




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase							:						int					       =  logging.get_logger(__name__)

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Union[str, Any]	):


      '''simple docstring'''



      lowerCamelCase_							=       DPTConfig(embedding_type='hybrid'	)

      if "large" in checkpoint_url:
            lowerCamelCase_							=       10_24
            lowerCamelCase_							=       40_96
            lowerCamelCase_							=       24
            lowerCamelCase_							=       16
            lowerCamelCase_							=       [5, 11, 17, 23]
            lowerCamelCase_							=       [2_56, 5_12, 10_24, 10_24]
            lowerCamelCase_							=       (1, 3_84, 3_84)

      if "nyu" or "midas" in checkpoint_url:
            lowerCamelCase_							=       7_68
            lowerCamelCase_							=       [1, 1, 1, 0.5]
            lowerCamelCase_							=       [2_56, 5_12, 7_68, 7_68]
            lowerCamelCase_							=       1_50
            lowerCamelCase_							=       16
            lowerCamelCase_							=       (1, 3_84, 3_84)
            lowerCamelCase_							=       False
            lowerCamelCase_							=       'project'

      if "ade" in checkpoint_url:
            lowerCamelCase_							=       True
            lowerCamelCase_							=       7_68
            lowerCamelCase_							=       [1, 1, 1, 0.5]
            lowerCamelCase_							=       1_50
            lowerCamelCase_							=       16
            lowerCamelCase_							=       'huggingface/label-files'
            lowerCamelCase_							=       'ade20k-id2label.json'
            lowerCamelCase_							=       json.load(open(cached_download(hf_hub_url(lowercase      ,				lowercase      ,				repo_type='dataset'	)	)      ,				'r'	)	)
            lowerCamelCase_							=       {int(lowercase	): v for k, v in idalabel.items()}
            lowerCamelCase_							=       idalabel
            lowerCamelCase_							=       {v: k for k, v in idalabel.items()}
            lowerCamelCase_							=       [1, 1_50, 4_80, 4_80]

      return config, expected_shape

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Optional[int]	):


      '''simple docstring'''



      lowerCamelCase_							=       ['pretrained.model.head.weight', 'pretrained.model.head.bias']
      for k in ignore_keys:
            state_dict.pop(lowercase      ,				lowercase	)

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			List[Any]	):


      '''simple docstring'''



      if (
          "pretrained.model" in name
          and "cls_token" not in name
          and "pos_embed" not in name
          and "patch_embed" not in name
      ):
            lowerCamelCase_							=       name.replace('pretrained.model'      ,				'dpt.encoder'	)
      if "pretrained.model" in name:
            lowerCamelCase_							=       name.replace('pretrained.model'      ,				'dpt.embeddings'	)
      if "patch_embed" in name:
            lowerCamelCase_							=       name.replace('patch_embed'      ,				''	)
      if "pos_embed" in name:
            lowerCamelCase_							=       name.replace('pos_embed'      ,				'position_embeddings'	)
      if "attn.proj" in name:
            lowerCamelCase_							=       name.replace('attn.proj'      ,				'attention.output.dense'	)
      if "proj" in name and "project" not in name:
            lowerCamelCase_							=       name.replace('proj'      ,				'projection'	)
      if "blocks" in name:
            lowerCamelCase_							=       name.replace('blocks'      ,				'layer'	)
      if "mlp.fc1" in name:
            lowerCamelCase_							=       name.replace('mlp.fc1'      ,				'intermediate.dense'	)
      if "mlp.fc2" in name:
            lowerCamelCase_							=       name.replace('mlp.fc2'      ,				'output.dense'	)
      if "norm1" in name and "backbone" not in name:
            lowerCamelCase_							=       name.replace('norm1'      ,				'layernorm_before'	)
      if "norm2" in name and "backbone" not in name:
            lowerCamelCase_							=       name.replace('norm2'      ,				'layernorm_after'	)
      if "scratch.output_conv" in name:
            lowerCamelCase_							=       name.replace('scratch.output_conv'      ,				'head'	)
      if "scratch" in name:
            lowerCamelCase_							=       name.replace('scratch'      ,				'neck'	)
      if "layer1_rn" in name:
            lowerCamelCase_							=       name.replace('layer1_rn'      ,				'convs.0'	)
      if "layer2_rn" in name:
            lowerCamelCase_							=       name.replace('layer2_rn'      ,				'convs.1'	)
      if "layer3_rn" in name:
            lowerCamelCase_							=       name.replace('layer3_rn'      ,				'convs.2'	)
      if "layer4_rn" in name:
            lowerCamelCase_							=       name.replace('layer4_rn'      ,				'convs.3'	)
      if "refinenet" in name:
            lowerCamelCase_							=       int(name[len('neck.refinenet'	) : len('neck.refinenet'	) + 1]	)
            # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
            lowerCamelCase_							=       name.replace(f"""refinenet{layer_idx}"""      ,				f"""fusion_stage.layers.{abs(layer_idx-4	)}"""	)
      if "out_conv" in name:
            lowerCamelCase_							=       name.replace('out_conv'      ,				'projection'	)
      if "resConfUnit1" in name:
            lowerCamelCase_							=       name.replace('resConfUnit1'      ,				'residual_layer1'	)
      if "resConfUnit2" in name:
            lowerCamelCase_							=       name.replace('resConfUnit2'      ,				'residual_layer2'	)
      if "conv1" in name:
            lowerCamelCase_							=       name.replace('conv1'      ,				'convolution1'	)
      if "conv2" in name:
            lowerCamelCase_							=       name.replace('conv2'      ,				'convolution2'	)
      # readout blocks
      if "pretrained.act_postprocess1.0.project.0" in name:
            lowerCamelCase_							=       name.replace('pretrained.act_postprocess1.0.project.0'      ,				'neck.reassemble_stage.readout_projects.0.0'	)
      if "pretrained.act_postprocess2.0.project.0" in name:
            lowerCamelCase_							=       name.replace('pretrained.act_postprocess2.0.project.0'      ,				'neck.reassemble_stage.readout_projects.1.0'	)
      if "pretrained.act_postprocess3.0.project.0" in name:
            lowerCamelCase_							=       name.replace('pretrained.act_postprocess3.0.project.0'      ,				'neck.reassemble_stage.readout_projects.2.0'	)
      if "pretrained.act_postprocess4.0.project.0" in name:
            lowerCamelCase_							=       name.replace('pretrained.act_postprocess4.0.project.0'      ,				'neck.reassemble_stage.readout_projects.3.0'	)

