




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

import requests







def lowerCamelCase__       (      _lowercase			,						_lowercase=None				):

     '''simple docstring'''


     UpperCAmelCase_      :							str       =      None
     if token is not None:
          UpperCAmelCase_      :							Tuple       =      {'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'''Bearer {token}'''}

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

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

          for i in range(_lowercase				):
               UpperCAmelCase_      :							Dict       =      requests.get(url + f'''&page={i + 2}'''			,						headers=_lowercase				).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__       (      _lowercase			,						_lowercase=None				):

     '''simple docstring'''


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

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

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

          for i in range(_lowercase				):
               UpperCAmelCase_      :							Any       =      requests.get(url + f'''&page={i + 2}'''			,						headers=_lowercase				).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__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


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

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







def lowerCamelCase__       (      _lowercase			,						_lowercase=None				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[int]       =      []
     UpperCAmelCase_      :							int       =      []
     UpperCAmelCase_      :							List[str]       =      None

     with zipfile.ZipFile(_lowercase				) as z:
          for filename in z.namelist():
               if not os.path.isdir(_lowercase				):
                    # read the file
                    if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                         with z.open(_lowercase				) as f:
                              for line in f:
                                   UpperCAmelCase_      :							Dict       =      line.decode('''UTF-8'''				).strip()
                                   if filename == "failures_line.txt":
                                        try:
                                             # `error_line` is the place where `error` occurs
                                             UpperCAmelCase_      :							Dict       =      line[: line.index(''': '''				)]
                                             UpperCAmelCase_      :							int       =      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
                                        UpperCAmelCase_      :							Tuple       =      line[len('''FAILED '''				) :]
                                        failed_tests.append(_lowercase				)
                                   elif filename == "job_name.txt":
                                        UpperCAmelCase_      :							Union[str, Any]       =      line

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

     UpperCAmelCase_      :							Any       =      None
     if job_name and job_links:
          UpperCAmelCase_      :							int       =      job_links.get(_lowercase			,						_lowercase				)

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

     return result







def lowerCamelCase__       (      _lowercase			,						_lowercase=None				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      []

     UpperCAmelCase_      :							Union[str, Any]       =      [os.path.join(_lowercase			,						_lowercase				) for p in os.listdir(_lowercase				) if p.endswith('''.zip'''				)]
     for p in paths:
          errors.extend(get_errors_from_single_artifact(_lowercase			,						job_links=_lowercase				)				)

     return errors







def lowerCamelCase__       (      _lowercase			,						_lowercase=None				):

     '''simple docstring'''


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

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







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


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

     return test







def lowerCamelCase__       (      _lowercase			,						_lowercase=None				):

     '''simple docstring'''


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

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

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







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


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

     return "\n".join(_lowercase				)







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


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

     return "\n".join(_lowercase				)


if __name__ == "__main__":
     __a          =			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          =			parser.parse_args()

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

     __a          =			get_job_links(args.workflow_run_id, token=args.token)
     __a          =			{}
     # 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          =			k.find(' / ')
                    __a          =			k[index + len(' / ') :]
               __a          =			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          =			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          =			get_all_errors(args.output_dir, job_links=job_links)

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

     # print the top 30 most common test errors
     __a          =			counter.most_common(30)
     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          =			reduce_by_error(errors)
     __a          =			reduce_by_model(errors)

     __a          =			make_github_table(reduced_by_error)
     __a          =			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)





import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[Any]       =      SwinConfig()
     UpperCAmelCase_      :							Dict       =      swin_name.split('''_'''				)

     UpperCAmelCase_      :							List[Any]       =      name_split[1]
     UpperCAmelCase_      :							Optional[Any]       =      int(name_split[4]				)
     UpperCAmelCase_      :							Union[str, Any]       =      int(name_split[3][-1]				)

     if model_size == "tiny":
          UpperCAmelCase_      :							Tuple       =      96
          UpperCAmelCase_      :							Optional[int]       =      (2, 2, 6, 2)
          UpperCAmelCase_      :							str       =      (3, 6, 12, 24)
     elif model_size == "small":
          UpperCAmelCase_      :							Dict       =      96
          UpperCAmelCase_      :							str       =      (2, 2, 18, 2)
          UpperCAmelCase_      :							List[str]       =      (3, 6, 12, 24)
     elif model_size == "base":
          UpperCAmelCase_      :							Tuple       =      128
          UpperCAmelCase_      :							Optional[int]       =      (2, 2, 18, 2)
          UpperCAmelCase_      :							Optional[int]       =      (4, 8, 16, 32)
     else:
          UpperCAmelCase_      :							List[str]       =      192
          UpperCAmelCase_      :							str       =      (2, 2, 18, 2)
          UpperCAmelCase_      :							Union[str, Any]       =      (6, 12, 24, 48)

     if "in22k" in swin_name:
          UpperCAmelCase_      :							int       =      21841
     else:
          UpperCAmelCase_      :							Tuple       =      1000
          UpperCAmelCase_      :							Any       =      '''huggingface/label-files'''
          UpperCAmelCase_      :							int       =      '''imagenet-1k-id2label.json'''
          UpperCAmelCase_      :							str       =      json.load(open(hf_hub_download(_lowercase			,						_lowercase			,						repo_type='''dataset'''				)			,						'''r'''				)				)
          UpperCAmelCase_      :							Optional[int]       =      {int(_lowercase				): v for k, v in idalabel.items()}
          UpperCAmelCase_      :							List[Any]       =      idalabel
          UpperCAmelCase_      :							int       =      {v: k for k, v in idalabel.items()}

