




"""simple docstring"""



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

from tabulate import DataRow, TableFormat, tabulate


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


SCREAMING_SNAKE_CASE				=       []
SCREAMING_SNAKE_CASE				=       []

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

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

SCREAMING_SNAKE_CASE				=       0
for log in Path().glob("*.log"):
      SCREAMING_SNAKE_CASE				=       0
      with open(log, "r") as f:
            for line in f:
                  SCREAMING_SNAKE_CASE				=       json.loads(line)
                  if line.get("nodeid", "") != "":
                        SCREAMING_SNAKE_CASE				=       line["nodeid"]
                        if line.get("duration", None) is not None:
                              SCREAMING_SNAKE_CASE				=       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])
      SCREAMING_SNAKE_CASE				=       []
      log.unlink()

SCREAMING_SNAKE_CASE				=       ""
SCREAMING_SNAKE_CASE				=       []
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"
                  SCREAMING_SNAKE_CASE				=       []
                  SCREAMING_SNAKE_CASE				=       {}
                  for test in failed_tests:
                        SCREAMING_SNAKE_CASE				=       test[0].split("::")
                        SCREAMING_SNAKE_CASE				=       data[0].split("/")[-1]
                        if data[0] not in filesafailed:
                              SCREAMING_SNAKE_CASE				=       [data[1:]]
                        else:
                              filesafailed[data[0]] += [data[1:]]
                        failed_table.append(data)

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

                  SCREAMING_SNAKE_CASE				=       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:
            SCREAMING_SNAKE_CASE				=       "Too many failed tests, please see the full report in the Action results."
            SCREAMING_SNAKE_CASE				=       len(err) + 10
            SCREAMING_SNAKE_CASE				=       message[: 3000 - offset] + f'\n...\n```\n{err}'
      print(f'### {message}')
else:
      SCREAMING_SNAKE_CASE				=       "No failed tests! 🤗"
      print(f'## {message}')
      payload.append(no_error_payload)

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

      SCREAMING_SNAKE_CASE				=       WebClient(token=os.environ["SLACK_API_TOKEN"])
      if message != "No failed tests! 🤗":
            SCREAMING_SNAKE_CASE				=       {
                "type": "section",
                "text": {
                    "type": "mrkdwn",
                    "text": message,
                },
            }
            payload.append(md_report)
            SCREAMING_SNAKE_CASE				=       {
                "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)
            SCREAMING_SNAKE_CASE				=       {
                "type": "context",
                "elements": [
                    {
                        "type": "plain_text",
                        "text": f'Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}',
                    }
                ],
            }
            payload.append(date_report)
      SCREAMING_SNAKE_CASE				=       client.chat_postMessage(channel="#accelerate-ci-daily", text=message, blocks=payload)
      SCREAMING_SNAKE_CASE				=       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
                  SCREAMING_SNAKE_CASE				=       ""
                  for i, row in enumerate(test_failures):
                        if row[0] != test_class:
                              SCREAMING_SNAKE_CASE				=       row[0]
                        else:
                              SCREAMING_SNAKE_CASE				=       ""

                  SCREAMING_SNAKE_CASE				=       {
                      "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],
                  )












"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






@dataclass
class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      torch.FloatTensor
       SCREAMING_SNAKE_CASE_   :      torch.FloatTensor
       SCREAMING_SNAKE_CASE_   :      Optional[torch.FloatTensor]							=  None






class 					_SCREAMING_SNAKE_CASE(   A    ,	A  ):
       SCREAMING_SNAKE_CASE_   :      Any							=  2




       @register_to_config
       def __init__(						self		,SCREAMING_SNAKE_CASE__ = 0.0_2		,SCREAMING_SNAKE_CASE__ = 1_00		,SCREAMING_SNAKE_CASE__ = 1.0_0_7		,SCREAMING_SNAKE_CASE__ = 80		,SCREAMING_SNAKE_CASE__ = 0.0_5		,SCREAMING_SNAKE_CASE__ = 50		,)			->      Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  sigma_max

             # setable values
             __SCREAMING_SNAKE_CASE   :int						=  None
             __SCREAMING_SNAKE_CASE   :np.IntTensor						=  None
             __SCREAMING_SNAKE_CASE   :torch.FloatTensor						=  None  # sigma(t_i)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)			->      torch.FloatTensor:





             """simple docstring"""






             return sample




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)			->      List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  num_inference_steps
             __SCREAMING_SNAKE_CASE   :int						=  np.arange(0		,self.num_inference_steps							)[::-1].copy()
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  torch.from_numpy(SCREAMING_SNAKE_CASE__							).to(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :Any						=  [
                 (
                     self.config.sigma_max**2
                     * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
                 )
                 for i in self.timesteps
             ]
             __SCREAMING_SNAKE_CASE   :List[str]						=  torch.tensor(SCREAMING_SNAKE_CASE__		,dtype=torch.floataa		,device=SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None							)			->      Tuple[torch.FloatTensor, float]:





             """simple docstring"""






             if self.config.s_min <= sigma <= self.config.s_max:
                   __SCREAMING_SNAKE_CASE   :List[str]						=  min(self.config.s_churn / self.num_inference_steps		,2**0.5 - 1							)
             else:
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  0

             # sample eps ~ N(0, S_noise^2 * I)
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  self.config.s_noise * randn_tensor(sample.shape		,generator=SCREAMING_SNAKE_CASE__							).to(sample.device							)
             __SCREAMING_SNAKE_CASE   :List[str]						=  sigma + gamma * sigma
             __SCREAMING_SNAKE_CASE   :str						=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

             return sample_hat, sigma_hat




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = True		,)			->      Union[KarrasVeOutput, Tuple]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :str						=  sample_hat + sigma_hat * model_output
             __SCREAMING_SNAKE_CASE   :Tuple						=  (sample_hat - pred_original_sample) / sigma_hat
             __SCREAMING_SNAKE_CASE   :List[Any]						=  sample_hat + (sigma_prev - sigma_hat) * derivative

             if not return_dict:
                   return (sample_prev, derivative)

             return KarrasVeOutput(
                 prev_sample=SCREAMING_SNAKE_CASE__		,derivative=SCREAMING_SNAKE_CASE__		,pred_original_sample=SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = True		,)			->      Union[KarrasVeOutput, Tuple]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE   :Any						=  sample_prev + sigma_prev * model_output
             __SCREAMING_SNAKE_CASE   :List[Any]						=  (sample_prev - pred_original_sample) / sigma_prev
             __SCREAMING_SNAKE_CASE   :Dict						=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

             if not return_dict:
                   return (sample_prev, derivative)

             return KarrasVeOutput(
                 prev_sample=SCREAMING_SNAKE_CASE__		,derivative=SCREAMING_SNAKE_CASE__		,pred_original_sample=SCREAMING_SNAKE_CASE__							)




       def     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)			->      Union[str, Any]:





             """simple docstring"""






             raise NotImplementedError()