





'''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 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,)							->							Optional[int]:







				'''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 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							Dict:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							List[Any]:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							List[str]:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							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,)     ,_lowerCamelCase     ,device=self.unet.device     ,dtype=torch.long )
							for _ in range(_lowerCamelCase ):
										with torch.enable_grad():
													x.requires_grad_()

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

													__lowercase										=							self.scheduler._get_variance(_lowerCamelCase )
													__lowercase										=							torch.exp(0.5 * posterior_variance )
													__lowercase										=							model_std * grad

										__lowercase										=							0
										__lowercase										=							x.detach()
										__lowercase										=							x + scale * grad
										__lowercase										=							self.reset_xa(_lowerCamelCase     ,_lowerCamelCase     ,self.action_dim )

							__lowercase										=							self.unet(x.permute(0     ,2     ,1 )     ,_lowerCamelCase ).sample.permute(0     ,2     ,1 )

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

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


	def __call__(self     ,_lowerCamelCase     ,_lowerCamelCase=64     ,_lowerCamelCase=32     ,_lowerCamelCase=2     ,_lowerCamelCase=0.1 )							->							Union[str, Any]:







				'''simple docstring'''





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

				__lowercase										=							{0: self.to_torch(_lowerCamelCase )}
				__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(_lowerCamelCase     ,device=self.unet.device )
				__lowercase										=							self.reset_xa(_lowerCamelCase     ,_lowerCamelCase     ,self.action_dim )
				__lowercase										=							self.to_torch(_lowerCamelCase )

				# run the diffusion process
				__lowercase						,		__lowercase										=							self.run_diffusion(_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )

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

				__lowercase										=							denorm_actions[selected_index, 0]
				return denorm_actions












'''simple docstring'''



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					=    {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE					=    {
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

_SCREAMING_SNAKE_CASE					=    {
    '''gpt-neox-20b''': 2_0_4_8,
}






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				List[Any]											=       VOCAB_FILES_NAMES
	a    :				Optional[Any]											=       PRETRAINED_VOCAB_FILES_MAP
	a    :				Optional[int]											=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a    :				List[str]											=       ["input_ids", "attention_mask"]


	def __init__(self     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase="<|endoftext|>"     ,_lowerCamelCase="<|endoftext|>"     ,_lowerCamelCase="<|endoftext|>"     ,_lowerCamelCase=False     ,**_lowerCamelCase     ,)							->							Optional[Any]:







				'''simple docstring'''





				super().__init__(
				    _lowerCamelCase     ,_lowerCamelCase     ,tokenizer_file=_lowerCamelCase     ,unk_token=_lowerCamelCase     ,bos_token=_lowerCamelCase     ,eos_token=_lowerCamelCase     ,add_prefix_space=_lowerCamelCase     ,**_lowerCamelCase     ,)

				__lowercase										=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
				if pre_tok_state.get('''add_prefix_space'''     ,_lowerCamelCase ) != add_prefix_space:
							__lowercase										=							getattr(_lowerCamelCase     ,pre_tok_state.pop('''type''' ) )
							__lowercase										=							add_prefix_space
							__lowercase										=							pre_tok_class(**_lowerCamelCase )

				__lowercase										=							add_prefix_space


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = None )							->							Tuple[str]:







				'''simple docstring'''





				__lowercase										=							self._tokenizer.model.save(_lowerCamelCase     ,name=_lowerCamelCase )
				return tuple(_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							List[int]:







				'''simple docstring'''





				__lowercase										=							[]
				for is_user, text in conversation.iter_texts():
							input_ids.extend(self.encode(_lowerCamelCase     ,add_special_tokens=_lowerCamelCase ) + [self.eos_token_id] )

				if len(_lowerCamelCase ) > self.model_max_length:
							__lowercase										=							input_ids[-self.model_max_length :]
				return input_ids





