
"""simple docstring"""




import argparse
import logging
import os
import sys
from collections import namedtuple

import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm

from transformers import BertTokenizer

from .utils_summarization import (
    CNNDMDataset,
    build_mask,
    compute_token_type_ids,
    encode_for_summarization,
    truncate_or_pad,
)


snake_case					  =						logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


snake_case					  =						namedtuple('Batch', ['document_names', 'batch_size', 'src', 'segs', 'mask_src', 'tgt_str'])

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      BertTokenizer.from_pretrained('bert-base-uncased',	do_lower_case=SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'     )
					model.to(args.device     )
					model.eval()

					SCREAMING_SNAKE_CASE           =      {
					    'BOS': tokenizer.vocab['[unused0]'],
					    'EOS': tokenizer.vocab['[unused1]'],
					    'PAD': tokenizer.vocab['[PAD]'],
					}

					if args.compute_rouge:
										SCREAMING_SNAKE_CASE           =      []
										SCREAMING_SNAKE_CASE           =      []

										import nltk
										import rouge

										nltk.download('punkt'     )
										SCREAMING_SNAKE_CASE           =      rouge.Rouge(
										    metrics=['rouge-n', 'rouge-l'],	max_n=2,	limit_length=SCREAMING_SNAKE_CASE_,	length_limit=args.beam_size,	length_limit_type='words',	apply_avg=SCREAMING_SNAKE_CASE_,	apply_best=SCREAMING_SNAKE_CASE_,	alpha=0.5,	weight_factor=1.2,	stemming=SCREAMING_SNAKE_CASE_,	)

					# these (unused) arguments are defined to keep the compatibility
					# with the legacy code and will be deleted in a next iteration.
					SCREAMING_SNAKE_CASE           =      ''
					SCREAMING_SNAKE_CASE           =      ''

					SCREAMING_SNAKE_CASE           =      build_data_iterator(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      build_predictor(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )

					logger.info('***** Running evaluation *****'     )
					logger.info('  Number examples = %d',	len(data_iterator.dataset     )     )
					logger.info('  Batch size = %d',	args.batch_size     )
					logger.info(''     )
					logger.info('***** Beam Search parameters *****'     )
					logger.info('  Beam size = %d',	args.beam_size     )
					logger.info('  Minimum length = %d',	args.min_length     )
					logger.info('  Maximum length = %d',	args.max_length     )
					logger.info('  Alpha (length penalty) = %.2f',	args.alpha     )
					logger.info('  Trigrams %s be blocked',	('will' if args.block_trigram else 'will NOT')     )

					for batch in tqdm(SCREAMING_SNAKE_CASE_     ):
										SCREAMING_SNAKE_CASE           =      predictor.translate_batch(SCREAMING_SNAKE_CASE_     )
										SCREAMING_SNAKE_CASE           =      predictor.from_batch(SCREAMING_SNAKE_CASE_     )
										SCREAMING_SNAKE_CASE           =      [format_summary(SCREAMING_SNAKE_CASE_     ) for t in translations]
										save_summaries(SCREAMING_SNAKE_CASE_,	args.summaries_output_dir,	batch.document_names     )

										if args.compute_rouge:
															reference_summaries += batch.tgt_str
															generated_summaries += summaries

					if args.compute_rouge:
										SCREAMING_SNAKE_CASE           =      rouge_evaluator.get_scores(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )
										SCREAMING_SNAKE_CASE           =      format_rouge_scores(SCREAMING_SNAKE_CASE_     )
										save_rouge_scores(SCREAMING_SNAKE_CASE_     )
										print(SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					for summary, document_name in zip(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
										# Prepare the summary file's name
										if "." in document_name:
															SCREAMING_SNAKE_CASE           =      '.'.join(document_name.split('.'     )[:-1]     )
															SCREAMING_SNAKE_CASE           =      document_name.split('.'     )[-1]
															SCREAMING_SNAKE_CASE           =      bare_document_name + '_summary.' + extension
										else:
															SCREAMING_SNAKE_CASE           =      document_name + '_summary'

