






from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class     snake_case_			(				__lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase							):


							"""simple docstring"""





							__UpperCAmelCase       =[r"h\.\d+\.attn\.bias", r"h\.\d+\.attn\.masked_bias"]




							@register_to_config
							def __init__( self					,						_A					,						_A					,						_A = None					,						_A = 5_0_2_5_7					,						_A = 1_0_2_4					,						_A = 7_6_8					,						_A = 1_2					,						_A = 1_2					,						_A = None					,						_A = "gelu_new"					,						_A = 0.1					,						_A = 0.1					,						_A = 0.1					,						_A = 1e-5					,						_A = 0.02					,						_A = True					,						_A = True					,						_A = False					,						_A = False					,						):
												super().__init__()

												__lowerCAmelCase					=							prefix_length

												if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
																	raise ValueError(
																	    F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
																	    F""" `n_embd`: {n_embd} are not equal."""							)

												__lowerCAmelCase					=							prefix_inner_dim
												__lowerCAmelCase					=							prefix_hidden_dim

												__lowerCAmelCase					=							(
												    nn.Linear(self.prefix_inner_dim					,						self.prefix_hidden_dim							)
												    if self.prefix_hidden_dim is not None
												    else nn.Identity()
												)
												__lowerCAmelCase					=							(
												    nn.Linear(self.prefix_hidden_dim					,						lowerCamelCase__							) if self.prefix_hidden_dim is not None else nn.Identity()
												)

												__lowerCAmelCase					=							GPTaConfig(
												    vocab_size=lowerCamelCase__					,						n_positions=lowerCamelCase__					,						n_embd=lowerCamelCase__					,						n_layer=lowerCamelCase__					,						n_head=lowerCamelCase__					,						n_inner=lowerCamelCase__					,						activation_function=lowerCamelCase__					,						resid_pdrop=lowerCamelCase__					,						embd_pdrop=lowerCamelCase__					,						attn_pdrop=lowerCamelCase__					,						layer_norm_epsilon=lowerCamelCase__					,						initializer_range=lowerCamelCase__					,						scale_attn_weights=lowerCamelCase__					,						use_cache=lowerCamelCase__					,						scale_attn_by_inverse_layer_idx=lowerCamelCase__					,						reorder_and_upcast_attn=lowerCamelCase__					,						)
												__lowerCAmelCase					=							GPTaLMHeadModel(lowerCamelCase__							)




							def        A__  ( self					,						_A					,						_A					,						_A = None					,						_A = None					,						):
												__lowerCAmelCase					=							self.transformer.transformer.wte(lowerCamelCase__							)
												__lowerCAmelCase					=							self.encode_prefix(lowerCamelCase__							)
												__lowerCAmelCase					=							self.decode_prefix(lowerCamelCase__							)
												__lowerCAmelCase					=							torch.cat((prefix_embeds, embedding_text)					,						dim=1							)

												if labels is not None:
																	__lowerCAmelCase					=							self.get_dummy_token(input_ids.shape[0]					,						input_ids.device							)
																	__lowerCAmelCase					=							torch.cat((dummy_token, input_ids)					,						dim=1							)
												__lowerCAmelCase					=							self.transformer(inputs_embeds=lowerCamelCase__					,						labels=lowerCamelCase__					,						attention_mask=lowerCamelCase__							)
												if self.prefix_hidden_dim is not None:
																	return out, hidden
												else:
																	return out




							def        A__  ( self					,						_A					,						_A							):
												return torch.zeros(lowerCamelCase__					,						self.prefix_length					,						dtype=torch.intaa					,						device=lowerCamelCase__							)




							def        A__  ( self					,						_A							):
												return self.encode_prefix(lowerCamelCase__							)




							@torch.no_grad()
							def        A__  ( self					,						_A					,						_A					,						_A							):
												__lowerCAmelCase					=							torch.split(lowerCamelCase__					,						1					,						dim=0							)
												__lowerCAmelCase					=							[]
												__lowerCAmelCase					=							[]
												for feature in features:
																	__lowerCAmelCase					=							self.decode_prefix(feature.to(lowerCamelCase__							)							)  # back to the clip feature
																	# Only support beam search for now
																	__lowerCAmelCase					=							self.generate_beam(
																	    input_embeds=lowerCamelCase__					,						device=lowerCamelCase__					,						eos_token_id=lowerCamelCase__							)
																	generated_tokens.append(output_tokens[0]							)
																	generated_seq_lengths.append(seq_lengths[0]							)
												__lowerCAmelCase					=							torch.stack(lowerCamelCase__							)
												__lowerCAmelCase					=							torch.stack(lowerCamelCase__							)
												return generated_tokens, generated_seq_lengths




