
"""simple docstring"""




import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


snake_case			       =		logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
					if "token" in model_name_or_path:
										return "rag_token"
					if "sequence" in model_name_or_path:
										return "rag_sequence"
					if "bart" in model_name_or_path:
										return "bart"
					return None

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					return max(metric_fn(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ) for gt in ground_truths      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     [line.strip() for line in open(SCREAMING_SNAKE_CASE_,					'r'      ).readlines()]
					SCREAMING_SNAKE_CASE           =     []

					if args.gold_data_mode == "qa":
										SCREAMING_SNAKE_CASE           =     pd.read_csv(SCREAMING_SNAKE_CASE_,					sep='\t',					header=SCREAMING_SNAKE_CASE_      )
										for answer_list in data[1]:
															SCREAMING_SNAKE_CASE           =     ast.literal_eval(SCREAMING_SNAKE_CASE_      )
															answers.append(SCREAMING_SNAKE_CASE_      )
					else:
										SCREAMING_SNAKE_CASE           =     [line.strip() for line in open(SCREAMING_SNAKE_CASE_,					'r'      ).readlines()]
										SCREAMING_SNAKE_CASE           =     [[reference] for reference in references]

					SCREAMING_SNAKE_CASE           =     SCREAMING_SNAKE_CASE           =     SCREAMING_SNAKE_CASE           =     0
					for prediction, ground_truths in zip(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
										total += 1
										em += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )
										fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )

					SCREAMING_SNAKE_CASE           =     100.0 * em / total
					SCREAMING_SNAKE_CASE           =     100.0 * fa / total

					logger.info(f'''F1: {fa:.2f}'''      )
					logger.info(f'''EM: {em:.2f}'''      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     args.k
					SCREAMING_SNAKE_CASE           =     [line.strip() for line in open(SCREAMING_SNAKE_CASE_,					'r'      ).readlines()]
					SCREAMING_SNAKE_CASE           =     [line.strip() for line in open(SCREAMING_SNAKE_CASE_,					'r'      ).readlines()]

					SCREAMING_SNAKE_CASE           =     SCREAMING_SNAKE_CASE           =     0
					for hypo, reference in zip(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
										SCREAMING_SNAKE_CASE           =     set(hypo.split('\t'      )[:k]      )
										SCREAMING_SNAKE_CASE           =     set(reference.split('\t'      )      )
										total += 1
										em += len(hypo_provenance & ref_provenance      ) / k

					SCREAMING_SNAKE_CASE           =     100.0 * em / total
					logger.info(f'''Precision@{k}: {em: .2f}'''      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					def strip_title(SCREAMING_SNAKE_CASE_      ):
										if title.startswith('"'      ):
															SCREAMING_SNAKE_CASE           =     title[1:]
										if title.endswith('"'      ):
															SCREAMING_SNAKE_CASE           =     title[:-1]
										return title

					SCREAMING_SNAKE_CASE           =     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
					    SCREAMING_SNAKE_CASE_,					return_tensors='pt',					padding=SCREAMING_SNAKE_CASE_,					truncation=SCREAMING_SNAKE_CASE_,					)['input_ids'].to(args.device      )

					SCREAMING_SNAKE_CASE           =     rag_model.rag.question_encoder(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     question_enc_outputs[0]

					SCREAMING_SNAKE_CASE           =     rag_model.retriever(
					    SCREAMING_SNAKE_CASE_,					question_enc_pool_output.cpu().detach().to(torch.floataa      ).numpy(),					prefix=rag_model.rag.generator.config.prefix,					n_docs=rag_model.config.n_docs,					return_tensors='pt',					)
					SCREAMING_SNAKE_CASE           =     rag_model.retriever.index.get_doc_dicts(result.doc_ids      )
					SCREAMING_SNAKE_CASE           =     []
					for docs in all_docs:
										SCREAMING_SNAKE_CASE           =     [strip_title(SCREAMING_SNAKE_CASE_      ) for title in docs['title']]
										provenance_strings.append('\t'.join(SCREAMING_SNAKE_CASE_      )      )
					return provenance_strings

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					with torch.no_grad():
										SCREAMING_SNAKE_CASE           =     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
										    SCREAMING_SNAKE_CASE_,					return_tensors='pt',					padding=SCREAMING_SNAKE_CASE_,					truncation=SCREAMING_SNAKE_CASE_      )

