

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

from tabulate import DataRow, TableFormat, tabulate


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


lowercase_			=  []
lowercase_			=  []

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

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

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

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

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

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

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

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

               lowercase_			=  {
                   "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],
               )




import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)


class 			A						(							nn.Module			):







    """simple docstring"""





    def __init__(				self			:	Union[str, Any]		)->   List[str]:





        '''simple docstring'''







        super().__init__()
        A__   				=       nn.Linear(3,4		)
        A__   				=       nn.BatchNormad(4		)
        A__   				=       nn.Linear(4,5		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Union[str, Any]		)->   Tuple:





        '''simple docstring'''







        return self.lineara(self.batchnorm(self.lineara(lowercase_		)		)		)




class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	List[str]		)->   Any:





        '''simple docstring'''







        A__   				=       ModelForTest()
        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,model.state_dict()		)
            A__   				=       os.path.join(lowercase_,'index.json'		)
            self.assertTrue(os.path.isfile(lowercase_		)		)
            # TODO: add tests on what is inside the index

            for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
                A__   				=       os.path.join(lowercase_,F'{key}.dat'		)
                self.assertTrue(os.path.isfile(lowercase_		)		)
                # TODO: add tests on the fact weights are properly loaded


    def 						snake_case__					(				self			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       [torch.floataa, torch.floataa, torch.bfloataa]

        for dtype in dtypes:
            A__   				=       torch.randn(2,3,dtype=lowercase_		)
            with TemporaryDirectory() as tmp_dir:
                A__   				=       offload_weight(lowercase_,'weight',lowercase_,{}		)
                A__   				=       os.path.join(lowercase_,'weight.dat'		)
                self.assertTrue(os.path.isfile(lowercase_		)		)
                self.assertDictEqual(lowercase_,{'weight': {'shape': [2, 3], 'dtype': str(lowercase_		).split('.'		)[1]}}		)

                A__   				=       load_offloaded_weight(lowercase_,index['weight']		)
                self.assertTrue(torch.equal(lowercase_,lowercase_		)		)


    def 						snake_case__					(				self			:	Optional[int]		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       ModelForTest()
        A__   				=       model.state_dict()
        A__   				=       {k: v for k, v in state_dict.items() if 'linear2' not in k}
        A__   				=       {k: v for k, v in state_dict.items() if 'linear2' in k}

        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,lowercase_		)
            A__   				=       OffloadedWeightsLoader(state_dict=lowercase_,save_folder=lowercase_		)

            # Every key is there with the right value
            self.assertEqual(sorted(lowercase_		),sorted(state_dict.keys()		)		)
            for key, param in state_dict.items():
                self.assertTrue(torch.allclose(lowercase_,weight_map[key]		)		)

        A__   				=       {k: v for k, v in state_dict.items() if 'weight' in k}
        A__   				=       {k: v for k, v in state_dict.items() if 'weight' not in k}

        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,lowercase_		)
            A__   				=       OffloadedWeightsLoader(state_dict=lowercase_,save_folder=lowercase_		)

            # Every key is there with the right value
            self.assertEqual(sorted(lowercase_		),sorted(state_dict.keys()		)		)
            for key, param in state_dict.items():
                self.assertTrue(torch.allclose(lowercase_,weight_map[key]		)		)

        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,lowercase_		)
            # Duplicates are removed
            A__   				=       OffloadedWeightsLoader(state_dict=lowercase_,save_folder=lowercase_		)

            # Every key is there with the right value
            self.assertEqual(sorted(lowercase_		),sorted(state_dict.keys()		)		)
            for key, param in state_dict.items():
                self.assertTrue(torch.allclose(lowercase_,weight_map[key]		)		)


    def 						snake_case__					(				self			:	int		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       {'a.1': 0, 'a.10': 1, 'a.2': 2}
        A__   				=       extract_submodules_state_dict(lowercase_,['a.1', 'a.2']		)
        self.assertDictEqual(lowercase_,{'a.1': 0, 'a.2': 2}		)

        A__   				=       {'a.1.a': 0, 'a.10.a': 1, 'a.2.a': 2}
        A__   				=       extract_submodules_state_dict(lowercase_,['a.1', 'a.2']		)
        self.assertDictEqual(lowercase_,{'a.1.a': 0, 'a.2.a': 2}		)

