
"""simple docstring"""







# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


_SCREAMING_SNAKE_CASE	       =      get_logger(__name__)







class 							__magic_name__     :
					_SCREAMING_SNAKE_CASE       :					List[str]     							=   'dummy_data'
					_SCREAMING_SNAKE_CASE       :					Dict     							=   'datasets'
					_SCREAMING_SNAKE_CASE       :					Dict     							=   False





					def __init__(				self   :							Tuple							,						snake_case_   :							str							,						snake_case_   :							str							,						snake_case_   :							Union[Version, str]							,						snake_case_   :							Optional[str] = None							,						snake_case_   :							bool = False							,						snake_case_   :							bool = True							,						snake_case_   :							Optional[List[Callable]] = None							,						):
												__snake_case     			=       0
												__snake_case     			=       dataset_name
												__snake_case     			=       cache_dir
												__snake_case     			=       use_local_dummy_data
												__snake_case     			=       config
												# download_callbacks take a single url as input
												__snake_case     			=       download_callbacks or []
												# if False, it doesn't load existing files and it returns the paths of the dummy files relative
												# to the dummy_data zip file root
												__snake_case     			=       load_existing_dummy_data

												# TODO(PVP, QL) might need to make this more general
												__snake_case     			=       str(snake_case_    )
												# to be downloaded
												__snake_case     			=       None
												__snake_case     			=       None





					@property
					def 							lowerCAmelCase      (				self   :							Any    ):
												if self._dummy_file is None:
																			__snake_case     			=       self.download_dummy_data()
												return self._dummy_file





					@property
					def 							lowerCAmelCase      (				self   :							Any    ):
												if self.config is not None:
																			# structure is dummy / config_name / version_name
																			return os.path.join("dummy"							,						self.config.name							,						self.version_name    )
												# structure is dummy / version_name
												return os.path.join("dummy"							,						self.version_name    )





					@property
					def 							lowerCAmelCase      (				self   :							str    ):
												return os.path.join(self.dummy_data_folder							,						"dummy_data.zip"    )





					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												__snake_case     			=       (
												    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
												)

												__snake_case     			=       cached_path(
												    snake_case_							,						cache_dir=self.cache_dir							,						extract_compressed_file=snake_case_							,						force_extract=snake_case_    )

												return os.path.join(snake_case_							,						self.dummy_file_name    )





					@property
					def 							lowerCAmelCase      (				self   :							str    ):
												return os.path.join(self.datasets_scripts_dir							,						self.dataset_name							,						self.dummy_zip_file    )





					@property
					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												if self._bucket_url is None:
																			__snake_case     			=       hf_github_url(self.dataset_name							,						self.dummy_zip_file.replace(os.sep							,						"/"    )    )
												return self._bucket_url





					@property
					def 							lowerCAmelCase      (				self   :							Tuple    ):
												# return full path if its a dir
												if os.path.isdir(self.dummy_file    ):
																			return self.dummy_file
												# else cut off path to file -> example `xsum`.
												return "/".join(self.dummy_file.replace(os.sep							,						"/"    ).split("/"    )[:-1]    )





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							int							,						*snake_case_   :							int    ):
												if self.load_existing_dummy_data:
																			# dummy data is downloaded and tested
																			__snake_case     			=       self.dummy_file
												else:
																			# dummy data cannot be downloaded and only the path to dummy file is returned
																			__snake_case     			=       self.dummy_file_name

												# special case when data_url is a dict
												if isinstance(snake_case_							,						snake_case_    ):
																			return self.create_dummy_data_dict(snake_case_							,						snake_case_    )
												elif isinstance(snake_case_							,						(list, tuple)    ):
																			return self.create_dummy_data_list(snake_case_							,						snake_case_    )
												else:
																			return self.create_dummy_data_single(snake_case_							,						snake_case_    )





					def 							lowerCAmelCase      (				self   :							str							,						snake_case_   :							int							,						*snake_case_   :							Optional[int]    ):
												return self.download_and_extract(snake_case_    )





					def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							str							,						snake_case_   :							Optional[int]    ):
												return self.download_and_extract(snake_case_    )





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							Tuple							,						*snake_case_   :							int							,						**snake_case_   :							Any    ):
												return path





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												return {}





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						snake_case_   :							str							,						snake_case_   :							List[str]    ):
												__snake_case     			=       {}
												for key, single_urls in data_url.items():
																			for download_callback in self.download_callbacks:
																										if isinstance(snake_case_							,						snake_case_    ):
																																	for single_url in single_urls:
																																								download_callback(snake_case_    )
																										else:
																																	__snake_case     			=       single_urls
																																	download_callback(snake_case_    )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
																			if isinstance(snake_case_							,						snake_case_    ):
																										__snake_case     			=       [os.path.join(snake_case_							,						urllib.parse.quote_plus(Path(snake_case_    ).name    )    ) for x in single_urls]
																			else:
																										__snake_case     			=       single_urls
																										__snake_case     			=       os.path.join(snake_case_							,						urllib.parse.quote_plus(Path(snake_case_    ).name    )    )
																			__snake_case     			=       value

