
"""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
)
A_							:			Any											= logging.getLogger(__name__)

def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      np.argmax(snake_case__		,				axis=1    )
			return np.sum(outputs == labels    )

def 	A       (     snake_case__    ):




			'''simple docstring'''


			with open(snake_case__		,				encoding="""utf_8"""    ) as f:
						SCREAMING_SNAKE_CASE__								=      csv.reader(snake_case__    )
						SCREAMING_SNAKE_CASE__								=      []
						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 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      []
			for dataset in encoded_datasets:
						SCREAMING_SNAKE_CASE__								=      len(snake_case__    )
						SCREAMING_SNAKE_CASE__								=      np.zeros((n_batch, 2, input_len)		,				dtype=np.intaa    )
						SCREAMING_SNAKE_CASE__								=      np.zeros((n_batch, 2)		,				dtype=np.intaa    )
						SCREAMING_SNAKE_CASE__								=      np.full((n_batch, 2, input_len)		,				fill_value=-1_00		,				dtype=np.intaa    )
						SCREAMING_SNAKE_CASE__								=      np.zeros((n_batch,)		,				dtype=np.intaa    )
						for (
						    i,
						    (story, conta, conta, mc_label),
						) in enumerate(snake_case__    ):
									SCREAMING_SNAKE_CASE__								=      [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									SCREAMING_SNAKE_CASE__								=      [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									SCREAMING_SNAKE_CASE__								=      with_conta
									SCREAMING_SNAKE_CASE__								=      with_conta
									SCREAMING_SNAKE_CASE__								=      len(snake_case__    ) - 1
									SCREAMING_SNAKE_CASE__								=      len(snake_case__    ) - 1
									SCREAMING_SNAKE_CASE__								=      with_conta
									SCREAMING_SNAKE_CASE__								=      with_conta
									SCREAMING_SNAKE_CASE__								=      mc_label
						SCREAMING_SNAKE_CASE__								=      (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 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      argparse.ArgumentParser()
			parser.add_argument("""--model_name"""		,				type=snake_case__		,				default="""openai-gpt"""		,				help="""pretrained model name"""    )
			parser.add_argument("""--do_train"""		,				action="""store_true"""		,				help="""Whether to run training."""    )
			parser.add_argument("""--do_eval"""		,				action="""store_true"""		,				help="""Whether to run eval on the dev set."""    )
			parser.add_argument(
			    """--output_dir"""		,				default=snake_case__		,				type=snake_case__		,				required=snake_case__		,				help="""The output directory where the model predictions and checkpoints will be written."""		,				)
			parser.add_argument("""--train_dataset"""		,				type=snake_case__		,				default=""""""    )
			parser.add_argument("""--eval_dataset"""		,				type=snake_case__		,				default=""""""    )
			parser.add_argument("""--seed"""		,				type=snake_case__		,				default=42    )
			parser.add_argument("""--num_train_epochs"""		,				type=snake_case__		,				default=3    )
			parser.add_argument("""--train_batch_size"""		,				type=snake_case__		,				default=8    )
			parser.add_argument("""--eval_batch_size"""		,				type=snake_case__		,				default=16    )
			parser.add_argument("""--adam_epsilon"""		,				default=1e-8		,				type=snake_case__		,				help="""Epsilon for Adam optimizer."""    )
			parser.add_argument("""--max_grad_norm"""		,				type=snake_case__		,				default=1    )
			parser.add_argument(
			    """--max_steps"""		,				default=-1		,				type=snake_case__		,				help=(
			        """If > 0: set total number of training                         steps to perform. Override num_train_epochs."""
			    )		,				)
			parser.add_argument(
			    """--gradient_accumulation_steps"""		,				type=snake_case__		,				default=1		,				help="""Number of updates steps to accumulate before                        performing a backward/update pass."""		,				)
			parser.add_argument("""--learning_rate"""		,				type=snake_case__		,				default=6.25e-5    )
			parser.add_argument("""--warmup_steps"""		,				default=0		,				type=snake_case__		,				help="""Linear warmup over warmup_steps."""    )
			parser.add_argument("""--lr_schedule"""		,				type=snake_case__		,				default="""warmup_linear"""    )
			parser.add_argument("""--weight_decay"""		,				type=snake_case__		,				default=0.01    )
			parser.add_argument("""--lm_coef"""		,				type=snake_case__		,				default=0.9    )
			parser.add_argument("""--n_valid"""		,				type=snake_case__		,				default=3_74    )

			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."""    )
			SCREAMING_SNAKE_CASE__								=      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    )

