



"""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
)
_lowercase : List[Any]      						=						logging.getLogger(__name__)






def      snake_case__   (		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			lowerCamelCase__		:		List[str]			=np.argmax(__lowerCamelCase							,		axis=1	)
			return np.sum(outputs == labels	)






def      snake_case__   (		__lowerCamelCase				:    List[str]	):
			"""simple docstring"""
			with open(__lowerCamelCase							,		encoding='''utf_8'''	) as f:
						lowerCamelCase__		:		Tuple			=csv.reader(__lowerCamelCase	)
						lowerCamelCase__		:		Dict			=[]
						next(__lowerCamelCase	)  # skip the first line
						for line in tqdm(__lowerCamelCase	):
									output.append((''' '''.join(line[1:5]	), line[5], line[6], int(line[-1]	) - 1)	)
			return output






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






def      snake_case__   (		):
			"""simple docstring"""
			lowerCamelCase__		:		Tuple			=argparse.ArgumentParser()
			parser.add_argument('''--model_name'''							,		type=__lowerCamelCase							,		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=__lowerCamelCase							,		type=__lowerCamelCase							,		required=__lowerCamelCase							,		help='''The output directory where the model predictions and checkpoints will be written.'''							,		)
			parser.add_argument('''--train_dataset'''							,		type=__lowerCamelCase							,		default=''''''	)
			parser.add_argument('''--eval_dataset'''							,		type=__lowerCamelCase							,		default=''''''	)
			parser.add_argument('''--seed'''							,		type=__lowerCamelCase							,		default=42	)
			parser.add_argument('''--num_train_epochs'''							,		type=__lowerCamelCase							,		default=3	)
			parser.add_argument('''--train_batch_size'''							,		type=__lowerCamelCase							,		default=8	)
			parser.add_argument('''--eval_batch_size'''							,		type=__lowerCamelCase							,		default=16	)
			parser.add_argument('''--adam_epsilon'''							,		default=1e-8							,		type=__lowerCamelCase							,		help='''Epsilon for Adam optimizer.'''	)
			parser.add_argument('''--max_grad_norm'''							,		type=__lowerCamelCase							,		default=1	)
			parser.add_argument(
			    '''--max_steps'''							,		default=-1							,		type=__lowerCamelCase							,		help=(
			        '''If > 0: set total number of training                         steps to perform. Override num_train_epochs.'''
			    )							,		)
			parser.add_argument(
			    '''--gradient_accumulation_steps'''							,		type=__lowerCamelCase							,		default=1							,		help='''Number of updates steps to accumulate before                        performing a backward/update pass.'''							,		)
			parser.add_argument('''--learning_rate'''							,		type=__lowerCamelCase							,		default=6.2_5e-5	)
			parser.add_argument('''--warmup_steps'''							,		default=0							,		type=__lowerCamelCase							,		help='''Linear warmup over warmup_steps.'''	)
			parser.add_argument('''--lr_schedule'''							,		type=__lowerCamelCase							,		default='''warmup_linear'''	)
			parser.add_argument('''--weight_decay'''							,		type=__lowerCamelCase							,		default=0.01	)
			parser.add_argument('''--lm_coef'''							,		type=__lowerCamelCase							,		default=0.9	)
			parser.add_argument('''--n_valid'''							,		type=__lowerCamelCase							,		default=374	)

			parser.add_argument('''--server_ip'''							,		type=__lowerCamelCase							,		default=''''''							,		help='''Can be used for distant debugging.'''	)
			parser.add_argument('''--server_port'''							,		type=__lowerCamelCase							,		default=''''''							,		help='''Can be used for distant debugging.'''	)
			lowerCamelCase__		:		int			=parser.parse_args()
			print(__lowerCamelCase	)

			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=__lowerCamelCase	)
						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	)

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

			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
			lowerCamelCase__		:		Optional[Any]			=['''_start_''', '''_delimiter_''', '''_classify_''']
			lowerCamelCase__		:		Tuple			=OpenAIGPTTokenizer.from_pretrained(args.model_name	)
			tokenizer.add_tokens(__lowerCamelCase	)
			lowerCamelCase__		:		Tuple			=tokenizer.convert_tokens_to_ids(__lowerCamelCase	)
			lowerCamelCase__		:		List[Any]			=OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name	)
			model.resize_token_embeddings(len(__lowerCamelCase	)	)
			model.to(__lowerCamelCase	)

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

			logger.info('''Encoding dataset...'''	)
			lowerCamelCase__		:		str			=load_rocstories_dataset(args.train_dataset	)
			lowerCamelCase__		:		Dict			=load_rocstories_dataset(args.eval_dataset	)
			lowerCamelCase__		:		Optional[int]			=(train_dataset, eval_dataset)
			lowerCamelCase__		:		Tuple			=tokenize_and_encode(__lowerCamelCase	)

			# Compute the max input length for the Transformer
			lowerCamelCase__		:		str			=model.config.n_positions // 2 - 2
			lowerCamelCase__		:		Any			=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	)
			lowerCamelCase__		:		int			=min(__lowerCamelCase							,		model.config.n_positions	)  # Max size of input for the pre-trained model

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

			lowerCamelCase__		:		Tuple			=TensorDataset(*__lowerCamelCase	)
			lowerCamelCase__		:		List[Any]			=RandomSampler(__lowerCamelCase	)
			lowerCamelCase__		:		int			=DataLoader(__lowerCamelCase							,		sampler=__lowerCamelCase							,		batch_size=args.train_batch_size	)

			lowerCamelCase__		:		Any			=TensorDataset(*__lowerCamelCase	)
			lowerCamelCase__		:		Any			=SequentialSampler(__lowerCamelCase	)
			lowerCamelCase__		:		List[str]			=DataLoader(__lowerCamelCase							,		sampler=__lowerCamelCase							,		batch_size=args.eval_batch_size	)

