"""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       a_      (     __UpperCamelCase			):




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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     str  ,						snake_case__						:     Tuple     ):
							return (x_in - self.means[key]) / self.stds[key]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Any  ,						snake_case__						:     Optional[int]     ):
							return x_in * self.stds[key] + self.means[key]




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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     str  ,						snake_case__						:     str  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Dict     ):
							lowerCAmelCase__        =   x.shape[0]
							lowerCAmelCase__        =   None
							for i in tqdm.tqdm(self.scheduler.timesteps     ):
									# create batch of timesteps to pass into model
									lowerCAmelCase__        =   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
													lowerCAmelCase__        =   self.value_function(x.permute(0  ,						2  ,						1     )  ,						snake_case__     ).sample
													lowerCAmelCase__        =   torch.autograd.grad([y.sum()]  ,						[x]     )[0]

													lowerCAmelCase__        =   self.scheduler._get_variance(snake_case__     )
													lowerCAmelCase__        =   torch.exp(0.5 * posterior_variance     )
													lowerCAmelCase__        =   model_std * grad

											lowerCAmelCase__        =   0
											lowerCAmelCase__        =   x.detach()
											lowerCAmelCase__        =   x + scale * grad
											lowerCAmelCase__        =   self.reset_xa(snake_case__  ,						snake_case__  ,						self.action_dim     )

									lowerCAmelCase__        =   self.unet(x.permute(0  ,						2  ,						1     )  ,						snake_case__     ).sample.permute(0  ,						2  ,						1     )

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

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




					def __call__(	self						:     int  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     int=64  ,						snake_case__						:     Dict=32  ,						snake_case__						:     Any=2  ,						snake_case__						:     Optional[Any]=0.1     ):
							# normalize the observations and create  batch dimension
							lowerCAmelCase__        =   self.normalize(snake_case__  ,						"""observations"""     )
							lowerCAmelCase__        =   obs[None].repeat(snake_case__  ,						axis=0     )

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

							# run the diffusion process
							lowerCAmelCase__   ,					lowerCAmelCase__        =   self.run_diffusion(snake_case__  ,						snake_case__  ,						snake_case__  ,						snake_case__     )

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

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

							lowerCAmelCase__        =   denorm_actions[selected_index, 0]
							return denorm_actions





"""simple docstring"""




import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402

class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   0




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32"""     )
							self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									lowerCAmelCase__        =   Path(snake_case__     ) / """config.json"""
									json.dump(
									    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)
									json.dump({"""model_type""": """clip"""}  ,						open(snake_case__  ,						"""w"""     )     )

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
									self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							# Ensure we can load the image processor from the feature extractor config
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									lowerCAmelCase__        =   Path(snake_case__     ) / """config.json"""
									json.dump(
									    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)
									json.dump({"""model_type""": """clip"""}  ,						open(snake_case__  ,						"""w"""     )     )

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
									self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   CLIPConfig()

									# Create a dummy config file with image_proceesor_type
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									lowerCAmelCase__        =   Path(snake_case__     ) / """config.json"""
									json.dump(
									    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)
									json.dump({"""model_type""": """clip"""}  ,						open(snake_case__  ,						"""w"""     )     )

									# remove image_processor_type to make sure config.json alone is enough to load image processor locally
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     ).to_dict()

									config_dict.pop("""image_processor_type"""     )
									lowerCAmelCase__        =   CLIPImageProcessor(**snake_case__     )

									# save in new folder
									model_config.save_pretrained(snake_case__     )
									config.save_pretrained(snake_case__     )

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )

									# make sure private variable is not incorrectly saved
									lowerCAmelCase__        =   json.loads(config.to_json_string()     )
									self.assertTrue("""_processor_class""" not in dict_as_saved     )

							self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									json.dump(
									    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
									self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							with self.assertRaisesRegex(
							    snake_case__  ,						"""clip-base is not a local folder and is not a valid model identifier"""     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""clip-base"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							with self.assertRaisesRegex(
							    snake_case__  ,						R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__  ,						revision="""aaaaaa"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							with self.assertRaisesRegex(
							    snake_case__  ,						"""hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""  ,						):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							# If remote code is not set, we will time out when asking whether to load the model.
							with self.assertRaises(snake_case__     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""     )
							# If remote code is disabled, we can't load this config.
							with self.assertRaises(snake_case__     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_image_processor"""  ,						trust_remote_code=snake_case__     )

							lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(
							    """hf-internal-testing/test_dynamic_image_processor"""  ,						trust_remote_code=snake_case__     )
							self.assertEqual(image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )

							# Test image processor can be reloaded.
							with tempfile.TemporaryDirectory() as tmp_dir:
									image_processor.save_pretrained(snake_case__     )
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__  ,						trust_remote_code=snake_case__     )
							self.assertEqual(reloaded_image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							try:
									AutoConfig.register("""custom"""  ,						snake_case__     )
									AutoImageProcessor.register(snake_case__  ,						snake_case__     )
									# Trying to register something existing in the Transformers library will raise an error
									with self.assertRaises(snake_case__     ):
											AutoImageProcessor.register(snake_case__  ,						snake_case__     )

									with tempfile.TemporaryDirectory() as tmpdirname:
											lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
											lowerCAmelCase__        =   Path(snake_case__     ) / """config.json"""
											json.dump(
											    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)
											json.dump({"""model_type""": """clip"""}  ,						open(snake_case__  ,						"""w"""     )     )

											lowerCAmelCase__        =   CustomImageProcessor.from_pretrained(snake_case__     )

									# Now that the config is registered, it can be used as any other config with the auto-API
									with tempfile.TemporaryDirectory() as tmp_dir:
											image_processor.save_pretrained(snake_case__     )
											lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
											self.assertIsInstance(snake_case__  ,						snake_case__     )

							finally:
									if "custom" in CONFIG_MAPPING._extra_content:
											del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
											del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):

							class       a_      (     __UpperCamelCase			):
												UpperCamelCase_ :	Tuple			=    True

							try:
									AutoConfig.register("""custom"""  ,						snake_case__     )
									AutoImageProcessor.register(snake_case__  ,						snake_case__     )
									# If remote code is not set, the default is to use local
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""     )
									self.assertEqual(image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )
									self.assertTrue(image_processor.is_local     )

									# If remote code is disabled, we load the local one.
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_image_processor"""  ,						trust_remote_code=snake_case__     )
									self.assertEqual(image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )
									self.assertTrue(image_processor.is_local     )

									# If remote is enabled, we load from the Hub
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_image_processor"""  ,						trust_remote_code=snake_case__     )
									self.assertEqual(image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )
									self.assertTrue(not hasattr(snake_case__  ,						"""is_local"""     )     )

							finally:
									if "custom" in CONFIG_MAPPING._extra_content:
											del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
											del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]




