





"""simple docstring"""
import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Optional[int]			):
 lowerCAmelCase    				= 384
 lowerCAmelCase    				= 7
 if "tiny" in model_name:
  lowerCAmelCase    				= 96
  lowerCAmelCase    				= (2, 2, 6, 2)
  lowerCAmelCase    				= (3, 6, 12, 24)
 elif "small" in model_name:
  lowerCAmelCase    				= 96
  lowerCAmelCase    				= (2, 2, 18, 2)
  lowerCAmelCase    				= (3, 6, 12, 24)
 elif "base" in model_name:
  lowerCAmelCase    				= 128
  lowerCAmelCase    				= (2, 2, 18, 2)
  lowerCAmelCase    				= (4, 8, 16, 32)
  lowerCAmelCase    				= 12
  lowerCAmelCase    				= 512
 elif "large" in model_name:
  lowerCAmelCase    				= 192
  lowerCAmelCase    				= (2, 2, 18, 2)
  lowerCAmelCase    				= (6, 12, 24, 48)
  lowerCAmelCase    				= 12
  lowerCAmelCase    				= 768

 # set label information
 lowerCAmelCase    				= 150
 lowerCAmelCase    				= 'huggingface/label-files'
 lowerCAmelCase    				= 'ade20k-id2label.json'
 lowerCAmelCase    				= json.load(open(hf_hub_download(_UpperCAmelCase  ,     _UpperCAmelCase  ,     repo_type='dataset'			)  ,     'r'			)			)
 lowerCAmelCase    				= {int(_UpperCAmelCase			): v for k, v in idalabel.items()}
 lowerCAmelCase    				= {v: k for k, v in idalabel.items()}

 lowerCAmelCase    				= SwinConfig(
     embed_dim=_UpperCAmelCase  ,     depths=_UpperCAmelCase  ,     num_heads=_UpperCAmelCase  ,     window_size=_UpperCAmelCase  ,     out_features=['stage1', 'stage2', 'stage3', 'stage4']  ,     )
 lowerCAmelCase    				= UperNetConfig(
     backbone_config=_UpperCAmelCase  ,     auxiliary_in_channels=_UpperCAmelCase  ,     num_labels=_UpperCAmelCase  ,     idalabel=_UpperCAmelCase  ,     labelaid=_UpperCAmelCase  ,     )

 return config

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]			):
 lowerCAmelCase    				= []

 # fmt: off
 # stem
 rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')			)
 rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')			)
 rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')			)
 rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.bias')			)
 # stages
 for i in range(len(config.backbone_config.depths			)			):
  for j in range(config.backbone_config.depths[i]			):
   rename_keys.append((F'backbone.stages.{i}.blocks.{j}.norm1.weight', F'backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')			)
   rename_keys.append((F'backbone.stages.{i}.blocks.{j}.norm1.bias', F'backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')			)
   rename_keys.append((F'backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table', F'backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')			)
   rename_keys.append((F'backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index', F'backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')			)
   rename_keys.append((F'backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight', F'backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')			)
   rename_keys.append((F'backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias', F'backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')			)
   rename_keys.append((F'backbone.stages.{i}.blocks.{j}.norm2.weight', F'backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')			)
   rename_keys.append((F'backbone.stages.{i}.blocks.{j}.norm2.bias', F'backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')			)
   rename_keys.append((F'backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight', F'backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')			)
   rename_keys.append((F'backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias', F'backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')			)
   rename_keys.append((F'backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight', F'backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight')			)
   rename_keys.append((F'backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias', F'backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias')			)

  if i < 3:
   rename_keys.append((F'backbone.stages.{i}.downsample.reduction.weight', F'backbone.encoder.layers.{i}.downsample.reduction.weight')			)
   rename_keys.append((F'backbone.stages.{i}.downsample.norm.weight', F'backbone.encoder.layers.{i}.downsample.norm.weight')			)
   rename_keys.append((F'backbone.stages.{i}.downsample.norm.bias', F'backbone.encoder.layers.{i}.downsample.norm.bias')			)
  rename_keys.append((F'backbone.norm{i}.weight', F'backbone.hidden_states_norms.stage{i+1}.weight')			)
  rename_keys.append((F'backbone.norm{i}.bias', F'backbone.hidden_states_norms.stage{i+1}.bias')			)

