UpperCamelCase   ,UpperCamelCase  )
			try:
				os.symlink(UpperCamelCase   ,UpperCamelCase  )
			except OSError:
				logger.warning(
				    'Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.'  )
	# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


lowerCAmelCase__										= os.path.join(DIFFUSERS_CACHE, "version_diffusers_cache.txt")
if not os.path.isfile(cache_version_file):
						lowerCAmelCase__										= 0
else:
						with open(cache_version_file) as f:
												try:
																		lowerCAmelCase__										= int(f.read())
												except ValueError:
																		lowerCAmelCase__										= 0

if cache_version < 1:
						lowerCAmelCase__										= os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
						if old_cache_is_not_empty:
												logger.warning(
												    "The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your "
												    "existing cached models. This is a one-time operation, you can interrupt it or run it "
												    "later by calling `diffusers.utils.hub_utils.move_cache()`."
												)
												try:
																		move_cache()
												except Exception as e:
																		lowerCAmelCase__										= "\n".join(traceback.format_tb(e.__traceback__))
																		logger.error(
																		    F'There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '
																		    "file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole "
																		    "message and we will do our best to help."
																		)

if cache_version < 1:
						try:
												os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
												with open(cache_version_file, "w") as f:
																		f.write("1")
						except Exception:
												logger.warning(
												    F'There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '
												    "the directory exists and can be written to."
												)







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase = None  )    ->    str:
	if variant is not None:
		UpperCAmelCase_		:      Dict       	=					weights_name.split('.'  )
		UpperCAmelCase_		:      Dict       	=					splits[:-1] + [variant] + splits[-1:]
		UpperCAmelCase_		:      Tuple       	=					'.'.join(UpperCamelCase  )

	return weights_name







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,*,
    UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase=None   ,)    ->    int:
	UpperCAmelCase_		:      List[Any]       	=					str(UpperCamelCase  )
	if os.path.isfile(UpperCamelCase  ):
		return pretrained_model_name_or_path
	elif os.path.isdir(UpperCamelCase  ):
		if os.path.isfile(os.path.join(UpperCamelCase   ,UpperCamelCase  )  ):
			# Load from a PyTorch checkpoint
			UpperCAmelCase_		:      int       	=					os.path.join(UpperCamelCase   ,UpperCamelCase  )
			return model_file
		elif subfolder is not None and os.path.isfile(
		    os.path.join(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )  ):
			UpperCAmelCase_		:      Dict       	=					os.path.join(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )
			return model_file
		else:
			raise EnvironmentError(
			    f"""Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."""  )
	else:
		# 1. First check if deprecated way of loading from branches is used
		if (
		    revision in DEPRECATED_REVISION_ARGS
		    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
		    and version.parse(version.parse(UpperCamelCase  ).base_version  ) >= version.parse('0.20.0'  )
		):
			try:
				UpperCAmelCase_		:      int       	=					hf_hub_download(
				    UpperCamelCase   ,filename=_add_variant(UpperCamelCase   ,UpperCamelCase  )   ,cache_dir=UpperCamelCase   ,force_download=UpperCamelCase   ,proxies=UpperCamelCase   ,resume_download=UpperCamelCase   ,local_files_only=UpperCamelCase   ,use_auth_token=UpperCamelCase   ,user_agent=UpperCamelCase   ,subfolder=UpperCamelCase   ,revision=revision or commit_hash   ,)
				warnings.warn(
				    f"""Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead."""   ,UpperCamelCase   ,)
				return model_file
			except:  # noqa: E722
				warnings.warn(
				    f"""You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(UpperCamelCase   ,UpperCamelCase  )} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(UpperCamelCase   ,UpperCamelCase  )}' so that the correct variant file can be added."""   ,UpperCamelCase   ,)
		try:
			# 2. Load model file as usual
			UpperCAmelCase_		:      int       	=					hf_hub_download(
			    UpperCamelCase   ,filename=UpperCamelCase   ,cache_dir=UpperCamelCase   ,force_download=UpperCamelCase   ,proxies=UpperCamelCase   ,resume_download=UpperCamelCase   ,local_files_only=UpperCamelCase   ,use_auth_token=UpperCamelCase   ,user_agent=UpperCamelCase   ,subfolder=UpperCamelCase   ,revision=revision or commit_hash   ,)
			return model_file

