



'''simple docstring'''






import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def   A			(		self    :  Optional[int]	)    ->	Optional[int]:
								UpperCAmelCase				: Tuple											=	tempfile.mkdtemp()
								UpperCAmelCase				: List[Any]											=	5

								# Realm tok
								UpperCAmelCase				: Dict											=	[
								    '''[UNK]''',
								    '''[CLS]''',
								    '''[SEP]''',
								    '''[PAD]''',
								    '''[MASK]''',
								    '''test''',
								    '''question''',
								    '''this''',
								    '''is''',
								    '''the''',
								    '''first''',
								    '''second''',
								    '''third''',
								    '''fourth''',
								    '''fifth''',
								    '''record''',
								    '''want''',
								    '''##want''',
								    '''##ed''',
								    '''wa''',
								    '''un''',
								    '''runn''',
								    '''##ing''',
								    ''',''',
								    '''low''',
								    '''lowest''',
								]
								UpperCAmelCase				: Dict											=	os.path.join(self.tmpdirname	,    '''realm_tokenizer'''	)
								os.makedirs(__snake_case	,    exist_ok=__snake_case	)
								UpperCAmelCase				: int											=	os.path.join(__snake_case	,    VOCAB_FILES_NAMES['''vocab_file''']	)
								with open(self.vocab_file	,    '''w'''	,    encoding='''utf-8'''	) as vocab_writer:
															vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]	)	)

								UpperCAmelCase				: Optional[Any]											=	os.path.join(self.tmpdirname	,    '''realm_block_records'''	)
								os.makedirs(__snake_case	,    exist_ok=__snake_case	)







	def   A			(		self    :  Union[str, Any]	)    ->	RealmTokenizer:
								return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname	,    '''realm_tokenizer'''	)	)







	def   A			(		self    :  str	)    ->	Optional[int]:
								shutil.rmtree(self.tmpdirname	)







	def   A			(		self    :  Tuple	)    ->	List[Any]:
								UpperCAmelCase				: Optional[int]											=	RealmConfig(num_block_records=self.num_block_records	)
								return config







	def   A			(		self    :  Tuple	)    ->	Union[str, Any]:
								UpperCAmelCase				: Dict											=	Dataset.from_dict(
								    {
								        '''id''': ['''0''', '''1'''],
								        '''question''': ['''foo''', '''bar'''],
								        '''answers''': [['''Foo''', '''Bar'''], ['''Bar''']],
								    }	)
								return dataset







	def   A			(		self    :  Optional[int]	)    ->	int:
								UpperCAmelCase				: List[Any]											=	np.array(
								    [
								        b'''This is the first record''',
								        b'''This is the second record''',
								        b'''This is the third record''',
								        b'''This is the fourth record''',
								        b'''This is the fifth record''',
								        b'''This is a longer longer longer record''',
								    ]	,    dtype=__snake_case	,    )
								return block_records







	def   A			(		self    :  Any	)    ->	int:
								UpperCAmelCase				: Union[str, Any]											=	RealmRetriever(
								    block_records=self.get_dummy_block_records()	,    tokenizer=self.get_tokenizer()	,    )
								return retriever







	def   A			(		self    :  Optional[Any]	)    ->	int:
								UpperCAmelCase				: Optional[Any]											=	self.get_config()
								UpperCAmelCase				: List[str]											=	self.get_dummy_retriever()
								UpperCAmelCase				: List[Any]											=	retriever.tokenizer

								UpperCAmelCase				: Tuple											=	np.array([0, 3]	,    dtype='''long'''	)
								UpperCAmelCase				: int											=	tokenizer(['''Test question''']	).input_ids
								UpperCAmelCase				: Optional[int]											=	tokenizer(
								    ['''the fourth''']	,    add_special_tokens=__snake_case	,    return_token_type_ids=__snake_case	,    return_attention_mask=__snake_case	,    ).input_ids
								UpperCAmelCase				: str											=	config.reader_seq_len

								UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: int											=	retriever(
								    __snake_case	,    __snake_case	,    answer_ids=__snake_case	,    max_length=__snake_case	,    return_tensors='''np'''	)

								self.assertEqual(len(__snake_case	)	,    2	)
								self.assertEqual(len(__snake_case	)	,    2	)
								self.assertEqual(len(__snake_case	)	,    2	)
								self.assertEqual(concat_inputs.input_ids.shape	,    (2, 10)	)
								self.assertEqual(concat_inputs.attention_mask.shape	,    (2, 10)	)
								self.assertEqual(concat_inputs.token_type_ids.shape	,    (2, 10)	)
								self.assertEqual(concat_inputs.special_tokens_mask.shape	,    (2, 10)	)
								self.assertEqual(
								    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]	)	,    ['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''first''', '''record''', '''[SEP]''']	,    )
								self.assertEqual(
								    tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]	)	,    ['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''fourth''', '''record''', '''[SEP]''']	,    )







	def   A			(		self    :  Optional[Any]	)    ->	Tuple:
								UpperCAmelCase				: Dict											=	self.get_config()
								UpperCAmelCase				: int											=	self.get_dummy_retriever()
								UpperCAmelCase				: List[Any]											=	retriever.tokenizer

								UpperCAmelCase				: Optional[Any]											=	np.array([0, 3, 5]	,    dtype='''long'''	)
								UpperCAmelCase				: Optional[Any]											=	tokenizer(['''Test question''']	).input_ids
								UpperCAmelCase				: List[Any]											=	tokenizer(
								    ['''the fourth''', '''longer longer''']	,    add_special_tokens=__snake_case	,    return_token_type_ids=__snake_case	,    return_attention_mask=__snake_case	,    ).input_ids
								UpperCAmelCase				: List[Any]											=	config.reader_seq_len

								UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: Optional[int]											=	retriever(
								    __snake_case	,    __snake_case	,    answer_ids=__snake_case	,    max_length=__snake_case	,    return_tensors='''np'''	)

								self.assertEqual([False, True, True]	,    __snake_case	)
								self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]	,    __snake_case	)
								self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]	,    __snake_case	)







	def   A			(		self    :  List[str]	)    ->	Optional[Any]:
								UpperCAmelCase				: str											=	self.get_dummy_retriever()
								retriever.save_pretrained(os.path.join(self.tmpdirname	,    '''realm_block_records'''	)	)

								# Test local path
								UpperCAmelCase				: List[str]											=	retriever.from_pretrained(os.path.join(self.tmpdirname	,    '''realm_block_records'''	)	)
								self.assertEqual(retriever.block_records[0]	,    b'''This is the first record'''	)

								# Test mocked remote path
								with patch('''transformers.models.realm.retrieval_realm.hf_hub_download'''	) as mock_hf_hub_download:
															UpperCAmelCase				: Union[str, Any]											=	os.path.join(
															    os.path.join(self.tmpdirname	,    '''realm_block_records'''	)	,    _REALM_BLOCK_RECORDS_FILENAME	)
															UpperCAmelCase				: Any											=	RealmRetriever.from_pretrained('''google/realm-cc-news-pretrained-openqa'''	)

								self.assertEqual(retriever.block_records[0]	,    b'''This is the first record'''	)










'''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        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






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







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







	def   A			(		self    :  Dict	,    __snake_case    :  Any	,    __snake_case    :  Dict	)    ->	Any:
								return x_in * self.stds[key] + self.means[key]







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







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

																													UpperCAmelCase				: Tuple											=	self.scheduler._get_variance(__snake_case	)
																													UpperCAmelCase				: Optional[int]											=	torch.exp(0.5 * posterior_variance	)
																													UpperCAmelCase				: str											=	model_std * grad

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

															UpperCAmelCase				: str											=	self.unet(x.permute(0	,    2	,    1	)	,    __snake_case	).sample.permute(0	,    2	,    1	)

															# TODO: verify deprecation of this kwarg
															UpperCAmelCase				: Optional[int]											=	self.scheduler.step(__snake_case	,    __snake_case	,    __snake_case	,    predict_epsilon=__snake_case	)['''prev_sample''']

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







	def __call__(		self    :  Any	,    __snake_case    :  List[str]	,    __snake_case    :  Optional[Any]=64	,    __snake_case    :  Optional[int]=32	,    __snake_case    :  Union[str, Any]=2	,    __snake_case    :  Optional[int]=0.1	)    ->	Tuple:
								# normalize the observations and create  batch dimension
								UpperCAmelCase				: Optional[Any]											=	self.normalize(__snake_case	,    '''observations'''	)
								UpperCAmelCase				: Optional[int]											=	obs[None].repeat(__snake_case	,    axis=0	)

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

								# run the diffusion process
								UpperCAmelCase							,	UpperCAmelCase				: Optional[int]											=	self.run_diffusion(__snake_case	,    __snake_case	,    __snake_case	,    __snake_case	)

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

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

								UpperCAmelCase				: Optional[int]											=	denorm_actions[selected_index, 0]
								return denorm_actions





