

import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.





def  _snake_case		(snake_case_					:     Union[str, Any]    ,       snake_case_					:     int    ,       snake_case_					:     Tuple    ,       snake_case_					:     Optional[int]    ,       snake_case_					:     Any    ,       snake_case_					:     Optional[int]  )							->		Union[str, Any]:
  # we don't care about the past rewards in this model

  __UpperCAmelCase	:  Dict		=     states.reshape(1    ,       -1    ,       model.config.state_dim  )
  __UpperCAmelCase	:  Any		=     actions.reshape(1    ,       -1    ,       model.config.act_dim  )
  __UpperCAmelCase	:  int		=     returns_to_go.reshape(1    ,       -1    ,       1  )
  __UpperCAmelCase	:  Tuple		=     timesteps.reshape(1    ,       -1  )

  if model.config.max_length is not None:
    __UpperCAmelCase	:  Optional[Any]		=     states[:, -model.config.max_length :]
    __UpperCAmelCase	:  Dict		=     actions[:, -model.config.max_length :]
    __UpperCAmelCase	:  Optional[Any]		=     returns_to_go[:, -model.config.max_length :]
    __UpperCAmelCase	:  List[str]		=     timesteps[:, -model.config.max_length :]

    # pad all tokens to sequence length
    __UpperCAmelCase	:  Tuple		=     torch.cat(
        [torch.zeros(model.config.max_length - states.shape[1]  ), torch.ones(states.shape[1]  )]  )
    __UpperCAmelCase	:  List[Any]		=     attention_mask.to(dtype=torch.long    ,       device=states.device  ).reshape(1    ,       -1  )
    __UpperCAmelCase	:  List[Any]		=     torch.cat(
        [
            torch.zeros(
                (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)    ,       device=states.device    ,       ),
            states,
        ]    ,       dim=1    ,       ).to(dtype=torch.floataa  )
    __UpperCAmelCase	:  Optional[Any]		=     torch.cat(
        [
            torch.zeros(
                (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)    ,       device=actions.device    ,       ),
            actions,
        ]    ,       dim=1    ,       ).to(dtype=torch.floataa  )
    __UpperCAmelCase	:  str		=     torch.cat(
        [
            torch.zeros(
                (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)    ,       device=returns_to_go.device    ,       ),
            returns_to_go,
        ]    ,       dim=1    ,       ).to(dtype=torch.floataa  )
    __UpperCAmelCase	:  Optional[int]		=     torch.cat(
        [
            torch.zeros(
                (timesteps.shape[0], model.config.max_length - timesteps.shape[1])    ,       device=timesteps.device  ),
            timesteps,
        ]    ,       dim=1    ,       ).to(dtype=torch.long  )
  else:
    __UpperCAmelCase	:  int		=     None

  __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  str		=     model(
      states=snake_case_    ,       actions=snake_case_    ,       rewards=snake_case_    ,       returns_to_go=snake_case_    ,       timesteps=snake_case_    ,       attention_mask=snake_case_    ,       return_dict=snake_case_    ,       )

  return action_preds[0, -1]


# build the environment

_SCREAMING_SNAKE_CASE							 =			gym.make('''Hopper-v3''')
_SCREAMING_SNAKE_CASE							 =			env.observation_space.shape[0]
_SCREAMING_SNAKE_CASE							 =			env.action_space.shape[0]
_SCREAMING_SNAKE_CASE							 =			1_0_0_0
_SCREAMING_SNAKE_CASE							 =			'''cuda'''
_SCREAMING_SNAKE_CASE							 =			1_0_0_0.0  # normalization for rewards/returns
_SCREAMING_SNAKE_CASE							 =			3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_SCREAMING_SNAKE_CASE							 =			np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
_SCREAMING_SNAKE_CASE							 =			np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
_SCREAMING_SNAKE_CASE							 =			torch.from_numpy(state_mean).to(device=device)
_SCREAMING_SNAKE_CASE							 =			torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_SCREAMING_SNAKE_CASE							 =			DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
_SCREAMING_SNAKE_CASE							 =			model.to(device)
model.eval()

for ep in range(1_0):
      _SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE							 =			0, 0
      _SCREAMING_SNAKE_CASE							 =			env.reset()
      _SCREAMING_SNAKE_CASE							 =			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
      _SCREAMING_SNAKE_CASE							 =			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
      _SCREAMING_SNAKE_CASE							 =			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
      _SCREAMING_SNAKE_CASE							 =			torch.zeros(0, device=device, dtype=torch.floataa)

      _SCREAMING_SNAKE_CASE							 =			torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
      for t in range(max_ep_len):
            env.render()
            # add padding
            _SCREAMING_SNAKE_CASE							 =			torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
            _SCREAMING_SNAKE_CASE							 =			torch.cat([rewards, torch.zeros(1, device=device)])

            _SCREAMING_SNAKE_CASE							 =			get_action(
                model,
                (states.to(dtype=torch.floataa) - state_mean) / state_std,
                actions.to(dtype=torch.floataa),
                rewards.to(dtype=torch.floataa),
                target_return.to(dtype=torch.floataa),
                timesteps.to(dtype=torch.long),
            )
            _SCREAMING_SNAKE_CASE							 =			action
            _SCREAMING_SNAKE_CASE							 =			action.detach().cpu().numpy()

            _SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE ,			_SCREAMING_SNAKE_CASE							 =			env.step(action)

            _SCREAMING_SNAKE_CASE							 =			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
            _SCREAMING_SNAKE_CASE							 =			torch.cat([states, cur_state], dim=0)
            _SCREAMING_SNAKE_CASE							 =			reward