      # resize blocks
      if "pretrained.act_postprocess1.3" in name:
            lowerCamelCase_							=       name.replace('pretrained.act_postprocess1.3'      ,				'neck.reassemble_stage.layers.0.projection'	)
      if "pretrained.act_postprocess1.4" in name:
            lowerCamelCase_							=       name.replace('pretrained.act_postprocess1.4'      ,				'neck.reassemble_stage.layers.0.resize'	)
      if "pretrained.act_postprocess2.3" in name:
            lowerCamelCase_							=       name.replace('pretrained.act_postprocess2.3'      ,				'neck.reassemble_stage.layers.1.projection'	)
      if "pretrained.act_postprocess2.4" in name:
            lowerCamelCase_							=       name.replace('pretrained.act_postprocess2.4'      ,				'neck.reassemble_stage.layers.1.resize'	)
      if "pretrained.act_postprocess3.3" in name:
            lowerCamelCase_							=       name.replace('pretrained.act_postprocess3.3'      ,				'neck.reassemble_stage.layers.2.projection'	)
      if "pretrained.act_postprocess4.3" in name:
            lowerCamelCase_							=       name.replace('pretrained.act_postprocess4.3'      ,				'neck.reassemble_stage.layers.3.projection'	)
      if "pretrained.act_postprocess4.4" in name:
            lowerCamelCase_							=       name.replace('pretrained.act_postprocess4.4'      ,				'neck.reassemble_stage.layers.3.resize'	)
      if "pretrained" in name:
            lowerCamelCase_							=       name.replace('pretrained'      ,				'dpt'	)
      if "bn" in name:
            lowerCamelCase_							=       name.replace('bn'      ,				'batch_norm'	)
      if "head" in name:
            lowerCamelCase_							=       name.replace('head'      ,				'head.head'	)
      if "encoder.norm" in name:
            lowerCamelCase_							=       name.replace('encoder.norm'      ,				'layernorm'	)
      if "auxlayer" in name:
            lowerCamelCase_							=       name.replace('auxlayer'      ,				'auxiliary_head.head'	)
      if "backbone" in name:
            lowerCamelCase_							=       name.replace('backbone'      ,				'backbone.bit.encoder'	)

      if ".." in name:
            lowerCamelCase_							=       name.replace('..'      ,				'.'	)

