


import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


UpperCAmelCase      =   (
    """https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"""
)


UpperCAmelCase      =   logging.get_logger(__name__)  # pylint: disable=invalid-name






def 	__lowerCAmelCase   ()->  Optional[Any]:




      """simple docstring"""

      snake_case_	=				'''https://pypi.org/pypi/diffusers/json'''
      snake_case_	=				json.loads(request.urlopen(SCREAMING_SNAKE_CASE ).read() )['''releases'''].keys()
      return sorted(SCREAMING_SNAKE_CASE , key=lambda SCREAMING_SNAKE_CASE : version.Version(SCREAMING_SNAKE_CASE ) )






def 	__lowerCAmelCase   ()->  List[Any]:




      """simple docstring"""

      if HF_MODULES_CACHE in sys.path:
            return

      sys.path.append(SCREAMING_SNAKE_CASE )
      os.makedirs(SCREAMING_SNAKE_CASE , exist_ok=SCREAMING_SNAKE_CASE )
      snake_case_	=				Path(SCREAMING_SNAKE_CASE ) / '''__init__.py'''
      if not init_path.exists():
            init_path.touch()






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  str:




      """simple docstring"""

      init_hf_modules()
      snake_case_	=				Path(SCREAMING_SNAKE_CASE ) / name
      # If the parent module does not exist yet, recursively create it.
      if not dynamic_module_path.parent.exists():
            create_dynamic_module(dynamic_module_path.parent )
      os.makedirs(SCREAMING_SNAKE_CASE , exist_ok=SCREAMING_SNAKE_CASE )
      snake_case_	=				dynamic_module_path / '''__init__.py'''
      if not init_path.exists():
            init_path.touch()






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      with open(SCREAMING_SNAKE_CASE , '''r''' , encoding='''utf-8''' ) as f:
            snake_case_	=				f.read()

      # Imports of the form `import .xxx`
      snake_case_	=				re.findall('''^\s*import\s+\.(\S+)\s*$''' , SCREAMING_SNAKE_CASE , flags=re.MULTILINE )
      # Imports of the form `from .xxx import yyy`
      relative_imports += re.findall('''^\s*from\s+\.(\S+)\s+import''' , SCREAMING_SNAKE_CASE , flags=re.MULTILINE )
      # Unique-ify
      return list(set(SCREAMING_SNAKE_CASE ) )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Any:




      """simple docstring"""

      snake_case_	=				False
      snake_case_	=				[module_file]
      snake_case_	=				[]

      # Let's recurse through all relative imports
      while not no_change:
            snake_case_	=				[]
            for f in files_to_check:
                  new_imports.extend(get_relative_imports(SCREAMING_SNAKE_CASE ) )

            snake_case_	=				Path(SCREAMING_SNAKE_CASE ).parent
            snake_case_	=				[str(module_path / m ) for m in new_imports]
            snake_case_	=				[f for f in new_import_files if f not in all_relative_imports]
            snake_case_	=				[f'''{f}.py''' for f in new_import_files]

            snake_case_	=				len(SCREAMING_SNAKE_CASE ) == 0
            all_relative_imports.extend(SCREAMING_SNAKE_CASE )

      return all_relative_imports






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Dict:




      """simple docstring"""

      with open(SCREAMING_SNAKE_CASE , '''r''' , encoding='''utf-8''' ) as f:
            snake_case_	=				f.read()

      # Imports of the form `import xxx`
      snake_case_	=				re.findall('''^\s*import\s+(\S+)\s*$''' , SCREAMING_SNAKE_CASE , flags=re.MULTILINE )
      # Imports of the form `from xxx import yyy`
      imports += re.findall('''^\s*from\s+(\S+)\s+import''' , SCREAMING_SNAKE_CASE , flags=re.MULTILINE )
      # Only keep the top-level module
      snake_case_	=				[imp.split('''.''' )[0] for imp in imports if not imp.startswith('''.''' )]

      # Unique-ify and test we got them all
      snake_case_	=				list(set(SCREAMING_SNAKE_CASE ) )
      snake_case_	=				[]
      for imp in imports:
            try:
                  importlib.import_module(SCREAMING_SNAKE_CASE )
            except ImportError:
                  missing_packages.append(SCREAMING_SNAKE_CASE )

      if len(SCREAMING_SNAKE_CASE ) > 0:
            raise ImportError(
                '''This modeling file requires the following packages that were not found in your environment: '''
                f'''{", ".join(SCREAMING_SNAKE_CASE )}. Run `pip install {" ".join(SCREAMING_SNAKE_CASE )}`''' )

      return get_relative_imports(SCREAMING_SNAKE_CASE )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Optional[Any]:




      """simple docstring"""

      snake_case_	=				module_path.replace(os.path.sep , '''.''' )
      snake_case_	=				importlib.import_module(SCREAMING_SNAKE_CASE )

      if class_name is None:
            return find_pipeline_class(SCREAMING_SNAKE_CASE )
      return getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  Optional[int]:




      """simple docstring"""

      from ..pipelines import DiffusionPipeline

      snake_case_	=				dict(inspect.getmembers(SCREAMING_SNAKE_CASE , inspect.isclass ) )

      snake_case_	=				None
      for cls_name, cls in cls_members.items():
            if (
                cls_name != DiffusionPipeline.__name__
                and issubclass(cls , SCREAMING_SNAKE_CASE )
                and cls.__module__.split('''.''' )[0] != "diffusers"
            ):
                  if pipeline_class is not None:
                        raise ValueError(
                            f'''Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'''
                            f''' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'''
                            f''' {loaded_module}.''' )
                  snake_case_	=				cls

      return pipeline_class






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = False , SCREAMING_SNAKE_CASE = False , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = False , )->  int:




      """simple docstring"""

      snake_case_	=				str(SCREAMING_SNAKE_CASE )

      snake_case_	=				os.path.join(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )

      if os.path.isfile(SCREAMING_SNAKE_CASE ):
            snake_case_	=				module_file_or_url
            snake_case_	=				'''local'''
      elif pretrained_model_name_or_path.count('''/''' ) == 0:
            snake_case_	=				get_diffusers_versions()
            # cut ".dev0"
            snake_case_	=				'''v''' + '''.'''.join(__version__.split('''.''' )[:3] )

            # retrieve github version that matches
            if revision is None:
                  snake_case_	=				latest_version if latest_version[1:] in available_versions else '''main'''
                  logger.info(f'''Defaulting to latest_version: {revision}.''' )
            elif revision in available_versions:
                  snake_case_	=				f'''v{revision}'''
            elif revision == "main":
                  snake_case_	=				revision
            else:
                  raise ValueError(
                      f'''`custom_revision`: {revision} does not exist. Please make sure to choose one of'''
                      f''' {", ".join(available_versions + ["main"] )}.''' )

            # community pipeline on GitHub
            snake_case_	=				COMMUNITY_PIPELINES_URL.format(revision=SCREAMING_SNAKE_CASE , pipeline=SCREAMING_SNAKE_CASE )
            try:
                  snake_case_	=				cached_download(
                      SCREAMING_SNAKE_CASE , cache_dir=SCREAMING_SNAKE_CASE , force_download=SCREAMING_SNAKE_CASE , proxies=SCREAMING_SNAKE_CASE , resume_download=SCREAMING_SNAKE_CASE , local_files_only=SCREAMING_SNAKE_CASE , use_auth_token=SCREAMING_SNAKE_CASE , )
                  snake_case_	=				'''git'''
                  snake_case_	=				pretrained_model_name_or_path + '''.py'''
            except EnvironmentError:
                  logger.error(f'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''' )
                  raise
      else:
            try:
                  # Load from URL or cache if already cached
                  snake_case_	=				hf_hub_download(
                      SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cache_dir=SCREAMING_SNAKE_CASE , force_download=SCREAMING_SNAKE_CASE , proxies=SCREAMING_SNAKE_CASE , resume_download=SCREAMING_SNAKE_CASE , local_files_only=SCREAMING_SNAKE_CASE , use_auth_token=SCREAMING_SNAKE_CASE , )
                  snake_case_	=				os.path.join('''local''' , '''--'''.join(pretrained_model_name_or_path.split('''/''' ) ) )
            except EnvironmentError:
                  logger.error(f'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.''' )
                  raise

