

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BertTokenizer,
    ViltConfig,
    ViltForImageAndTextRetrieval,
    ViltForImagesAndTextClassification,
    ViltForMaskedLM,
    ViltForQuestionAnswering,
    ViltImageProcessor,
    ViltProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase					:			Tuple  				=			logging.get_logger(__name__)






def        UpperCamelCase_(							_snake_case   :  Union[str, Any]       ,     _snake_case   :  Union[str, Any]=False       ,     _snake_case   :  int=False       ,     _snake_case   :  int=False	):




			"""simple docstring"""






			__a        =[]
			for i in range(config.num_hidden_layers	):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append((F'transformer.blocks.{i}.norm1.weight', F'vilt.encoder.layer.{i}.layernorm_before.weight')	)
						rename_keys.append((F'transformer.blocks.{i}.norm1.bias', F'vilt.encoder.layer.{i}.layernorm_before.bias')	)
						rename_keys.append(
						    (F'transformer.blocks.{i}.attn.proj.weight', F'vilt.encoder.layer.{i}.attention.output.dense.weight')	)
						rename_keys.append(
						    (F'transformer.blocks.{i}.attn.proj.bias', F'vilt.encoder.layer.{i}.attention.output.dense.bias')	)
						rename_keys.append((F'transformer.blocks.{i}.norm2.weight', F'vilt.encoder.layer.{i}.layernorm_after.weight')	)
						rename_keys.append((F'transformer.blocks.{i}.norm2.bias', F'vilt.encoder.layer.{i}.layernorm_after.bias')	)
						rename_keys.append(
						    (F'transformer.blocks.{i}.mlp.fc1.weight', F'vilt.encoder.layer.{i}.intermediate.dense.weight')	)
						rename_keys.append((F'transformer.blocks.{i}.mlp.fc1.bias', F'vilt.encoder.layer.{i}.intermediate.dense.bias')	)
						rename_keys.append((F'transformer.blocks.{i}.mlp.fc2.weight', F'vilt.encoder.layer.{i}.output.dense.weight')	)
						rename_keys.append((F'transformer.blocks.{i}.mlp.fc2.bias', F'vilt.encoder.layer.{i}.output.dense.bias')	)

			# embeddings
			rename_keys.extend(
			    [
			        # text embeddings
			        ('text_embeddings.word_embeddings.weight', 'vilt.embeddings.text_embeddings.word_embeddings.weight'),
			        (
			            'text_embeddings.position_embeddings.weight',
			            'vilt.embeddings.text_embeddings.position_embeddings.weight',
			        ),
			        ('text_embeddings.position_ids', 'vilt.embeddings.text_embeddings.position_ids'),
			        (
			            'text_embeddings.token_type_embeddings.weight',
			            'vilt.embeddings.text_embeddings.token_type_embeddings.weight',
			        ),
			        ('text_embeddings.LayerNorm.weight', 'vilt.embeddings.text_embeddings.LayerNorm.weight'),
			        ('text_embeddings.LayerNorm.bias', 'vilt.embeddings.text_embeddings.LayerNorm.bias'),
			        # patch embeddings
			        ('transformer.cls_token', 'vilt.embeddings.cls_token'),
			        ('transformer.patch_embed.proj.weight', 'vilt.embeddings.patch_embeddings.projection.weight'),
			        ('transformer.patch_embed.proj.bias', 'vilt.embeddings.patch_embeddings.projection.bias'),
			        ('transformer.pos_embed', 'vilt.embeddings.position_embeddings'),
			        # token type embeddings
			        ('token_type_embeddings.weight', 'vilt.embeddings.token_type_embeddings.weight'),
			    ]	)

			# final layernorm + pooler
			rename_keys.extend(
			    [
			        ('transformer.norm.weight', 'vilt.layernorm.weight'),
			        ('transformer.norm.bias', 'vilt.layernorm.bias'),
			        ('pooler.dense.weight', 'vilt.pooler.dense.weight'),
			        ('pooler.dense.bias', 'vilt.pooler.dense.bias'),
			    ]	)

