



"""simple docstring"""




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

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

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase    : Any							=							logging.get_logger(__name__)

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Optional[int]						,  SCREAMING_SNAKE_CASE__     :						Optional[Any]=False    )  ->       Tuple:





     '''simple docstring'''

     _UpperCAmelCase      :     str        =   OrderedDict()
     for key, value in state_dict.items():
          if encoder_only and not key.startswith("head"    ):
               _UpperCAmelCase      :     Dict        =   'segformer.encoder.' + key
          if key.startswith("backbone"    ):
               _UpperCAmelCase      :     Optional[int]        =   key.replace("backbone"						,  "segformer.encoder"    )
          if "patch_embed" in key:
               # replace for example patch_embed1 by patch_embeddings.0
               _UpperCAmelCase      :     Dict        =   key[key.find("patch_embed"    ) + len("patch_embed"    )]
               _UpperCAmelCase      :     Union[str, Any]        =   key.replace(f'patch_embed{idx}'						,  f'patch_embeddings.{int(lowercase__    )-1}'    )
          if "norm" in key:
               _UpperCAmelCase      :     Tuple        =   key.replace("norm"						,  "layer_norm"    )
          if "segformer.encoder.layer_norm" in key:
               # replace for example layer_norm1 by layer_norm.0
               _UpperCAmelCase      :     int        =   key[key.find("segformer.encoder.layer_norm"    ) + len("segformer.encoder.layer_norm"    )]
               _UpperCAmelCase      :     Optional[Any]        =   key.replace(f'layer_norm{idx}'						,  f'layer_norm.{int(lowercase__    )-1}'    )
          if "layer_norm1" in key:
               _UpperCAmelCase      :     Any        =   key.replace("layer_norm1"						,  "layer_norm_1"    )
          if "layer_norm2" in key:
               _UpperCAmelCase      :     Dict        =   key.replace("layer_norm2"						,  "layer_norm_2"    )
          if "block" in key:
               # replace for example block1 by block.0
               _UpperCAmelCase      :     Dict        =   key[key.find("block"    ) + len("block"    )]
               _UpperCAmelCase      :     str        =   key.replace(f'block{idx}'						,  f'block.{int(lowercase__    )-1}'    )
          if "attn.q" in key:
               _UpperCAmelCase      :     Union[str, Any]        =   key.replace("attn.q"						,  "attention.self.query"    )
          if "attn.proj" in key:
               _UpperCAmelCase      :     List[Any]        =   key.replace("attn.proj"						,  "attention.output.dense"    )
          if "attn" in key:
               _UpperCAmelCase      :     Tuple        =   key.replace("attn"						,  "attention.self"    )
          if "fc1" in key:
               _UpperCAmelCase      :     int        =   key.replace("fc1"						,  "dense1"    )
          if "fc2" in key:
               _UpperCAmelCase      :     Optional[Any]        =   key.replace("fc2"						,  "dense2"    )
          if "linear_pred" in key:
               _UpperCAmelCase      :     Dict        =   key.replace("linear_pred"						,  "classifier"    )
          if "linear_fuse" in key:
               _UpperCAmelCase      :     List[str]        =   key.replace("linear_fuse.conv"						,  "linear_fuse"    )
               _UpperCAmelCase      :     Tuple        =   key.replace("linear_fuse.bn"						,  "batch_norm"    )
          if "linear_c" in key:
               # replace for example linear_c4 by linear_c.3
               _UpperCAmelCase      :     Optional[int]        =   key[key.find("linear_c"    ) + len("linear_c"    )]
               _UpperCAmelCase      :     Optional[int]        =   key.replace(f'linear_c{idx}'						,  f'linear_c.{int(lowercase__    )-1}'    )
          if key.startswith("head"    ):
               _UpperCAmelCase      :     Union[str, Any]        =   key.replace("head"						,  "classifier"    )
          _UpperCAmelCase      :     int        =   value

     return new_state_dict

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Any						,  SCREAMING_SNAKE_CASE__     :						List[str]    )  ->       Any:





     '''simple docstring'''

     for i in range(config.num_encoder_blocks    ):
          for j in range(config.depths[i]    ):
               # read in weights + bias of keys and values (which is a single matrix in the original implementation)
               _UpperCAmelCase      :     Optional[int]        =   state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.weight'    )
               _UpperCAmelCase      :     str        =   state_dict.pop(f'segformer.encoder.block.{i}.{j}.attention.self.kv.bias'    )
               # next, add keys and values (in that order) to the state dict
               _UpperCAmelCase      :     Dict        =   kv_weight[
                   : config.hidden_sizes[i], :
               ]
               _UpperCAmelCase      :     List[Any]        =   kv_bias[: config.hidden_sizes[i]]
               _UpperCAmelCase      :     int        =   kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               _UpperCAmelCase      :     str        =   kv_bias[
                   config.hidden_sizes[i] :
               ]

def 						__snake_case						(				)  ->       Any:





     '''simple docstring'''

     _UpperCAmelCase      :     Any        =   'http://images.cocodataset.org/val2017/000000039769.jpg'
     _UpperCAmelCase      :     Tuple        =   Image.open(requests.get(lowercase__						,  stream=lowercase__    ).raw    )

     return image

@torch.no_grad()
def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Optional[Any]						,  SCREAMING_SNAKE_CASE__     :						Tuple						,  SCREAMING_SNAKE_CASE__     :						Optional[int]    )  ->       List[str]:





     '''simple docstring'''

     _UpperCAmelCase      :     Optional[Any]        =   SegformerConfig()
     _UpperCAmelCase      :     Union[str, Any]        =   False

     # set attributes based on model_name
     _UpperCAmelCase      :     Union[str, Any]        =   'huggingface/label-files'
     if "segformer" in model_name:
          _UpperCAmelCase      :     Tuple        =   model_name[len("segformer."    ) : len("segformer."    ) + 2]
          if "ade" in model_name:
               _UpperCAmelCase      :     List[Any]        =   150
               _UpperCAmelCase      :     Tuple        =   'ade20k-id2label.json'
               _UpperCAmelCase      :     str        =   (1, 150, 128, 128)
          elif "city" in model_name:
               _UpperCAmelCase      :     List[Any]        =   19
               _UpperCAmelCase      :     Dict        =   'cityscapes-id2label.json'
               _UpperCAmelCase      :     Dict        =   (1, 19, 128, 128)
          else:
               raise ValueError(f'Model {model_name} not supported'    )
     elif "mit" in model_name:
          _UpperCAmelCase      :     List[Any]        =   True
          _UpperCAmelCase      :     str        =   model_name[4:6]
          _UpperCAmelCase      :     Any        =   1_000
          _UpperCAmelCase      :     Any        =   'imagenet-1k-id2label.json'
          _UpperCAmelCase      :     Union[str, Any]        =   (1, 1_000)
     else:
          raise ValueError(f'Model {model_name} not supported'    )

     # set config attributes
     _UpperCAmelCase      :     Union[str, Any]        =   json.load(open(hf_hub_download(lowercase__						,  lowercase__						,  repo_type="dataset"    )						,  "r"    )    )
     _UpperCAmelCase      :     Optional[Any]        =   {int(lowercase__    ): v for k, v in idalabel.items()}
     _UpperCAmelCase      :     Tuple        =   idalabel
     _UpperCAmelCase      :     Tuple        =   {v: k for k, v in idalabel.items()}
     if size == "b0":
          pass
     elif size == "b1":
          _UpperCAmelCase      :     Dict        =   [64, 128, 320, 512]
          _UpperCAmelCase      :     List[str]        =   256
     elif size == "b2":
          _UpperCAmelCase      :     int        =   [64, 128, 320, 512]
          _UpperCAmelCase      :     str        =   768
          _UpperCAmelCase      :     Optional[Any]        =   [3, 4, 6, 3]
     elif size == "b3":
          _UpperCAmelCase      :     Any        =   [64, 128, 320, 512]
          _UpperCAmelCase      :     Any        =   768
          _UpperCAmelCase      :     int        =   [3, 4, 18, 3]
     elif size == "b4":
          _UpperCAmelCase      :     Tuple        =   [64, 128, 320, 512]
          _UpperCAmelCase      :     int        =   768
          _UpperCAmelCase      :     Union[str, Any]        =   [3, 8, 27, 3]
     elif size == "b5":
          _UpperCAmelCase      :     Dict        =   [64, 128, 320, 512]
          _UpperCAmelCase      :     int        =   768
          _UpperCAmelCase      :     Any        =   [3, 6, 40, 3]
     else:
          raise ValueError(f'Size {size} not supported'    )

