


import argparse
import json

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

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     Dict					=					[2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 1_8, 2]
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					True if 'large' in model_name or 'huge' in model_name else False
			SCREAMING_SNAKE_CASE_		:     List[str]					=					True if 'large' in model_name or 'huge' in model_name else False
			SCREAMING_SNAKE_CASE_		:     Tuple					=					True if 'large' in model_name or 'huge' in model_name else False

			if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
						if "fl3" in model_name:
									SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[3, 3, 3, 3]
									SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[5, 5, 5, 5]
						elif "fl4" in model_name:
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[4, 4, 4, 4]
									SCREAMING_SNAKE_CASE_		:     Tuple					=					[3, 3, 3, 3]

			if "tiny" in model_name or "small" in model_name or "base" in model_name:
						SCREAMING_SNAKE_CASE_		:     str					=					[3, 3, 3, 3]
						if "lrf" in model_name:
									SCREAMING_SNAKE_CASE_		:     Any					=					[3, 3, 3, 3]
						else:
									SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[2, 2, 2, 2]

			if "tiny" in model_name:
						SCREAMING_SNAKE_CASE_		:     Any					=					9_6
			elif "small" in model_name:
						SCREAMING_SNAKE_CASE_		:     str					=					9_6
			elif "base" in model_name:
						SCREAMING_SNAKE_CASE_		:     Any					=					1_2_8
			elif "large" in model_name:
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					1_9_2
			elif "xlarge" in model_name:
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					2_5_6
			elif "huge" in model_name:
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					3_5_2

			# set label information
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					'huggingface/label-files'
			if "large" in model_name or "huge" in model_name:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					'imagenet-22k-id2label.json'
			else:
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'imagenet-1k-id2label.json'

			SCREAMING_SNAKE_CASE_		:     Any					=					json.load(open(hf_hub_download(A__,      A__,      repo_type='dataset'				),      'r'				)				)
			SCREAMING_SNAKE_CASE_		:     str					=					{int(A__				): v for k, v in idalabel.items()}
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					{v: k for k, v in idalabel.items()}

			SCREAMING_SNAKE_CASE_		:     int					=					FocalNetConfig(
			    embed_dim=A__,      depths=A__,      focal_levels=A__,      focal_windows=A__,      use_conv_embed=A__,      idalabel=A__,      labelaid=A__,      use_post_layernorm=A__,      use_layerscale=A__,      )

			return config






def 						a__			(				A__				):
			if "patch_embed.proj" in name:
						SCREAMING_SNAKE_CASE_		:     str					=					name.replace('patch_embed.proj',      'embeddings.patch_embeddings.projection'				)
			if "patch_embed.norm" in name:
						SCREAMING_SNAKE_CASE_		:     int					=					name.replace('patch_embed.norm',      'embeddings.norm'				)
			if "layers" in name:
						SCREAMING_SNAKE_CASE_		:     Any					=					'encoder.' + name
			if "encoder.layers" in name:
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					name.replace('encoder.layers',      'encoder.stages'				)
			if "downsample.proj" in name:
						SCREAMING_SNAKE_CASE_		:     str					=					name.replace('downsample.proj',      'downsample.projection'				)
			if "blocks" in name:
						SCREAMING_SNAKE_CASE_		:     Any					=					name.replace('blocks',      'layers'				)
			if "modulation.f.weight" in name or "modulation.f.bias" in name:
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					name.replace('modulation.f',      'modulation.projection_in'				)
			if "modulation.h.weight" in name or "modulation.h.bias" in name:
						SCREAMING_SNAKE_CASE_		:     Dict					=					name.replace('modulation.h',      'modulation.projection_context'				)
			if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					name.replace('modulation.proj',      'modulation.projection_out'				)

			if name == "norm.weight":
						SCREAMING_SNAKE_CASE_		:     int					=					'layernorm.weight'
			if name == "norm.bias":
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					'layernorm.bias'

			if "head" in name:
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					name.replace('head',      'classifier'				)
			else:
						SCREAMING_SNAKE_CASE_		:     List[str]					=					'focalnet.' + name

			return name






def 						a__			(				A__,      A__,      A__=False				):
			# fmt: off
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					{
			    'focalnet-tiny': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth',
			    'focalnet-tiny-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth',
			    'focalnet-small': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth',
			    'focalnet-small-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth',
			    'focalnet-base': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth',
			    'focalnet-base-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth',
			    'focalnet-large-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
			    'focalnet-large-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
			    'focalnet-xlarge-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
			    'focalnet-xlarge-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
			}
			# fmt: on