			# classifier head(s)
			if vqa_model:
						# classification head
						rename_keys.extend(
						    [
						        ('vqa_classifier.0.weight', 'classifier.0.weight'),
						        ('vqa_classifier.0.bias', 'classifier.0.bias'),
						        ('vqa_classifier.1.weight', 'classifier.1.weight'),
						        ('vqa_classifier.1.bias', 'classifier.1.bias'),
						        ('vqa_classifier.3.weight', 'classifier.3.weight'),
						        ('vqa_classifier.3.bias', 'classifier.3.bias'),
						    ]	)
			elif nlvr_model:
						# classification head
						rename_keys.extend(
						    [
						        ('nlvr2_classifier.0.weight', 'classifier.0.weight'),
						        ('nlvr2_classifier.0.bias', 'classifier.0.bias'),
						        ('nlvr2_classifier.1.weight', 'classifier.1.weight'),
						        ('nlvr2_classifier.1.bias', 'classifier.1.bias'),
						        ('nlvr2_classifier.3.weight', 'classifier.3.weight'),
						        ('nlvr2_classifier.3.bias', 'classifier.3.bias'),
						    ]	)
			else:
						pass

			return rename_keys






def        UpperCamelCase_(							_snake_case   :  Optional[int]       ,     _snake_case   :  Tuple	):




			"""simple docstring"""






			for i in range(config.num_hidden_layers	):
						__a        ='vilt.'
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						__a        =state_dict.pop(F'transformer.blocks.{i}.attn.qkv.weight'	)
						__a        =state_dict.pop(F'transformer.blocks.{i}.attn.qkv.bias'	)
						# next, add query, keys and values (in that order) to the state dict
						__a        =in_proj_weight[
						    : config.hidden_size, :
						]
						__a        =in_proj_bias[: config.hidden_size]
						__a        =in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						__a        =in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						__a        =in_proj_weight[
						    -config.hidden_size :, :
						]
						__a        =in_proj_bias[-config.hidden_size :]






def        UpperCamelCase_(							_snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			__a        =['head.weight', 'head.bias']
			for k in ignore_keys:
						state_dict.pop(_snake_case       ,     _snake_case	)






def        UpperCamelCase_(							_snake_case   :  Union[str, Any]       ,     _snake_case   :  Dict       ,     _snake_case   :  Optional[int]	):




			"""simple docstring"""






			__a        =dct.pop(_snake_case	)
			__a        =val






@torch.no_grad()
def        UpperCamelCase_(							_snake_case   :  str       ,     _snake_case   :  Optional[int]	):




			"""simple docstring"""






			__a        =ViltConfig(image_size=384       ,     patch_size=32       ,     tie_word_embeddings=_snake_case	)
			__a        =False
			__a        =False
			__a        =False
			__a        =False
			if "vqa" in checkpoint_url:
						__a        =True
						__a        =3129
						__a        ='huggingface/label-files'
						__a        ='vqa2-id2label.json'
						__a        =json.load(open(hf_hub_download(_snake_case       ,     _snake_case       ,     repo_type='dataset'	)       ,     'r'	)	)
						__a        ={int(_snake_case	): v for k, v in idalabel.items()}
						__a        =idalabel
						__a        ={v: k for k, v in idalabel.items()}
						__a        =ViltForQuestionAnswering(_snake_case	)
			elif "nlvr" in checkpoint_url:
						__a        =True
						__a        =2
						__a        ={0: 'False', 1: 'True'}
						__a        ={v: k for k, v in config.idalabel.items()}
						__a        =3
						__a        =ViltForImagesAndTextClassification(_snake_case	)
			elif "irtr" in checkpoint_url:
						__a        =True
						__a        =ViltForImageAndTextRetrieval(_snake_case	)
			elif "mlm_itm" in checkpoint_url:
						__a        =True
						__a        =ViltForMaskedLM(_snake_case	)
			else:
						raise ValueError('Unknown model type'	)

			# load state_dict of original model, remove and rename some keys
			__a        =torch.hub.load_state_dict_from_url(_snake_case       ,     map_location='cpu'	)['state_dict']
			__a        =create_rename_keys(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
			for src, dest in rename_keys:
						rename_key(_snake_case       ,     _snake_case       ,     _snake_case	)
			read_in_q_k_v(_snake_case       ,     _snake_case	)
			if mlm_model or irtr_model:
						__a        =['itm_score.fc.weight', 'itm_score.fc.bias']
						for k in ignore_keys:
									state_dict.pop(_snake_case       ,     _snake_case	)

    # load state dict into HuggingFace model
			model.eval()
			if mlm_model:
						__a      ,    __a        =model.load_state_dict(_snake_case       ,     strict=_snake_case	)
						assert missing_keys == ["mlm_score.decoder.bias"]
			else:
						model.load_state_dict(_snake_case	)

			# Define processor
			__a        =ViltImageProcessor(size=384	)
			__a        =BertTokenizer.from_pretrained('bert-base-uncased'	)
			__a        =ViltProcessor(_snake_case       ,     _snake_case	)

