




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 						__lowerCAmelCase      (      __magic_name__					):
							def __init__(		self		:Optional[Any]						, __magic_name__		:UNetaDModel						, __magic_name__		:UNetaDModel						, __magic_name__		:DDPMScheduler						, __magic_name__		:Optional[Any]						, ):







								'''simple docstring'''







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







								'''simple docstring'''







								return (x_in - self.means[key]) / self.stds[key]
							def  lowerCamelCase__				(		self		:Tuple						, __magic_name__		:List[str]						, __magic_name__		:str							):







								'''simple docstring'''







								return x_in * self.stds[key] + self.means[key]
							def  lowerCamelCase__				(		self		:Dict						, __magic_name__		:Union[str, Any]							):







								'''simple docstring'''







								if type(__magic_name__							) is dict:
									return {k: self.to_torch(__magic_name__							) for k, v in x_in.items()}
								elif torch.is_tensor(__magic_name__							):
									return x_in.to(self.unet.device							)
								return torch.tensor(__magic_name__						, device=self.unet.device							)
							def  lowerCamelCase__				(		self		:Optional[int]						, __magic_name__		:Tuple						, __magic_name__		:List[str]						, __magic_name__		:Optional[Any]							):







								'''simple docstring'''







								for key, val in cond.items():
									a									=				val.clone()
								return x_in
							def  lowerCamelCase__				(		self		:List[str]						, __magic_name__		:List[Any]						, __magic_name__		:Optional[Any]						, __magic_name__		:Union[str, Any]						, __magic_name__		:int							):







								'''simple docstring'''







								a									=				x.shape[0]
								a									=				None
								for i in tqdm.tqdm(self.scheduler.timesteps							):
									# create batch of timesteps to pass into model
									a									=				torch.full((batch_size,)						, __magic_name__						, device=self.unet.device						, dtype=torch.long							)
									for _ in range(__magic_name__							):
										with torch.enable_grad():
											x.requires_grad_()

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

											a									=				self.scheduler._get_variance(__magic_name__							)
											a									=				torch.exp(0.5 * posterior_variance							)
											a									=				model_std * grad

										a									=				0
										a									=				x.detach()
										a									=				x + scale * grad
										a									=				self.reset_xa(__magic_name__						, __magic_name__						, self.action_dim							)

									a									=				self.unet(x.permute(0						, 2						, 1							)						, __magic_name__							).sample.permute(0						, 2						, 1							)

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

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





							def __call__(		self		:str						, __magic_name__		:Tuple						, __magic_name__		:Union[str, Any]=64						, __magic_name__		:List[str]=32						, __magic_name__		:Tuple=2						, __magic_name__		:Optional[int]=0.1							):







								'''simple docstring'''







								a									=				self.normalize(__magic_name__						, """observations"""							)
								a									=				obs[None].repeat(__magic_name__						, axis=0							)

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

								# run the diffusion process
								a							,					a									=				self.run_diffusion(__magic_name__						, __magic_name__						, __magic_name__						, __magic_name__							)

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

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

								a									=				denorm_actions[selected_index, 0]
								return denorm_actions







import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__UpperCamelCase   :			Tuple          = "."

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
__UpperCamelCase   :			List[str]          = [
    "Assert",
    "AssignVariableOp",
    "EmptyTensorList",
    "MergeV2Checkpoints",
    "ReadVariableOp",
    "ResourceGather",
    "RestoreV2",
    "SaveV2",
    "ShardedFilename",
    "StatefulPartitionedCall",
    "StaticRegexFullMatch",
    "VarHandleOp",
]





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Union[str, Any]:
	a									=				SavedModel()
	a									=				[]

	with open(os.path.join(__lowerCamelCase				,     """utils"""				,     """tf_ops"""				,     """onnx.json"""       )       ) as f:
		a									=				json.load(__lowerCamelCase       )["""opsets"""]

	for i in range(1				,     opset + 1       ):
		onnx_ops.extend(onnx_opsets[str(__lowerCamelCase       )]       )

	with open(__lowerCamelCase				,     """rb"""       ) as f:
		saved_model.ParseFromString(f.read()       )

	a									=				set()

	# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
	for meta_graph in saved_model.meta_graphs:
		# Add operations in the graph definition
		model_op_names.update(node.op for node in meta_graph.graph_def.node       )

		# Go through the functions in the graph definition
		for func in meta_graph.graph_def.library.function:
			# Add operations in each function
			model_op_names.update(node.op for node in func.node_def       )

    # Convert to list, sorted if you want
	a									=				sorted(__lowerCamelCase       )
	a									=				[]

	for op in model_op_names:
		if op not in onnx_ops and op not in INTERNAL_OPS:
			incompatible_ops.append(__lowerCamelCase       )

	if strict and len(__lowerCamelCase       ) > 0:
		raise Exception(f'Found the following incompatible ops for the opset {opset}:\n' + incompatible_ops       )
	elif len(__lowerCamelCase       ) > 0:
		print(f'Found the following incompatible ops for the opset {opset}:'       )
		print(*__lowerCamelCase				,     sep="""\n"""       )
	else:
		print(f'The saved model {saved_model_path} can properly be converted with ONNX.'       )


if __name__ == "__main__":
		__UpperCamelCase   :			Optional[Any]          = argparse.ArgumentParser()
		parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
		parser.add_argument(
		    "--opset", default=12, type=int, help="The ONNX opset against which the model has to be tested."
		)
		parser.add_argument(
		    "--framework", choices=["onnx"], default="onnx", help="Frameworks against which to test the saved model."
		)
		parser.add_argument(
		    "--strict", action="store_true", help="Whether make the checking strict (raise errors) or not (raise warnings)"
		)
		__UpperCamelCase   :			str          = parser.parse_args()

		if args.framework == "onnx":
				onnx_compliancy(args.saved_model_path, args.strict, args.opset)