      if "stem.conv" in name:
            lowerCamelCase_							=       name.replace('stem.conv'      ,				'bit.embedder.convolution'	)
      if "blocks" in name:
            lowerCamelCase_							=       name.replace('blocks'      ,				'layers'	)
      if "convolution" in name and "backbone" in name:
            lowerCamelCase_							=       name.replace('convolution'      ,				'conv'	)
      if "layer" in name and "backbone" in name:
            lowerCamelCase_							=       name.replace('layer'      ,				'layers'	)
      if "backbone.bit.encoder.bit" in name:
            lowerCamelCase_							=       name.replace('backbone.bit.encoder.bit'      ,				'backbone.bit'	)
      if "embedder.conv" in name:
            lowerCamelCase_							=       name.replace('embedder.conv'      ,				'embedder.convolution'	)
      if "backbone.bit.encoder.stem.norm" in name:
            lowerCamelCase_							=       name.replace('backbone.bit.encoder.stem.norm'      ,				'backbone.bit.embedder.norm'	)
      return name

def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Any      ,				lowercase      :			str	):


      '''simple docstring'''



      for i in range(config.num_hidden_layers	):
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            lowerCamelCase_							=       state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""	)
            lowerCamelCase_							=       state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias"""	)
            # next, add query, keys and values (in that order) to the state dict
            lowerCamelCase_							=       in_proj_weight[: config.hidden_size, :]
            lowerCamelCase_							=       in_proj_bias[: config.hidden_size]
            lowerCamelCase_							=       in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            lowerCamelCase_							=       in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            lowerCamelCase_							=       in_proj_weight[
                -config.hidden_size :, :
            ]
            lowerCamelCase_							=       in_proj_bias[-config.hidden_size :]

def      _SCREAMING_SNAKE_CASE     (				):


      '''simple docstring'''



      lowerCamelCase_							=       'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowerCamelCase_							=       Image.open(requests.get(lowercase      ,				stream=lowercase	).raw	)
      return im

@torch.no_grad()
def      _SCREAMING_SNAKE_CASE     (				lowercase      :			Any      ,				lowercase      :			str      ,				lowercase      :			List[Any]      ,				lowercase      :			Any      ,				lowercase      :			Union[str, Any]	):


      '''simple docstring'''



      lowerCamelCase_	,							lowerCamelCase_							=       get_dpt_config(lowercase	)
      # load original state_dict from URL
      # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
      lowerCamelCase_							=       torch.load(lowercase      ,				map_location='cpu'	)
      # remove certain keys
      remove_ignore_keys_(lowercase	)
      # rename keys
      for key in state_dict.copy().keys():
            lowerCamelCase_							=       state_dict.pop(lowercase	)
            lowerCamelCase_							=       val
      # read in qkv matrices
      read_in_q_k_v(lowercase      ,				lowercase	)

      # load HuggingFace model
      lowerCamelCase_							=       DPTForSemanticSegmentation(lowercase	) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowercase	)
      model.load_state_dict(lowercase	)
      model.eval()

      # Check outputs on an image
      lowerCamelCase_							=       4_80 if 'ade' in checkpoint_url else 3_84
      lowerCamelCase_							=       DPTImageProcessor(size=lowercase	)

      lowerCamelCase_							=       prepare_img()
      lowerCamelCase_							=       image_processor(lowercase      ,				return_tensors='pt'	)

      # forward pass
      lowerCamelCase_							=       model(**lowercase	).logits if 'ade' in checkpoint_url else model(**lowercase	).predicted_depth

      if show_prediction:
            lowerCamelCase_							=       (
                torch.nn.functional.interpolate(
                    outputs.unsqueeze(1	)      ,				size=(image.size[1], image.size[0])      ,				mode='bicubic'      ,				align_corners=lowercase      ,				)
                .squeeze()
                .cpu()
                .numpy()
            )

            Image.fromarray((prediction / prediction.max()) * 2_55	).show()

      if pytorch_dump_folder_path is not None:
            Path(lowercase	).mkdir(exist_ok=lowercase	)
            print(f"""Saving model 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 push_to_hub:
            model.push_to_hub('ybelkada/dpt-hybrid-midas'	)
            image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'	)


if __name__ == "__main__":
 lowerCamelCase							:						Union[str, Any]					       =  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--checkpoint_url",
     default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
     type=str,
     help="URL of the original DPT checkpoint you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default=None,
     type=str,
     required=False,
     help="Path to the output PyTorch model directory.",
 )
 parser.add_argument(
     "--push_to_hub",
     action="store_true",
 )
 parser.add_argument(
     "--model_name",
     default="dpt-large",
     type=str,
     help="Name of the model, in case you're pushing to the hub.",
 )
 parser.add_argument(
     "--show_prediction",
     action="store_true",
 )

 lowerCamelCase							:						int					       =  parser.parse_args()
 convert_dpt_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
 )









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)