		except RepositoryNotFoundError:
			raise EnvironmentError(
			    f"""{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier """
			    'listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '
			    'token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '
			    'login`.'  )
		except RevisionNotFoundError:
			raise EnvironmentError(
			    f"""{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for """
			    'this model name. Check the model page at '
			    f"""'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."""  )
		except EntryNotFoundError:
			raise EnvironmentError(
			    f"""{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."""  )
		except HTTPError as err:
			raise EnvironmentError(
			    f"""There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"""  )
		except ValueError:
			raise EnvironmentError(
			    f"""We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"""
			    f""" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"""
			    f""" directory containing a file named {weights_name} or"""
			    ' \nCheckout your internet connection or see how to run the library in'
			    ' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.'  )
		except EnvironmentError:
			raise EnvironmentError(
			    f"""Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from """
			    '\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '
			    f"""Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory """
			    f"""containing a file named {weights_name}"""  )









'''simple docstring'''


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
)
lowerCAmelCase__										= logging.getLogger(__name__)







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Union[str, Any]:
	UpperCAmelCase_		:      Any       	=					np.argmax(UpperCamelCase   ,axis=1  )
	return np.sum(outputs == labels  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    List[str]:
	with open(UpperCamelCase   ,encoding='utf_8'  ) as f:
		UpperCAmelCase_		:      int       	=					csv.reader(UpperCamelCase  )
		UpperCAmelCase_		:      Tuple       	=					[]
		next(UpperCamelCase  )  # skip the first line
		for line in tqdm(UpperCamelCase  ):
			output.append((' '.join(line[1:5]  ), line[5], line[6], int(line[-1]  ) - 1)  )
	return output







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Optional[int]:
	UpperCAmelCase_		:      str       	=					[]
	for dataset in encoded_datasets:
		UpperCAmelCase_		:      str       	=					len(UpperCamelCase  )
		UpperCAmelCase_		:      str       	=					np.zeros((n_batch, 2, input_len)   ,dtype=np.intaa  )
		UpperCAmelCase_		:      List[Any]       	=					np.zeros((n_batch, 2)   ,dtype=np.intaa  )
		UpperCAmelCase_		:      Any       	=					np.full((n_batch, 2, input_len)   ,fill_value=-1_0_0   ,dtype=np.intaa  )
		UpperCAmelCase_		:      List[Any]       	=					np.zeros((n_batch,)   ,dtype=np.intaa  )
		for (
		    i,
		    (story, conta, conta, mc_label),
		) in enumerate(UpperCamelCase  ):
			UpperCAmelCase_		:      Union[str, Any]       	=					[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			UpperCAmelCase_		:      List[Any]       	=					[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			UpperCAmelCase_		:      Any       	=					with_conta
			UpperCAmelCase_		:      Tuple       	=					with_conta
			UpperCAmelCase_		:      Union[str, Any]       	=					len(UpperCamelCase  ) - 1
			UpperCAmelCase_		:      int       	=					len(UpperCamelCase  ) - 1
			UpperCAmelCase_		:      Any       	=					with_conta
			UpperCAmelCase_		:      Any       	=					with_conta
			UpperCAmelCase_		:      Optional[int]       	=					mc_label
		UpperCAmelCase_		:      Tuple       	=					(input_ids, mc_token_ids, lm_labels, mc_labels)
		tensor_datasets.append(tuple(torch.tensor(UpperCamelCase  ) for t in all_inputs  )  )
	return tensor_datasets







