

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 _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			def __init__(	self							:       Optional[Any]					,					a							:       UNetaDModel					,					a							:       UNetaDModel					,					a							:       DDPMScheduler					,					a							:       Dict					,					)							->				List[str]:

				"""simple docstring"""

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

				"""simple docstring"""

				return (x_in - self.means[key]) / self.stds[key]
			def 							__UpperCamelCase       (	self							:       Any					,					a							:       List[str]					,					a							:       Optional[int]		)							->				List[str]:

				"""simple docstring"""

				return x_in * self.stds[key] + self.means[key]
			def 							__UpperCamelCase       (	self							:       Union[str, Any]					,					a							:       int		)							->				List[str]:

				"""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							:       List[Any]					,					a							:       Optional[Any]					,					a							:       List[Any]					,					a							:       List[str]		)							->				Dict:

				"""simple docstring"""

				for key, val in cond.items():
					SCREAMING_SNAKE_CASE      :					List[Any]    =				val.clone()
				return x_in
			def 							__UpperCamelCase       (	self							:       Optional[int]					,					a							:       str					,					a							:       List[str]					,					a							:       Any					,					a							:       int		)							->				List[Any]:

				"""simple docstring"""

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

							SCREAMING_SNAKE_CASE      :					Dict    =				self.scheduler._get_variance(a		)
							SCREAMING_SNAKE_CASE      :					int    =				torch.exp(0.5 * posterior_variance		)
							SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				model_std * grad

						SCREAMING_SNAKE_CASE      :					int    =				0
						SCREAMING_SNAKE_CASE      :					List[str]    =				x.detach()
						SCREAMING_SNAKE_CASE      :					Any    =				x + scale * grad
						SCREAMING_SNAKE_CASE      :					Dict    =				self.reset_xa(a					,					a					,					self.action_dim		)

					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.unet(x.permute(0					,					2					,					1		)					,					a		).sample.permute(0					,					2					,					1		)

					# TODO: verify deprecation of this kwarg
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.scheduler.step(a					,					a					,					a					,					predict_epsilon=a		)["prev_sample"]

					# apply conditions to the trajectory (set the initial state)
					SCREAMING_SNAKE_CASE      :					Dict    =				self.reset_xa(a					,					a					,					self.action_dim		)
					SCREAMING_SNAKE_CASE      :					Tuple    =				self.to_torch(a		)
				return x, y






			def __call__(	self							:       Dict					,					a							:       int					,					a							:       int=64					,					a							:       Union[str, Any]=32					,					a							:       str=2					,					a							:       Tuple=0.1		)							->				Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.normalize(a					,					"observations"		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				obs[None].repeat(a					,					axis=0		)

				SCREAMING_SNAKE_CASE      :					str    =				{0: self.to_torch(a		)}
				SCREAMING_SNAKE_CASE      :					Dict    =				(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)
				SCREAMING_SNAKE_CASE      :					str    =				randn_tensor(a					,					device=self.unet.device		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.reset_xa(a					,					a					,					self.action_dim		)
				SCREAMING_SNAKE_CASE      :					Dict    =				self.to_torch(a		)

				# run the diffusion process
				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					int    =				self.run_diffusion(a					,					a					,					a					,					a		)

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

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

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				denorm_actions[selected_index, 0]
				return denorm_actions






"""simple docstring"""
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration






def 					A__				(			UpperCamelCase       ):
							A		=  [
							    "encoder.version",
							    "decoder.version",
							    "model.encoder.version",
							    "model.decoder.version",
							    "decoder.output_projection.weight",
							    "_float_tensor",
							    "encoder.embed_positions._float_tensor",
							    "decoder.embed_positions._float_tensor",
							]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase					,  UpperCamelCase       )






def 					A__				(			UpperCamelCase       ):
							A		=  list(s_dict.keys()       )
							for key in keys:
														if "transformer_layers" in key:
																					A		=  s_dict.pop(UpperCamelCase       )
														elif "subsample" in key:
																					A		=  s_dict.pop(UpperCamelCase       )






def 					A__				(			UpperCamelCase       ):
							A,	A		=  emb.weight.shape
							A		=  nn.Linear(UpperCamelCase					,  UpperCamelCase					,  bias=UpperCamelCase       )
							A		=  emb.weight.data
							return lin_layer






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  torch.load(UpperCamelCase					,  map_location="cpu"       )
							A		=  mam_aaa["args"]
							A		=  mam_aaa["model"]
							A		=  state_dict["decoder.output_projection.weight"]

							remove_ignore_keys_(UpperCamelCase       )
							rename_keys(UpperCamelCase       )

							A		=  state_dict["decoder.embed_tokens.weight"].shape[0]

							A		=  args.share_decoder_input_output_embed

							A		=  [int(UpperCamelCase       ) for i in args.conv_kernel_sizes.split(","       )]
							A		=  SpeechaTextConfig(
							    vocab_size=UpperCamelCase					,  max_source_positions=args.max_source_positions					,  max_target_positions=args.max_target_positions					,  encoder_layers=args.encoder_layers					,  decoder_layers=args.decoder_layers					,  encoder_attention_heads=args.encoder_attention_heads					,  decoder_attention_heads=args.decoder_attention_heads					,  encoder_ffn_dim=args.encoder_ffn_embed_dim					,  decoder_ffn_dim=args.decoder_ffn_embed_dim					,  d_model=args.encoder_embed_dim					,  dropout=args.dropout					,  attention_dropout=args.attention_dropout					,  activation_dropout=args.activation_dropout					,  activation_function="relu"					,  num_conv_layers=len(UpperCamelCase       )					,  conv_channels=args.conv_channels					,  conv_kernel_sizes=UpperCamelCase					,  input_feat_per_channel=args.input_feat_per_channel					,  input_channels=args.input_channels					,  tie_word_embeddings=UpperCamelCase					,  num_beams=5					,  max_length=200					,  use_cache=UpperCamelCase					,  decoder_start_token_id=2					,  early_stopping=UpperCamelCase					,  )

							A		=  SpeechaTextForConditionalGeneration(UpperCamelCase       )
							A,	A		=  model.model.load_state_dict(UpperCamelCase					,  strict=UpperCamelCase       )
							if len(UpperCamelCase       ) > 0 and not set(UpperCamelCase       ) <= {
							    "encoder.embed_positions.weights",
							    "decoder.embed_positions.weights",
							}:
														raise ValueError(
														    "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"
														    F" but all the following weights are missing {missing}"       )

							if tie_embeds:
														A		=  make_linear_from_emb(model.model.decoder.embed_tokens       )
							else:
														A		=  lm_head_weights

							model.save_pretrained(UpperCamelCase       )


if __name__ == "__main__":
		_snake_case     :		Optional[int]       						= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
		parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		_snake_case     :		str       						= parser.parse_args()
		convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)


