





"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType






class  _SCREAMING_SNAKE_CASE(       A  ):




       def __init__(						self		,SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  data




       def __iter__(						self							)      ->				Tuple:





             """simple docstring"""






             for element in self.data:
                   yield element




def 					__lowerCamelCase       (							a_					:		Optional[Any]=True							)      ->				int:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  Accelerator(even_batches=a_							)
      assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
      return accelerator



def 					__lowerCamelCase       (							a_					:		Accelerator			,       a_					:		int			,       a_					:		int			,       a_					:		bool = False							)      ->				List[str]:
      if iterable:
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  DummyIterableDataset(torch.as_tensor(range(a_							)							)							)
      else:
            __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  TensorDataset(torch.as_tensor(range(a_							)							)							)

      __SCREAMING_SNAKE_CASE       :int		    =  DataLoader(a_			,       batch_size=a_							)
      __SCREAMING_SNAKE_CASE       :Any		    =  accelerator.prepare(a_							)

      return dl



def 					__lowerCamelCase       (							a_					:		Accelerator			,       a_					:		int			,       a_					:		int			,       a_					:		List[int]			,       a_					:		List[int]			,       )      ->				Tuple:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  create_dataloader(accelerator=a_			,       dataset_size=a_			,       batch_size=a_							)

      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  [len(batch[0]							) for batch in dl]

      if accelerator.process_index == 0:
            assert batch_sizes == process_0_expected_batch_sizes
      elif accelerator.process_index == 1:
            assert batch_sizes == process_1_expected_batch_sizes



def 					__lowerCamelCase       (							)      ->				Dict:
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  create_accelerator()

      # without padding, we would expect a different number of batches
      verify_dataloader_batch_sizes(
          a_			,       dataset_size=3			,       batch_size=1			,       process_0_expected_batch_sizes=[1, 1]			,       process_1_expected_batch_sizes=[1, 1]			,       )

      # without padding, we would expect the same number of batches, but different sizes
      verify_dataloader_batch_sizes(
          a_			,       dataset_size=7			,       batch_size=2			,       process_0_expected_batch_sizes=[2, 2]			,       process_1_expected_batch_sizes=[2, 2]			,       )



def 					__lowerCamelCase       (							)      ->				str:
      __SCREAMING_SNAKE_CASE       :Tuple		    =  create_accelerator(even_batches=a_							)

      verify_dataloader_batch_sizes(
          a_			,       dataset_size=3			,       batch_size=1			,       process_0_expected_batch_sizes=[1, 1]			,       process_1_expected_batch_sizes=[1]			,       )

      verify_dataloader_batch_sizes(
          a_			,       dataset_size=7			,       batch_size=2			,       process_0_expected_batch_sizes=[2, 2]			,       process_1_expected_batch_sizes=[2, 1]			,       )



def 					__lowerCamelCase       (							)      ->				Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :Dict		    =  create_accelerator(even_batches=a_							)

      __SCREAMING_SNAKE_CASE       :str		    =  torch.nn.Linear(1			,       1							)
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  accelerator.prepare(a_							)

      __SCREAMING_SNAKE_CASE       :str		    =  create_dataloader(a_			,       dataset_size=3			,       batch_size=1							)

      __SCREAMING_SNAKE_CASE       :List[str]		    =  []
      with accelerator.join_uneven_inputs([ddp_model]							):
            for batch_idx, batch in enumerate(a_							):
                  __SCREAMING_SNAKE_CASE       :List[str]		    =  ddp_model(batch[0].float()							)
                  __SCREAMING_SNAKE_CASE       :Any		    =  output.sum()
                  loss.backward()
                  batch_idxs.append(a_							)

      accelerator.wait_for_everyone()

      if accelerator.process_index == 0:
            assert batch_idxs == [0, 1]
      elif accelerator.process_index == 1:
            assert batch_idxs == [0]



def 					__lowerCamelCase       (							a_					:		List[Any]							)      ->				int:
      with warnings.catch_warnings(record=a_							) as w:
            with accelerator.join_uneven_inputs([Mock()]							):
                  pass

            assert issubclass(w[-1].category			,       a_							)
            assert "only supported for multi-GPU" in str(w[-1].message							)



def 					__lowerCamelCase       (							)      ->				Optional[int]:
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  True
      __SCREAMING_SNAKE_CASE       :List[str]		    =  False
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  create_accelerator(even_batches=a_							)
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  torch.nn.Linear(1			,       1							)
      __SCREAMING_SNAKE_CASE       :Tuple		    =  accelerator.prepare(a_							)
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  create_dataloader(a_			,       dataset_size=3			,       batch_size=1							)
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  create_dataloader(a_			,       dataset_size=3			,       batch_size=1							)

