
"""simple docstring"""




import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase=None						):






  """simple docstring"""



  _lowercase:			Optional[int]       =     None
  if token is not None:
    _lowercase:			List[str]       =     {'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'''Bearer {token}'''}

  _lowercase:			Union[str, Any]       =     f'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
  _lowercase:			List[Any]       =     requests.get(SCREAMING_SNAKE_CASE_ ,				headers=SCREAMING_SNAKE_CASE_						).json()
  _lowercase:			Union[str, Any]       =     {}

  try:
    job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}						)
    _lowercase:			Tuple       =     math.ceil((result['''total_count'''] - 100) / 100						)

    for i in range(SCREAMING_SNAKE_CASE_						):
      _lowercase:			Any       =     requests.get(url + f'''&page={i + 2}''' ,				headers=SCREAMING_SNAKE_CASE_						).json()
      job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}						)

    return job_links
  except Exception:
    print(f'''Unknown error, could not fetch links:\n{traceback.format_exc()}'''						)

  return {}




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase=None						):






  """simple docstring"""



  _lowercase:			List[Any]       =     None
  if token is not None:
    _lowercase:			Tuple       =     {'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'''Bearer {token}'''}

  _lowercase:			int       =     f'''https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'''
  _lowercase:			Optional[Any]       =     requests.get(SCREAMING_SNAKE_CASE_ ,				headers=SCREAMING_SNAKE_CASE_						).json()
  _lowercase:			Optional[Any]       =     {}

  try:
    artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']}						)
    _lowercase:			Tuple       =     math.ceil((result['''total_count'''] - 100) / 100						)

    for i in range(SCREAMING_SNAKE_CASE_						):
      _lowercase:			str       =     requests.get(url + f'''&page={i + 2}''' ,				headers=SCREAMING_SNAKE_CASE_						).json()
      artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']}						)

    return artifacts
  except Exception:
    print(f'''Unknown error, could not fetch links:\n{traceback.format_exc()}'''						)

  return {}




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Optional[int]       =     None
  if token is not None:
    _lowercase:			Tuple       =     {'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'''Bearer {token}'''}

  _lowercase:			Dict       =     requests.get(SCREAMING_SNAKE_CASE_ ,				headers=SCREAMING_SNAKE_CASE_ ,				allow_redirects=SCREAMING_SNAKE_CASE_						)
  _lowercase:			int       =     result.headers['''Location''']
  _lowercase:			str       =     requests.get(SCREAMING_SNAKE_CASE_ ,				allow_redirects=SCREAMING_SNAKE_CASE_						)
  _lowercase:			Any       =     os.path.join(SCREAMING_SNAKE_CASE_ ,				f'''{artifact_name}.zip'''						)
  with open(SCREAMING_SNAKE_CASE_ ,				'''wb'''						) as fp:
    fp.write(response.content						)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase=None						):






  """simple docstring"""



  _lowercase:			List[str]       =     []
  _lowercase:			Any       =     []
  _lowercase:			List[str]       =     None

  with zipfile.ZipFile(SCREAMING_SNAKE_CASE_						) as z:
    for filename in z.namelist():
      if not os.path.isdir(SCREAMING_SNAKE_CASE_						):
        # read the file
        if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
          with z.open(SCREAMING_SNAKE_CASE_						) as f:
            for line in f:
              _lowercase:			Dict       =     line.decode('''UTF-8'''						).strip()
              if filename == "failures_line.txt":
                try:
                  # `error_line` is the place where `error` occurs
                  _lowercase:			Tuple       =     line[: line.index(''': '''						)]
                  _lowercase:			Tuple       =     line[line.index(''': '''						) + len(''': '''						) :]
                  errors.append([error_line, error]						)
                except Exception:
                  # skip un-related lines
                  pass
              elif filename == "summary_short.txt" and line.startswith('''FAILED '''						):
                # `test` is the test method that failed
                _lowercase:			int       =     line[len('''FAILED '''						) :]
                failed_tests.append(SCREAMING_SNAKE_CASE_						)
              elif filename == "job_name.txt":
                _lowercase:			Any       =     line

  if len(SCREAMING_SNAKE_CASE_						) != len(SCREAMING_SNAKE_CASE_						):
    raise ValueError(
        f'''`errors` and `failed_tests` should have the same number of elements. Got {len(SCREAMING_SNAKE_CASE_						)} for `errors` '''
        f'''and {len(SCREAMING_SNAKE_CASE_						)} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'''
        ''' problem.'''						)

