






"""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       __A		(   SCREAMING_SNAKE_CASE_			):

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

					def   lowerCamelCase__   (			self  :      Any     ,		__snake_case  :      Dict     ,		__snake_case  :      int					)			->							Optional[int]:
											return (x_in - self.means[key]) / self.stds[key]

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      List[Any]					)			->							Optional[int]:
											return x_in * self.stds[key] + self.means[key]

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      List[Any]					)			->							Tuple:
											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  :      str     ,		__snake_case  :      str     ,		__snake_case  :      Dict     ,		__snake_case  :      Tuple					)			->							Optional[Any]:
											for key, val in cond.items():
																	__magic_name__:	int							=       val.clone()
											return x_in

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

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

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

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

																	# TODO: verify deprecation of this kwarg
																	__magic_name__:	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)
																	__magic_name__:	Dict							=       self.reset_xa(__snake_case     ,		__snake_case     ,		self.action_dim					)
																	__magic_name__:	Union[str, Any]							=       self.to_torch(__snake_case					)
											return x, y






					def __call__(			self  :      Optional[Any]     ,		__snake_case  :      int     ,		__snake_case  :      Tuple=6_4     ,		__snake_case  :      Union[str, Any]=3_2     ,		__snake_case  :      str=2     ,		__snake_case  :      Any=0.1					)			->							int:
											# normalize the observations and create  batch dimension
											__magic_name__:	List[str]							=       self.normalize(__snake_case     ,		"""observations"""					)
											__magic_name__:	Optional[int]							=       obs[None].repeat(__snake_case     ,		axis=0					)

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

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

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

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

											__magic_name__:	int							=       denorm_actions[selected_index, 0]
											return denorm_actions











"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


__lowerCamelCase							=  logging.get_logger(__name__)

__lowerCamelCase							=  {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCamelCase							=  {
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

__lowerCamelCase							=  {
    'squeezebert/squeezebert-uncased': 5_12,
    'squeezebert/squeezebert-mnli': 5_12,
    'squeezebert/squeezebert-mnli-headless': 5_12,
}


__lowerCamelCase							=  {
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				VOCAB_FILES_NAMES
					UpperCAmelCase__								=				PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__								=				PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__								=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__								=				SqueezeBertTokenizer

					def __init__(			self  :      Tuple     ,		__snake_case  :      str=None     ,		__snake_case  :      Optional[Any]=None     ,		__snake_case  :      str=True     ,		__snake_case  :      str="[UNK]"     ,		__snake_case  :      int="[SEP]"     ,		__snake_case  :      int="[PAD]"     ,		__snake_case  :      Any="[CLS]"     ,		__snake_case  :      List[Any]="[MASK]"     ,		__snake_case  :      Union[str, Any]=True     ,		__snake_case  :      Dict=None     ,		**__snake_case  :      str     ,		)			->							List[Any]:
											super().__init__(
											    __snake_case     ,		tokenizer_file=__snake_case     ,		do_lower_case=__snake_case     ,		unk_token=__snake_case     ,		sep_token=__snake_case     ,		pad_token=__snake_case     ,		cls_token=__snake_case     ,		mask_token=__snake_case     ,		tokenize_chinese_chars=__snake_case     ,		strip_accents=__snake_case     ,		**__snake_case     ,		)

											__magic_name__:	List[Any]							=       json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
											if (
											    normalizer_state.get("""lowercase"""     ,		__snake_case					) != do_lower_case
											    or normalizer_state.get("""strip_accents"""     ,		__snake_case					) != strip_accents
											    or normalizer_state.get("""handle_chinese_chars"""     ,		__snake_case					) != tokenize_chinese_chars
											):
																	__magic_name__:	Dict							=       getattr(__snake_case     ,		normalizer_state.pop("""type"""					)					)
																	__magic_name__:	Optional[Any]							=       do_lower_case
																	__magic_name__:	Any							=       strip_accents
																	__magic_name__:	Any							=       tokenize_chinese_chars
																	__magic_name__:	Union[str, Any]							=       normalizer_class(**__snake_case					)

											__magic_name__:	Optional[Any]							=       do_lower_case

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      List[Any]     ,		__snake_case  :      int=None					)			->							Optional[int]:
											__magic_name__:	int							=       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

											if token_ids_a:
																	output += token_ids_a + [self.sep_token_id]

											return output

					def   lowerCamelCase__   (			self  :      Any     ,		__snake_case  :      List[int]     ,		__snake_case  :      Optional[List[int]] = None					)			->							List[int]:
											__magic_name__:	Dict							=       [self.sep_token_id]
											__magic_name__:	List[str]							=       [self.cls_token_id]
											if token_ids_a is None:
																	return len(cls + token_ids_a + sep					) * [0]
											return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      str     ,		__snake_case  :      Optional[str] = None					)			->							Tuple[str]:
											__magic_name__:	Any							=       self._tokenizer.model.save(__snake_case     ,		name=__snake_case					)
											return tuple(__snake_case					)



