



'''simple docstring'''






import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
						import os

						import jax.numpy as jnp
						from jax import jit

						from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
						from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

						UpperCamelCase__:     int										=    "0.12"  # assumed parallelism: 8


if is_torch_available():
						import torch


def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		List[Any]=None						)   ->     List[Any]:
							if rng is None:
														UpperCAmelCase				: Optional[int]											=	random.Random()

							UpperCAmelCase				: List[Any]											=	1
							for dim in shape:
														total_dims *= dim

							UpperCAmelCase				: int											=	[]
							for _ in range(_lowerCAmelCase						):
														values.append(rng.randint(0     ,   vocab_size - 1						)						)

							UpperCAmelCase				: Any											=	np.array(_lowerCAmelCase     ,   dtype=jnp.intaa						).reshape(_lowerCAmelCase						)

							return output


def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		Union[str, Any]=None						)   ->     List[Any]:
							UpperCAmelCase				: Tuple											=	ids_tensor(_lowerCAmelCase     ,   vocab_size=2     ,   rng=_lowerCAmelCase						)
							# make sure that at least one token is attended to for each batch
							UpperCAmelCase				: Union[str, Any]											=	1
							return attn_mask

@require_flax
class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	lowerCamelCase__    				= None
	lowerCamelCase__    				= ()







	def   A			(		self    :  Any	)    ->	Dict:
								UpperCAmelCase							,	UpperCAmelCase				: Tuple											=	self.model_tester.prepare_config_and_inputs_for_common()

								# cut to half length & take max batch_size 3
								UpperCAmelCase				: Optional[Any]											=	2
								UpperCAmelCase				: Optional[int]											=	inputs['''input_ids'''].shape[-1] // 2
								UpperCAmelCase				: int											=	inputs['''input_ids'''][:max_batch_size, :sequence_length]

								UpperCAmelCase				: Any											=	jnp.ones_like(__snake_case	)
								UpperCAmelCase				: str											=	attention_mask[:max_batch_size, :sequence_length]

								# generate max 5 tokens
								UpperCAmelCase				: List[Any]											=	input_ids.shape[-1] + 5
								if config.eos_token_id is not None and config.pad_token_id is None:
															# hack to allow generate for models such as GPT2 as is done in `generate()`
															UpperCAmelCase				: List[str]											=	config.eos_token_id
								return config, input_ids, attention_mask, max_length







	@is_pt_flax_cross_test
	def   A			(		self    :  List[str]	)    ->	str:
								UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: int											=	self._get_input_ids_and_config()
								UpperCAmelCase				: Any											=	False
								UpperCAmelCase				: List[Any]											=	max_length
								UpperCAmelCase				: Dict											=	0

								for model_class in self.all_generative_model_classes:
															UpperCAmelCase				: Optional[int]											=	model_class(__snake_case	)

															UpperCAmelCase				: Dict											=	model_class.__name__[4:]  # Skip the "Flax" at the beginning
															UpperCAmelCase				: str											=	getattr(__snake_case	,    __snake_case	)
															UpperCAmelCase				: str											=	pt_model_class(__snake_case	).eval()
															UpperCAmelCase				: Union[str, Any]											=	load_flax_weights_in_pytorch_model(__snake_case	,    flax_model.params	)

															UpperCAmelCase				: Any											=	flax_model.generate(__snake_case	).sequences
															UpperCAmelCase				: Optional[Any]											=	pt_model.generate(torch.tensor(__snake_case	,    dtype=torch.long	)	)

															if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																						UpperCAmelCase				: str											=	flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

															self.assertListEqual(pt_generation_outputs.numpy().tolist()	,    flax_generation_outputs.tolist()	)







	def   A			(		self    :  List[str]	)    ->	Dict:
								UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: Union[str, Any]											=	self._get_input_ids_and_config()
								UpperCAmelCase				: Dict											=	False
								UpperCAmelCase				: Any											=	max_length

								for model_class in self.all_generative_model_classes:
															UpperCAmelCase				: int											=	model_class(__snake_case	)

															UpperCAmelCase				: Tuple											=	model.generate(__snake_case	).sequences
															self.assertEqual(generation_outputs.shape[-1]	,    __snake_case	)

															UpperCAmelCase				: List[Any]											=	jit(model.generate	)
															UpperCAmelCase				: Any											=	jit_generate(__snake_case	).sequences

															self.assertListEqual(generation_outputs.tolist()	,    jit_generation_outputs.tolist()	)







	def   A			(		self    :  str	)    ->	List[Any]:
								UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: Optional[int]											=	self._get_input_ids_and_config()
								UpperCAmelCase				: List[Any]											=	True
								UpperCAmelCase				: List[Any]											=	max_length

								for model_class in self.all_generative_model_classes:
															UpperCAmelCase				: Optional[int]											=	model_class(__snake_case	)