			SCREAMING_SNAKE_CASE__								=      torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""    )
			SCREAMING_SNAKE_CASE__								=      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
			SCREAMING_SNAKE_CASE__								=      ["""_start_""", """_delimiter_""", """_classify_"""]
			SCREAMING_SNAKE_CASE__								=      OpenAIGPTTokenizer.from_pretrained(args.model_name    )
			tokenizer.add_tokens(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      tokenizer.convert_tokens_to_ids(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      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__    ):
						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..."""    )
			SCREAMING_SNAKE_CASE__								=      load_rocstories_dataset(args.train_dataset    )
			SCREAMING_SNAKE_CASE__								=      load_rocstories_dataset(args.eval_dataset    )
			SCREAMING_SNAKE_CASE__								=      (train_dataset, eval_dataset)
			SCREAMING_SNAKE_CASE__								=      tokenize_and_encode(snake_case__    )

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

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

			SCREAMING_SNAKE_CASE__								=      TensorDataset(*snake_case__    )
			SCREAMING_SNAKE_CASE__								=      RandomSampler(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      DataLoader(snake_case__		,				sampler=snake_case__		,				batch_size=args.train_batch_size    )

			SCREAMING_SNAKE_CASE__								=      TensorDataset(*snake_case__    )
			SCREAMING_SNAKE_CASE__								=      SequentialSampler(snake_case__    )
			SCREAMING_SNAKE_CASE__								=      DataLoader(snake_case__		,				sampler=snake_case__		,				batch_size=args.eval_batch_size    )

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

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

			if args.do_train:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      0, 0, None
						model.train()
						for _ in trange(int(args.num_train_epochs    )		,				desc="""Epoch"""    ):
									SCREAMING_SNAKE_CASE__								=      0
									SCREAMING_SNAKE_CASE__								=      0
									SCREAMING_SNAKE_CASE__								=      tqdm(snake_case__		,				desc="""Training"""    )
									for step, batch in enumerate(snake_case__    ):
												SCREAMING_SNAKE_CASE__								=      tuple(t.to(snake_case__    ) for t in batch    )
												SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      batch
												SCREAMING_SNAKE_CASE__								=      model(snake_case__		,				mc_token_ids=snake_case__		,				lm_labels=snake_case__		,				mc_labels=snake_case__    )
												SCREAMING_SNAKE_CASE__								=      args.lm_coef * losses[0] + losses[1]
												loss.backward()
												optimizer.step()
												scheduler.step()
												optimizer.zero_grad()
												tr_loss += loss.item()
												SCREAMING_SNAKE_CASE__								=      (
												    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
												)
												nb_tr_steps += 1
												SCREAMING_SNAKE_CASE__								=      """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
						SCREAMING_SNAKE_CASE__								=      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`
						SCREAMING_SNAKE_CASE__								=      os.path.join(args.output_dir		,				snake_case__    )
						SCREAMING_SNAKE_CASE__								=      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
						SCREAMING_SNAKE_CASE__								=      OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir    )
						SCREAMING_SNAKE_CASE__								=      OpenAIGPTTokenizer.from_pretrained(args.output_dir    )
						model.to(snake_case__    )

			if args.do_eval:
						model.eval()
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      0, 0
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      0, 0
						for batch in tqdm(snake_case__		,				desc="""Evaluating"""    ):
									SCREAMING_SNAKE_CASE__								=      tuple(t.to(snake_case__    ) for t in batch    )
									SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      batch
									with torch.no_grad():
												SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      model(
												    snake_case__		,				mc_token_ids=snake_case__		,				lm_labels=snake_case__		,				mc_labels=snake_case__    )

									SCREAMING_SNAKE_CASE__								=      mc_logits.detach().cpu().numpy()
									SCREAMING_SNAKE_CASE__								=      mc_labels.to("""cpu"""    ).numpy()
									SCREAMING_SNAKE_CASE__								=      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

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

						SCREAMING_SNAKE_CASE__								=      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()





"""simple docstring"""




import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
A_							:			Tuple											= logging.getLogger(__name__)


A_							:			Tuple											= "Hello world! cécé herlolip"