            _SCREAMING_SNAKE_CASE							 =			target_return[0, -1] - (reward / scale)
            _SCREAMING_SNAKE_CASE							 =			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
            _SCREAMING_SNAKE_CASE							 =			torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

            episode_return += reward
            episode_length += 1

            if done:
                  break













import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_a       =    '''src/transformers'''

# Matches is_xxx_available()
_a       =    re.compile(r'''is\_([a-z_]*)_available()''')
# Matches from xxx import bla
_a       =    re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
_a       =    re.compile(r'''^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)''')


_a       =    '''
{0} = None
'''

_a       =    '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


_a       =    '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''


def __A       ( __lowerCAmelCase      )->    str:





						"""simple docstring"""



						if _re_test_backend.search(__lowerCAmelCase      ) is None:
												return None
						_UpperCAmelCase							   =     [b[0] for b in _re_backend.findall(__lowerCAmelCase      )]
						backends.sort()
						return "_and_".join(__lowerCAmelCase      )


def __A       ( )->    int:





						"""simple docstring"""



						with open(os.path.join(__lowerCAmelCase					,						'__init__.py'      )					,						'r'					,						encoding='utf-8'					,						newline='\n'      ) as f:
												_UpperCAmelCase							   =     f.readlines()

						# Get to the point we do the actual imports for type checking
						_UpperCAmelCase							   =     0
						while not lines[line_index].startswith('if TYPE_CHECKING'      ):
												line_index += 1

						_UpperCAmelCase							   =     {}
						# Go through the end of the file
						while line_index < len(__lowerCAmelCase      ):
												# If the line is an if is_backend_available, we grab all objects associated.
												_UpperCAmelCase							   =     find_backend(lines[line_index]      )
												if backend is not None:
																		while not lines[line_index].startswith('    else:'      ):
																								line_index += 1
																		line_index += 1

																		_UpperCAmelCase							   =     []
																		# Until we unindent, add backend objects to the list
																		while len(lines[line_index]      ) <= 1 or lines[line_index].startswith(' ' * 8      ):
																								_UpperCAmelCase							   =     lines[line_index]
																								_UpperCAmelCase							   =     _re_single_line_import.search(__lowerCAmelCase      )
																								if single_line_import_search is not None:
																														objects.extend(single_line_import_search.groups()[0].split(', '      )      )
																								elif line.startswith(' ' * 12      ):
																														objects.append(line[12:-2]      )
																								line_index += 1

																		_UpperCAmelCase							   =     objects
												else:
																		line_index += 1

						return backend_specific_objects


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase      )->    Any:





						"""simple docstring"""



						if name.isupper():
												return DUMMY_CONSTANT.format(__lowerCAmelCase      )
						elif name.islower():
												return DUMMY_FUNCTION.format(__lowerCAmelCase					,						__lowerCAmelCase      )
						else:
												return DUMMY_CLASS.format(__lowerCAmelCase					,						__lowerCAmelCase      )


def __A       ( __lowerCAmelCase=None      )->    Union[str, Any]:





						"""simple docstring"""



						if backend_specific_objects is None:
												_UpperCAmelCase							   =     read_init()
						# For special correspondence backend to module name as used in the function requires_modulename
						_UpperCAmelCase							   =     {}

						for backend, objects in backend_specific_objects.items():
												_UpperCAmelCase							   =     '[' + ', '.join(F"""\"{b}\"""" for b in backend.split('_and_'      )      ) + ']'
												_UpperCAmelCase							   =     '# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
												dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
												dummy_file += "\n".join([create_dummy_object(__lowerCAmelCase					,						__lowerCAmelCase      ) for o in objects]      )
												_UpperCAmelCase							   =     dummy_file

						return dummy_files


def __A       ( __lowerCAmelCase=False      )->    int:





						"""simple docstring"""



						_UpperCAmelCase							   =     create_dummy_files()
						# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
						_UpperCAmelCase							   =     {'torch': 'pt'}

						# Locate actual dummy modules and read their content.
						_UpperCAmelCase							   =     os.path.join(__lowerCAmelCase					,						'utils'      )
						_UpperCAmelCase							   =     {
						    backend: os.path.join(__lowerCAmelCase					,						F"""dummy_{short_names.get(__lowerCAmelCase					,						__lowerCAmelCase      )}_objects.py"""      )
						    for backend in dummy_files.keys()
						}

						_UpperCAmelCase							   =     {}
						for backend, file_path in dummy_file_paths.items():
												if os.path.isfile(__lowerCAmelCase      ):
																		with open(__lowerCAmelCase					,						'r'					,						encoding='utf-8'					,						newline='\n'      ) as f:
																								_UpperCAmelCase							   =     f.read()
												else:
																		_UpperCAmelCase							   =     ''

						for backend in dummy_files.keys():
												if dummy_files[backend] != actual_dummies[backend]:
																		if overwrite:
																								print(
																								    F"""Updating transformers.utils.dummy_{short_names.get(__lowerCAmelCase					,						__lowerCAmelCase      )}_objects.py as the main """
																								    '__init__ has new objects.'      )
																								with open(dummy_file_paths[backend]					,						'w'					,						encoding='utf-8'					,						newline='\n'      ) as f:
																														f.write(dummy_files[backend]      )
																		else:
																								raise ValueError(
																								    'The main __init__ has objects that are not present in '
																								    F"""transformers.utils.dummy_{short_names.get(__lowerCAmelCase					,						__lowerCAmelCase      )}_objects.py. Run `make fix-copies` """
																								    'to fix this.'      )


if __name__ == "__main__":
		_a       =    argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		_a       =    parser.parse_args()

		check_dummies(args.fix_and_overwrite)
