




'''simple docstring'''





import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


a		:	Optional[int]       =      logging.get_logger(__name__)  # pylint: disable=invalid-name


a		:	str       =      '''lfs-multipart-upload'''




class      UpperCamelCase       (	snake_case__       ):



  @staticmethod
  def     a_							(			__lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      parser.add_parser(
          """lfs-enable-largefiles"""		,     help=(
              """Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."""
          )		,     )
      enable_parser.add_argument("""path"""		,     type=__lowerCAmelCase		,     help="""Local path to repository you want to configure.""" )
      enable_parser.set_defaults(func=lambda __lowerCAmelCase : LfsEnableCommand(__lowerCAmelCase ) )

      _a				:							Union[str, Any]							       =      parser.add_parser(
          __lowerCAmelCase		,     help=(
              """Deprecated: use `huggingface-cli` instead. """
              """Command will get called by git-lfs, do not call it directly."""
          )		,     )
      upload_parser.set_defaults(func=lambda __lowerCAmelCase : LfsUploadCommand(__lowerCAmelCase ) )






class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							List[str]							       =      args



  def     a_							(			self ):







      '''simple docstring'''


      warnings.warn(
          """Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead.""" )
      _a				:							Dict							       =      os.path.abspath(self.args.path )
      if not os.path.isdir(__lowerCAmelCase ):
          print("""This does not look like a valid git repo.""" )
          exit(1 )
      subprocess.run(
          """git config lfs.customtransfer.multipart.path transformers-cli""".split()		,     check=__lowerCAmelCase		,     cwd=__lowerCAmelCase )
      subprocess.run(
          f'''git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'''.split()		,     check=__lowerCAmelCase		,     cwd=__lowerCAmelCase		,     )
      print("""Local repo set up for largefiles""" )






def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Tuple							       =      json.dumps(UpperCAmelCase_						) + """\n"""
    sys.stdout.write(UpperCAmelCase_						)
    sys.stdout.flush()





def UpperCAmelCase    (       ):
    _a				:							Optional[Any]							       =      json.loads(sys.stdin.readline().strip()						)

    if "terminate" in (msg.get("""type"""						), msg.get("""event"""						)):
        # terminate message received
        return None

    if msg.get("""event"""						) not in ("download", "upload"):
        logger.critical("""Received unexpected message"""						)
        sys.exit(1						)

    return msg




class      UpperCamelCase       (	snake_case__       ):



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      filepath
      _a				:							str							       =      seek_from
      _a				:							int							       =      read_limit
      _a				:							List[Any]							       =      0



  def __enter__(			self ):







      '''simple docstring'''


      _a				:							Dict							       =      open(self.filepath		,     """rb""" )
      self.f.seek(self.seek_from )
      return self



  def __len__(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      os.fstat(self.f.fileno() ).st_size
      return min(self.read_limit		,     total_length - self.seek_from )



  def     a_							(			self		,     __lowerCAmelCase=-1 ):







      '''simple docstring'''


      if self.n_seen >= self.read_limit:
          return b""
      _a				:							Optional[Any]							       =      self.read_limit - self.n_seen
      _a				:							Any							       =      self.f.read(remaining_amount if n < 0 else min(__lowerCAmelCase		,     __lowerCAmelCase ) )
      self.n_seen += len(__lowerCAmelCase )
      return data



  def __iter__(			self ):







      '''simple docstring'''


      yield self.read(n=4 * 1024 * 1024 )



  def __exit__(			self		,     *__lowerCAmelCase ):







      '''simple docstring'''


      self.f.close()






class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							int							       =      args



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							List[Any]							       =      json.loads(sys.stdin.readline().strip() )
      if not (init_msg.get("""event""" ) == "init" and init_msg.get("""operation""" ) == "upload"):
          write_msg({"""error""": {"""code""": 32, """message""": """Wrong lfs init operation"""}} )
          sys.exit(1 )

      # The transfer process should use the information it needs from the
      # initiation structure, and also perform any one-off setup tasks it
      # needs to do. It should then respond on stdout with a simple empty
      # confirmation structure, as follows:
      write_msg({} )

      # After the initiation exchange, git-lfs will send any number of
      # transfer requests to the stdin of the transfer process, in a serial sequence.
      while True:
          _a				:							Optional[Any]							       =      read_msg()
          if msg is None:
              # When all transfers have been processed, git-lfs will send
              # a terminate event to the stdin of the transfer process.
              # On receiving this message the transfer process should
              # clean up and terminate. No response is expected.
              sys.exit(0 )