			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					model_name_to_url[model_name]
			print('Checkpoint URL: ',      A__				)
			SCREAMING_SNAKE_CASE_		:     str					=					torch.hub.load_state_dict_from_url(A__,      map_location='cpu'				)['model']

			# rename keys
			for key in state_dict.copy().keys():
						SCREAMING_SNAKE_CASE_		:     Dict					=					state_dict.pop(A__				)
						SCREAMING_SNAKE_CASE_		:     Dict					=					val

			SCREAMING_SNAKE_CASE_		:     List[Any]					=					get_focalnet_config(A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					FocalNetForImageClassification(A__				)
			model.eval()

			# load state dict
			model.load_state_dict(A__				)

			# verify conversion
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'http://images.cocodataset.org/val2017/000000039769.jpg'

			SCREAMING_SNAKE_CASE_		:     List[Any]					=					BitImageProcessor(
			    do_resize=A__,      size={'shortest_edge': 2_5_6},      resample=PILImageResampling.BILINEAR,      do_center_crop=A__,      crop_size=2_2_4,      do_normalize=A__,      image_mean=A__,      image_std=A__,      )
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					Image.open(requests.get(A__,      stream=A__				).raw				)
			SCREAMING_SNAKE_CASE_		:     str					=					processor(images=A__,      return_tensors='pt'				)

			SCREAMING_SNAKE_CASE_		:     int					=					transforms.Compose(
			    [
			        transforms.Resize(2_5_6				),
			        transforms.CenterCrop(2_2_4				),
			        transforms.ToTensor(),
			        transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06],      std=[0.2_29, 0.2_24, 0.2_25]				),
			    ]				)

			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					image_transforms(A__				).unsqueeze(0				)

			# verify pixel_values
			assert torch.allclose(inputs.pixel_values,      A__,      atol=1E-4				)

			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					model(**A__				)

			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					outputs.logits.argmax(-1				).item()
			print('Predicted class:',      model.config.idalabel[predicted_class_idx]				)

			print('First values of logits:',      outputs.logits[0, :3]				)

			if model_name == "focalnet-tiny":
						SCREAMING_SNAKE_CASE_		:     Dict					=					torch.tensor([0.21_66, -0.43_68, 0.21_91]				)
			elif model_name == "focalnet-tiny-lrf":
						SCREAMING_SNAKE_CASE_		:     Any					=					torch.tensor([1.16_69, 0.01_25, -0.16_95]				)
			elif model_name == "focalnet-small":
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					torch.tensor([0.49_17, -0.04_30, 0.13_41]				)
			elif model_name == "focalnet-small-lrf":
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					torch.tensor([-0.25_88, -0.53_42, -0.23_31]				)
			elif model_name == "focalnet-base":
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					torch.tensor([-0.16_55, -0.40_90, -0.17_30]				)
			elif model_name == "focalnet-base-lrf":
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.tensor([0.53_06, -0.04_83, -0.39_28]				)
			assert torch.allclose(outputs.logits[0, :3],      A__,      atol=1E-4				)
			print('Looks ok!'				)

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

			if push_to_hub:
						print(F'''Pushing model and processor of {model_name} to the hub...'''				)
						model.push_to_hub(F'''{model_name}'''				)
						processor.push_to_hub(F'''{model_name}'''				)


if __name__ == "__main__":
				lowerCAmelCase__			:      List[Any]								=argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='focalnet-tiny',
				    type=str,
				    help='Name of the FocalNet 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 to push the model and processor to the hub.',
				)

				lowerCAmelCase__			:      Optional[int]								=parser.parse_args()
				convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












'''simple docstring'''


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

import requests


def 						__a(SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       Tuple=None     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   None
    if token is not None:
        _lowerCAmelCase						      =   {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