										SCREAMING_SNAKE_CASE           =     inputs_dict.input_ids.to(args.device      )
										SCREAMING_SNAKE_CASE           =     inputs_dict.attention_mask.to(args.device      )
										SCREAMING_SNAKE_CASE           =     rag_model.generate(  # rag_model overwrites generate
										    SCREAMING_SNAKE_CASE_,					attention_mask=SCREAMING_SNAKE_CASE_,					num_beams=args.num_beams,					min_length=args.min_length,					max_length=args.max_length,					early_stopping=SCREAMING_SNAKE_CASE_,					num_return_sequences=1,					bad_words_ids=[[0, 0]],					)
										SCREAMING_SNAKE_CASE           =     rag_model.retriever.generator_tokenizer.batch_decode(SCREAMING_SNAKE_CASE_,					skip_special_tokens=SCREAMING_SNAKE_CASE_      )

										if args.print_predictions:
															for q, a in zip(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
																				logger.info('Q: {} - A: {}'.format(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )      )

										return answers

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE           =     argparse.ArgumentParser()
					parser.add_argument(
					    '--model_type',					choices=['rag_sequence', 'rag_token', 'bart'],					type=SCREAMING_SNAKE_CASE_,					help=(
					        'RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'
					        ' model_name_or_path'
					    ),					)
					parser.add_argument(
					    '--index_name',					default=SCREAMING_SNAKE_CASE_,					choices=['exact', 'compressed', 'legacy'],					type=SCREAMING_SNAKE_CASE_,					help='RAG model retriever type',					)
					parser.add_argument(
					    '--index_path',					default=SCREAMING_SNAKE_CASE_,					type=SCREAMING_SNAKE_CASE_,					help='Path to the retrieval index',					)
					parser.add_argument('--n_docs',					default=5,					type=SCREAMING_SNAKE_CASE_,					help='Number of retrieved docs'      )
					parser.add_argument(
					    '--model_name_or_path',					default=SCREAMING_SNAKE_CASE_,					type=SCREAMING_SNAKE_CASE_,					required=SCREAMING_SNAKE_CASE_,					help='Path to pretrained checkpoints or model identifier from huggingface.co/models',					)
					parser.add_argument(
					    '--eval_mode',					choices=['e2e', 'retrieval'],					default='e2e',					type=SCREAMING_SNAKE_CASE_,					help=(
					        'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
					        ' precision@k.'
					    ),					)
					parser.add_argument('--k',					default=1,					type=SCREAMING_SNAKE_CASE_,					help='k for the precision@k calculation'      )
					parser.add_argument(
					    '--evaluation_set',					default=SCREAMING_SNAKE_CASE_,					type=SCREAMING_SNAKE_CASE_,					required=SCREAMING_SNAKE_CASE_,					help='Path to a file containing evaluation samples',					)
					parser.add_argument(
					    '--gold_data_path',					default=SCREAMING_SNAKE_CASE_,					type=SCREAMING_SNAKE_CASE_,					required=SCREAMING_SNAKE_CASE_,					help='Path to a tab-separated file with gold samples',					)
					parser.add_argument(
					    '--gold_data_mode',					default='qa',					type=SCREAMING_SNAKE_CASE_,					choices=['qa', 'ans'],					help=(
					        'Format of the gold data file'
					        'qa - a single line in the following format: question [tab] answer_list'
					        'ans - a single line of the gold file contains the expected answer string'
					    ),					)
					parser.add_argument(
					    '--predictions_path',					type=SCREAMING_SNAKE_CASE_,					default='predictions.txt',					help='Name of the predictions file, to be stored in the checkpoints directory',					)
					parser.add_argument(
					    '--eval_all_checkpoints',					action='store_true',					help='Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number',					)
					parser.add_argument(
					    '--eval_batch_size',					default=8,					type=SCREAMING_SNAKE_CASE_,					help='Batch size per GPU/CPU for evaluation.',					)
					parser.add_argument(
					    '--recalculate',					help='Recalculate predictions even if the prediction file exists',					action='store_true',					)
					parser.add_argument(
					    '--num_beams',					default=4,					type=SCREAMING_SNAKE_CASE_,					help='Number of beams to be used when generating answers',					)
					parser.add_argument('--min_length',					default=1,					type=SCREAMING_SNAKE_CASE_,					help='Min length of the generated answers'      )
					parser.add_argument('--max_length',					default=5_0,					type=SCREAMING_SNAKE_CASE_,					help='Max length of the generated answers'      )

					parser.add_argument(
					    '--print_predictions',					action='store_true',					help='If True, prints predictions while evaluating.',					)
					parser.add_argument(
					    '--print_docs',					action='store_true',					help='If True, prints docs retried while generating.',					)
					SCREAMING_SNAKE_CASE           =     parser.parse_args()
					SCREAMING_SNAKE_CASE           =     torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )
					return args