 # decode head
 rename_keys.extend(
     [
         ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
         ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
         ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
         ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
     ]			)
 # fmt: on

 return rename_keys

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    Tuple  ,     _UpperCAmelCase					:    int			):
 lowerCAmelCase    				= dct.pop(_UpperCAmelCase			)
 lowerCAmelCase    				= val

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]  ,     _UpperCAmelCase					:    Union[str, Any]			):
 lowerCAmelCase    				= [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
 for i in range(len(backbone_config.depths			)			):
  lowerCAmelCase    				= num_features[i]
  for j in range(backbone_config.depths[i]			):
   # fmt: off
   # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
   lowerCAmelCase    				= state_dict.pop(F'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'			)
   lowerCAmelCase    				= state_dict.pop(F'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias'			)
   # next, add query, keys and values (in that order) to the state dict
   lowerCAmelCase    				= in_proj_weight[:dim, :]
   lowerCAmelCase    				= in_proj_bias[: dim]
   lowerCAmelCase    				= in_proj_weight[
       dim : dim * 2, :
   ]
   lowerCAmelCase    				= in_proj_bias[
       dim : dim * 2
   ]
   lowerCAmelCase    				= in_proj_weight[
       -dim :, :
   ]
   lowerCAmelCase    				= in_proj_bias[-dim :]
   # fmt: on

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[str]			):
 lowerCAmelCase				,lowerCAmelCase    				= x.shape
 lowerCAmelCase    				= x.reshape(_UpperCAmelCase  ,     4  ,     in_channel // 4			)
 lowerCAmelCase    				= x[:, [0, 2, 1, 3], :].transpose(1  ,     2			).reshape(_UpperCAmelCase  ,     _UpperCAmelCase			)
 return x

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 lowerCAmelCase				,lowerCAmelCase    				= x.shape
 lowerCAmelCase    				= x.reshape(_UpperCAmelCase  ,     in_channel // 4  ,     4			)
 lowerCAmelCase    				= x[:, :, [0, 2, 1, 3]].transpose(1  ,     2			).reshape(_UpperCAmelCase  ,     _UpperCAmelCase			)

 return x

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Optional[Any]			):
 lowerCAmelCase    				= x.shape[0]
 lowerCAmelCase    				= x.reshape(4  ,     in_channel // 4			)
 lowerCAmelCase    				= x[[0, 2, 1, 3], :].transpose(0  ,     1			).reshape(_UpperCAmelCase			)
 return x

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Union[str, Any]			):
 lowerCAmelCase    				= x.shape[0]
 lowerCAmelCase    				= x.reshape(in_channel // 4  ,     4			)
 lowerCAmelCase    				= x[:, [0, 2, 1, 3]].transpose(0  ,     1			).reshape(_UpperCAmelCase			)
 return x

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Union[str, Any]  ,     _UpperCAmelCase					:    Union[str, Any]  ,     _UpperCAmelCase					:    int			):
 lowerCAmelCase    				= {
     'upernet-swin-tiny': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth',
     'upernet-swin-small': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth',
     'upernet-swin-base': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth',
     'upernet-swin-large': 'https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth',
 }
 lowerCAmelCase    				= model_name_to_url[model_name]
 lowerCAmelCase    				= torch.hub.load_state_dict_from_url(_UpperCAmelCase  ,     map_location='cpu'  ,     file_name=_UpperCAmelCase			)[
     'state_dict'
 ]

 for name, param in state_dict.items():
  print(_UpperCAmelCase  ,     param.shape			)

 lowerCAmelCase    				= get_upernet_config(_UpperCAmelCase			)
 lowerCAmelCase    				= UperNetForSemanticSegmentation(_UpperCAmelCase			)
 model.eval()

 # replace "bn" => "batch_norm"
 for key in state_dict.copy().keys():
  lowerCAmelCase    				= state_dict.pop(_UpperCAmelCase			)
  if "bn" in key:
   lowerCAmelCase    				= key.replace('bn'  ,     'batch_norm'			)
  lowerCAmelCase    				= val