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

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

        for i in range(SCREAMING_SNAKE_CASE_     ):
            _lowerCAmelCase						      =   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 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       Dict=None     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   None
    if token is not None:
        _lowerCAmelCase						      =   {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

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

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

        for i in range(SCREAMING_SNAKE_CASE_     ):
            _lowerCAmelCase						      =   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 						__a(SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       Dict     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   None
    if token is not None:
        _lowerCAmelCase						      =   {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

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


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       Optional[Any]=None     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   []
    _lowerCAmelCase						      =   []
    _lowerCAmelCase						      =   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:
                            _lowerCAmelCase						      =   line.decode("UTF-8"     ).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    _lowerCAmelCase						      =   line[: line.index(": "     )]
                                    _lowerCAmelCase						      =   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
                                _lowerCAmelCase						      =   line[len("FAILED "     ) :]
                                failed_tests.append(SCREAMING_SNAKE_CASE_     )
                            elif filename == "job_name.txt":
                                _lowerCAmelCase						      =   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."     )

    _lowerCAmelCase						      =   None
    if job_name and job_links:
        _lowerCAmelCase						      =   job_links.get(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )

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

    return result


def 						__a(SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       Tuple=None     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   []

    _lowerCAmelCase						      =   [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 						__a(SCREAMING_SNAKE_CASE_	:       List[str]		,							SCREAMING_SNAKE_CASE_	:       str=None     ):



    '''simple docstring'''





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

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


def 						__a(SCREAMING_SNAKE_CASE_	:       List[str]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   test.split("::"     )[0]
    if test.startswith("tests/models/"     ):
        _lowerCAmelCase						      =   test.split("/"     )[2]
    else:
        _lowerCAmelCase						      =   None

    return test


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       Tuple=None     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   [(x[0], x[1], get_model(x[2]     )) for x in logs]
    _lowerCAmelCase						      =   [x for x in logs if x[2] is not None]
    _lowerCAmelCase						      =   {x[2] for x in logs}

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

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


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[int]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   "| no. | error | status |"
    _lowerCAmelCase						      =   "|-:|:-|:-|"
    _lowerCAmelCase						      =   [header, sep]
    for error in reduced_by_error:
        _lowerCAmelCase						      =   reduced_by_error[error]["count"]
        _lowerCAmelCase						      =   F'''| {count} | {error[:100]} |  |'''
        lines.append(SCREAMING_SNAKE_CASE_     )

    return "\n".join(SCREAMING_SNAKE_CASE_     )


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[int]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   "| model | no. of errors | major error | count |"
    _lowerCAmelCase						      =   "|-:|-:|-:|-:|"
    _lowerCAmelCase						      =   [header, sep]
    for model in reduced_by_model:
        _lowerCAmelCase						      =   reduced_by_model[model]["count"]
        _lowerCAmelCase      ,      _lowerCAmelCase						      =   list(reduced_by_model[model]["errors"].items()     )[0]
        _lowerCAmelCase						      =   F'''| {model} | {count} | {error[:60]} | {_count} |'''
        lines.append(SCREAMING_SNAKE_CASE_     )

    return "\n".join(SCREAMING_SNAKE_CASE_     )


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

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

       _SCREAMING_SNAKE_CASE									=				get_job_links(args.workflow_run_id, token=args.token)
       _SCREAMING_SNAKE_CASE									=				{}
       # 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:
                            _SCREAMING_SNAKE_CASE									=				k.find(" / ")
                            _SCREAMING_SNAKE_CASE									=				k[index + len(" / ") :]
                     _SCREAMING_SNAKE_CASE									=				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)

       _SCREAMING_SNAKE_CASE									=				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)

       _SCREAMING_SNAKE_CASE									=				get_all_errors(args.output_dir, job_links=job_links)

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

       # print the top 30 most common test errors
       _SCREAMING_SNAKE_CASE									=				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)

       _SCREAMING_SNAKE_CASE									=				reduce_by_error(errors)
       _SCREAMING_SNAKE_CASE									=				reduce_by_model(errors)

       _SCREAMING_SNAKE_CASE									=				make_github_table(reduced_by_error)
       _SCREAMING_SNAKE_CASE									=				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)


