






'''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) > 3_0_0_0:
    SCREAMING_SNAKE_CASE_					   =				'Too many failed tests, please see the full report in the Action results.'
    SCREAMING_SNAKE_CASE_					   =				len(err) + 1_0
    SCREAMING_SNAKE_CASE_					   =				message[: 3_0_0_0 - 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"""

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def  __snake_case							(	__A      :						Union[str, Any]							,		__A      :						Any							,		__A      :						Dict     )     ->       Tuple:






				'''simple docstring'''
				# Initialise PyTorch model
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     BertConfig.from_json_file(__A     )
				print(F"""Building PyTorch model from configuration: {config}"""     )
				SCREAMING_SNAKE_CASE						:							List[Any]							=     BertForPreTraining(__A     )

				# Load weights from tf checkpoint
				load_tf_weights_in_bert(__A							,		__A							,		__A     )

				# Save pytorch-model
				print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
				torch.save(model.state_dict()							,		__A     )


if __name__ == "__main__":
							A_						:    int				=      argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
							)
							parser.add_argument(
							    '--bert_config_file',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        'The config json file corresponding to the pre-trained BERT model. \n'
							        'This specifies the model architecture.'
							    ),
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							A_						:    str				=      parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