			# Forward pass on example inputs (image + text)
			if nlvr_model:
						__a        =Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'       ,     stream=_snake_case	).raw	)
						__a        =Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'       ,     stream=_snake_case	).raw	)
						__a        =(
						    'The left image contains twice the number of dogs as the right image, and at least two dogs in total are'
						    ' standing.'
						)
						__a        =processor(_snake_case       ,     _snake_case       ,     return_tensors='pt'	)
						__a        =processor(_snake_case       ,     _snake_case       ,     return_tensors='pt'	)
						__a        =model(
						    input_ids=encoding_a.input_ids       ,     pixel_values=encoding_a.pixel_values       ,     pixel_values_a=encoding_a.pixel_values       ,     )
			else:
						__a        =Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg'       ,     stream=_snake_case	).raw	)
						if mlm_model:
									__a        ='a bunch of [MASK] laying on a [MASK].'
						else:
									__a        ='How many cats are there?'
						__a        =processor(_snake_case       ,     _snake_case       ,     return_tensors='pt'	)
						__a        =model(**_snake_case	)

			# Verify outputs
			if mlm_model:
						__a        =torch.Size([1, 11, 30522]	)
						__a        =torch.tensor([-12.5_061, -12.5_123, -12.5_174]	)
						assert outputs.logits.shape == expected_shape
						assert torch.allclose(outputs.logits[0, 0, :3]       ,     _snake_case       ,     atol=1e-4	)

						# verify masked token prediction equals "cats"
						__a        =outputs.logits[0, 4, :].argmax(-1	).item()
						assert tokenizer.decode([predicted_id]	) == "cats"
			elif vqa_model:
						__a        =torch.Size([1, 3129]	)
						__a        =torch.tensor([-15.9_495, -18.1_472, -10.3_041]	)
						assert torch.allclose(outputs.logits[0, :3]       ,     _snake_case       ,     atol=1e-4	)
						assert outputs.logits.shape == expected_shape
						assert torch.allclose(outputs.logits[0, 0, :3]       ,     _snake_case       ,     atol=1e-4	)

						# verify vqa prediction equals "2"
						__a        =outputs.logits.argmax(-1	).item()
						assert model.config.idalabel[predicted_idx] == "2"
			elif nlvr_model:
						__a        =torch.Size([1, 2]	)
						__a        =torch.tensor([-2.8_721, 2.1_291]	)
						assert torch.allclose(outputs.logits[0, :3]       ,     _snake_case       ,     atol=1e-4	)
						assert outputs.logits.shape == expected_shape

			Path(_snake_case	).mkdir(exist_ok=_snake_case	)
			print(F'Saving model and processor to {pytorch_dump_folder_path}'	)
			model.save_pretrained(_snake_case	)
			processor.save_pretrained(_snake_case	)


if __name__ == "__main__":
						_lowerCAmelCase					:			Tuple  				=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint_url",
						    default="https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt",
						    type=str,
						    help="URL of the checkpoint you'd like to convert.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
						)

						_lowerCAmelCase					:			Optional[int]  				=			parser.parse_args()
						convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









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






def        UpperCamelCase_(							_snake_case   :  Optional[Any]       ,     _snake_case   :  Any	):




			"""simple docstring"""






			__a        =np.argmax(_snake_case       ,     axis=1	)
			return np.sum(outputs == labels	)






def        UpperCamelCase_(							_snake_case   :  List[str]	):




			"""simple docstring"""






			with open(_snake_case       ,     encoding='utf_8'	) as f:
						__a        =csv.reader(_snake_case	)
						__a        =[]
						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        UpperCamelCase_(							_snake_case   :  Union[str, Any]       ,     _snake_case   :  Any       ,     _snake_case   :  Dict       ,     _snake_case   :  Any       ,     _snake_case   :  Union[str, Any]       ,     _snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =[]
			for dataset in encoded_datasets:
						__a        =len(_snake_case	)
						__a        =np.zeros((n_batch, 2, input_len)       ,     dtype=np.intaa	)
						__a        =np.zeros((n_batch, 2)       ,     dtype=np.intaa	)
						__a        =np.full((n_batch, 2, input_len)       ,     fill_value=-100       ,     dtype=np.intaa	)
						__a        =np.zeros((n_batch,)       ,     dtype=np.intaa	)
						for (
						    i,
						    (story, conta, conta, mc_label),
						) in enumerate(_snake_case	):
									__a        =[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									__a        =[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									__a        =with_conta
									__a        =with_conta
									__a        =len(_snake_case	) - 1
									__a        =len(_snake_case	) - 1
									__a        =with_conta
									__a        =with_conta
									__a        =mc_label
						__a        =(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        UpperCamelCase_(							):