			# Prepare optimizer
			if args.do_train:
						if args.max_steps > 0:
									lowerCamelCase__		:		str			=args.max_steps
									lowerCamelCase__		:		Dict			=args.max_steps // (len(__lowerCamelCase	) // args.gradient_accumulation_steps) + 1
						else:
									lowerCamelCase__		:		Optional[Any]			=len(__lowerCamelCase	) // args.gradient_accumulation_steps * args.num_train_epochs

						lowerCamelCase__		:		Dict			=list(model.named_parameters()	)
						lowerCamelCase__		:		int			=['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
						lowerCamelCase__		:		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},
						]
						lowerCamelCase__		:		int			=AdamW(__lowerCamelCase							,		lr=args.learning_rate							,		eps=args.adam_epsilon	)
						lowerCamelCase__		:		int			=get_linear_schedule_with_warmup(
						    __lowerCamelCase							,		num_warmup_steps=args.warmup_steps							,		num_training_steps=__lowerCamelCase	)

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

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

						# If we save using the predefined names, we can load using `from_pretrained`
						lowerCamelCase__		:		Any			=os.path.join(args.output_dir							,		__lowerCamelCase	)
						lowerCamelCase__		:		Optional[int]			=os.path.join(args.output_dir							,		__lowerCamelCase	)

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

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

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

									lowerCamelCase__		:		Dict			=mc_logits.detach().cpu().numpy()
									lowerCamelCase__		:		Optional[Any]			=mc_labels.to('''cpu'''	).numpy()
									lowerCamelCase__		:		Optional[int]			=accuracy(__lowerCamelCase							,		__lowerCamelCase	)

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

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

						lowerCamelCase__		:		int			=eval_loss / nb_eval_steps
						lowerCamelCase__		:		str			=eval_accuracy / nb_eval_examples
						lowerCamelCase__		:		str			=tr_loss / nb_tr_steps if args.do_train else None
						lowerCamelCase__		:		str			={'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}

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


if __name__ == "__main__":
		main()









"""simple docstring"""






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


_lowercase : Tuple      						=						logging.get_logger(__name__)

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							['pixel_values']



					def __init__(       self							:  Optional[Any],  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Union[int, float] = 1 / 255,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  int = 8,  **lowerCamelCase							:  Tuple,  )->					None:
								super().__init__(**lowerCamelCase     )

								lowerCamelCase__		:		int			=do_rescale
								lowerCamelCase__		:		Dict			=rescale_factor
								lowerCamelCase__		:		Union[str, Any]			=do_pad
								lowerCamelCase__		:		Union[str, Any]			=pad_size



					def 		snake_case       (       self							:  int,  lowerCamelCase							:  np.ndarray,  lowerCamelCase							:  float,  lowerCamelCase							:  Optional[Union[str, ChannelDimension]] = None,  **lowerCamelCase							:  int     )->					np.ndarray:
								return rescale(lowerCamelCase,  scale=lowerCamelCase,  data_format=lowerCamelCase,  **lowerCamelCase     )



					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  np.ndarray,  lowerCamelCase							:  int,  lowerCamelCase							:  Optional[Union[str, ChannelDimension]] = None     )->					List[Any]:
								lowerCamelCase__	,   lowerCamelCase__		:		Optional[int]			=get_image_size(lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=(old_height // size + 1) * size - old_height
								lowerCamelCase__		:		List[str]			=(old_width // size + 1) * size - old_width

								return pad(lowerCamelCase,  ((0, pad_height), (0, pad_width)),  mode='''symmetric''',  data_format=lowerCamelCase     )




					def 		snake_case       (       self							:  List[Any],  lowerCamelCase							:  ImageInput,  lowerCamelCase							:  Optional[bool] = None,  lowerCamelCase							:  Optional[float] = None,  lowerCamelCase							:  Optional[bool] = None,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  Optional[Union[str, TensorType]] = None,  lowerCamelCase							:  Union[str, ChannelDimension] = ChannelDimension.FIRST,  **lowerCamelCase							:  Union[str, Any],  )->					Dict:
								lowerCamelCase__		:		List[str]			=do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase__		:		Tuple			=rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase__		:		str			=do_pad if do_pad is not None else self.do_pad
								lowerCamelCase__		:		int			=pad_size if pad_size is not None else self.pad_size

								lowerCamelCase__		:		Optional[int]			=make_list_of_images(lowerCamelCase     )

								if not valid_images(lowerCamelCase     ):
											raise ValueError(
											    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
											    '''torch.Tensor, tf.Tensor or jax.ndarray.'''     )

								if do_rescale and rescale_factor is None:
											raise ValueError('''Rescale factor must be specified if do_rescale is True.'''     )

								# All transformations expect numpy arrays.
								lowerCamelCase__		:		Tuple			=[to_numpy_array(lowerCamelCase     ) for image in images]

								if do_rescale:
											lowerCamelCase__		:		Tuple			=[self.rescale(image=lowerCamelCase,  scale=lowerCamelCase     ) for image in images]

								if do_pad:
											lowerCamelCase__		:		Tuple			=[self.pad(lowerCamelCase,  size=lowerCamelCase     ) for image in images]

								lowerCamelCase__		:		int			=[to_channel_dimension_format(lowerCamelCase,  lowerCamelCase     ) for image in images]

								lowerCamelCase__		:		Dict			={'''pixel_values''': images}
								return BatchFeature(data=lowerCamelCase,  tensor_type=lowerCamelCase     )