												# make sure that values are unique
												if all(isinstance(snake_case_							,						snake_case_    ) for i in dummy_data_dict.values()    ) and len(set(dummy_data_dict.values()    )    ) < len(
												    dummy_data_dict.values()    ):
																			# append key to value to make its name unique
																			__snake_case     			=       {key: value + key for key, value in dummy_data_dict.items()}

												return dummy_data_dict





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							List[Any]							,						snake_case_   :							Dict    ):
												__snake_case     			=       []
												# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
												__snake_case     			=       all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}"							,						snake_case_    )    ) for url in data_url    )
												__snake_case     			=       all(
												    url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed"    ) for url in data_url    )
												if data_url and (is_tf_records or is_pubmed_records):
																			__snake_case     			=       [data_url[0]] * len(snake_case_    )
												for single_url in data_url:
																			for download_callback in self.download_callbacks:
																										download_callback(snake_case_    )
																			# we force the name of each key to be the last file / folder name of the url path
																			# if the url has arguments, we need to encode them with urllib.parse.quote_plus
																			__snake_case     			=       os.path.join(snake_case_							,						urllib.parse.quote_plus(single_url.split("/"    )[-1]    )    )
																			dummy_data_list.append(snake_case_    )
												return dummy_data_list





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Tuple							,						snake_case_   :							Tuple    ):
												for download_callback in self.download_callbacks:
																			download_callback(snake_case_    )
												# we force the name of each key to be the last file / folder name of the url path
												# if the url has arguments, we need to encode them with urllib.parse.quote_plus
												__snake_case     			=       os.path.join(snake_case_							,						urllib.parse.quote_plus(data_url.split("/"    )[-1]    )    )
												if os.path.exists(snake_case_    ) or not self.load_existing_dummy_data:
																			return value
												else:
																			# Backward compatibility, maybe deprecate at one point.
																			# For many datasets with single url calls to dl_manager.download_and_extract,
																			# the dummy_data.zip file is actually the zipped downloaded file
																			# while now we expected the dummy_data.zip file to be a directory containing
																			# the downloaded file.
																			return path_to_dummy_data





					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												pass





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												pass





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							Any    ):
												def _iter_archive_members(snake_case_   :							List[Any]    ):
																			# this preserves the order of the members inside the ZIP archive
																			__snake_case     			=       Path(self.dummy_file    ).parent
																			__snake_case     			=       path.relative_to(snake_case_    )
																			with ZipFile(self.local_path_to_dummy_data    ) as zip_file:
																										__snake_case     			=       zip_file.namelist()
																			for member in members:
																										if member.startswith(relative_path.as_posix()    ):
																																	yield dummy_parent_path.joinpath(snake_case_    )

												__snake_case     			=       Path(snake_case_    )
												__snake_case     			=       _iter_archive_members(snake_case_    ) if self.use_local_dummy_data else path.rglob("*"    )
												for file_path in file_paths:
																			if file_path.is_file() and not file_path.name.startswith((".", "__")    ):
																										yield file_path.relative_to(snake_case_    ).as_posix(), file_path.open("rb"    )





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							str    ):
												if not isinstance(snake_case_							,						snake_case_    ):
																			__snake_case     			=       [paths]
												for path in paths:
																			if os.path.isfile(snake_case_    ):
																										if os.path.basename(snake_case_    ).startswith((".", "__")    ):
																																	return
																										yield path
																			else:
																										for dirpath, dirnames, filenames in os.walk(snake_case_    ):
																																	if os.path.basename(snake_case_    ).startswith((".", "__")    ):
																																								continue
																																	dirnames.sort()
																																	for filename in sorted(snake_case_    ):
																																								if filename.startswith((".", "__")    ):
																																															continue
																																								yield os.path.join(snake_case_							,						snake_case_    )


"""simple docstring"""







import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler







class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							Any							,						snake_case_   :							UNetaDModel							,						snake_case_   :							UNetaDModel							,						snake_case_   :							DDPMScheduler							,						snake_case_   :							Any							,						):
												super().__init__()
												__snake_case     			=       value_function
												__snake_case     			=       unet
												__snake_case     			=       scheduler
												__snake_case     			=       env
												__snake_case     			=       env.get_dataset()
												__snake_case     			=       {}
												for key in self.data.keys():
																			try:
																										__snake_case     			=       self.data[key].mean()
																			except:  # noqa: E722
																										pass
												__snake_case     			=       {}
												for key in self.data.keys():
																			try:
																										__snake_case     			=       self.data[key].std()
																			except:  # noqa: E722
																										pass
												__snake_case     			=       env.observation_space.shape[0]
												__snake_case     			=       env.action_space.shape[0]





