






'''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     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  ):
       super().__init__()
       UpperCamelCase_					: Any            =				value_function
       UpperCamelCase_					: Any            =				unet
       UpperCamelCase_					: Optional[int]            =				scheduler
       UpperCamelCase_					: Tuple            =				env
       UpperCamelCase_					: int            =				env.get_dataset()
       UpperCamelCase_					: Optional[Any]            =				{}
       for key in self.data.keys():
             try:
                   UpperCamelCase_					: str            =				self.data[key].mean()
             except:  # noqa: E722
                   pass
       UpperCamelCase_					: str            =				{}
       for key in self.data.keys():
             try:
                   UpperCamelCase_					: Union[str, Any]            =				self.data[key].std()
             except:  # noqa: E722
                   pass
       UpperCamelCase_					: str            =				env.observation_space.shape[0]
       UpperCamelCase_					: Optional[int]            =				env.action_space.shape[0]
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       return (x_in - self.means[key]) / self.stds[key]
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       return x_in * self.stds[key] + self.means[key]
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       if type(__lowerCAmelCase			) is dict:
             return {k: self.to_torch(__lowerCAmelCase			) for k, v in x_in.items()}
       elif torch.is_tensor(__lowerCAmelCase			):
             return x_in.to(self.unet.device			)
       return torch.tensor(__lowerCAmelCase      ,  device=self.unet.device			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       for key, val in cond.items():
             UpperCamelCase_					: Optional[Any]            =				val.clone()
       return x_in
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Union[str, Any]            =				x.shape[0]
       UpperCamelCase_					: List[Any]            =				None
       for i in tqdm.tqdm(self.scheduler.timesteps			):
             # create batch of timesteps to pass into model
             UpperCamelCase_					: Tuple            =				torch.full((batch_size,)      ,  __lowerCAmelCase      ,  device=self.unet.device      ,  dtype=torch.long			)
             for _ in range(__lowerCAmelCase			):
                   with torch.enable_grad():
                         x.requires_grad_()

                         # permute to match dimension for pre-trained models
                         UpperCamelCase_					: Tuple            =				self.value_function(x.permute(0      ,  2      ,  1			)      ,  __lowerCAmelCase			).sample
                         UpperCamelCase_					: Any            =				torch.autograd.grad([y.sum()]      ,  [x]			)[0]

                         UpperCamelCase_					: str            =				self.scheduler._get_variance(__lowerCAmelCase			)
                         UpperCamelCase_					: str            =				torch.exp(0.5 * posterior_variance			)
                         UpperCamelCase_					: Any            =				model_std * grad

                   UpperCamelCase_					: Tuple            =				0
                   UpperCamelCase_					: Optional[int]            =				x.detach()
                   UpperCamelCase_					: int            =				x + scale * grad
                   UpperCamelCase_					: Dict            =				self.reset_xa(__lowerCAmelCase      ,  __lowerCAmelCase      ,  self.action_dim			)

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

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

             # apply conditions to the trajectory (set the initial state)
             UpperCamelCase_					: Tuple            =				self.reset_xa(__lowerCAmelCase      ,  __lowerCAmelCase      ,  self.action_dim			)
             UpperCamelCase_					: Dict            =				self.to_torch(__lowerCAmelCase			)
       return x, y





 def __call__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=64      ,  __lowerCAmelCase=32      ,  __lowerCAmelCase=2      ,  __lowerCAmelCase=0.1			):
       # normalize the observations and create  batch dimension
       UpperCamelCase_					: int            =				self.normalize(__lowerCAmelCase      ,  """observations"""			)
       UpperCamelCase_					: List[str]            =				obs[None].repeat(__lowerCAmelCase      ,  axis=0			)

       UpperCamelCase_					: Tuple            =				{0: self.to_torch(__lowerCAmelCase			)}
       UpperCamelCase_					: Dict            =				(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)
       UpperCamelCase_					: Tuple            =				randn_tensor(__lowerCAmelCase      ,  device=self.unet.device			)
       UpperCamelCase_					: List[Any]            =				self.reset_xa(__lowerCAmelCase      ,  __lowerCAmelCase      ,  self.action_dim			)
       UpperCamelCase_					: Optional[Any]            =				self.to_torch(__lowerCAmelCase			)

       # run the diffusion process
       UpperCamelCase_     ,      UpperCamelCase_					: List[Any]            =				self.run_diffusion(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)

       # sort output trajectories by value
       UpperCamelCase_					: int            =				y.argsort(0      ,  descending=__lowerCAmelCase			).squeeze()
       UpperCamelCase_					: Tuple            =				x[sorted_idx]
       UpperCamelCase_					: Dict            =				sorted_values[:, :, : self.action_dim]
       UpperCamelCase_					: Union[str, Any]            =				actions.detach().cpu().numpy()
       UpperCamelCase_					: List[Any]            =				self.de_normalize(__lowerCAmelCase      ,  key="""actions"""			)

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

       UpperCamelCase_					: Any            =				denorm_actions[selected_index, 0]
       return denorm_actions