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     {}
					if args.model_type is None:
										SCREAMING_SNAKE_CASE           =     infer_model_type(args.model_name_or_path      )
										assert args.model_type is not None
					if args.model_type.startswith('rag'      ):
										SCREAMING_SNAKE_CASE           =     RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
										SCREAMING_SNAKE_CASE           =     args.n_docs
										if args.index_name is not None:
															SCREAMING_SNAKE_CASE           =     args.index_name
										if args.index_path is not None:
															SCREAMING_SNAKE_CASE           =     args.index_path
					else:
										SCREAMING_SNAKE_CASE           =     BartForConditionalGeneration

					SCREAMING_SNAKE_CASE           =     (
					    [f.path for f in os.scandir(args.model_name_or_path      ) if f.is_dir()]
					    if args.eval_all_checkpoints
					    else [args.model_name_or_path]
					)

					logger.info('Evaluate the following checkpoints: %s',					SCREAMING_SNAKE_CASE_      )

					SCREAMING_SNAKE_CASE           =     get_scores if args.eval_mode == 'e2e' else get_precision_at_k
					SCREAMING_SNAKE_CASE           =     evaluate_batch_eae if args.eval_mode == 'e2e' else evaluate_batch_retrieval

					for checkpoint in checkpoints:
										if os.path.exists(args.predictions_path      ) and (not args.recalculate):
															logger.info('Calculating metrics based on an existing predictions file: {}'.format(args.predictions_path      )      )
															score_fn(SCREAMING_SNAKE_CASE_,					args.predictions_path,					args.gold_data_path      )
															continue

										logger.info('***** Running evaluation for {} *****'.format(SCREAMING_SNAKE_CASE_      )      )
										logger.info('  Batch size = %d',					args.eval_batch_size      )
										logger.info('  Predictions will be stored under {}'.format(args.predictions_path      )      )

										if args.model_type.startswith('rag'      ):
															SCREAMING_SNAKE_CASE           =     RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE_,					**SCREAMING_SNAKE_CASE_      )
															SCREAMING_SNAKE_CASE           =     model_class.from_pretrained(SCREAMING_SNAKE_CASE_,					retriever=SCREAMING_SNAKE_CASE_,					**SCREAMING_SNAKE_CASE_      )
															model.retriever.init_retrieval()
										else:
															SCREAMING_SNAKE_CASE           =     model_class.from_pretrained(SCREAMING_SNAKE_CASE_,					**SCREAMING_SNAKE_CASE_      )
										model.to(args.device      )

										with open(args.evaluation_set,					'r'      ) as eval_file, open(args.predictions_path,					'w'      ) as preds_file:
															SCREAMING_SNAKE_CASE           =     []
															for line in tqdm(SCREAMING_SNAKE_CASE_      ):
																				questions.append(line.strip()      )
																				if len(SCREAMING_SNAKE_CASE_      ) == args.eval_batch_size:
																									SCREAMING_SNAKE_CASE           =     evaluate_batch_fn(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )
																									preds_file.write('\n'.join(SCREAMING_SNAKE_CASE_      ) + '\n'      )
																									preds_file.flush()
																									SCREAMING_SNAKE_CASE           =     []
															if len(SCREAMING_SNAKE_CASE_      ) > 0:
																				SCREAMING_SNAKE_CASE           =     evaluate_batch_fn(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )
																				preds_file.write('\n'.join(SCREAMING_SNAKE_CASE_      )      )
																				preds_file.flush()

															score_fn(SCREAMING_SNAKE_CASE_,					args.predictions_path,					args.gold_data_path      )


if __name__ == "__main__":
	snake_case			       =		get_args()
	main(args)


"""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