							@torch.no_grad()
							def        A__  ( self					,						_A=None					,						_A=None					,						_A=None					,						_A = 5					,						_A = 6_7					,						_A = 1.0					,						_A = None					,						):
												__lowerCAmelCase					=							eos_token_id
												__lowerCAmelCase					=							None
												__lowerCAmelCase					=							None
												__lowerCAmelCase					=							torch.ones(lowerCamelCase__					,						device=lowerCamelCase__					,						dtype=torch.int							)
												__lowerCAmelCase					=							torch.zeros(lowerCamelCase__					,						device=lowerCamelCase__					,						dtype=torch.bool							)

												if input_embeds is not None:
																	__lowerCAmelCase					=							input_embeds
												else:
																	__lowerCAmelCase					=							self.transformer.transformer.wte(lowerCamelCase__							)

												for i in range(lowerCamelCase__							):
																	__lowerCAmelCase					=							self.transformer(inputs_embeds=lowerCamelCase__							)
																	__lowerCAmelCase					=							outputs.logits
																	__lowerCAmelCase					=							logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
																	__lowerCAmelCase					=							logits.softmax(-1							).log()

																	if scores is None:
																						__lowerCAmelCase					=							logits.topk(lowerCamelCase__					,						-1							)
																						__lowerCAmelCase					=							generated.expand(lowerCamelCase__					,						*generated.shape[1:]							)
																						__lowerCAmelCase					=							next_tokens.permute(1					,						0							), scores.squeeze(0							)
																						if tokens is None:
																											__lowerCAmelCase					=							next_tokens
																						else:
																											__lowerCAmelCase					=							tokens.expand(lowerCamelCase__					,						*tokens.shape[1:]							)
																											__lowerCAmelCase					=							torch.cat((tokens, next_tokens)					,						dim=1							)
																	else:
																						__lowerCAmelCase					=							-float(np.inf							)
																						__lowerCAmelCase					=							0
																						__lowerCAmelCase					=							scores[:, None] + logits
																						seq_lengths[~is_stopped] += 1
																						__lowerCAmelCase					=							scores_sum / seq_lengths[:, None]
																						__lowerCAmelCase					=							scores_sum_average.view(-1							).topk(lowerCamelCase__					,						-1							)
																						__lowerCAmelCase					=							next_tokens // scores_sum.shape[1]
																						__lowerCAmelCase					=							seq_lengths[next_tokens_source]
																						__lowerCAmelCase					=							next_tokens % scores_sum.shape[1]
																						__lowerCAmelCase					=							next_tokens.unsqueeze(1							)
																						__lowerCAmelCase					=							tokens[next_tokens_source]
																						__lowerCAmelCase					=							torch.cat((tokens, next_tokens)					,						dim=1							)
																						__lowerCAmelCase					=							generated[next_tokens_source]
																						__lowerCAmelCase					=							scores_sum_average * seq_lengths
																						__lowerCAmelCase					=							is_stopped[next_tokens_source]

																	__lowerCAmelCase					=							self.transformer.transformer.wte(next_tokens.squeeze()							).view(generated.shape[0]					,						1					,						-1							)
																	__lowerCAmelCase					=							torch.cat((generated, next_token_embed)					,						dim=1							)
																	__lowerCAmelCase					=							is_stopped + next_tokens.eq(lowerCamelCase__							).squeeze()
																	if is_stopped.all():
																						break

												__lowerCAmelCase					=							scores / seq_lengths
												__lowerCAmelCase					=							scores.argsort(descending=lowerCamelCase__							)
												# tokens tensors are already padded to max_seq_length
												__lowerCAmelCase					=							[tokens[i] for i in order]
												__lowerCAmelCase					=							torch.stack(lowerCamelCase__					,						dim=0							)
												__lowerCAmelCase					=							torch.tensor([seq_lengths[i] for i in order]					,						dtype=seq_lengths.dtype							)
												return output_texts, seq_lengths









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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							np.argmax(UpperCAmelCase__		,					axis=1      )
					return np.sum(outputs == labels      )


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					with open(UpperCAmelCase__		,					encoding='utf_8'      ) as f:
										__lowerCAmelCase					=							csv.reader(UpperCAmelCase__      )
										__lowerCAmelCase					=							[]
										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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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

					"""simple docstring"""






					__lowerCAmelCase					=							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=42      )
					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=16      )
					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=374      )

					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.'      )
					__lowerCAmelCase					=							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      )

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

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

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

					__lowerCAmelCase					=							TensorDataset(*UpperCAmelCase__      )
					__lowerCAmelCase					=							RandomSampler(UpperCAmelCase__      )
					__lowerCAmelCase					=							DataLoader(UpperCAmelCase__		,					sampler=UpperCAmelCase__		,					batch_size=args.train_batch_size      )

					__lowerCAmelCase					=							TensorDataset(*UpperCAmelCase__      )
					__lowerCAmelCase					=							SequentialSampler(UpperCAmelCase__      )
					__lowerCAmelCase					=							DataLoader(UpperCAmelCase__		,					sampler=UpperCAmelCase__		,					batch_size=args.eval_batch_size      )

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

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

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

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

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

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

										__lowerCAmelCase					=							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()