										SCREAMING_SNAKE_CASE           =      os.path.join(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )
										with open(SCREAMING_SNAKE_CASE_,	'w'     ) as output:
															output.write(SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      translation
					SCREAMING_SNAKE_CASE           =      (
					    raw_summary.replace('[unused0]',	''     )
					    .replace('[unused3]',	''     )
					    .replace('[PAD]',	''     )
					    .replace('[unused1]',	''     )
					    .replace(R' +',	' '     )
					    .replace(' [unused2] ',	'. '     )
					    .replace('[unused2]',	''     )
					    .strip()
					)

					return summary

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					return """\n
****** ROUGE SCORES ******

** ROUGE 1
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE 2
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE L
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}""".format(
					    scores['rouge-1']['f'],	scores['rouge-1']['p'],	scores['rouge-1']['r'],	scores['rouge-2']['f'],	scores['rouge-2']['p'],	scores['rouge-2']['r'],	scores['rouge-l']['f'],	scores['rouge-l']['p'],	scores['rouge-l']['r'],	)

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					with open('rouge_scores.txt',	'w'     ) as output:
										output.write(SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      load_and_cache_examples(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      SequentialSampler(SCREAMING_SNAKE_CASE_     )

					def collate_fn(SCREAMING_SNAKE_CASE_     ):
										return collate(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	block_size=5_1_2,	device=args.device     )

					SCREAMING_SNAKE_CASE           =      DataLoader(
					    SCREAMING_SNAKE_CASE_,	sampler=SCREAMING_SNAKE_CASE_,	batch_size=args.batch_size,	collate_fn=SCREAMING_SNAKE_CASE_,	)

					return iterator

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      CNNDMDataset(args.documents_dir     )
					return dataset

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      [x for x in data if not len(x[1]     ) == 0]  # remove empty_files
					SCREAMING_SNAKE_CASE           =      [name for name, _, _ in data]
					SCREAMING_SNAKE_CASE           =      [' '.join(SCREAMING_SNAKE_CASE_     ) for _, _, summary_list in data]

					SCREAMING_SNAKE_CASE           =      [encode_for_summarization(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ) for _, story, summary in data]
					SCREAMING_SNAKE_CASE           =      torch.tensor(
					    [truncate_or_pad(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	tokenizer.pad_token_id     ) for story, _ in encoded_text]     )
					SCREAMING_SNAKE_CASE           =      compute_token_type_ids(SCREAMING_SNAKE_CASE_,	tokenizer.cls_token_id     )
					SCREAMING_SNAKE_CASE           =      build_mask(SCREAMING_SNAKE_CASE_,	tokenizer.pad_token_id     )

					SCREAMING_SNAKE_CASE           =      Batch(
					    document_names=SCREAMING_SNAKE_CASE_,	batch_size=len(SCREAMING_SNAKE_CASE_     ),	src=encoded_stories.to(SCREAMING_SNAKE_CASE_     ),	segs=encoder_token_type_ids.to(SCREAMING_SNAKE_CASE_     ),	mask_src=encoder_mask.to(SCREAMING_SNAKE_CASE_     ),	tgt_str=SCREAMING_SNAKE_CASE_,	)

					return batch

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      summary_tokens.to('cpu'     ).numpy()
					SCREAMING_SNAKE_CASE           =      tokenizer.decode(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      summary.split('.'     )
					SCREAMING_SNAKE_CASE           =      [s + '.' for s in sentences]
					return sentences