  _lowercase:			int       =     None
  if job_name and job_links:
    _lowercase:			Dict       =     job_links.get(SCREAMING_SNAKE_CASE_ ,				SCREAMING_SNAKE_CASE_						)

  # A list with elements of the form (line of error, error, failed test)
  _lowercase:			str       =     [x + [y] + [job_link] for x, y in zip(SCREAMING_SNAKE_CASE_ ,				SCREAMING_SNAKE_CASE_						)]

  return result




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase=None						):






  """simple docstring"""



  _lowercase:			Optional[Any]       =     []

  _lowercase:			Optional[int]       =     [os.path.join(SCREAMING_SNAKE_CASE_ ,				SCREAMING_SNAKE_CASE_						) for p in os.listdir(SCREAMING_SNAKE_CASE_						) if p.endswith('''.zip'''						)]
  for p in paths:
    errors.extend(get_errors_from_single_artifact(SCREAMING_SNAKE_CASE_ ,				job_links=SCREAMING_SNAKE_CASE_						)						)

  return errors




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase=None						):






  """simple docstring"""



  _lowercase:			Tuple       =     Counter()
  counter.update([x[1] for x in logs]						)
  _lowercase:			Tuple       =     counter.most_common()
  _lowercase:			Optional[Any]       =     {}
  for error, count in counts:
    if error_filter is None or error not in error_filter:
      _lowercase:			str       =     {'''count''': count, '''failed_tests''': [(x[2], x[0]) for x in logs if x[1] == error]}

  _lowercase:			Optional[int]       =     dict(sorted(r.items() ,				key=lambda _UpperCamelCase						: item[1]["count"] ,				reverse=SCREAMING_SNAKE_CASE_						)						)
  return r




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Optional[int]       =     test.split('''::'''						)[0]
  if test.startswith('''tests/models/'''						):
    _lowercase:			Union[str, Any]       =     test.split('''/'''						)[2]
  else:
    _lowercase:			Optional[Any]       =     None

  return test




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase=None						):






  """simple docstring"""



  _lowercase:			Dict       =     [(x[0], x[1], get_model(x[2]						)) for x in logs]
  _lowercase:			List[str]       =     [x for x in logs if x[2] is not None]
  _lowercase:			Optional[Any]       =     {x[2] for x in logs}

  _lowercase:			List[Any]       =     {}
  for test in tests:
    _lowercase:			int       =     Counter()
    # count by errors in `test`
    counter.update([x[1] for x in logs if x[2] == test]						)
    _lowercase:			Optional[int]       =     counter.most_common()
    _lowercase:			Optional[int]       =     {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
    _lowercase:			Optional[int]       =     sum(error_counts.values()						)
    if n_errors > 0:
      _lowercase:			str       =     {'''count''': n_errors, '''errors''': error_counts}

  _lowercase:			Dict       =     dict(sorted(r.items() ,				key=lambda _UpperCamelCase						: item[1]["count"] ,				reverse=SCREAMING_SNAKE_CASE_						)						)
  return r




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Optional[Any]       =     '''| no. | error | status |'''
  _lowercase:			Any       =     '''|-:|:-|:-|'''
  _lowercase:			List[Any]       =     [header, sep]
  for error in reduced_by_error:
    _lowercase:			Dict       =     reduced_by_error[error]['''count''']
    _lowercase:			Optional[Any]       =     f'''| {count} | {error[:100]} |  |'''
    lines.append(SCREAMING_SNAKE_CASE_						)

  return "\n".join(SCREAMING_SNAKE_CASE_						)