     UpperCAmelCase_      :							Tuple       =      img_size
     UpperCAmelCase_      :							List[Any]       =      num_classes
     UpperCAmelCase_      :							str       =      embed_dim
     UpperCAmelCase_      :							Optional[int]       =      depths
     UpperCAmelCase_      :							Dict       =      num_heads
     UpperCAmelCase_      :							Optional[int]       =      window_size

     return config







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     if "patch_embed.proj" in name:
          UpperCAmelCase_      :							str       =      name.replace('''patch_embed.proj'''			,						'''embeddings.patch_embeddings.projection'''				)
     if "patch_embed.norm" in name:
          UpperCAmelCase_      :							List[Any]       =      name.replace('''patch_embed.norm'''			,						'''embeddings.norm'''				)
     if "layers" in name:
          UpperCAmelCase_      :							Tuple       =      '''encoder.''' + name
     if "attn.proj" in name:
          UpperCAmelCase_      :							Tuple       =      name.replace('''attn.proj'''			,						'''attention.output.dense'''				)
     if "attn" in name:
          UpperCAmelCase_      :							int       =      name.replace('''attn'''			,						'''attention.self'''				)
     if "norm1" in name:
          UpperCAmelCase_      :							Union[str, Any]       =      name.replace('''norm1'''			,						'''layernorm_before'''				)
     if "norm2" in name:
          UpperCAmelCase_      :							List[Any]       =      name.replace('''norm2'''			,						'''layernorm_after'''				)
     if "mlp.fc1" in name:
          UpperCAmelCase_      :							Tuple       =      name.replace('''mlp.fc1'''			,						'''intermediate.dense'''				)
     if "mlp.fc2" in name:
          UpperCAmelCase_      :							Optional[int]       =      name.replace('''mlp.fc2'''			,						'''output.dense'''				)

     if name == "norm.weight":
          UpperCAmelCase_      :							Optional[Any]       =      '''layernorm.weight'''
     if name == "norm.bias":
          UpperCAmelCase_      :							List[str]       =      '''layernorm.bias'''

     if "head" in name:
          UpperCAmelCase_      :							Union[str, Any]       =      name.replace('''head'''			,						'''classifier'''				)
     else:
          UpperCAmelCase_      :							Union[str, Any]       =      '''swin.''' + name

     return name







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     for key in orig_state_dict.copy().keys():
          UpperCAmelCase_      :							str       =      orig_state_dict.pop(_lowercase				)

          if "mask" in key:
               continue
          elif "qkv" in key:
               UpperCAmelCase_      :							str       =      key.split('''.'''				)
               UpperCAmelCase_      :							str       =      int(key_split[1]				)
               UpperCAmelCase_      :							Optional[Any]       =      int(key_split[3]				)
               UpperCAmelCase_      :							Optional[int]       =      model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               if "weight" in key:
                    UpperCAmelCase_      :							Any       =      val[:dim, :]
                    UpperCAmelCase_      :							Optional[int]       =      val[
                        dim : dim * 2, :
                    ]
                    UpperCAmelCase_      :							List[str]       =      val[-dim:, :]
               else:
                    UpperCAmelCase_      :							Any       =      val[
                        :dim
                    ]
                    UpperCAmelCase_      :							Optional[int]       =      val[
                        dim : dim * 2
                    ]
                    UpperCAmelCase_      :							Union[str, Any]       =      val[
                        -dim:
                    ]
          else:
               UpperCAmelCase_      :							Optional[Any]       =      val

     return orig_state_dict







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      timm.create_model(_lowercase			,						pretrained=_lowercase				)
     timm_model.eval()

     UpperCAmelCase_      :							Any       =      get_swin_config(_lowercase				)
     UpperCAmelCase_      :							Union[str, Any]       =      SwinForImageClassification(_lowercase				)
     model.eval()

     UpperCAmelCase_      :							Tuple       =      convert_state_dict(timm_model.state_dict()			,						_lowercase				)
     model.load_state_dict(_lowercase				)

     UpperCAmelCase_      :							List[Any]       =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''

     UpperCAmelCase_      :							int       =      AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''			,						'''-'''				)				)				)
     UpperCAmelCase_      :							Optional[Any]       =      Image.open(requests.get(_lowercase			,						stream=_lowercase				).raw				)
     UpperCAmelCase_      :							Union[str, Any]       =      image_processor(images=_lowercase			,						return_tensors='''pt'''				)

     UpperCAmelCase_      :							Optional[Any]       =      timm_model(inputs['''pixel_values''']				)
     UpperCAmelCase_      :							Dict       =      model(**_lowercase				).logits

     assert torch.allclose(_lowercase			,						_lowercase			,						atol=1E-3				)

     print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''				)
     model.save_pretrained(_lowercase				)

     print(f'''Saving image processor to {pytorch_dump_folder_path}'''				)
     image_processor.save_pretrained(_lowercase				)


if __name__ == "__main__":
     __a          =			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--swin_name',
         default='swin_tiny_patch4_window7_224',
         type=str,
         help='Name of the Swin timm model you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )

     __a          =			parser.parse_args()
     convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)