     # load image processor (only resize + normalize)
     _UpperCAmelCase      :     Dict        =   SegformerImageProcessor(
         image_scale=(512, 512)						,  keep_ratio=lowercase__						,  align=lowercase__						,  do_random_crop=lowercase__    )

     # prepare image
     _UpperCAmelCase      :     Dict        =   prepare_img()
     _UpperCAmelCase      :     Optional[Any]        =   image_processor(images=lowercase__						,  return_tensors="pt"    ).pixel_values

     logger.info(f'Converting model {model_name}...'    )

     # load original state dict
     if encoder_only:
          _UpperCAmelCase      :     Any        =   torch.load(lowercase__						,  map_location=torch.device("cpu"    )    )
     else:
          _UpperCAmelCase      :     Dict        =   torch.load(lowercase__						,  map_location=torch.device("cpu"    )    )['state_dict']

     # rename keys
     _UpperCAmelCase      :     Dict        =   rename_keys(lowercase__						,  encoder_only=lowercase__    )
     if not encoder_only:
          del state_dict["decode_head.conv_seg.weight"]
          del state_dict["decode_head.conv_seg.bias"]

     # key and value matrices need special treatment
     read_in_k_v(lowercase__						,  lowercase__    )

     # create HuggingFace model and load state dict
     if encoder_only:
          _UpperCAmelCase      :     Union[str, Any]        =   False
          _UpperCAmelCase      :     str        =   SegformerForImageClassification(lowercase__    )
     else:
          _UpperCAmelCase      :     Optional[int]        =   SegformerForSemanticSegmentation(lowercase__    )
     model.load_state_dict(lowercase__    )
     model.eval()

     # forward pass
     _UpperCAmelCase      :     List[Any]        =   model(lowercase__    )
     _UpperCAmelCase      :     Optional[Any]        =   outputs.logits