    # Check we have all the requirements in our environment
      snake_case_	=				check_imports(SCREAMING_SNAKE_CASE )

      # Now we move the module inside our cached dynamic modules.
      snake_case_	=				DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
      create_dynamic_module(SCREAMING_SNAKE_CASE )
      snake_case_	=				Path(SCREAMING_SNAKE_CASE ) / full_submodule
      if submodule == "local" or submodule == "git":
            # We always copy local files (we could hash the file to see if there was a change, and give them the name of
            # that hash, to only copy when there is a modification but it seems overkill for now).
            # The only reason we do the copy is to avoid putting too many folders in sys.path.
            shutil.copy(SCREAMING_SNAKE_CASE , submodule_path / module_file )
            for module_needed in modules_needed:
                  snake_case_	=				f'''{module_needed}.py'''
                  shutil.copy(os.path.join(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) , submodule_path / module_needed )
      else:
            # Get the commit hash
            # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
            if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
                  snake_case_	=				use_auth_token
            elif use_auth_token is True:
                  snake_case_	=				HfFolder.get_token()
            else:
                  snake_case_	=				None

            snake_case_	=				model_info(SCREAMING_SNAKE_CASE , revision=SCREAMING_SNAKE_CASE , token=SCREAMING_SNAKE_CASE ).sha

            # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
            # benefit of versioning.
            snake_case_	=				submodule_path / commit_hash
            snake_case_	=				full_submodule + os.path.sep + commit_hash
            create_dynamic_module(SCREAMING_SNAKE_CASE )

            if not (submodule_path / module_file).exists():
                  shutil.copy(SCREAMING_SNAKE_CASE , submodule_path / module_file )
            # Make sure we also have every file with relative
            for module_needed in modules_needed:
                  if not (submodule_path / module_needed).exists():
                        get_cached_module_file(
                            SCREAMING_SNAKE_CASE , f'''{module_needed}.py''' , cache_dir=SCREAMING_SNAKE_CASE , force_download=SCREAMING_SNAKE_CASE , resume_download=SCREAMING_SNAKE_CASE , proxies=SCREAMING_SNAKE_CASE , use_auth_token=SCREAMING_SNAKE_CASE , revision=SCREAMING_SNAKE_CASE , local_files_only=SCREAMING_SNAKE_CASE , )
      return os.path.join(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = False , SCREAMING_SNAKE_CASE = False , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = False , **SCREAMING_SNAKE_CASE , )->  str:




      """simple docstring"""

      snake_case_	=				get_cached_module_file(
          SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cache_dir=SCREAMING_SNAKE_CASE , force_download=SCREAMING_SNAKE_CASE , resume_download=SCREAMING_SNAKE_CASE , proxies=SCREAMING_SNAKE_CASE , use_auth_token=SCREAMING_SNAKE_CASE , revision=SCREAMING_SNAKE_CASE , local_files_only=SCREAMING_SNAKE_CASE , )
      return get_class_in_module(SCREAMING_SNAKE_CASE , final_module.replace('''.py''' , '''''' ) )



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_						(			lowerCamelCase__	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       return (x_in - self.means[key]) / self.stds[key]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       return x_in * self.stds[key] + self.means[key]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       if type(_UpperCAmelCase ) is dict:
             return {k: self.to_torch(_UpperCAmelCase ) for k, v in x_in.items()}
       elif torch.is_tensor(_UpperCAmelCase ):
             return x_in.to(self.unet.device )
       return torch.tensor(_UpperCAmelCase		,				device=self.unet.device )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       for key, val in cond.items():
             snake_case_	=				val.clone()
       return x_in



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       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,)		,				_UpperCAmelCase		,				device=self.unet.device		,				dtype=torch.long )
             for _ in range(_UpperCAmelCase ):
                   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 )		,				_UpperCAmelCase ).sample
                         snake_case_	=				torch.autograd.grad([y.sum()]		,				[x] )[0]

                         snake_case_	=				self.scheduler._get_variance(_UpperCAmelCase )
                         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(_UpperCAmelCase		,				_UpperCAmelCase		,				self.action_dim )

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

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

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





 def __call__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=64		,				_UpperCAmelCase=32		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.1 ):
       # normalize the observations and create  batch dimension
       snake_case_	=				self.normalize(_UpperCAmelCase		,				'''observations''' )
       snake_case_	=				obs[None].repeat(_UpperCAmelCase		,				axis=0 )

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

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

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

       snake_case_	=				denorm_actions[selected_index, 0]
       return denorm_actions