															UpperCAmelCase				: Union[str, Any]											=	model.generate(__snake_case	).sequences
															self.assertEqual(generation_outputs.shape[-1]	,    __snake_case	)

															UpperCAmelCase				: Any											=	jit(model.generate	)
															UpperCAmelCase				: List[str]											=	jit_generate(__snake_case	).sequences

															self.assertListEqual(generation_outputs.tolist()	,    jit_generation_outputs.tolist()	)







	def   A			(		self    :  str	)    ->	List[str]:
								UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: Tuple											=	self._get_input_ids_and_config()
								UpperCAmelCase				: List[Any]											=	False
								UpperCAmelCase				: Tuple											=	max_length
								UpperCAmelCase				: int											=	2

								for model_class in self.all_generative_model_classes:
															UpperCAmelCase				: Dict											=	model_class(__snake_case	)

															UpperCAmelCase				: Optional[int]											=	model.generate(__snake_case	).sequences
															self.assertEqual(generation_outputs.shape[-1]	,    __snake_case	)

															UpperCAmelCase				: Union[str, Any]											=	jit(model.generate	)
															UpperCAmelCase				: Optional[Any]											=	jit_generate(__snake_case	).sequences

															self.assertListEqual(generation_outputs.tolist()	,    jit_generation_outputs.tolist()	)







	def   A			(		self    :  int	)    ->	Tuple:
								UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: Optional[Any]											=	self._get_input_ids_and_config()
								UpperCAmelCase				: Optional[int]											=	False
								UpperCAmelCase				: Optional[int]											=	max_length
								UpperCAmelCase				: List[Any]											=	2
								UpperCAmelCase				: Dict											=	2

								for model_class in self.all_generative_model_classes:
															UpperCAmelCase				: str											=	model_class(__snake_case	)

															UpperCAmelCase				: Dict											=	model.generate(__snake_case	).sequences
															self.assertEqual(generation_outputs.shape[0]	,    input_ids.shape[0] * config.num_return_sequences	)







	def   A			(		self    :  Union[str, Any]	)    ->	Any:
								UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: List[Any]											=	self._get_input_ids_and_config()
								UpperCAmelCase				: Optional[int]											=	True
								UpperCAmelCase				: List[Any]											=	max_length
								UpperCAmelCase				: Dict											=	0.8
								UpperCAmelCase				: Union[str, Any]											=	10
								UpperCAmelCase				: Dict											=	0.3
								UpperCAmelCase				: str											=	1
								UpperCAmelCase				: int											=	8
								UpperCAmelCase				: List[str]											=	9

								for model_class in self.all_generative_model_classes:
															UpperCAmelCase				: str											=	model_class(__snake_case	)

															UpperCAmelCase				: List[Any]											=	model.generate(__snake_case	).sequences
															self.assertEqual(generation_outputs.shape[-1]	,    __snake_case	)

															UpperCAmelCase				: Any											=	jit(model.generate	)
															UpperCAmelCase				: Tuple											=	jit_generate(__snake_case	).sequences

															self.assertListEqual(generation_outputs.tolist()	,    jit_generation_outputs.tolist()	)







	def   A			(		self    :  Any	)    ->	Optional[Any]:
								UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: Union[str, Any]											=	self._get_input_ids_and_config()
								UpperCAmelCase				: Optional[Any]											=	max_length
								UpperCAmelCase				: Union[str, Any]											=	1
								UpperCAmelCase				: Optional[Any]											=	8
								UpperCAmelCase				: Any											=	9

								for model_class in self.all_generative_model_classes:
															UpperCAmelCase				: Tuple											=	model_class(__snake_case	)

															UpperCAmelCase				: int											=	model.generate(__snake_case	).sequences
															self.assertEqual(generation_outputs.shape[-1]	,    __snake_case	)

															UpperCAmelCase				: Any											=	jit(model.generate	)
															UpperCAmelCase				: List[str]											=	jit_generate(__snake_case	).sequences

															self.assertListEqual(generation_outputs.tolist()	,    jit_generation_outputs.tolist()	)







	def   A			(		self    :  str	)    ->	List[str]:
								UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: List[Any]											=	self._get_input_ids_and_config()
								UpperCAmelCase				: List[str]											=	max_length
								UpperCAmelCase				: List[str]											=	2
								UpperCAmelCase				: Tuple											=	1
								UpperCAmelCase				: Any											=	8
								UpperCAmelCase				: Union[str, Any]											=	9

								for model_class in self.all_generative_model_classes:
															UpperCAmelCase				: Union[str, Any]											=	model_class(__snake_case	)