     # set expected_slice based on model name
     # ADE20k checkpoints
     if model_name == "segformer.b0.512x512.ade.160k":
          _UpperCAmelCase      :     List[str]        =   torch.tensor(
              [
                  [[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
                  [[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
                  [[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
              ]    )
     elif model_name == "segformer.b1.512x512.ade.160k":
          _UpperCAmelCase      :     Union[str, Any]        =   torch.tensor(
              [
                  [[-7.5_820, -8.7_231, -8.3_215], [-8.0_600, -10.3_529, -10.0_304], [-7.5_208, -9.4_103, -9.6_239]],
                  [[-12.6_918, -13.8_994, -13.7_137], [-13.3_196, -15.7_523, -15.4_789], [-12.9_343, -14.8_757, -14.9_689]],
                  [[-11.1_911, -11.9_421, -11.3_243], [-11.3_342, -13.6_839, -13.3_581], [-10.3_909, -12.1_832, -12.4_858]],
              ]    )
     elif model_name == "segformer.b2.512x512.ade.160k":
          _UpperCAmelCase      :     Union[str, Any]        =   torch.tensor(
              [
                  [[-11.8_173, -14.3_850, -16.3_128], [-14.5_648, -16.5_804, -18.6_568], [-14.7_223, -15.7_387, -18.4_218]],
                  [[-15.7_290, -17.9_171, -19.4_423], [-18.3_105, -19.9_448, -21.4_661], [-17.9_296, -18.6_497, -20.7_910]],
                  [[-15.0_783, -17.0_336, -18.2_789], [-16.8_771, -18.6_870, -20.1_612], [-16.2_454, -17.1_426, -19.5_055]],
              ]    )
     elif model_name == "segformer.b3.512x512.ade.160k":
          _UpperCAmelCase      :     Union[str, Any]        =   torch.tensor(
              [
                  [[-9.0_878, -10.2_081, -10.1_891], [-9.3_144, -10.7_941, -10.9_843], [-9.2_294, -10.3_855, -10.5_704]],
                  [[-12.2_316, -13.9_068, -13.6_102], [-12.9_161, -14.3_702, -14.3_235], [-12.5_233, -13.7_174, -13.7_932]],
                  [[-14.6_275, -15.2_490, -14.9_727], [-14.3_400, -15.9_687, -16.2_827], [-14.1_484, -15.4_033, -15.8_937]],
              ]    )
     elif model_name == "segformer.b4.512x512.ade.160k":
          _UpperCAmelCase      :     int        =   torch.tensor(
              [
                  [[-12.3_144, -13.2_447, -14.0_802], [-13.3_614, -14.5_816, -15.6_117], [-13.3_340, -14.4_433, -16.2_219]],
                  [[-19.2_781, -20.4_128, -20.7_506], [-20.6_153, -21.6_566, -22.0_998], [-19.9_800, -21.0_430, -22.1_494]],
                  [[-18.8_739, -19.7_804, -21.1_834], [-20.1_233, -21.6_765, -23.2_944], [-20.0_315, -21.2_641, -23.6_944]],
              ]    )
     elif model_name == "segformer.b5.640x640.ade.160k":
          _UpperCAmelCase      :     Tuple        =   torch.tensor(
              [
                  [[-9.5_524, -12.0_835, -11.7_348], [-10.5_229, -13.6_446, -14.5_662], [-9.5_842, -12.8_851, -13.9_414]],
                  [[-15.3_432, -17.5_323, -17.0_818], [-16.3_330, -18.9_255, -19.2_101], [-15.1_340, -17.7_848, -18.3_971]],
                  [[-12.6_072, -14.9_486, -14.6_631], [-13.7_629, -17.0_907, -17.7_745], [-12.7_899, -16.1_695, -17.1_671]],
              ]    )
     # Cityscapes checkpoints
     elif model_name == "segformer.b0.1024x1024.city.160k":
          _UpperCAmelCase      :     Optional[int]        =   torch.tensor(
              [
                  [[-11.9_295, -13.4_057, -14.8_106], [-13.3_431, -14.8_179, -15.3_781], [-14.2_836, -15.5_942, -16.1_588]],
                  [[-11.4_906, -12.8_067, -13.6_564], [-13.1_189, -14.0_500, -14.1_543], [-13.8_748, -14.5_136, -14.8_789]],
                  [[0.5_374, 0.1_067, -0.4_742], [0.1_141, -0.2_255, -0.7_099], [-0.3_000, -0.5_924, -1.3_105]],
              ]    )
     elif model_name == "segformer.b0.512x1024.city.160k":
          _UpperCAmelCase      :     Dict        =   torch.tensor(
              [
                  [[-7.8_217, -9.8_767, -10.1_717], [-9.4_438, -10.9_058, -11.4_047], [-9.7_939, -12.3_495, -12.1_079]],
                  [[-7.1_514, -9.5_336, -10.0_860], [-9.7_776, -11.6_822, -11.8_439], [-10.1_411, -12.7_655, -12.8_972]],