'''simple docstring'''

import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


UpperCamelCase      					=TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow("", "|", "|"),
    datarow=DataRow("", "|", "|"),
    padding=1,
    with_header_hide=None,
)


UpperCamelCase      					=[]
UpperCamelCase      					=[]

UpperCamelCase      					={"type": "section", "text": {"type": "plain_text", "text": "No failed tests! 🤗", "emoji": True}}

UpperCamelCase      					=[
    {
        "type": "header",
        "text": {
            "type": "plain_text",
            "text": f"🤗 Accelerate nightly {os.environ.get('TEST_TYPE', '')} test results",
            "emoji": True,
        },
    }
]

UpperCamelCase      					=0
for log in Path().glob("*.log"):
      UpperCamelCase      					=0
      with open(log, "r") as f:
            for line in f:
                  UpperCamelCase      					=json.loads(line)
                  if line.get("nodeid", "") != "":
                        UpperCamelCase      					=line["nodeid"]
                        if line.get("duration", None) is not None:
                              UpperCamelCase      					=f"{line['duration']:.4f}"
                              if line.get("outcome", "") == "failed":
                                    section_num_failed += 1
                                    failed.append([test, duration, log.name.split("_")[0]])
                                    total_num_failed += 1
      group_info.append([str(log), section_num_failed, failed])
      UpperCamelCase      					=[]
      log.unlink()

UpperCamelCase      					=""
UpperCamelCase      					=[]
if total_num_failed > 0:
      for name, num_failed, failed_tests in group_info:
            if num_failed > 0:
                  if num_failed == 1:
                        message += f"*{name[1:]}: {num_failed} failed test*\n"
                  else:
                        message += f"*{name[1:]}: {num_failed} failed tests*\n"
                  UpperCamelCase      					=[]
                  UpperCamelCase      					={}
                  for test in failed_tests:
                        UpperCamelCase      					=test[0].split("::")
                        UpperCamelCase      					=data[0].split("/")[-1]
                        if data[0] not in filesafailed:
                              UpperCamelCase      					=[data[1:]]
                        else:
                              filesafailed[data[0]] += [data[1:]]
                        failed_table.append(data)

                  UpperCamelCase      					=[test[0] for test in failed_table]
                  UpperCamelCase      					=list(set(files))
                  # Count number of instances in failed_tests
                  UpperCamelCase      					=[]
                  for file in individual_files:
                        table.append([file, len(filesafailed[file])])

                  UpperCamelCase      					=tabulate(
                      table,
                      headers=["Test Location", "Num Failed"],
                      tablefmt=hf_table_format,
                      stralign="right",
                  )
                  message += f"\n```\n{failed_table}\n```"
                  all_filesafailed.append(filesafailed)
      if len(message) > 3000:
            UpperCamelCase      					="Too many failed tests, please see the full report in the Action results."
            UpperCamelCase      					=len(err) + 10
            UpperCamelCase      					=message[: 3000 - offset] + f"\n...\n```\n{err}"
      print(f"### {message}")
else:
      UpperCamelCase      					="No failed tests! 🤗"
      print(f"## {message}")
      payload.append(no_error_payload)

if os.environ.get("TEST_TYPE", "") != "":
      from slack_sdk import WebClient

      UpperCamelCase      					=WebClient(token=os.environ["SLACK_API_TOKEN"])
      if message != "No failed tests! 🤗":
            UpperCamelCase      					={
                "type": "section",
                "text": {
                    "type": "mrkdwn",
                    "text": message,
                },
            }
            payload.append(md_report)
            UpperCamelCase      					={
                "type": "section",
                "text": {
                    "type": "mrkdwn",
                    "text": "*For more details:*",
                },
                "accessory": {
                    "type": "button",
                    "text": {
                        "type": "plain_text",
                        "text": "Check Action results",
                        "emoji": True,
                    },
                    "url": f"https://github.com/{os.environ['GITHUB_REPOSITORY']}/actions/runs/{os.environ['GITHUB_RUN_ID']}",
                },
            }
            payload.append(action_button)
            UpperCamelCase      					={
                "type": "context",
                "elements": [
                    {
                        "type": "plain_text",
                        "text": f"Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}",
                    }
                ],
            }
            payload.append(date_report)
      UpperCamelCase      					=client.chat_postMessage(channel="#accelerate-ci-daily", text=message, blocks=payload)
      UpperCamelCase      					=response.data["ts"]
      for failed_file in all_filesafailed:
            for test_location, test_failures in failed_file.items():
                  # Keep only the first instance of the test name
                  UpperCamelCase      					=""
                  for i, row in enumerate(test_failures):
                        if row[0] != test_class:
                              UpperCamelCase      					=row[0]
                        else:
                              UpperCamelCase      					=""

                  UpperCamelCase      					={
                      "type": "section",
                      "text": {
                          "type": "mrkdwn",
                          "text": f"Test location: {test_location}\n```\n{tabulate(test_failures, headers=['Class', 'Test'], tablefmt=hf_table_format, stralign='right')}\n```",
                      },
                  }

                  client.chat_postMessage(
                      channel="#accelerate-ci-daily",
                      thread_ts=ts,
                      blocks=[payload],
                  )