def     UpperCamelCase_    (						):
					SCREAMING_SNAKE_CASE           =      argparse.ArgumentParser()
					parser.add_argument(
					    '--documents_dir',	default=SCREAMING_SNAKE_CASE_,	type=SCREAMING_SNAKE_CASE_,	required=SCREAMING_SNAKE_CASE_,	help='The folder where the documents to summarize are located.',	)
					parser.add_argument(
					    '--summaries_output_dir',	default=SCREAMING_SNAKE_CASE_,	type=SCREAMING_SNAKE_CASE_,	required=SCREAMING_SNAKE_CASE_,	help='The folder in wich the summaries should be written. Defaults to the folder where the documents are',	)
					parser.add_argument(
					    '--compute_rouge',	default=SCREAMING_SNAKE_CASE_,	type=SCREAMING_SNAKE_CASE_,	required=SCREAMING_SNAKE_CASE_,	help='Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.',	)
					# EVALUATION options
					parser.add_argument(
					    '--no_cuda',	default=SCREAMING_SNAKE_CASE_,	type=SCREAMING_SNAKE_CASE_,	help='Whether to force the execution on CPU.',	)
					parser.add_argument(
					    '--batch_size',	default=4,	type=SCREAMING_SNAKE_CASE_,	help='Batch size per GPU/CPU for training.',	)
					# BEAM SEARCH arguments
					parser.add_argument(
					    '--min_length',	default=5_0,	type=SCREAMING_SNAKE_CASE_,	help='Minimum number of tokens for the summaries.',	)
					parser.add_argument(
					    '--max_length',	default=2_0_0,	type=SCREAMING_SNAKE_CASE_,	help='Maixmum number of tokens for the summaries.',	)
					parser.add_argument(
					    '--beam_size',	default=5,	type=SCREAMING_SNAKE_CASE_,	help='The number of beams to start with for each example.',	)
					parser.add_argument(
					    '--alpha',	default=0.95,	type=SCREAMING_SNAKE_CASE_,	help='The value of alpha for the length penalty in the beam search.',	)
					parser.add_argument(
					    '--block_trigram',	default=SCREAMING_SNAKE_CASE_,	type=SCREAMING_SNAKE_CASE_,	help='Whether to block the existence of repeating trigrams in the text generated by beam search.',	)
					SCREAMING_SNAKE_CASE           =      parser.parse_args()

					# Select device (distibuted not available)
					SCREAMING_SNAKE_CASE           =      torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'     )

					# Check the existence of directories
					if not args.summaries_output_dir:
										SCREAMING_SNAKE_CASE           =      args.documents_dir

					if not documents_dir_is_valid(args.documents_dir     ):
										raise FileNotFoundError(
										    'We could not find the directory you specified for the documents to summarize, or it was empty. Please'
										    ' specify a valid path.'     )
					os.makedirs(args.summaries_output_dir,	exist_ok=SCREAMING_SNAKE_CASE_     )

					evaluate(SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					if not os.path.exists(SCREAMING_SNAKE_CASE_     ):
										return False

					SCREAMING_SNAKE_CASE           =      os.listdir(SCREAMING_SNAKE_CASE_     )
					if len(SCREAMING_SNAKE_CASE_     ) == 0:
										return False

					return True


if __name__ == "__main__":
	main()


"""simple docstring"""




import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					# we don't care about the past rewards in this model

					SCREAMING_SNAKE_CASE           =      states.reshape(1,	-1,	model.config.state_dim     )
					SCREAMING_SNAKE_CASE           =      actions.reshape(1,	-1,	model.config.act_dim     )
					SCREAMING_SNAKE_CASE           =      returns_to_go.reshape(1,	-1,	1     )
					SCREAMING_SNAKE_CASE           =      timesteps.reshape(1,	-1     )

					if model.config.max_length is not None:
										SCREAMING_SNAKE_CASE           =      states[:, -model.config.max_length :]
										SCREAMING_SNAKE_CASE           =      actions[:, -model.config.max_length :]
										SCREAMING_SNAKE_CASE           =      returns_to_go[:, -model.config.max_length :]
										SCREAMING_SNAKE_CASE           =      timesteps[:, -model.config.max_length :]

