
"""simple docstring"""







from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_SCREAMING_SNAKE_CASE						       =      logging.get_logger(__name__)
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""simple docstring"""


							if isinstance(SCREAMING_SNAKE_CASE__							,    np.ndarray ):
														return list(tensor.shape )

							__snake_case     			=       tf.shape(SCREAMING_SNAKE_CASE__ )

							if tensor.shape == tf.TensorShape(SCREAMING_SNAKE_CASE__ ):
														return dynamic

							__snake_case     			=       tensor.shape.as_list()

							return [dynamic[i] if s is None else s for i, s in enumerate(SCREAMING_SNAKE_CASE__ )]
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = None							,    SCREAMING_SNAKE_CASE = None )  ->     Optional[Any]:







							"""simple docstring"""


							return tf.nn.softmax(logits=logits + 1e-9							,    axis=SCREAMING_SNAKE_CASE__							,    name=SCREAMING_SNAKE_CASE__ )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=1e-5							,    SCREAMING_SNAKE_CASE=-1 )  ->     Tuple:







							"""simple docstring"""


							if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__ ):
														raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis." )

							# Get mean and variance on the axis to be normalized
							__snake_case     			=       tf.nn.moments(SCREAMING_SNAKE_CASE__							,    axes=[axis]							,    keepdims=SCREAMING_SNAKE_CASE__ )

							if axis != -1:
														# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
														# on every dimension except axis
														__snake_case     			=       [1] * inputs.shape.rank
														__snake_case     			=       shape_list(SCREAMING_SNAKE_CASE__ )[axis]
														__snake_case     			=       tf.reshape(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__ )
														__snake_case     			=       tf.reshape(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__ )

							# Compute layer normalization using the batch_normalization
							# function.
							__snake_case     			=       tf.nn.batch_normalization(
							    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    offset=SCREAMING_SNAKE_CASE__							,    scale=SCREAMING_SNAKE_CASE__							,    variance_epsilon=SCREAMING_SNAKE_CASE__							,    )
							return outputs
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=0							,    SCREAMING_SNAKE_CASE=-1 )  ->     List[Any]:







							"""simple docstring"""


							if end_dim < 0:
														end_dim += input.shape.rank
							if start_dim < 0:
														start_dim += input.shape.rank

							if start_dim == end_dim:
														return input

							__snake_case     			=       tf.shape(SCREAMING_SNAKE_CASE__ )
							__snake_case     			=       tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
							__snake_case     			=       tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]							,    axis=0 )
							return tf.reshape(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__ )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Tuple:







							"""simple docstring"""


							if not isinstance(SCREAMING_SNAKE_CASE__							,    tf.Tensor ):
														__snake_case     			=       tf.convert_to_tensor(SCREAMING_SNAKE_CASE__ )  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														__snake_case     			=       encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														__snake_case     			=       encoder_attention_mask[:, None, None, :]
							# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
							# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
							# /transformer/transformer_layers.py#L270
							# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
							# encoder_extended_attention_mask.transpose(-1, -2))
							__snake_case     			=       (
							    tf.cast(1							,    encoder_attention_mask.dtype ) - encoder_extended_attention_mask
							) * encoder_extended_attention_mask.dtype.min

							return encoder_extended_attention_mask
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = "input_ids" )  ->     List[Any]:







							"""simple docstring"""


							tf.debugging.assert_less(
							    SCREAMING_SNAKE_CASE__							,    tf.cast(SCREAMING_SNAKE_CASE__							,    dtype=tensor.dtype )							,    message=(
							        F'''The maximum value of {tensor_name} ({tf.math.reduce_max(SCREAMING_SNAKE_CASE__ )}) must be smaller than the embedding '''
							        F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
							    )							,    )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Any:







							"""simple docstring"""


							__snake_case     			=       6_45_12
							# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
							# because in that case even chunking the array would not make the saving
							# possible.
							__snake_case     			=       [x for x in data if len(SCREAMING_SNAKE_CASE__ ) > HDF5_OBJECT_HEADER_LIMIT]

							# Expecting this to never be true.
							if bad_attributes:
														raise RuntimeError(
														    "The following attributes cannot be saved to HDF5 file because "
														    F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
														    F'''bytes: {bad_attributes}''' )

							__snake_case     			=       np.asarray(SCREAMING_SNAKE_CASE__ )

							__snake_case     			=       1
							__snake_case     			=       np.array_split(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__ )

							# This will never loop forever thanks to the test above.
							while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
														num_chunks += 1
														__snake_case     			=       np.array_split(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__ )

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(SCREAMING_SNAKE_CASE__ ):
																					__snake_case     			=       chunk_data
							else:
														__snake_case     			=       data
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     List[str]:







							"""simple docstring"""


							if name in group.attrs:
														__snake_case     			=       [n.decode("utf8" ) if hasattr(SCREAMING_SNAKE_CASE__							,    "decode" ) else n for n in group.attrs[name]]
							else:
														__snake_case     			=       []
														__snake_case     			=       0
														while "%s%d" % (name, chunk_id) in group.attrs:
																					data.extend(
																					    [n.decode("utf8" ) if hasattr(SCREAMING_SNAKE_CASE__							,    "decode" ) else n for n in group.attrs["%s%d" % (name, chunk_id)]] )
																					chunk_id += 1
							return data



def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							def _expand_single_ad_tensor(SCREAMING_SNAKE_CASE ):
														if isinstance(SCREAMING_SNAKE_CASE__							,    tf.Tensor ) and t.shape.rank == 1:
																					return tf.expand_dims(SCREAMING_SNAKE_CASE__							,    axis=-1 )
														return t

							return tf.nest.map_structure(_expand_single_ad_tensor							,    SCREAMING_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 							__magic_name__     (		lowercase__   ):





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





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							str							,						snake_case_   :							Any    ):
												return (x_in - self.means[key]) / self.stds[key]





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Optional[Any]    ):
												return x_in * self.stds[key] + self.means[key]





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Dict    ):
												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   :							List[str]							,						snake_case_   :							Tuple							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Union[str, Any]    ):
												for key, val in cond.items():
																			__snake_case     			=       val.clone()
												return x_in





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

																																	__snake_case     			=       self.scheduler._get_variance(snake_case_    )
																																	__snake_case     			=       torch.exp(0.5 * posterior_variance    )
																																	__snake_case     			=       model_std * grad

																										__snake_case     			=       0
																										__snake_case     			=       x.detach()
																										__snake_case     			=       x + scale * grad
																										__snake_case     			=       self.reset_xa(snake_case_							,						snake_case_							,						self.action_dim    )

																			__snake_case     			=       self.unet(x.permute(0							,						2							,						1    )							,						snake_case_    ).sample.permute(0							,						2							,						1    )

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

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





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

												__snake_case     			=       {0: self.to_torch(snake_case_    )}
												__snake_case     			=       (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)
												__snake_case     			=       randn_tensor(snake_case_							,						device=self.unet.device    )
												__snake_case     			=       self.reset_xa(snake_case_							,						snake_case_							,						self.action_dim    )
												__snake_case     			=       self.to_torch(snake_case_    )

												# run the diffusion process
												__snake_case			,   __snake_case     			=       self.run_diffusion(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )

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

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

												__snake_case     			=       denorm_actions[selected_index, 0]
												return denorm_actions
