






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 			_UpperCamelCase(       __lowerCamelCase		):

		def __init__(					self		:    str    ,       SCREAMING_SNAKE_CASE__		:    UNetaDModel    ,       SCREAMING_SNAKE_CASE__		:    UNetaDModel    ,       SCREAMING_SNAKE_CASE__		:    DDPMScheduler    ,       SCREAMING_SNAKE_CASE__		:    str    ,       ):



					'''simple docstring'''



					super().__init__()
					__a						:  Tuple									=     value_function
					__a						:  List[Any]									=     unet
					__a						:  Optional[int]									=     scheduler
					__a						:  Optional[Any]									=     env
					__a						:  str									=     env.get_dataset()
					__a						:  Tuple									=     {}
					for key in self.data.keys():
								try:
											__a						:  str									=     self.data[key].mean()
								except:  # noqa: E722
											pass
					__a						:  str									=     {}
					for key in self.data.keys():
								try:
											__a						:  Any									=     self.data[key].std()
								except:  # noqa: E722
											pass
					__a						:  Optional[int]									=     env.observation_space.shape[0]
					__a						:  List[str]									=     env.action_space.shape[0]

		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    Tuple    ):



					'''simple docstring'''



					return (x_in - self.means[key]) / self.stds[key]

		def    __lowerCAmelCase  (					self		:    str    ,       SCREAMING_SNAKE_CASE__		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    Any    ):



					'''simple docstring'''



					return x_in * self.stds[key] + self.means[key]

		def    __lowerCAmelCase  (					self		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]    ):



					'''simple docstring'''



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

		def    __lowerCAmelCase  (					self		:    Any    ,       SCREAMING_SNAKE_CASE__		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    Tuple    ):



					'''simple docstring'''



					for key, val in cond.items():
								__a						:  str									=     val.clone()
					return x_in

		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    str    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]    ):



					'''simple docstring'''



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

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

														__a						:  Union[str, Any]									=     self.scheduler._get_variance(SCREAMING_SNAKE_CASE__    )
														__a						:  Dict									=     torch.exp(0.5 * posterior_variance    )
														__a						:  Tuple									=     model_std * grad

											__a						:  Tuple									=     0
											__a						:  str									=     x.detach()
											__a						:  List[str]									=     x + scale * grad
											__a						:  Optional[Any]									=     self.reset_xa(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       self.action_dim    )

								__a						:  str									=     self.unet(x.permute(0    ,       2    ,       1    )    ,       SCREAMING_SNAKE_CASE__    ).sample.permute(0    ,       2    ,       1    )

								# TODO: verify deprecation of this kwarg
								__a						:  Union[str, Any]									=     self.scheduler.step(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       predict_epsilon=SCREAMING_SNAKE_CASE__    )['prev_sample']

								# apply conditions to the trajectory (set the initial state)
								__a						:  int									=     self.reset_xa(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       self.action_dim    )
								__a						:  List[str]									=     self.to_torch(SCREAMING_SNAKE_CASE__    )
					return x, y



		def __call__(					self		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]=6_4    ,       SCREAMING_SNAKE_CASE__		:    Dict=3_2    ,       SCREAMING_SNAKE_CASE__		:    int=2    ,       SCREAMING_SNAKE_CASE__		:    Any=0.1    ):



					'''simple docstring'''



					__a						:  Any									=     self.normalize(SCREAMING_SNAKE_CASE__    ,       'observations'    )
					__a						:  Dict									=     obs[None].repeat(SCREAMING_SNAKE_CASE__    ,       axis=0    )

					__a						:  Union[str, Any]									=     {0: self.to_torch(SCREAMING_SNAKE_CASE__    )}
					__a						:  Union[str, Any]									=     (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)
					__a						:  Optional[Any]									=     randn_tensor(SCREAMING_SNAKE_CASE__    ,       device=self.unet.device    )
					__a						:  str									=     self.reset_xa(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       self.action_dim    )
					__a						:  Any									=     self.to_torch(SCREAMING_SNAKE_CASE__    )

					# run the diffusion process
					__a						,					__a						:  Dict									=     self.run_diffusion(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )

					# sort output trajectories by value
					__a						:  List[str]									=     y.argsort(0    ,       descending=SCREAMING_SNAKE_CASE__    ).squeeze()
					__a						:  Optional[Any]									=     x[sorted_idx]
					__a						:  List[Any]									=     sorted_values[:, :, : self.action_dim]
					__a						:  str									=     actions.detach().cpu().numpy()
					__a						:  int									=     self.de_normalize(SCREAMING_SNAKE_CASE__    ,       key='actions'    )

					# select the action with the highest value
					if y is not None:
								__a						:  List[str]									=     0
					else:
								# if we didn't run value guiding, select a random action
								__a						:  List[str]									=     np.random.randint(0    ,       SCREAMING_SNAKE_CASE__    )

					__a						:  Any									=     denorm_actions[selected_index, 0]
					return denorm_actions










import pytest


lowercase_													=      '''__dummy_dataset1__'''

lowercase_													=      '''
import json
import os

import datasets


REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features(
            {
                "tokens": datasets.Sequence(datasets.Value("string")),
                "ner_tags": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            "O",
                            "B-PER",
                            "I-PER",
                            "B-ORG",
                            "I-ORG",
                            "B-LOC",
                            "I-LOC",
                        ]
                    )
                ),
                "langs": datasets.Sequence(datasets.Value("string")),
                "spans": datasets.Sequence(datasets.Value("string")),
            }
        )
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager):
        dl_path = dl_manager.download(URLS)
        return [
            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, "r", encoding="utf-8") as f:
            for i, line in enumerate(f):
                yield i, json.loads(line)
'''






@pytest.fixture
def        __lowerCAmelCase		(	)	->    Any:
					return DATASET_LOADING_SCRIPT_NAME






@pytest.fixture
def        __lowerCAmelCase		(	)	->    Union[str, Any]:
					return DATASET_LOADING_SCRIPT_CODE






@pytest.fixture
def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Tuple					, __lowerCamelCase						:     str						)	->    List[str]:
					__lowerCAmelCase						 =dataset_loading_script_name
					__lowerCAmelCase						 =tmp_path / """datasets""" / script_name
					script_dir.mkdir(parents=__lowerCamelCase						)
					__lowerCAmelCase						 =script_dir / f"""{script_name}.py"""
					with open(__lowerCamelCase					, """w"""						) as f:
										f.write(__lowerCamelCase						)
					return str(__lowerCamelCase						)




