



'''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__( lowerCamelCase__     ):







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







    def       lowercase_	(				self			:  Optional[int]						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  str     ):
      return (x_in - self.means[key]) / self.stds[key]







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  List[Any]						,      __snake_case			:  Dict     ):
      return x_in * self.stds[key] + self.means[key]







    def       lowercase_	(				self			:  List[Any]						,      __snake_case			:  List[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       lowercase_	(				self			:  Dict						,      __snake_case			:  Dict						,      __snake_case			:  Optional[Any]						,      __snake_case			:  str     ):
      for key, val in cond.items():
        a						: List[str]					       =					val.clone()
      return x_in







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

            a						: Dict					       =					self.scheduler._get_variance(__snake_case     )
            a						: int					       =					torch.exp(0.5 * posterior_variance     )
            a						: Optional[Any]					       =					model_std * grad

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

        a						: Union[str, Any]					       =					self.unet(x.permute(0						,      2						,      1     )						,      __snake_case     ).sample.permute(0						,      2						,      1     )

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

        # apply conditions to the trajectory (set the initial state)
        a						: Any					       =					self.reset_xa(__snake_case						,      __snake_case						,      self.action_dim     )
        a						: List[Any]					       =					self.to_torch(__snake_case     )
      return x, y







    def __call__(				self			:  int						,      __snake_case			:  List[Any]						,      __snake_case			:  Optional[int]=64						,      __snake_case			:  Optional[int]=32						,      __snake_case			:  Tuple=2						,      __snake_case			:  Union[str, Any]=0.1     ):
      # normalize the observations and create  batch dimension
      a						: Optional[Any]					       =					self.normalize(__snake_case						,      'observations'     )
      a						: Dict					       =					obs[None].repeat(__snake_case						,      axis=0     )

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

      # run the diffusion process
      a       ,     a						: Dict					       =					self.run_diffusion(__snake_case						,      __snake_case						,      __snake_case						,      __snake_case     )

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

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

      a						: Optional[int]					       =					denorm_actions[selected_index, 0]
      return denorm_actions




'''simple docstring'''






import argparse
import os
import re

import packaging.version


lowerCAmelCase: List[str]							=						'examples/'
lowerCAmelCase: List[Any]							=						{
    'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
    'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
lowerCAmelCase: str							=						{
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
lowerCAmelCase: str							=						'README.md'
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  with open(_A			,      'r'			,      encoding='utf-8'			,      newline='\n'    ) as f:
    a						: Tuple					       =					f.read()
  a       ,     a						: Tuple					       =					REPLACE_PATTERNS[pattern]
  a						: Dict					       =					replace.replace('VERSION'			,      _A    )
  a						: Dict					       =					re_pattern.sub(_A			,      _A    )
  with open(_A			,      'w'			,      encoding='utf-8'			,      newline='\n'    ) as f:
    f.write(_A    )
def 				lowerCamelCase__	(						_A    ):
  for folder, directories, fnames in os.walk(_A    ):
    # Removing some of the folders with non-actively maintained examples from the walk
    if "research_projects" in directories:
      directories.remove('research_projects'    )
    if "legacy" in directories:
      directories.remove('legacy'    )
    for fname in fnames:
      if fname.endswith('.py'    ):
        update_version_in_file(os.path.join(_A			,      _A    )			,      _A			,      pattern='examples'    )
def 				lowerCamelCase__	(						_A			,      _A=False    ):
  for pattern, fname in REPLACE_FILES.items():
    update_version_in_file(_A			,      _A			,      _A    )
  if not patch:
    update_version_in_examples(_A    )
def 				lowerCamelCase__	(						):
  a						: Tuple					       =					'🤗 Transformers currently provides the following architectures'
  a						: Any					       =					'1. Want to contribute a new model?'
  with open(_A			,      'r'			,      encoding='utf-8'			,      newline='\n'    ) as f:
    a						: Tuple					       =					f.readlines()

  # Find the start of the list.
  a						: Optional[int]					       =					0
  while not lines[start_index].startswith(_start_prompt    ):
    start_index += 1
  start_index += 1

  a						: Optional[int]					       =					start_index
  # Update the lines in the model list.
  while not lines[index].startswith(_end_prompt    ):
    if lines[index].startswith('1.'    ):
      a						: List[Any]					       =					lines[index].replace(
          'https://huggingface.co/docs/transformers/main/model_doc'			,      'https://huggingface.co/docs/transformers/model_doc'			,      )
    index += 1

  with open(_A			,      'w'			,      encoding='utf-8'			,      newline='\n'    ) as f:
    f.writelines(_A    )
def 				lowerCamelCase__	(						):
  with open(REPLACE_FILES['init']			,      'r'    ) as f:
    a						: Union[str, Any]					       =					f.read()
  a						: Tuple					       =					REPLACE_PATTERNS['init'][0].search(_A    ).groups()[0]
  return packaging.version.parse(_A    )
def 				lowerCamelCase__	(						_A=False    ):
  a						: int					       =					get_version()
  if patch and default_version.is_devrelease:
    raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!'    )
  if default_version.is_devrelease:
    a						: Any					       =					default_version.base_version
  elif patch:
    a						: Dict					       =					f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
  else:
    a						: Union[str, Any]					       =					f"""{default_version.major}.{default_version.minor + 1}.0"""

  # Now let's ask nicely if that's the right one.
  a						: List[Any]					       =					input(f"""Which version are you releasing? [{default_version}]"""    )
  if len(_A    ) == 0:
    a						: Union[str, Any]					       =					default_version

  print(f"""Updating version to {version}."""    )
  global_version_update(_A			,      patch=_A    )
  if not patch:
    print('Cleaning main README, don\'t forget to run `make fix-copies`.'    )
    clean_main_ref_in_model_list()
def 				lowerCamelCase__	(						):
  a						: int					       =					get_version()
  a						: Any					       =					f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
  a						: int					       =					current_version.base_version

  # Check with the user we got that right.
  a						: Tuple					       =					input(f"""Which version are we developing now? [{dev_version}]"""    )
  if len(_A    ) == 0:
    a						: Optional[int]					       =					dev_version

  print(f"""Updating version to {version}."""    )
  global_version_update(_A    )
  print('Cleaning main README, don\'t forget to run `make fix-copies`.'    )
  clean_main_ref_in_model_list()


if __name__ == "__main__":
     lowerCAmelCase: Tuple							=						argparse.ArgumentParser()
     parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
     parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
     lowerCAmelCase: Optional[Any]							=						parser.parse_args()
     if not args.post_release:
          pre_release_work(patch=args.patch)
     elif args.patch:
          print('Nothing to do after a patch :-)')
     else:
          post_release_work()