 # rename keys
 lowerCAmelCase    				= create_rename_keys(_UpperCAmelCase			)
 for src, dest in rename_keys:
  rename_key(_UpperCAmelCase  ,     _UpperCAmelCase  ,     _UpperCAmelCase			)
 read_in_q_k_v(_UpperCAmelCase  ,     config.backbone_config			)

 # fix downsample parameters
 for key, value in state_dict.items():
  if "downsample" in key:
   if "reduction" in key:
    lowerCAmelCase    				= reverse_correct_unfold_reduction_order(_UpperCAmelCase			)
   if "norm" in key:
    lowerCAmelCase    				= reverse_correct_unfold_norm_order(_UpperCAmelCase			)

 model.load_state_dict(_UpperCAmelCase			)

 # verify on image
 lowerCAmelCase    				= 'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
 lowerCAmelCase    				= Image.open(requests.get(_UpperCAmelCase  ,     stream=_UpperCAmelCase			).raw			).convert('RGB'			)

 lowerCAmelCase    				= SegformerImageProcessor()
 lowerCAmelCase    				= processor(_UpperCAmelCase  ,     return_tensors='pt'			).pixel_values

 with torch.no_grad():
  lowerCAmelCase    				= model(_UpperCAmelCase			)
  lowerCAmelCase    				= outputs.logits

 print(logits.shape			)
 print('First values of logits:'  ,     logits[0, 0, :3, :3]			)
 # assert values
 if model_name == "upernet-swin-tiny":
  lowerCAmelCase    				= torch.tensor(
      [[-7.5958, -7.5958, -7.4302], [-7.5958, -7.5958, -7.4302], [-7.4797, -7.4797, -7.3068]]			)
 elif model_name == "upernet-swin-small":
  lowerCAmelCase    				= torch.tensor(
      [[-7.1921, -7.1921, -6.9532], [-7.1921, -7.1921, -6.9532], [-7.0908, -7.0908, -6.8534]]			)
 elif model_name == "upernet-swin-base":
  lowerCAmelCase    				= torch.tensor(
      [[-6.5851, -6.5851, -6.4330], [-6.5851, -6.5851, -6.4330], [-6.4763, -6.4763, -6.3254]]			)
 elif model_name == "upernet-swin-large":
  lowerCAmelCase    				= torch.tensor(
      [[-7.5297, -7.5297, -7.3802], [-7.5297, -7.5297, -7.3802], [-7.4044, -7.4044, -7.2586]]			)
 print('Logits:'  ,     outputs.logits[0, 0, :3, :3]			)
 assert torch.allclose(outputs.logits[0, 0, :3, :3]  ,     _UpperCAmelCase  ,     atol=1e-4			)
 print('Looks ok!'			)

 if pytorch_dump_folder_path is not None:
  print(F'Saving model {model_name} to {pytorch_dump_folder_path}'			)
  model.save_pretrained(_UpperCAmelCase			)
  print(F'Saving processor to {pytorch_dump_folder_path}'			)
  processor.save_pretrained(_UpperCAmelCase			)

 if push_to_hub:
  print(F'Pushing model and processor for {model_name} to hub'			)
  model.push_to_hub(F'openmmlab/{model_name}'			)
  processor.push_to_hub(F'openmmlab/{model_name}'			)


if __name__ == "__main__":
       __UpperCamelCase     :						Tuple						       =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--model_name''',
           default='''upernet-swin-tiny''',
           type=str,
           choices=[f'''upernet-swin-{size}''' for size in ['''tiny''', '''small''', '''base''', '''large''']],
           help='''Name of the Swin + UperNet 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.'''
       )
       parser.add_argument(
           '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
       )

       __UpperCamelCase     :						int						       =  parser.parse_args()
       convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











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

import requests





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Tuple:
	a									=				None
	if token is not None:
		a									=				{"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

	a									=				f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
	a									=				requests.get(__lowerCamelCase				,     headers=__lowerCamelCase       ).json()
	a									=				{}

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

		for i in range(__lowerCamelCase       ):
			a									=				requests.get(url + f'&page={i + 2}'				,     headers=__lowerCamelCase       ).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 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Dict:
	a									=				None
	if token is not None:
		a									=				{"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

	a									=				f'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
	a									=				requests.get(__lowerCamelCase				,     headers=__lowerCamelCase       ).json()
	a									=				{}