      with accelerator.join_uneven_inputs([ddp_model]			,       even_batches=a_							):
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  train_dl.batch_sampler.even_batches
            __SCREAMING_SNAKE_CASE       :List[str]		    =  valid_dl.batch_sampler.even_batches

      assert train_dl_overridden_value == overridden_even_batches
      assert valid_dl_overridden_value == overridden_even_batches
      assert train_dl.batch_sampler.even_batches == default_even_batches
      assert valid_dl.batch_sampler.even_batches == default_even_batches



def 					__lowerCamelCase       (							)      ->				Dict:
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  True
      __SCREAMING_SNAKE_CASE       :int		    =  False
      __SCREAMING_SNAKE_CASE       :Dict		    =  create_accelerator(even_batches=a_							)
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  torch.nn.Linear(1			,       1							)
      __SCREAMING_SNAKE_CASE       :Any		    =  accelerator.prepare(a_							)
      create_dataloader(a_			,       dataset_size=3			,       batch_size=1			,       iterable=a_							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  create_dataloader(a_			,       dataset_size=3			,       batch_size=1							)

      with warnings.catch_warnings():
            warnings.filterwarnings('''ignore'''							)
            try:
                  with accelerator.join_uneven_inputs([ddp_model]			,       even_batches=a_							):
                        __SCREAMING_SNAKE_CASE       :Optional[int]		    =  batch_dl.batch_sampler.even_batches
            except AttributeError:
                  # ensure attribute error is not raised when processing iterable dl
                  raise AssertionError

      assert batch_dl_overridden_value == overridden_even_batches
      assert batch_dl.batch_sampler.even_batches == default_even_batches



def 					__lowerCamelCase       (							)      ->				str:
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  create_accelerator()
      __SCREAMING_SNAKE_CASE       :Tuple		    =  torch.nn.Linear(1			,       1							)
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  accelerator.prepare(a_							)
      create_dataloader(a_			,       dataset_size=3			,       batch_size=1			,       iterable=a_							)

      with warnings.catch_warnings(record=a_							) as w:
            with accelerator.join_uneven_inputs([ddp_model]			,       even_batches=a_							):
                  pass

            assert issubclass(w[-1].category			,       a_							)
            assert "only supported for map-style datasets" in str(w[-1].message							)



def 					__lowerCamelCase       (							)      ->				Optional[Any]:
      __SCREAMING_SNAKE_CASE       :str		    =  create_accelerator()

      accelerator.print('''Test that even_batches variable ensures uniform batches across processes'''							)
      test_default_ensures_even_batch_sizes()

      accelerator.print('''Run tests with even_batches disabled'''							)
      test_can_disable_even_batches()

      accelerator.print('''Test joining uneven inputs'''							)
      test_can_join_uneven_inputs()

      accelerator.print('''Test overriding even_batches when joining uneven inputs'''							)
      test_join_can_override_even_batches()

      accelerator.print('''Test overriding even_batches for mixed dataloader types'''							)
      test_join_can_override_for_mixed_type_dataloaders()

      accelerator.print('''Test overriding even_batches raises a warning for iterable dataloaders'''							)
      test_join_raises_warning_for_iterable_when_overriding_even_batches()

      accelerator.print('''Test join with non DDP distributed raises warning'''							)
      __SCREAMING_SNAKE_CASE       :Tuple		    =  accelerator.state.distributed_type
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  DistributedType.FSDP
      test_join_raises_warning_for_non_ddp_distributed(a_							)
      __SCREAMING_SNAKE_CASE       :int		    =  original_state


if __name__ == "__main__":
  main()






"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


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


lowerCamelCase_       =		[]
lowerCamelCase_       =		[]

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

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

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

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

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

      lowerCamelCase_       =		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) > 3_0_0_0:
    lowerCamelCase_       =		"Too many failed tests, please see the full report in the Action results."
    lowerCamelCase_       =		len(err) + 1_0
    lowerCamelCase_       =		message[: 3_0_0_0 - offset] + f'\n...\n```\n{err}'
  print(f'### {message}')
else:
  lowerCamelCase_       =		"No failed tests! 🤗"
  print(f'## {message}')
  payload.append(no_error_payload)

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

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

      lowerCamelCase_       =		{
          "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],
      )