															UpperCAmelCase				: Optional[int]											=	model.generate(__snake_case	).sequences
															self.assertEqual(generation_outputs.shape[-1]	,    __snake_case	)

															UpperCAmelCase				: Dict											=	jit(model.generate	)
															UpperCAmelCase				: Optional[int]											=	jit_generate(__snake_case	).sequences

															self.assertListEqual(generation_outputs.tolist()	,    jit_generation_outputs.tolist()	)







	def   A			(		self    :  Dict	)    ->	Union[str, Any]:
								UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: int											=	self._get_input_ids_and_config()

								# pad attention mask on the left
								UpperCAmelCase				: int											=	attention_mask.at[(0, 0)].set(0	)

								UpperCAmelCase				: int											=	False
								UpperCAmelCase				: int											=	max_length

								for model_class in self.all_generative_model_classes:
															UpperCAmelCase				: Tuple											=	model_class(__snake_case	)

															UpperCAmelCase				: List[str]											=	model.generate(__snake_case	,    attention_mask=__snake_case	).sequences
															self.assertEqual(generation_outputs.shape[-1]	,    __snake_case	)

															UpperCAmelCase				: Optional[int]											=	jit(model.generate	)
															UpperCAmelCase				: List[Any]											=	jit_generate(__snake_case	,    attention_mask=__snake_case	).sequences

															self.assertListEqual(generation_outputs.tolist()	,    jit_generation_outputs.tolist()	)







	def   A			(		self    :  Tuple	)    ->	Union[str, Any]:
								UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: Dict											=	self._get_input_ids_and_config()

								# pad attention mask on the left
								UpperCAmelCase				: List[str]											=	attention_mask.at[(0, 0)].set(0	)

								UpperCAmelCase				: Tuple											=	True
								UpperCAmelCase				: Union[str, Any]											=	max_length

								for model_class in self.all_generative_model_classes:
															UpperCAmelCase				: int											=	model_class(__snake_case	)

															UpperCAmelCase				: int											=	model.generate(__snake_case	,    attention_mask=__snake_case	).sequences
															self.assertEqual(generation_outputs.shape[-1]	,    __snake_case	)

															UpperCAmelCase				: List[str]											=	jit(model.generate	)
															UpperCAmelCase				: List[Any]											=	jit_generate(__snake_case	,    attention_mask=__snake_case	).sequences

															self.assertListEqual(generation_outputs.tolist()	,    jit_generation_outputs.tolist()	)







	def   A			(		self    :  Optional[int]	)    ->	Union[str, Any]:
								UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: List[str]											=	self._get_input_ids_and_config()

								# pad attention mask on the left
								UpperCAmelCase				: Optional[Any]											=	attention_mask.at[(0, 0)].set(0	)

								UpperCAmelCase				: Dict											=	2
								UpperCAmelCase				: int											=	max_length

								for model_class in self.all_generative_model_classes:
															UpperCAmelCase				: List[Any]											=	model_class(__snake_case	)

															UpperCAmelCase				: Optional[Any]											=	model.generate(__snake_case	,    attention_mask=__snake_case	).sequences
															self.assertEqual(generation_outputs.shape[-1]	,    __snake_case	)

															UpperCAmelCase				: Any											=	jit(model.generate	)
															UpperCAmelCase				: Union[str, Any]											=	jit_generate(__snake_case	,    attention_mask=__snake_case	).sequences

															self.assertListEqual(generation_outputs.tolist()	,    jit_generation_outputs.tolist()	)

@require_flax
class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  Union[str, Any]	)    ->	Any:
								UpperCAmelCase				: Union[str, Any]											=	AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-bert'''	)
								UpperCAmelCase				: int											=	FlaxAutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-bert-flax-only'''	)

								UpperCAmelCase				: List[str]											=	'''Hello world'''
								UpperCAmelCase				: str											=	tokenizer(__snake_case	,    return_tensors='''np'''	).input_ids

								# typos are quickly detected (the correct argument is `do_sample`)
								with self.assertRaisesRegex(__snake_case	,    '''do_samples'''	):
															model.generate(__snake_case	,    do_samples=__snake_case	)

								# arbitrary arguments that will not be used anywhere are also not accepted
								with self.assertRaisesRegex(__snake_case	,    '''foo'''	):
															UpperCAmelCase				: Optional[Any]											=	{'''foo''': '''bar'''}
															model.generate(__snake_case	,    **__snake_case	)










'''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





