


"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     Optional[int]			) ->      Optional[int]:
		__a           =			AutoConfig.from_pretrained(lowerCAmelCase__			)
		__a           =			FlaxAutoModelForSeqaSeqLM.from_config(config=lowerCAmelCase__			)
		__a           =			checkpoints.load_tax_checkpoint(lowerCAmelCase__			)

		__a           =			'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

		if config.model_type == "t5":
				__a           =			'''SelfAttention'''
		if config.model_type == "longt5" and config.encoder_attention_type == "local":
				__a           =			'''LocalSelfAttention'''
		elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
				__a           =			'''TransientGlobalSelfAttention'''
		else:
				raise ValueError(
				    '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
				    ''' attribute with a value from [\'local\', \'transient-global].'''			)

		# Encoder
		for layer_index in range(config.num_layers			):
				__a           =			f'''layers_{str(lowerCAmelCase__			)}'''

				# Self-Attention
				__a           =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
				__a           =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
				__a           =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
				__a           =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

				# Global input layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						__a           =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

				# Layer Normalization
				__a           =			tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

				if split_mlp_wi:
						__a           =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
						__a           =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
				else:
						__a           =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

				__a           =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

				# Layer Normalization
				__a           =			tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

				# Assigning
				__a           =			flax_model.params['''encoder''']['''block'''][str(lowerCAmelCase__			)]['''layer''']
				__a           =			tax_attention_key
				__a           =			tax_attention_out
				__a           =			tax_attention_query
				__a           =			tax_attention_value

				__a           =			tax_attention_layer_norm

				# Global input layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
						__a           =			tax_global_layer_norm

				if split_mlp_wi:
						__a           =			tax_mlp_wi_a
						__a           =			tax_mlp_wi_a
				else:
						__a           =			tax_mlp_wi

				__a           =			tax_mlp_wo
				__a           =			tax_mlp_layer_norm

				__a           =			flax_model_encoder_layer_block

		# Only for layer 0:
		__a           =			tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
		__a           =			tax_encoder_rel_embedding

		# Side/global relative position_bias + layer norm
		if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
				__a           =			tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
				__a           =			tax_encoder_global_rel_embedding

		# Assigning
		__a           =			tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
		__a           =			tax_encoder_norm

		# Decoder
		for layer_index in range(config.num_layers			):
				__a           =			f'''layers_{str(lowerCAmelCase__			)}'''

				# Self-Attention
				__a           =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
				__a           =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
				__a           =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
				__a           =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

				# Layer Normalization
				__a           =			tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
				    '''scale'''
				]

				# Encoder-Decoder-Attention
				__a           =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
				__a           =			tax_enc_dec_attention_module['''key''']['''kernel''']
				__a           =			tax_enc_dec_attention_module['''out''']['''kernel''']
				__a           =			tax_enc_dec_attention_module['''query''']['''kernel''']
				__a           =			tax_enc_dec_attention_module['''value''']['''kernel''']

				# Layer Normalization
				__a           =			tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

				# MLP
				if split_mlp_wi:
						__a           =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
						__a           =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
				else:
						__a           =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

				__a           =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

				# Layer Normalization
				__a           =			tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

				# Assigning
				__a           =			flax_model.params['''decoder''']['''block'''][str(lowerCAmelCase__			)]['''layer''']
				__a           =			tax_attention_key
				__a           =			tax_attention_out
				__a           =			tax_attention_query
				__a           =			tax_attention_value

				__a           =			tax_pre_attention_layer_norm

				__a           =			tax_enc_dec_attention_key
				__a           =			tax_enc_dec_attention_out
				__a           =			tax_enc_dec_attention_query
				__a           =			tax_enc_dec_attention_value

				__a           =			tax_cross_layer_norm

				if split_mlp_wi:
						__a           =			tax_mlp_wi_a
						__a           =			tax_mlp_wi_a
				else:
						__a           =			tax_mlp_wi