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

		for i in range(__lowerCamelCase       ):
			a									=				requests.get(url + f'&page={i + 2}'				,     headers=__lowerCamelCase       ).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 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Optional[int]:
	a									=				None
	if token is not None:
		a									=				{"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

	a									=				requests.get(__lowerCamelCase				,     headers=__lowerCamelCase				,     allow_redirects=__lowerCamelCase       )
	a									=				result.headers["""Location"""]
	a									=				requests.get(__lowerCamelCase				,     allow_redirects=__lowerCamelCase       )
	a									=				os.path.join(__lowerCamelCase				,     f'{artifact_name}.zip'       )
	with open(__lowerCamelCase				,     """wb"""       ) as fp:
		fp.write(response.content       )





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Tuple:
	a									=				[]
	a									=				[]
	a									=				None

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

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

	a									=				None
	if job_name and job_links:
		a									=				job_links.get(__lowerCamelCase				,     __lowerCamelCase       )

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

	return result





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Dict:
	a									=				[]

	a									=				[os.path.join(__lowerCamelCase				,     __lowerCamelCase       ) for p in os.listdir(__lowerCamelCase       ) if p.endswith(""".zip"""       )]
	for p in paths:
		errors.extend(get_errors_from_single_artifact(__lowerCamelCase				,     job_links=__lowerCamelCase       )       )

	return errors





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Tuple:
	a									=				Counter()
	counter.update([x[1] for x in logs]       )
	a									=				counter.most_common()
	a									=				{}
	for error, count in counts:
		if error_filter is None or error not in error_filter:
			a									=				{"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}

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





def 						__A						(      __lowerCamelCase       ) ->			List[str]:
	a									=				test.split("""::"""       )[0]
	if test.startswith("""tests/models/"""       ):
		a									=				test.split("""/"""       )[2]
	else:
		a									=				None

	return test





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Any:
	a									=				[(x[0], x[1], get_model(x[2]       )) for x in logs]
	a									=				[x for x in logs if x[2] is not None]
	a									=				{x[2] for x in logs}

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

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





def 						__A						(      __lowerCamelCase       ) ->			Optional[int]:
	a									=				"""| no. | error | status |"""
	a									=				"""|-:|:-|:-|"""
	a									=				[header, sep]
	for error in reduced_by_error:
		a									=				reduced_by_error[error]["""count"""]
		a									=				f'| {count} | {error[:100]} |  |'
		lines.append(__lowerCamelCase       )

	return "\n".join(__lowerCamelCase       )





def 						__A						(      __lowerCamelCase       ) ->			int:
	a									=				"""| model | no. of errors | major error | count |"""
	a									=				"""|-:|-:|-:|-:|"""
	a									=				[header, sep]
	for model in reduced_by_model:
		a									=				reduced_by_model[model]["""count"""]
		a							,					a									=				list(reduced_by_model[model]["""errors"""].items()       )[0]
		a									=				f'| {model} | {count} | {error[:60]} | {_count} |'
		lines.append(__lowerCamelCase       )

	return "\n".join(__lowerCamelCase       )


if __name__ == "__main__":
		__UpperCamelCase   :			Dict          = 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.")
		__UpperCamelCase   :			Any          = parser.parse_args()

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

		__UpperCamelCase   :			Optional[Any]          = get_job_links(args.workflow_run_id, token=args.token)
		__UpperCamelCase   :			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:
								__UpperCamelCase   :			List[str]          = k.find(" / ")
								__UpperCamelCase   :			List[Any]          = k[index + len(" / ") :]
						__UpperCamelCase   :			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)

		__UpperCamelCase   :			Tuple          = 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)

		__UpperCamelCase   :			int          = get_all_errors(args.output_dir, job_links=job_links)

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

		# print the top 30 most common test errors
		__UpperCamelCase   :			Any          = 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)

		__UpperCamelCase   :			Union[str, Any]          = reduce_by_error(errors)
		__UpperCamelCase   :			Dict          = reduce_by_model(errors)

		__UpperCamelCase   :			Union[str, Any]          = make_github_table(reduced_by_error)
		__UpperCamelCase   :			Union[str, 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)

