



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 SCREAMING_SNAKE_CASE__	(						SCREAMING_SNAKE_CASE_       ):




    '''simple docstring'''






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






    def      _lowerCAmelCase   (			self,						lowerCamelCase__,						lowerCamelCase__						):
        return (x_in - self.means[key]) / self.stds[key]






    def      _lowerCAmelCase   (			self,						lowerCamelCase__,						lowerCamelCase__						):
        return x_in * self.stds[key] + self.means[key]






    def      _lowerCAmelCase   (			self,						lowerCamelCase__						):
        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,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__						):
        for key, val in cond.items():
            A					:			int		=  val.clone()
        return x_in






    def      _lowerCAmelCase   (			self,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__						):
        A					:			str		=  x.shape[0]
        A					:			Optional[Any]		=  None
        for i in tqdm.tqdm(self.scheduler.timesteps						):
            # create batch of timesteps to pass into model
            A					:			Dict		=  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
                    A					:			int		=  self.value_function(x.permute(0,						2,						1						),						__snake_case						).sample
                    A					:			str		=  torch.autograd.grad([y.sum()],						[x]						)[0]

                    A					:			Optional[int]		=  self.scheduler._get_variance(__snake_case						)
                    A					:			Optional[Any]		=  torch.exp(0.5 * posterior_variance						)
                    A					:			List[Any]		=  model_std * grad

                A					:			List[str]		=  0
                A					:			Any		=  x.detach()
                A					:			Dict		=  x + scale * grad
                A					:			Optional[Any]		=  self.reset_xa(__snake_case,						__snake_case,						self.action_dim						)

            A					:			int		=  self.unet(x.permute(0,						2,						1						),						__snake_case						).sample.permute(0,						2,						1						)

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

            # apply conditions to the trajectory (set the initial state)
            A					:			Dict		=  self.reset_xa(__snake_case,						__snake_case,						self.action_dim						)
            A					:			Union[str, Any]		=  self.to_torch(__snake_case						)
        return x, y






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

        A					:			str		=  {0: self.to_torch(__snake_case						)}
        A					:			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					:			int		=  randn_tensor(__snake_case,						device=self.unet.device						)
        A					:			Union[str, Any]		=  self.reset_xa(__snake_case,						__snake_case,						self.action_dim						)
        A					:			int		=  self.to_torch(__snake_case						)

        # run the diffusion process
        A					:			int		=  self.run_diffusion(__snake_case,						__snake_case,						__snake_case,						__snake_case						)

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

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

        A					:			int		=  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.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				"openai/whisper-base"
					UpperCAmelCase__								=				(
					    "This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
					    "transcribed text."
					)
					UpperCAmelCase__								=				"transcriber"
					UpperCAmelCase__								=				WhisperProcessor
					UpperCAmelCase__								=				WhisperForConditionalGeneration

					UpperCAmelCase__								=				["audio"]
					UpperCAmelCase__								=				["text"]

					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      List[str]					)			->							Dict:
											return self.pre_processor(__snake_case     ,		return_tensors="""pt"""					).input_features

					def   lowerCamelCase__   (			self  :      int     ,		__snake_case  :      Union[str, Any]					)			->							List[str]:
											return self.model.generate(inputs=__snake_case					)






					def   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      List[Any]					)			->							Any:
											return self.pre_processor.batch_decode(__snake_case     ,		skip_special_tokens=__snake_case					)[0]