			"""simple docstring"""






			__a        =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=6.2_5e-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.'	)
			__a        =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	)

			__a        =torch.device('cuda' if torch.cuda.is_available() else 'cpu'	)
			__a        =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
			__a        =['_start_', '_delimiter_', '_classify_']
			__a        =OpenAIGPTTokenizer.from_pretrained(args.model_name	)
			tokenizer.add_tokens(_snake_case	)
			__a        =tokenizer.convert_tokens_to_ids(_snake_case	)
			__a        =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   :  int	):
						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...'	)
			__a        =load_rocstories_dataset(args.train_dataset	)
			__a        =load_rocstories_dataset(args.eval_dataset	)
			__a        =(train_dataset, eval_dataset)
			__a        =tokenize_and_encode(_snake_case	)

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

			# Prepare inputs tensors and dataloaders
			__a        =pre_process_datasets(_snake_case       ,     _snake_case       ,     _snake_case       ,     *_snake_case	)
			__a      ,    __a        =tensor_datasets[0], tensor_datasets[1]

			__a        =TensorDataset(*_snake_case	)
			__a        =RandomSampler(_snake_case	)
			__a        =DataLoader(_snake_case       ,     sampler=_snake_case       ,     batch_size=args.train_batch_size	)

			__a        =TensorDataset(*_snake_case	)
			__a        =SequentialSampler(_snake_case	)
			__a        =DataLoader(_snake_case       ,     sampler=_snake_case       ,     batch_size=args.eval_batch_size	)

			# Prepare optimizer
			if args.do_train:
						if args.max_steps > 0:
									__a        =args.max_steps
									__a        =args.max_steps // (len(_snake_case	) // args.gradient_accumulation_steps) + 1
						else:
									__a        =len(_snake_case	) // args.gradient_accumulation_steps * args.num_train_epochs

						__a        =list(model.named_parameters()	)
						__a        =['bias', 'LayerNorm.bias', 'LayerNorm.weight']
						__a        =[
						    {
						        '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},
						]
						__a        =AdamW(_snake_case       ,     lr=args.learning_rate       ,     eps=args.adam_epsilon	)
						__a        =get_linear_schedule_with_warmup(
						    _snake_case       ,     num_warmup_steps=args.warmup_steps       ,     num_training_steps=_snake_case	)

			if args.do_train:
						__a      ,    __a      ,    __a        =0, 0, None
						model.train()
						for _ in trange(int(args.num_train_epochs	)       ,     desc='Epoch'	):
									__a        =0
									__a        =0
									__a        =tqdm(_snake_case       ,     desc='Training'	)
									for step, batch in enumerate(_snake_case	):
												__a        =tuple(t.to(_snake_case	) for t in batch	)
												__a      ,    __a      ,    __a      ,    __a        =batch
												__a        =model(_snake_case       ,     mc_token_ids=_snake_case       ,     lm_labels=_snake_case       ,     mc_labels=_snake_case	)
												__a        =args.lm_coef * losses[0] + losses[1]
												loss.backward()
												optimizer.step()
												scheduler.step()
												optimizer.zero_grad()
												tr_loss += loss.item()
												__a        =(
												    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
												)
												nb_tr_steps += 1
												__a        ='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
						__a        =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`
						__a        =os.path.join(args.output_dir       ,     _snake_case	)
						__a        =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
						__a        =OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir	)
						__a        =OpenAIGPTTokenizer.from_pretrained(args.output_dir	)
						model.to(_snake_case	)

			if args.do_eval:
						model.eval()
						__a      ,    __a        =0, 0
						__a      ,    __a        =0, 0
						for batch in tqdm(_snake_case       ,     desc='Evaluating'	):
									__a        =tuple(t.to(_snake_case	) for t in batch	)
									__a      ,    __a      ,    __a      ,    __a        =batch
									with torch.no_grad():
												__a      ,    __a      ,    __a      ,    __a        =model(
												    _snake_case       ,     mc_token_ids=_snake_case       ,     lm_labels=_snake_case       ,     mc_labels=_snake_case	)

									__a        =mc_logits.detach().cpu().numpy()
									__a        =mc_labels.to('cpu'	).numpy()
									__a        =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

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

						__a        =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()