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Dict       =     '''| model | no. of errors | major error | count |'''
  _lowercase:			Tuple       =     '''|-:|-:|-:|-:|'''
  _lowercase:			Optional[Any]       =     [header, sep]
  for model in reduced_by_model:
    _lowercase:			Optional[Any]       =     reduced_by_model[model]['''count''']
    _lowercase ,				_lowercase:			Optional[Any]       =     list(reduced_by_model[model]['''errors'''].items()						)[0]
    _lowercase:			Optional[int]       =     f'''| {model} | {count} | {error[:60]} | {_count} |'''
    lines.append(SCREAMING_SNAKE_CASE_						)

  return "\n".join(SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
  A__   :					int							=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
  parser.add_argument(
      '--output_dir',
      type=str,
      required=True,
      help='Where to store the downloaded artifacts and other result files.',
  )
  parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
  A__   :					str							=   parser.parse_args()

  os.makedirs(args.output_dir, exist_ok=True)

  A__   :					List[Any]							=   get_job_links(args.workflow_run_id, token=args.token)
  A__   :					List[str]							=   {}
  # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
  # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
  if _job_links:
    for k, v in _job_links.items():
      # This is how GitHub actions combine job names.
      if " / " in k:
        A__   :					Dict							=   k.find(' / ')
        A__   :					Optional[Any]							=   k[index + len(' / ') :]
      A__   :					List[str]							=   v
  with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp:
    json.dump(job_links, fp, ensure_ascii=False, indent=4)

  A__   :					List[str]							=   get_artifacts_links(args.workflow_run_id, token=args.token)
  with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
    json.dump(artifacts, fp, ensure_ascii=False, indent=4)

  for idx, (name, url) in enumerate(artifacts.items()):
    download_artifact(name, url, args.output_dir, args.token)
    # Be gentle to GitHub
    time.sleep(1)

  A__   :					List[str]							=   get_all_errors(args.output_dir, job_links=job_links)

  # `e[1]` is the error
  A__   :					str							=   Counter()
  counter.update([e[1] for e in errors])

  # print the top 30 most common test errors
  A__   :					List[str]							=   counter.most_common(3_0)
  for item in most_common:
    print(item)

  with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp:
    json.dump(errors, fp, ensure_ascii=False, indent=4)

  A__   :					Optional[Any]							=   reduce_by_error(errors)
  A__   :					Optional[int]							=   reduce_by_model(errors)

  A__   :					Optional[int]							=   make_github_table(reduced_by_error)
  A__   :					Optional[Any]							=   make_github_table_per_model(reduced_by_model)

  with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp:
    fp.write(sa)
  with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp:
    fp.write(sa)


"""simple docstring"""




import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A__   :					List[Any]							=   logging.get_logger()




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase = True						):






  """simple docstring"""



  print(f'''Converting {name}...'''						)

  with torch.no_grad():
    if hidden_sizes == 128:
      if name[-1] == "S":
        _lowercase:			int       =     timm.create_model('''levit_128s''' ,				pretrained=_UpperCamelCase						)
      else:
        _lowercase:			int       =     timm.create_model('''levit_128''' ,				pretrained=_UpperCamelCase						)
    if hidden_sizes == 192:
      _lowercase:			str       =     timm.create_model('''levit_192''' ,				pretrained=_UpperCamelCase						)
    if hidden_sizes == 256:
      _lowercase:			int       =     timm.create_model('''levit_256''' ,				pretrained=_UpperCamelCase						)
    if hidden_sizes == 384:
      _lowercase:			Dict       =     timm.create_model('''levit_384''' ,				pretrained=_UpperCamelCase						)

    from_model.eval()
    _lowercase:			Any       =     LevitForImageClassificationWithTeacher(_UpperCamelCase						).eval()
    _lowercase:			Union[str, Any]       =     OrderedDict()

    _lowercase:			Optional[Any]       =     from_model.state_dict()
    _lowercase:			List[Any]       =     list(from_model.state_dict().keys()						)
    _lowercase:			int       =     list(our_model.state_dict().keys()						)
    print(len(_UpperCamelCase						) ,				len(_UpperCamelCase						)						)
    for i in range(len(_UpperCamelCase						)						):
      _lowercase:			Any       =     weights[og_keys[i]]
    our_model.load_state_dict(_UpperCamelCase						)

    _lowercase:			int       =     torch.randn((2, 3, 224, 224)						)
    _lowercase:			List[str]       =     from_model(_UpperCamelCase						)
    _lowercase:			Optional[int]       =     our_model(_UpperCamelCase						).logits

  assert torch.allclose(_UpperCamelCase ,				_UpperCamelCase						), "The model logits don't match the original one."