def 						SCREAMING_SNAKE_CASE(  )    ->    str:
	UpperCAmelCase_		:      Union[str, Any]       	=					argparse.ArgumentParser()
	parser.add_argument('--model_name'   ,type=UpperCamelCase   ,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=UpperCamelCase   ,type=UpperCamelCase   ,required=UpperCamelCase   ,help='The output directory where the model predictions and checkpoints will be written.'   ,)
	parser.add_argument('--train_dataset'   ,type=UpperCamelCase   ,default=''  )
	parser.add_argument('--eval_dataset'   ,type=UpperCamelCase   ,default=''  )
	parser.add_argument('--seed'   ,type=UpperCamelCase   ,default=4_2  )
	parser.add_argument('--num_train_epochs'   ,type=UpperCamelCase   ,default=3  )
	parser.add_argument('--train_batch_size'   ,type=UpperCamelCase   ,default=8  )
	parser.add_argument('--eval_batch_size'   ,type=UpperCamelCase   ,default=1_6  )
	parser.add_argument('--adam_epsilon'   ,default=1e-8   ,type=UpperCamelCase   ,help='Epsilon for Adam optimizer.'  )
	parser.add_argument('--max_grad_norm'   ,type=UpperCamelCase   ,default=1  )
	parser.add_argument(
	    '--max_steps'   ,default=-1   ,type=UpperCamelCase   ,help=(
	        'If > 0: set total number of training                         steps to perform. Override num_train_epochs.'
	    )   ,)
	parser.add_argument(
	    '--gradient_accumulation_steps'   ,type=UpperCamelCase   ,default=1   ,help='Number of updates steps to accumulate before                        performing a backward/update pass.'   ,)
	parser.add_argument('--learning_rate'   ,type=UpperCamelCase   ,default=6.25e-5  )
	parser.add_argument('--warmup_steps'   ,default=0   ,type=UpperCamelCase   ,help='Linear warmup over warmup_steps.'  )
	parser.add_argument('--lr_schedule'   ,type=UpperCamelCase   ,default='warmup_linear'  )
	parser.add_argument('--weight_decay'   ,type=UpperCamelCase   ,default=0.01  )
	parser.add_argument('--lm_coef'   ,type=UpperCamelCase   ,default=0.9  )
	parser.add_argument('--n_valid'   ,type=UpperCamelCase   ,default=3_7_4  )

	parser.add_argument('--server_ip'   ,type=UpperCamelCase   ,default=''   ,help='Can be used for distant debugging.'  )
	parser.add_argument('--server_port'   ,type=UpperCamelCase   ,default=''   ,help='Can be used for distant debugging.'  )
	UpperCAmelCase_		:      Optional[Any]       	=					parser.parse_args()
	print(UpperCamelCase  )

	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=UpperCamelCase  )
		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  )

	UpperCAmelCase_		:      Union[str, Any]       	=					torch.device('cuda' if torch.cuda.is_available() else 'cpu'  )
	UpperCAmelCase_		:      Dict       	=					torch.cuda.device_count()
	logger.info('device: {}, n_gpu {}'.format(UpperCamelCase   ,UpperCamelCase  )  )

	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
	UpperCAmelCase_		:      Optional[int]       	=					['_start_', '_delimiter_', '_classify_']
	UpperCAmelCase_		:      Union[str, Any]       	=					OpenAIGPTTokenizer.from_pretrained(args.model_name  )
	tokenizer.add_tokens(UpperCamelCase  )
	UpperCAmelCase_		:      Optional[Any]       	=					tokenizer.convert_tokens_to_ids(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name  )
	model.resize_token_embeddings(len(UpperCamelCase  )  )
	model.to(UpperCamelCase  )

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

	logger.info('Encoding dataset...'  )
	UpperCAmelCase_		:      List[str]       	=					load_rocstories_dataset(args.train_dataset  )
	UpperCAmelCase_		:      Optional[Any]       	=					load_rocstories_dataset(args.eval_dataset  )
	UpperCAmelCase_		:      Dict       	=					(train_dataset, eval_dataset)
	UpperCAmelCase_		:      Union[str, Any]       	=					tokenize_and_encode(UpperCamelCase  )

	# Compute the max input length for the Transformer
	UpperCAmelCase_		:      Dict       	=					model.config.n_positions // 2 - 2
	UpperCAmelCase_		:      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  )
	UpperCAmelCase_		:      Dict       	=					min(UpperCamelCase   ,model.config.n_positions  )  # Max size of input for the pre-trained model

	# Prepare inputs tensors and dataloaders
	UpperCAmelCase_		:      Optional[int]       	=					pre_process_datasets(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,*UpperCamelCase  )
	UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					tensor_datasets[0], tensor_datasets[1]

	UpperCAmelCase_		:      Optional[int]       	=					TensorDataset(*UpperCamelCase  )
	UpperCAmelCase_		:      Optional[Any]       	=					RandomSampler(UpperCamelCase  )
	UpperCAmelCase_		:      int       	=					DataLoader(UpperCamelCase   ,sampler=UpperCamelCase   ,batch_size=args.train_batch_size  )

	UpperCAmelCase_		:      int       	=					TensorDataset(*UpperCamelCase  )
	UpperCAmelCase_		:      Optional[int]       	=					SequentialSampler(UpperCamelCase  )
	UpperCAmelCase_		:      Tuple       	=					DataLoader(UpperCamelCase   ,sampler=UpperCamelCase   ,batch_size=args.eval_batch_size  )