                  [[0.3_021, 0.0_805, -0.2_310], [-0.0_328, -0.1_605, -0.2_714], [-0.1_408, -0.5_477, -0.6_976]],
              ]    )
     elif model_name == "segformer.b0.640x1280.city.160k":
          _UpperCAmelCase      :     Optional[Any]        =   torch.tensor(
              [
                  [
                      [-1.1372E01, -1.2787E01, -1.3477E01],
                      [-1.2536E01, -1.4194E01, -1.4409E01],
                      [-1.3217E01, -1.4888E01, -1.5327E01],
                  ],
                  [
                      [-1.4791E01, -1.7122E01, -1.8277E01],
                      [-1.7163E01, -1.9192E01, -1.9533E01],
                      [-1.7897E01, -1.9991E01, -2.0315E01],
                  ],
                  [
                      [7.6723E-01, 4.1921E-01, -7.7878E-02],
                      [4.7772E-01, 9.5557E-03, -2.8082E-01],
                      [3.6032E-01, -2.4826E-01, -5.1168E-01],
                  ],
              ]    )
     elif model_name == "segformer.b0.768x768.city.160k":
          _UpperCAmelCase      :     Optional[Any]        =   torch.tensor(
              [
                  [[-9.4_959, -11.3_087, -11.7_479], [-11.0_025, -12.6_540, -12.3_319], [-11.4_064, -13.0_487, -12.9_905]],
                  [[-9.8_905, -11.3_084, -12.0_854], [-11.1_726, -12.7_698, -12.9_583], [-11.5_985, -13.3_278, -14.1_774]],
                  [[0.2_213, 0.0_192, -0.2_466], [-0.1_731, -0.4_213, -0.4_874], [-0.3_126, -0.6_541, -1.1_389]],
              ]    )
     elif model_name == "segformer.b1.1024x1024.city.160k":
          _UpperCAmelCase      :     Dict        =   torch.tensor(
              [
                  [[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
                  [[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
                  [[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
              ]    )
     elif model_name == "segformer.b2.1024x1024.city.160k":
          _UpperCAmelCase      :     Tuple        =   torch.tensor(
              [
                  [[-16.0_976, -16.4_856, -17.3_962], [-16.6_234, -19.0_342, -19.7_685], [-16.0_900, -18.0_661, -19.1_180]],
                  [[-18.4_750, -18.8_488, -19.5_074], [-19.4_030, -22.1_570, -22.5_977], [-19.1_191, -20.8_486, -22.3_783]],
                  [[-4.5_178, -5.5_037, -6.5_109], [-5.0_884, -7.2_174, -8.0_334], [-4.4_156, -5.8_117, -7.2_970]],
              ]    )
     elif model_name == "segformer.b3.1024x1024.city.160k":
          _UpperCAmelCase      :     List[str]        =   torch.tensor(
              [
                  [[-14.2_081, -14.4_732, -14.1_977], [-14.5_867, -16.4_423, -16.6_356], [-13.4_441, -14.9_685, -16.8_696]],
                  [[-14.4_576, -14.7_073, -15.0_451], [-15.0_816, -17.6_237, -17.9_873], [-14.4_213, -16.0_199, -18.5_992]],
                  [[-4.7_349, -4.9_588, -5.0_966], [-4.3_210, -6.9_325, -7.2_591], [-3.4_312, -4.7_484, -7.1_917]],
              ]    )
     elif model_name == "segformer.b4.1024x1024.city.160k":
          _UpperCAmelCase      :     Optional[int]        =   torch.tensor(
              [
                  [[-11.7_737, -11.9_526, -11.3_273], [-13.6_692, -14.4_574, -13.8_878], [-13.8_937, -14.6_924, -15.9_345]],
                  [[-14.6_706, -14.5_330, -14.1_306], [-16.1_502, -16.8_180, -16.4_269], [-16.8_338, -17.8_939, -20.1_746]],
                  [[1.0_491, 0.8_289, 1.0_310], [1.1_044, 0.5_219, 0.8_055], [1.0_899, 0.6_926, 0.5_590]],
              ]    )
     elif model_name == "segformer.b5.1024x1024.city.160k":
          _UpperCAmelCase      :     Union[str, Any]        =   torch.tensor(
              [
                  [[-12.5_641, -13.4_777, -13.0_684], [-13.9_587, -15.8_983, -16.6_557], [-13.3_109, -15.7_350, -16.3_141]],
                  [[-14.7_074, -15.4_352, -14.5_944], [-16.6_353, -18.1_663, -18.6_120], [-15.1_702, -18.0_329, -18.1_547]],
                  [[-1.7_990, -2.0_951, -1.7_784], [-2.6_397, -3.8_245, -3.9_686], [-1.5_264, -2.8_126, -2.9_316]],
              ]    )
     else:
          _UpperCAmelCase      :     Optional[int]        =   logits.argmax(-1    ).item()
          print("Predicted class:"						,  model.config.idalabel[predicted_class_idx]    )