A_							:			Dict											= namedtuple(
    "BertAbsConfig",
    [
        "temp_dir",
        "large",
        "use_bert_emb",
        "finetune_bert",
        "encoder",
        "share_emb",
        "max_pos",
        "enc_layers",
        "enc_hidden_size",
        "enc_heads",
        "enc_ff_size",
        "enc_dropout",
        "dec_layers",
        "dec_hidden_size",
        "dec_heads",
        "dec_ff_size",
        "dec_dropout",
    ],
)

def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      BertAbsConfig(
			    temp_dir="""."""		,				finetune_bert=snake_case__		,				large=snake_case__		,				share_emb=snake_case__		,				use_bert_emb=snake_case__		,				encoder="""bert"""		,				max_pos=5_12		,				enc_layers=6		,				enc_hidden_size=5_12		,				enc_heads=8		,				enc_ff_size=5_12		,				enc_dropout=0.2		,				dec_layers=6		,				dec_hidden_size=7_68		,				dec_heads=8		,				dec_ff_size=20_48		,				dec_dropout=0.2		,				)
			SCREAMING_SNAKE_CASE__								=      torch.load(snake_case__		,				lambda snake_case__		,				snake_case__    : storage    )
			SCREAMING_SNAKE_CASE__								=      AbsSummarizer(snake_case__		,				torch.device("""cpu"""    )		,				snake_case__    )
			original.eval()

			SCREAMING_SNAKE_CASE__								=      BertAbsSummarizer(snake_case__		,				torch.device("""cpu"""    )    )
			new_model.eval()

			# -------------------
			# Convert the weights
			# -------------------

			logging.info("""convert the model"""    )
			new_model.bert.load_state_dict(original.bert.state_dict()    )
			new_model.decoder.load_state_dict(original.decoder.state_dict()    )
			new_model.generator.load_state_dict(original.generator.state_dict()    )

			# ----------------------------------
			# Make sure the outpus are identical
			# ----------------------------------

			logging.info("""Make sure that the models' outputs are identical"""    )
			SCREAMING_SNAKE_CASE__								=      BertTokenizer.from_pretrained("""bert-base-uncased"""    )

			# prepare the model inputs
			SCREAMING_SNAKE_CASE__								=      tokenizer.encode("""This is sample éàalj'-."""    )
			encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(snake_case__    ))    )
			SCREAMING_SNAKE_CASE__								=      torch.tensor(snake_case__    ).unsqueeze(0    )
			SCREAMING_SNAKE_CASE__								=      tokenizer.encode("""This is sample 3 éàalj'-."""    )
			decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(snake_case__    ))    )
			SCREAMING_SNAKE_CASE__								=      torch.tensor(snake_case__    ).unsqueeze(0    )

			# failsafe to make sure the weights reset does not affect the
			# loaded weights.
			assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight    )    ) == 0

			# forward pass
			SCREAMING_SNAKE_CASE__								=      encoder_input_ids
			SCREAMING_SNAKE_CASE__								=      decoder_input_ids
			SCREAMING_SNAKE_CASE__								=      SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      SCREAMING_SNAKE_CASE__								=      None
			SCREAMING_SNAKE_CASE__								=      None

			# The original model does not apply the geneator layer immediatly but rather in
			# the beam search (where it combines softmax + linear layer). Since we already
			# apply the softmax in our generation process we only apply the linear layer here.
			# We make sure that the outputs of the full stack are identical
			SCREAMING_SNAKE_CASE__								=      original(snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )[0]
			SCREAMING_SNAKE_CASE__								=      original.generator(snake_case__    )

			SCREAMING_SNAKE_CASE__								=      new_model(
			    snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__    )[0]
			SCREAMING_SNAKE_CASE__								=      new_model.generator(snake_case__    )

			SCREAMING_SNAKE_CASE__								=      torch.max(torch.abs(output_converted_model - output_original_model    )    ).item()
			print("""Maximum absolute difference beween weights: {:.2f}""".format(snake_case__    )    )
			SCREAMING_SNAKE_CASE__								=      torch.max(torch.abs(output_converted_generator - output_original_generator    )    ).item()
			print("""Maximum absolute difference beween weights: {:.2f}""".format(snake_case__    )    )

			SCREAMING_SNAKE_CASE__								=      torch.allclose(snake_case__		,				snake_case__		,				atol=1e-3    )
			if are_identical:
						logging.info("""all weights are equal up to 1e-3"""    )
			else:
						raise ValueError("""the weights are different. The new model is likely different from the original one."""    )

			# The model has been saved with torch.save(model) and this is bound to the exact
			# directory structure. We save the state_dict instead.
			logging.info("""saving the model's state dictionary"""    )
			torch.save(
			    new_model.state_dict()		,				"""./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"""    )


if __name__ == "__main__":
						A_							:			Dict											= argparse.ArgumentParser()
						parser.add_argument(
						    "--bertabs_checkpoint_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path the official PyTorch dump.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default=None,
						    type=str,
						    required=True,
						    help="Path to the output PyTorch model.",
						)
						A_							:			Tuple											= parser.parse_args()

						convert_bertabs_checkpoints(
						    args.bertabs_checkpoint_path,
						    args.pytorch_dump_folder_path,
						)