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

		UpperCAmelCase_		:      Any       	=					list(model.named_parameters()  )
		UpperCAmelCase_		:      Any       	=					['bias', 'LayerNorm.bias', 'LayerNorm.weight']
		UpperCAmelCase_		:      Dict       	=					[
		    {
		        '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},
		]
		UpperCAmelCase_		:      Any       	=					AdamW(UpperCamelCase   ,lr=args.learning_rate   ,eps=args.adam_epsilon  )
		UpperCAmelCase_		:      List[str]       	=					get_linear_schedule_with_warmup(
		    UpperCamelCase   ,num_warmup_steps=args.warmup_steps   ,num_training_steps=UpperCamelCase  )

	if args.do_train:
		UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      List[Any]       	=					0, 0, None
		model.train()
		for _ in trange(int(args.num_train_epochs  )   ,desc='Epoch'  ):
			UpperCAmelCase_		:      Union[str, Any]       	=					0
			UpperCAmelCase_		:      Any       	=					0
			UpperCAmelCase_		:      List[Any]       	=					tqdm(UpperCamelCase   ,desc='Training'  )
			for step, batch in enumerate(UpperCamelCase  ):
				UpperCAmelCase_		:      Optional[Any]       	=					tuple(t.to(UpperCamelCase  ) for t in batch  )
				UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      Tuple       	=					batch
				UpperCAmelCase_		:      Dict       	=					model(UpperCamelCase   ,mc_token_ids=UpperCamelCase   ,lm_labels=UpperCamelCase   ,mc_labels=UpperCamelCase  )
				UpperCAmelCase_		:      int       	=					args.lm_coef * losses[0] + losses[1]
				loss.backward()
				optimizer.step()
				scheduler.step()
				optimizer.zero_grad()
				tr_loss += loss.item()
				UpperCAmelCase_		:      Dict       	=					(
				    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
				)
				nb_tr_steps += 1
				UpperCAmelCase_		:      List[Any]       	=					'Training loss: {:.2e} lr: {:.2e}'.format(UpperCamelCase   ,scheduler.get_lr()[0]  )

    # Save a trained model
	if args.do_train:
		# Save a trained model, configuration and tokenizer
		UpperCAmelCase_		:      int       	=					model.module if hasattr(UpperCamelCase   ,'module'  ) else model  # Only save the model itself

		# If we save using the predefined names, we can load using `from_pretrained`
		UpperCAmelCase_		:      int       	=					os.path.join(args.output_dir   ,UpperCamelCase  )
		UpperCAmelCase_		:      Any       	=					os.path.join(args.output_dir   ,UpperCamelCase  )

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

		# Load a trained model and vocabulary that you have fine-tuned
		UpperCAmelCase_		:      int       	=					OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir  )
		UpperCAmelCase_		:      Dict       	=					OpenAIGPTTokenizer.from_pretrained(args.output_dir  )
		model.to(UpperCamelCase  )

	if args.do_eval:
		model.eval()
		UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					0, 0
		UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					0, 0
		for batch in tqdm(UpperCamelCase   ,desc='Evaluating'  ):
			UpperCAmelCase_		:      Union[str, Any]       	=					tuple(t.to(UpperCamelCase  ) for t in batch  )
			UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					batch
			with torch.no_grad():
				UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					model(
				    UpperCamelCase   ,mc_token_ids=UpperCamelCase   ,lm_labels=UpperCamelCase   ,mc_labels=UpperCamelCase  )

			UpperCAmelCase_		:      Optional[Any]       	=					mc_logits.detach().cpu().numpy()
			UpperCAmelCase_		:      int       	=					mc_labels.to('cpu'  ).numpy()
			UpperCAmelCase_		:      Optional[int]       	=					accuracy(UpperCamelCase   ,UpperCamelCase  )

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

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

		UpperCAmelCase_		:      str       	=					eval_loss / nb_eval_steps
		UpperCAmelCase_		:      str       	=					eval_accuracy / nb_eval_examples
		UpperCAmelCase_		:      List[Any]       	=					tr_loss / nb_tr_steps if args.do_train else None
		UpperCAmelCase_		:      Dict       	=					{'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

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


if __name__ == "__main__":
						main()