     # verify logits
     if not encoder_only:
          assert logits.shape == expected_shape
          assert torch.allclose(logits[0, :3, :3, :3]						,  lowercase__						,  atol=1E-2    )

     # finally, save model and image processor
     logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'    )
     Path(lowercase__    ).mkdir(exist_ok=lowercase__    )
     model.save_pretrained(lowercase__    )
     image_processor.save_pretrained(lowercase__    )


if __name__ == "__main__":
       _lowerCAmelCase    : Dict							=							argparse.ArgumentParser()

       parser.add_argument(
           "--model_name",
           default="segformer.b0.512x512.ade.160k",
           type=str,
           help="Name of the model you'd like to convert.",
       )
       parser.add_argument(
           "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
       )
       _lowerCAmelCase    : Tuple							=							parser.parse_args()
       convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





"""simple docstring"""




import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Tuple						,  SCREAMING_SNAKE_CASE__     :						List[str]=7    )  ->       Tuple:





     '''simple docstring'''

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

     # The id of a workflow (not of a workflow run)
     _UpperCAmelCase      :     Any        =   "636036"

     _UpperCAmelCase      :     Dict        =   f'https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'
     # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
     url += f'?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'

     _UpperCAmelCase      :     Tuple        =   requests.get(SCREAMING_SNAKE_CASE__						,  headers=SCREAMING_SNAKE_CASE__    ).json()

     return result["workflow_runs"]

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Union[str, Any]    )  ->       Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase      :     int        =   get_daily_ci_runs(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[int]        =   None
     for workflow_run in workflow_runs:
          if workflow_run["status"] == "completed":
               _UpperCAmelCase      :     str        =   workflow_run["id"]
               break

     return workflow_run_id

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict						,  SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]    )  ->       List[str]:





     '''simple docstring'''

     _UpperCAmelCase      :     List[str]        =   get_last_daily_ci_runs(SCREAMING_SNAKE_CASE__    )
     if workflow_run_id is not None:
          _UpperCAmelCase      :     Any        =   get_artifacts_links(worflow_run_id=SCREAMING_SNAKE_CASE__						,  token=SCREAMING_SNAKE_CASE__    )
          for artifact_name in artifact_names:
               if artifact_name in artifacts_links:
                    _UpperCAmelCase      :     List[str]        =   artifacts_links[artifact_name]
                    download_artifact(
                        artifact_name=SCREAMING_SNAKE_CASE__						,  artifact_url=SCREAMING_SNAKE_CASE__						,  output_dir=SCREAMING_SNAKE_CASE__						,  token=SCREAMING_SNAKE_CASE__    )

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Tuple						,  SCREAMING_SNAKE_CASE__     :						Dict						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]    )  ->       Union[str, Any]:





     '''simple docstring'''

     get_last_daily_ci_artifacts(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )

     _UpperCAmelCase      :     Any        =   {}
     for artifact_name in artifact_names:
          _UpperCAmelCase      :     Dict        =   os.path.join(SCREAMING_SNAKE_CASE__						,  f'{artifact_name}.zip'    )
          if os.path.isfile(SCREAMING_SNAKE_CASE__    ):
               _UpperCAmelCase      :     str        =   {}
               with zipfile.ZipFile(SCREAMING_SNAKE_CASE__    ) as z:
                    for filename in z.namelist():
                         if not os.path.isdir(SCREAMING_SNAKE_CASE__    ):
                              # read the file
                              with z.open(SCREAMING_SNAKE_CASE__    ) as f:
                                   _UpperCAmelCase      :     List[str]        =   f.read().decode("UTF-8"    )

     return results
