


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 			_a				(     UpperCamelCase__     ):

							def __init__(				self:      Tuple       ,		UpperCamelCase_:      UNetaDModel       ,		UpperCamelCase_:      UNetaDModel       ,		UpperCamelCase_:      DDPMScheduler       ,		UpperCamelCase_:      str       ,		)     ->  List[Any]:





									"""simple docstring"""







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

							def  lowerCamelCase_					(				self:      Dict       ,		UpperCamelCase_:      List[str]       ,		UpperCamelCase_:      Tuple				)     ->  List[Any]:





									"""simple docstring"""







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

							def  lowerCamelCase_					(				self:      List[Any]       ,		UpperCamelCase_:      Optional[Any]       ,		UpperCamelCase_:      List[str]				)     ->  Dict:





									"""simple docstring"""







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

							def  lowerCamelCase_					(				self:      int       ,		UpperCamelCase_:      Union[str, Any]				)     ->  Any:





									"""simple docstring"""







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

							def  lowerCamelCase_					(				self:      Optional[int]       ,		UpperCamelCase_:      Optional[int]       ,		UpperCamelCase_:      Optional[Any]       ,		UpperCamelCase_:      Union[str, Any]				)     ->  Optional[int]:





									"""simple docstring"""







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

							def  lowerCamelCase_					(				self:      Tuple       ,		UpperCamelCase_:      Tuple       ,		UpperCamelCase_:      Union[str, Any]       ,		UpperCamelCase_:      Tuple       ,		UpperCamelCase_:      str				)     ->  str:





									"""simple docstring"""







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

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

															lowercase__				= self.scheduler._get_variance(UpperCamelCase_				)
															lowercase__				= torch.exp(0.5 * posterior_variance				)
															lowercase__				= model_std * grad

													lowercase__				= 0
													lowercase__				= x.detach()
													lowercase__				= x + scale * grad
													lowercase__				= self.reset_xa(UpperCamelCase_       ,		UpperCamelCase_       ,		self.action_dim				)

											lowercase__				= self.unet(x.permute(0       ,		2       ,		1				)       ,		UpperCamelCase_				).sample.permute(0       ,		2       ,		1				)

											# TODO: verify deprecation of this kwarg
											lowercase__				= self.scheduler.step(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		predict_epsilon=UpperCamelCase_				)['''prev_sample''']

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


							def __call__(				self:      Any       ,		UpperCamelCase_:      List[str]       ,		UpperCamelCase_:      Optional[int]=64       ,		UpperCamelCase_:      Any=32       ,		UpperCamelCase_:      Any=2       ,		UpperCamelCase_:      Any=0.1				)     ->  Optional[Any]:





									"""simple docstring"""







									lowercase__				= self.normalize(UpperCamelCase_       ,		'''observations'''				)
									lowercase__				= obs[None].repeat(UpperCamelCase_       ,		axis=0				)

									lowercase__				= {0: self.to_torch(UpperCamelCase_				)}
									lowercase__				= (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)
									lowercase__				= randn_tensor(UpperCamelCase_       ,		device=self.unet.device				)
									lowercase__				= self.reset_xa(UpperCamelCase_       ,		UpperCamelCase_       ,		self.action_dim				)
									lowercase__				= self.to_torch(UpperCamelCase_				)

									# run the diffusion process
									lowercase__	,						lowercase__				= self.run_diffusion(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_				)

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

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

									lowercase__				= denorm_actions[selected_index, 0]
									return denorm_actions









"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							int									=		"""philschmid/bart-large-cnn-samsum"""
				__snake_case :							Optional[int]									=		(
				    """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
				    """and returns a summary of the text."""
				)
				__snake_case :							Dict									=		"""summarizer"""
				__snake_case :							str									=		AutoTokenizer
				__snake_case :							List[str]									=		AutoModelForSeqaSeqLM

				__snake_case :							List[Any]									=		["""text"""]
				__snake_case :							Dict									=		["""text"""]




				def        __lowercase	(  self	:int    ,     __lowercase	:List[Any]      ):
											return self.pre_processor(__lowercase    ,     return_tensors='''pt'''    ,     truncation=__lowercase      )




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:Optional[int]      ):
											return self.model.generate(**__lowercase      )[0]




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:str      ):
											return self.pre_processor.decode(__lowercase    ,     skip_special_tokens=__lowercase    ,     clean_up_tokenization_spaces=__lowercase      )