										# pad all tokens to sequence length
										SCREAMING_SNAKE_CASE           =      torch.cat(
										    [torch.zeros(model.config.max_length - states.shape[1]     ), torch.ones(states.shape[1]     )]     )
										SCREAMING_SNAKE_CASE           =      attention_mask.to(dtype=torch.long,	device=states.device     ).reshape(1,	-1     )
										SCREAMING_SNAKE_CASE           =      torch.cat(
										    [
										        torch.zeros(
										            (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim),	device=states.device,	),
										        states,
										    ],	dim=1,	).to(dtype=torch.floataa     )
										SCREAMING_SNAKE_CASE           =      torch.cat(
										    [
										        torch.zeros(
										            (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim),	device=actions.device,	),
										        actions,
										    ],	dim=1,	).to(dtype=torch.floataa     )
										SCREAMING_SNAKE_CASE           =      torch.cat(
										    [
										        torch.zeros(
										            (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1),	device=returns_to_go.device,	),
										        returns_to_go,
										    ],	dim=1,	).to(dtype=torch.floataa     )
										SCREAMING_SNAKE_CASE           =      torch.cat(
										    [
										        torch.zeros(
										            (timesteps.shape[0], model.config.max_length - timesteps.shape[1]),	device=timesteps.device     ),
										        timesteps,
										    ],	dim=1,	).to(dtype=torch.long     )
					else:
										SCREAMING_SNAKE_CASE           =      None

					SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      model(
					    states=SCREAMING_SNAKE_CASE_,	actions=SCREAMING_SNAKE_CASE_,	rewards=SCREAMING_SNAKE_CASE_,	returns_to_go=SCREAMING_SNAKE_CASE_,	timesteps=SCREAMING_SNAKE_CASE_,	attention_mask=SCREAMING_SNAKE_CASE_,	return_dict=SCREAMING_SNAKE_CASE_,	)

					return action_preds[0, -1]


# build the environment

snake_case					  =						gym.make('Hopper-v3')
snake_case					  =						env.observation_space.shape[0]
snake_case					  =						env.action_space.shape[0]
snake_case					  =						1_0_0_0
snake_case					  =						'cuda'
snake_case					  =						1_0_0_0.0  # normalization for rewards/returns
snake_case					  =						3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
snake_case					  =						np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
snake_case					  =						np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
snake_case					  =						torch.from_numpy(state_mean).to(device=device)
snake_case					  =						torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
snake_case					  =						DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
snake_case					  =						model.to(device)
model.eval()

for ep in range(1_0):
	snake_case  ,     snake_case					  =						0, 0
	snake_case					  =						env.reset()
	snake_case					  =						torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
	snake_case					  =						torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
	snake_case					  =						torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
	snake_case					  =						torch.zeros(0, device=device, dtype=torch.floataa)

	snake_case					  =						torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
	for t in range(max_ep_len):
		env.render()
		# add padding
		snake_case					  =						torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
		snake_case					  =						torch.cat([rewards, torch.zeros(1, device=device)])

		snake_case					  =						get_action(
		    model,
		    (states.to(dtype=torch.floataa) - state_mean) / state_std,
		    actions.to(dtype=torch.floataa),
		    rewards.to(dtype=torch.floataa),
		    target_return.to(dtype=torch.floataa),
		    timesteps.to(dtype=torch.long),
		)
		snake_case					  =						action
		snake_case					  =						action.detach().cpu().numpy()

		snake_case  ,     snake_case  ,     snake_case  ,     snake_case					  =						env.step(action)

		snake_case					  =						torch.from_numpy(state).to(device=device).reshape(1, state_dim)
		snake_case					  =						torch.cat([states, cur_state], dim=0)
		snake_case					  =						reward

		snake_case					  =						target_return[0, -1] - (reward / scale)
		snake_case					  =						torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
		snake_case					  =						torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

		episode_return += reward
		episode_length += 1

		if done:
			break
