'''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) > 30_00:
				UpperCamelCase_      	=	'''Too many failed tests, please see the full report in the Action results.'''
				UpperCamelCase_      	=	len(err) + 10
				UpperCamelCase_      	=	message[: 30_00 - 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],
						)












"""simple docstring"""

import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
a							      =     logging.get_logger(__name__)

a							      =     [
    ('''bert.bert''', '''visual_bert'''),
    ('''bert.cls''', '''cls'''),
    ('''bert.classifier''', '''cls'''),
    ('''token_type_embeddings_visual''', '''visual_token_type_embeddings'''),
    ('''position_embeddings_visual''', '''visual_position_embeddings'''),
    ('''projection''', '''visual_projection'''),
]

a							      =     [
    '''nlvr2_coco_pre_trained.th''',
    '''nlvr2_fine_tuned.th''',
    '''nlvr2_pre_trained.th''',
    '''vcr_coco_pre_train.th''',
    '''vcr_fine_tune.th''',
    '''vcr_pre_train.th''',
    '''vqa_coco_pre_trained.th''',
    '''vqa_fine_tuned.th''',
    '''vqa_pre_trained.th''',
]






def 		_snake_case	(   _snake_case				:   Optional[Any]				)       ->   str:






      '''simple docstring'''


      _A     =  torch.load(_snake_case							,    map_location='cpu'				)
      return sd






def 		_snake_case	(   _snake_case				:   Union[str, Any]							,    _snake_case				:   str							,    _snake_case				:   Tuple=rename_keys_prefix				)       ->   List[str]:






      '''simple docstring'''


      _A     =  OrderedDict()
      _A     =  torch.arange(config.max_position_embeddings				).expand((1, -1)				)
      # detector_d = OrderedDict()
      for key in d:
            if "detector" in key:
                  # detector_d[key.replace('detector.','')] = d[key]
                  continue
            _A     =  key
            for name_pair in rename_keys_prefix:
                  _A     =  new_key.replace(name_pair[0]							,    name_pair[1]				)
            _A     =  d[key]
            if key == "bert.cls.predictions.decoder.weight":
                  # Old bert code didn't have `decoder.bias`, but was added separately
                  _A     =  new_d['cls.predictions.bias']
      return new_d






@torch.no_grad()
def 		_snake_case	(   _snake_case				:   List[str]							,    _snake_case				:   Dict				)       ->   Dict:






      '''simple docstring'''


      assert (
          checkpoint_path.split('/'				)[-1] in ACCEPTABLE_CHECKPOINTS
      ), F'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''

      # Get Config
      if "pre" in checkpoint_path:
            _A     =  'pretraining'
            if "vcr" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 5_12}
            elif "vqa_advanced" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 20_48}
            elif "vqa" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 20_48}
            elif "nlvr" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 10_24}
            else:
                  raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.'''				)
      else:
            if "vcr" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 5_12}
                  _A     =  'multichoice'
            elif "vqa_advanced" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 20_48}
                  _A     =  'vqa_advanced'
            elif "vqa" in checkpoint_path:
                  _A     =  {'visual_embedding_dim': 20_48, 'num_labels': 31_29}
                  _A     =  'vqa'
            elif "nlvr" in checkpoint_path:
                  _A     =  {
                      'visual_embedding_dim': 10_24,
                      'num_labels': 2,
                  }
                  _A     =  'nlvr'

      _A     =  VisualBertConfig(**_snake_case				)

      # Load State Dict
      _A     =  load_state_dict(_snake_case				)

      _A     =  get_new_dict(_snake_case							,    _snake_case				)

      if model_type == "pretraining":
            _A     =  VisualBertForPreTraining(_snake_case				)
      elif model_type == "vqa":
            _A     =  VisualBertForQuestionAnswering(_snake_case				)
      elif model_type == "nlvr":
            _A     =  VisualBertForVisualReasoning(_snake_case				)
      elif model_type == "multichoice":
            _A     =  VisualBertForMultipleChoice(_snake_case				)

      model.load_state_dict(_snake_case				)
      # Save Checkpoints
      Path(_snake_case				).mkdir(exist_ok=_snake_case				)
      model.save_pretrained(_snake_case				)


if __name__ == "__main__":
       a							      =     argparse.ArgumentParser()
       # Required parameters
       parser.add_argument('''orig_checkpoint_path''', type=str, help='''A path to .th on local filesystem.''')
       parser.add_argument('''pytorch_dump_folder_path''', type=str, help='''Path to the output PyTorch model.''')
       a							      =     parser.parse_args()
       convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)

