
"""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


"""simple docstring"""




import os
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


snake_case			       =		'block_records.npy'


snake_case			       =		logging.get_logger(__name__)

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					import tensorflow.compat.va as tf

					SCREAMING_SNAKE_CASE           =     tf.data.TFRecordDataset(SCREAMING_SNAKE_CASE_,					buffer_size=5_1_2 * 1_0_2_4 * 1_0_2_4      )
					SCREAMING_SNAKE_CASE           =     blocks_dataset.batch(SCREAMING_SNAKE_CASE_,					drop_remainder=SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     next(blocks_dataset.take(1      ).as_numpy_iterator()      )

					return np_record




class 					UpperCamelCase     :

				"""simple docstring"""



				def __init__(    self		,     lowercase__		,     lowercase__		,     lowercase__=2		,     lowercase__=1000		,     lowercase__=100		,     lowercase__=100000		,     )   ->   Dict:
									"""simple docstring"""







									from scann.scann_ops.py.scann_ops_pybind import builder as Builder

									SCREAMING_SNAKE_CASE           =     Builder(db=lowercase__		,     num_neighbors=lowercase__		,     distance_measure='dot_product'      )
									SCREAMING_SNAKE_CASE           =     builder.tree(
									    num_leaves=lowercase__		,     num_leaves_to_search=lowercase__		,     training_sample_size=lowercase__      )
									SCREAMING_SNAKE_CASE           =     builder.score_ah(dimensions_per_block=lowercase__      )

									SCREAMING_SNAKE_CASE           =     builder.build()




				def 							A (    self		,     lowercase__      )   ->   str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     self.searcher.search_batched(question_projection.detach().cpu()      )
									return retrieved_block_ids.astype('int64'      )






class 					UpperCamelCase     :

				"""simple docstring"""



				def __init__(    self		,     lowercase__		,     lowercase__      )   ->   Dict:
									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE           =     block_records
									SCREAMING_SNAKE_CASE           =     tokenizer


				def __call__(    self		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__=None		,     lowercase__="pt"      )   ->   Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     np.take(self.block_records		,     indices=lowercase__		,     axis=0      )

									SCREAMING_SNAKE_CASE           =     self.tokenizer.decode(question_input_ids[0]		,     skip_special_tokens=lowercase__      )

									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     []
									for retrieved_block in retrieved_blocks:
														text.append(lowercase__      )
														text_pair.append(retrieved_block.decode()      )

									SCREAMING_SNAKE_CASE           =     self.tokenizer(
									    lowercase__		,     lowercase__		,     padding=lowercase__		,     truncation=lowercase__		,     return_special_tokens_mask=lowercase__		,     max_length=lowercase__      )
									SCREAMING_SNAKE_CASE           =     concat_inputs.convert_to_tensors(lowercase__      )

									if answer_ids is not None:
														return self.block_has_answer(lowercase__		,     lowercase__      ) + (concat_inputs_tensors,)
									else:
														return (None, None, None, concat_inputs_tensors)


				@classmethod
				def 							A (    cls		,     lowercase__		,     *lowercase__		,     **lowercase__      )   ->   Optional[Any]:
									"""simple docstring"""







									if os.path.isdir(lowercase__      ):
														SCREAMING_SNAKE_CASE           =     os.path.join(lowercase__		,     _REALM_BLOCK_RECORDS_FILENAME      )
									else:
														SCREAMING_SNAKE_CASE           =     hf_hub_download(
														    repo_id=lowercase__		,     filename=_REALM_BLOCK_RECORDS_FILENAME		,     **lowercase__      )
									SCREAMING_SNAKE_CASE           =     np.load(lowercase__		,     allow_pickle=lowercase__      )

									SCREAMING_SNAKE_CASE           =     AutoTokenizer.from_pretrained(lowercase__		,     *lowercase__		,     **lowercase__      )

									return cls(lowercase__		,     lowercase__      )


				def 							A (    self		,     lowercase__      )   ->   List[str]:
									"""simple docstring"""







									np.save(os.path.join(lowercase__		,     _REALM_BLOCK_RECORDS_FILENAME      )		,     self.block_records      )
									# save tokenizer
									self.tokenizer.save_pretrained(lowercase__      )




				def 							A (    self		,     lowercase__		,     lowercase__      )   ->   Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     []
									SCREAMING_SNAKE_CASE           =     0

									for input_id in concat_inputs.input_ids:
														SCREAMING_SNAKE_CASE           =     input_id.tolist()
														# Check answers between two [SEP] tokens
														SCREAMING_SNAKE_CASE           =     input_id_list.index(self.tokenizer.sep_token_id      )
														SCREAMING_SNAKE_CASE           =     first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id      )

														start_pos.append([]      )
														end_pos.append([]      )
														for answer in answer_ids:
																			for idx in range(first_sep_idx + 1		,     lowercase__      ):
																								if answer[0] == input_id_list[idx]:
																													if input_id_list[idx : idx + len(lowercase__      )] == answer:
																																		start_pos[-1].append(lowercase__      )
																																		end_pos[-1].append(idx + len(lowercase__      ) - 1      )

														if len(start_pos[-1]      ) == 0:
																			has_answers.append(lowercase__      )
														else:
																			has_answers.append(lowercase__      )
																			if len(start_pos[-1]      ) > max_answers:
																								SCREAMING_SNAKE_CASE           =     len(start_pos[-1]      )

        # Pad -1 to max_answers
									for start_pos_, end_pos_ in zip(lowercase__		,     lowercase__      ):
														if len(start_pos_      ) < max_answers:
																			SCREAMING_SNAKE_CASE           =     [-1] * (max_answers - len(start_pos_      ))
																			start_pos_ += padded
																			end_pos_ += padded
									return has_answers, start_pos, end_pos