          _a				:							Tuple							       =      msg["""oid"""]
          _a				:							Optional[Any]							       =      msg["""path"""]
          _a				:							Any							       =      msg["""action"""]["""href"""]
          _a				:							int							       =      msg["""action"""]["""header"""]
          _a				:							Optional[int]							       =      int(header.pop("""chunk_size""" ) )
          _a				:							List[str]							       =      list(header.values() )

          _a				:							Optional[Any]							       =      []
          for i, presigned_url in enumerate(__lowerCAmelCase ):
              with FileSlice(__lowerCAmelCase		,     seek_from=i * chunk_size		,     read_limit=__lowerCAmelCase ) as data:
                  _a				:							Any							       =      requests.put(__lowerCAmelCase		,     data=__lowerCAmelCase )
                  r.raise_for_status()
                  parts.append(
                      {
                          """etag""": r.headers.get("""etag""" ),
                          """partNumber""": i + 1,
                      } )
                  # In order to support progress reporting while data is uploading / downloading,
                  # the transfer process should post messages to stdout
                  write_msg(
                      {
                          """event""": """progress""",
                          """oid""": oid,
                          """bytesSoFar""": (i + 1) * chunk_size,
                          """bytesSinceLast""": chunk_size,
                      } )
                  # Not precise but that's ok.

          _a				:							List[Any]							       =      requests.post(
              __lowerCAmelCase		,     json={
                  """oid""": oid,
                  """parts""": parts,
              }		,     )
          r.raise_for_status()

          write_msg({"""event""": """complete""", """oid""": oid} )



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 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    )->	Optional[Any]:






      '''simple docstring'''





      UpperCAmelCase     :			Any     	=states.reshape(1		,       -1		,       model.config.state_dim    )
      UpperCAmelCase     :			Optional[int]     	=actions.reshape(1		,       -1		,       model.config.act_dim    )
      UpperCAmelCase     :			Optional[Any]     	=returns_to_go.reshape(1		,       -1		,       1    )
      UpperCAmelCase     :			Optional[int]     	=timesteps.reshape(1		,       -1    )

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

            # pad all tokens to sequence length
            UpperCAmelCase     :			Optional[int]     	=torch.cat(
                [torch.zeros(model.config.max_length - states.shape[1]    ), torch.ones(states.shape[1]    )]    )
            UpperCAmelCase     :			int     	=attention_mask.to(dtype=torch.long		,       device=states.device    ).reshape(1		,       -1    )
            UpperCAmelCase     :			Optional[int]     	=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     :			Tuple     	=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     :			Tuple     	=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     :			Union[str, Any]     	=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     :			Tuple     	=None

      UpperCAmelCase	,    UpperCAmelCase	,    UpperCAmelCase     :			int     	=model(
          states=__lowerCAmelCase		,       actions=__lowerCAmelCase		,       rewards=__lowerCAmelCase		,       returns_to_go=__lowerCAmelCase		,       timesteps=__lowerCAmelCase		,       attention_mask=__lowerCAmelCase		,       return_dict=__lowerCAmelCase		,       )

      return action_preds[0, -1]


# build the environment

__snake_case         =     gym.make('''Hopper-v3''')
__snake_case         =     env.observation_space.shape[0]
__snake_case         =     env.action_space.shape[0]
__snake_case         =     10_00
__snake_case         =     '''cuda'''
__snake_case         =     1000.0  # normalization for rewards/returns
__snake_case         =     36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case         =     np.array(
    [
        1.31_1279,
        -0.0846_9521,
        -0.538_2719,
        -0.0720_1576,
        0.0493_2366,
        2.106_6856,
        -0.1501_7354,
        0.0087_8345,
        -0.284_8186,
        -0.1854_0096,
        -0.2846_1286,
    ]
)
__snake_case         =     np.array(
    [
        0.1779_0751,
        0.0544_4621,
        0.2129_7139,
        0.1453_0419,
        0.612_4444,
        0.8517_4465,
        1.451_5252,
        0.675_1696,
        1.53_6239,
        1.616_0746,
        5.607_2536,
    ]
)
__snake_case         =     torch.from_numpy(state_mean).to(device=device)
__snake_case         =     torch.from_numpy(state_std).to(device=device)

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

for ep in range(10):
     __snake_case   ,	__snake_case         =     0, 0
     __snake_case         =     env.reset()
     __snake_case         =     torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
     __snake_case         =     torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
     __snake_case         =     torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
     __snake_case         =     torch.zeros(0, device=device, dtype=torch.floataa)

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

          __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),
          )
          __snake_case         =     action
          __snake_case         =     action.detach().cpu().numpy()

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

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

          __snake_case         =     target_return[0, -1] - (reward / scale)
          __snake_case         =     torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
          __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