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							str							,						snake_case_   :							Any    ):
												return (x_in - self.means[key]) / self.stds[key]





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Optional[Any]    ):
												return x_in * self.stds[key] + self.means[key]





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Dict    ):
												if type(snake_case_    ) is dict:
																			return {k: self.to_torch(snake_case_    ) for k, v in x_in.items()}
												elif torch.is_tensor(snake_case_    ):
																			return x_in.to(self.unet.device    )
												return torch.tensor(snake_case_							,						device=self.unet.device    )





					def 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							Tuple							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Union[str, Any]    ):
												for key, val in cond.items():
																			__snake_case     			=       val.clone()
												return x_in





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Tuple							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Any							,						snake_case_   :							Optional[Any]    ):
												__snake_case     			=       x.shape[0]
												__snake_case     			=       None
												for i in tqdm.tqdm(self.scheduler.timesteps    ):
																			# create batch of timesteps to pass into model
																			__snake_case     			=       torch.full((batch_size,)							,						snake_case_							,						device=self.unet.device							,						dtype=torch.long    )
																			for _ in range(snake_case_    ):
																										with torch.enable_grad():
																																	x.requires_grad_()

																																	# permute to match dimension for pre-trained models
																																	__snake_case     			=       self.value_function(x.permute(0							,						2							,						1    )							,						snake_case_    ).sample
																																	__snake_case     			=       torch.autograd.grad([y.sum()]							,						[x]    )[0]

																																	__snake_case     			=       self.scheduler._get_variance(snake_case_    )
																																	__snake_case     			=       torch.exp(0.5 * posterior_variance    )
																																	__snake_case     			=       model_std * grad

																										__snake_case     			=       0
																										__snake_case     			=       x.detach()
																										__snake_case     			=       x + scale * grad
																										__snake_case     			=       self.reset_xa(snake_case_							,						snake_case_							,						self.action_dim    )

																			__snake_case     			=       self.unet(x.permute(0							,						2							,						1    )							,						snake_case_    ).sample.permute(0							,						2							,						1    )

																			# TODO: verify deprecation of this kwarg
																			__snake_case     			=       self.scheduler.step(snake_case_							,						snake_case_							,						snake_case_							,						predict_epsilon=snake_case_    )["prev_sample"]

																			# apply conditions to the trajectory (set the initial state)
																			__snake_case     			=       self.reset_xa(snake_case_							,						snake_case_							,						self.action_dim    )
																			__snake_case     			=       self.to_torch(snake_case_    )
												return x, y





					def __call__(				self   :							Union[str, Any]							,						snake_case_   :							List[Any]							,						snake_case_   :							Any=64							,						snake_case_   :							str=32							,						snake_case_   :							List[str]=2							,						snake_case_   :							Tuple=0.1    ):
												# normalize the observations and create  batch dimension
												__snake_case     			=       self.normalize(snake_case_							,						"observations"    )
												__snake_case     			=       obs[None].repeat(snake_case_							,						axis=0    )

												__snake_case     			=       {0: self.to_torch(snake_case_    )}
												__snake_case     			=       (batch_size, planning_horizon, self.state_dim + self.action_dim)

												# generate initial noise and apply our conditions (to make the trajectories start at current state)
												__snake_case     			=       randn_tensor(snake_case_							,						device=self.unet.device    )
												__snake_case     			=       self.reset_xa(snake_case_							,						snake_case_							,						self.action_dim    )
												__snake_case     			=       self.to_torch(snake_case_    )

												# run the diffusion process
												__snake_case			,   __snake_case     			=       self.run_diffusion(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )

												# sort output trajectories by value
												__snake_case     			=       y.argsort(0							,						descending=snake_case_    ).squeeze()
												__snake_case     			=       x[sorted_idx]
												__snake_case     			=       sorted_values[:, :, : self.action_dim]
												__snake_case     			=       actions.detach().cpu().numpy()
												__snake_case     			=       self.de_normalize(snake_case_							,						key="actions"    )

												# select the action with the highest value
												if y is not None:
																			__snake_case     			=       0
												else:
																			# if we didn't run value guiding, select a random action
																			__snake_case     			=       np.random.randint(0							,						snake_case_    )

												__snake_case     			=       denorm_actions[selected_index, 0]
												return denorm_actions