				__a           =			tax_mlp_wo

				__a           =			txa_mlp_layer_norm

				__a           =			flax_model_decoder_layer_block

		# Decoder Normalization
		__a           =			tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
		__a           =			txa_decoder_norm

		# Only for layer 0:
		__a           =			tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
		__a           =			tax_decoder_rel_embedding

		# Token Embeddings
		__a           =			tax_model['''target''']['''token_embedder''']['''embedding''']
		__a           =			txa_token_embeddings

		# LM Head (only in v1.1 and LongT5 checkpoints)
		if "logits_dense" in tax_model["target"]["decoder"]:
				__a           =			tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

		flax_model.save_pretrained(lowerCAmelCase__			)
		print('''T5X Model was sucessfully converted!'''			)


if __name__ == "__main__":
				lowercase_			     =  argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
				)
				parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
				parser.add_argument(
				    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
				)
				lowercase_			     =  parser.parse_args()
				convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








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					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      A_				,      A_				,      A_				,      A_				,      )  ->					Optional[int]:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase		=     value_function
				UpperCamelCase		=     unet
				UpperCamelCase		=     scheduler
				UpperCamelCase		=     env
				UpperCamelCase		=     env.get_dataset()
				UpperCamelCase		=     {}
				for key in self.data.keys():
						try:
								UpperCamelCase		=     self.data[key].mean()
						except:  # noqa: E722
								pass
				UpperCamelCase		=     {}
				for key in self.data.keys():
						try:
								UpperCamelCase		=     self.data[key].std()
						except:  # noqa: E722
								pass
				UpperCamelCase		=     env.observation_space.shape[0]
				UpperCamelCase		=     env.action_space.shape[0]



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Optional[Any]:
				"""simple docstring"""
				return (x_in - self.means[key]) / self.stds[key]



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				return x_in * self.stds[key] + self.means[key]



		def __UpperCamelCase ( self				,      A_       )  ->					Any:
				"""simple docstring"""
				if type(A_       ) is dict:
						return {k: self.to_torch(A_       ) for k, v in x_in.items()}
				elif torch.is_tensor(A_       ):
						return x_in.to(self.unet.device       )
				return torch.tensor(A_				,      device=self.unet.device       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				for key, val in cond.items():
						UpperCamelCase		=     val.clone()
				return x_in



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     x.shape[0]
				UpperCamelCase		=     None
				for i in tqdm.tqdm(self.scheduler.timesteps       ):
						# create batch of timesteps to pass into model
						UpperCamelCase		=     torch.full((batch_size,)				,      A_				,      device=self.unet.device				,      dtype=torch.long       )
						for _ in range(A_       ):
								with torch.enable_grad():
										x.requires_grad_()

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

										UpperCamelCase		=     self.scheduler._get_variance(A_       )
										UpperCamelCase		=     torch.exp(0.5 * posterior_variance       )
										UpperCamelCase		=     model_std * grad

								UpperCamelCase		=     0
								UpperCamelCase		=     x.detach()
								UpperCamelCase		=     x + scale * grad
								UpperCamelCase		=     self.reset_xa(A_				,      A_				,      self.action_dim       )

						UpperCamelCase		=     self.unet(x.permute(0				,      2				,      1       )				,      A_       ).sample.permute(0				,      2				,      1       )

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

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



		def __call__( self				,      A_				,      A_=64				,      A_=32				,      A_=2				,      A_=0.1       )  ->					List[str]:
				"""simple docstring"""
				# normalize the observations and create  batch dimension
				UpperCamelCase		=     self.normalize(A_				,      'observations'       )
				UpperCamelCase		=     obs[None].repeat(A_				,      axis=0       )

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

				# run the diffusion process
				UpperCamelCase	,  UpperCamelCase		=     self.run_diffusion(A_				,      A_				,      A_				,      A_       )

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

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

				UpperCamelCase		=     denorm_actions[selected_index, 0]
				return denorm_actions