  _lowercase:			Union[str, Any]       =     name
  print(_UpperCamelCase						)

  if push_to_hub:
    our_model.save_pretrained(save_directory / checkpoint_name						)
    _lowercase:			Dict       =     LevitImageProcessor()
    image_processor.save_pretrained(save_directory / checkpoint_name						)

    print(f'''Pushed {checkpoint_name}'''						)




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase = None ,				_UpperCamelCase = True						):






  """simple docstring"""



  _lowercase:			int       =     '''imagenet-1k-id2label.json'''
  _lowercase:			int       =     1_000
  _lowercase:			Tuple       =     (1, num_labels)

  _lowercase:			Dict       =     '''huggingface/label-files'''
  _lowercase:			Optional[int]       =     num_labels
  _lowercase:			List[str]       =     json.load(open(hf_hub_download(_UpperCamelCase ,				_UpperCamelCase ,				repo_type='''dataset'''						) ,				'''r'''						)						)
  _lowercase:			List[str]       =     {int(_UpperCamelCase						): v for k, v in idalabel.items()}

  _lowercase:			int       =     idalabel
  _lowercase:			Tuple       =     {v: k for k, v in idalabel.items()}

  _lowercase:			Tuple       =     partial(_UpperCamelCase ,				num_labels=_UpperCamelCase ,				idalabel=_UpperCamelCase ,				labelaid=_UpperCamelCase						)

  _lowercase:			Union[str, Any]       =     {
      '''levit-128S''': 128,
      '''levit-128''': 128,
      '''levit-192''': 192,
      '''levit-256''': 256,
      '''levit-384''': 384,
  }

  _lowercase:			int       =     {
      '''levit-128S''': ImageNetPreTrainedConfig(
          hidden_sizes=[128, 256, 384] ,				num_attention_heads=[4, 6, 8] ,				depths=[2, 3, 4] ,				key_dim=[16, 16, 16] ,				drop_path_rate=0 ,				),
      '''levit-128''': ImageNetPreTrainedConfig(
          hidden_sizes=[128, 256, 384] ,				num_attention_heads=[4, 8, 12] ,				depths=[4, 4, 4] ,				key_dim=[16, 16, 16] ,				drop_path_rate=0 ,				),
      '''levit-192''': ImageNetPreTrainedConfig(
          hidden_sizes=[192, 288, 384] ,				num_attention_heads=[3, 5, 6] ,				depths=[4, 4, 4] ,				key_dim=[32, 32, 32] ,				drop_path_rate=0 ,				),
      '''levit-256''': ImageNetPreTrainedConfig(
          hidden_sizes=[256, 384, 512] ,				num_attention_heads=[4, 6, 8] ,				depths=[4, 4, 4] ,				key_dim=[32, 32, 32] ,				drop_path_rate=0 ,				),
      '''levit-384''': ImageNetPreTrainedConfig(
          hidden_sizes=[384, 512, 768] ,				num_attention_heads=[6, 9, 12] ,				depths=[4, 4, 4] ,				key_dim=[32, 32, 32] ,				drop_path_rate=0.1 ,				),
  }

  if model_name:
    convert_weight_and_push(
        names_to_hidden_sizes[model_name] ,				_UpperCamelCase ,				names_to_config[model_name] ,				_UpperCamelCase ,				_UpperCamelCase						)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(names_to_hidden_sizes[model_name] ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase						)
  return config, expected_shape


if __name__ == "__main__":
  A__   :					Any							=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--model_name',
      default=None,
      type=str,
      help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default='levit-dump-folder/',
      type=Path,
      required=False,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
  parser.add_argument(
      '--no-push_to_hub',
      dest='push_to_hub',
      action='store_false',
      help='Do not push model and image processor to the hub',
  )

  A__   :					Optional[Any]							=   parser.parse_args()
  A__   :					Path							=   args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
