


"""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()
_snake_case          =							logging.get_logger(__name__)



def   snake_case						(				_a:     List[str]		,   _a:     List[Any]=False							)->		Tuple:
      '''simple docstring'''


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

      return new_state_dict



def   snake_case						(				_a:     Optional[Any]		,   _a:     List[Any]							)->		Optional[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)
                  lowerCamelCase__           =		state_dict.pop(F'segformer.encoder.block.{i}.{j}.attention.self.kv.weight'							)
                  lowerCamelCase__           =		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
                  lowerCamelCase__           =		kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  lowerCamelCase__           =		kv_bias[: config.hidden_sizes[i]]
                  lowerCamelCase__           =		kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  lowerCamelCase__           =		kv_bias[
                      config.hidden_sizes[i] :
                  ]



def   snake_case						(				)->		Tuple:
      '''simple docstring'''


      lowerCamelCase__           =		'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowerCamelCase__           =		Image.open(requests.get(lowerCAmelCase__		,   stream=lowerCAmelCase__							).raw							)

      return image



@torch.no_grad()
def   snake_case						(				_a:     int		,   _a:     List[str]		,   _a:     Optional[int]							)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		SegformerConfig()
      lowerCamelCase__           =		False

      # set attributes based on model_name
      lowerCamelCase__           =		'huggingface/label-files'
      if "segformer" in model_name:
            lowerCamelCase__           =		model_name[len('segformer.'							) : len('segformer.'							) + 2]
            if "ade" in model_name:
                  lowerCamelCase__           =		150
                  lowerCamelCase__           =		'ade20k-id2label.json'
                  lowerCamelCase__           =		(1, 150, 128, 128)
            elif "city" in model_name:
                  lowerCamelCase__           =		19
                  lowerCamelCase__           =		'cityscapes-id2label.json'
                  lowerCamelCase__           =		(1, 19, 128, 128)
            else:
                  raise ValueError(F'Model {model_name} not supported'							)
      elif "mit" in model_name:
            lowerCamelCase__           =		True
            lowerCamelCase__           =		model_name[4:6]
            lowerCamelCase__           =		1000
            lowerCamelCase__           =		'imagenet-1k-id2label.json'
            lowerCamelCase__           =		(1, 1000)
      else:
            raise ValueError(F'Model {model_name} not supported'							)

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

      # load image processor (only resize + normalize)
      lowerCamelCase__           =		SegformerImageProcessor(
          image_scale=(512, 512)		,   keep_ratio=lowerCAmelCase__		,   align=lowerCAmelCase__		,   do_random_crop=lowerCAmelCase__							)

      # prepare image
      lowerCamelCase__           =		prepare_img()
      lowerCamelCase__           =		image_processor(images=lowerCAmelCase__		,   return_tensors='pt'							).pixel_values

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

      # load original state dict
      if encoder_only:
            lowerCamelCase__           =		torch.load(lowerCAmelCase__		,   map_location=torch.device('cpu'							)							)
      else:
            lowerCamelCase__           =		torch.load(lowerCAmelCase__		,   map_location=torch.device('cpu'							)							)['state_dict']

      # rename keys
      lowerCamelCase__           =		rename_keys(lowerCAmelCase__		,   encoder_only=lowerCAmelCase__							)
      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(lowerCAmelCase__		,   lowerCAmelCase__							)

      # create HuggingFace model and load state dict
      if encoder_only:
            lowerCamelCase__           =		False
            lowerCamelCase__           =		SegformerForImageClassification(lowerCAmelCase__							)
      else:
            lowerCamelCase__           =		SegformerForSemanticSegmentation(lowerCAmelCase__							)
      model.load_state_dict(lowerCAmelCase__							)
      model.eval()

      # forward pass
      lowerCamelCase__           =		model(lowerCAmelCase__							)
      lowerCamelCase__           =		outputs.logits

      # set expected_slice based on model name
      # ADE20k checkpoints
      if model_name == "segformer.b0.512x512.ade.160k":
            lowerCamelCase__           =		torch.tensor(
                [
                    [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
                    [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
                    [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
                ]							)
      elif model_name == "segformer.b1.512x512.ade.160k":
            lowerCamelCase__           =		torch.tensor(
                [
                    [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
                    [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
                    [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
                ]							)
      elif model_name == "segformer.b2.512x512.ade.160k":
            lowerCamelCase__           =		torch.tensor(
                [
                    [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
                    [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
                    [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
                ]							)
      elif model_name == "segformer.b3.512x512.ade.160k":
            lowerCamelCase__           =		torch.tensor(
                [
                    [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
                    [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
                    [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
                ]							)
      elif model_name == "segformer.b4.512x512.ade.160k":
            lowerCamelCase__           =		torch.tensor(
                [
                    [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
                    [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
                    [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
                ]							)
      elif model_name == "segformer.b5.640x640.ade.160k":
            lowerCamelCase__           =		torch.tensor(
                [
                    [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
                    [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
                    [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
                ]							)
      # Cityscapes checkpoints
      elif model_name == "segformer.b0.1024x1024.city.160k":
            lowerCamelCase__           =		torch.tensor(
                [
                    [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
                    [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
                    [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
                ]							)
      elif model_name == "segformer.b0.512x1024.city.160k":
            lowerCamelCase__           =		torch.tensor(
                [
                    [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
                    [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
                    [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
                ]							)
      elif model_name == "segformer.b0.640x1280.city.160k":
            lowerCamelCase__           =		torch.tensor(
                [
                    [
                        [-1.1_372E01, -1.2_787E01, -1.3_477E01],
                        [-1.2_536E01, -1.4_194E01, -1.4_409E01],
                        [-1.3_217E01, -1.4_888E01, -1.5_327E01],
                    ],
                    [
                        [-1.4_791E01, -1.7_122E01, -1.8_277E01],
                        [-1.7_163E01, -1.9_192E01, -1.9_533E01],
                        [-1.7_897E01, -1.9_991E01, -2.0_315E01],
                    ],
                    [
                        [7.6_723E-01, 4.1_921E-01, -7.7_878E-02],
                        [4.7_772E-01, 9.5_557E-03, -2.8_082E-01],
                        [3.6_032E-01, -2.4_826E-01, -5.1_168E-01],
                    ],
                ]							)
      elif model_name == "segformer.b0.768x768.city.160k":
            lowerCamelCase__           =		torch.tensor(
                [
                    [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
                    [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
                    [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
                ]							)
      elif model_name == "segformer.b1.1024x1024.city.160k":
            lowerCamelCase__           =		torch.tensor(
                [
                    [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
                    [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
                    [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
                ]							)
      elif model_name == "segformer.b2.1024x1024.city.160k":
            lowerCamelCase__           =		torch.tensor(
                [
                    [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
                    [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
                    [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
                ]							)
      elif model_name == "segformer.b3.1024x1024.city.160k":
            lowerCamelCase__           =		torch.tensor(
                [
                    [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
                    [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
                    [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
                ]							)
      elif model_name == "segformer.b4.1024x1024.city.160k":
            lowerCamelCase__           =		torch.tensor(
                [
                    [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
                    [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
                    [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
                ]							)
      elif model_name == "segformer.b5.1024x1024.city.160k":
            lowerCamelCase__           =		torch.tensor(
                [
                    [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
                    [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
                    [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
                ]							)
      else:
            lowerCamelCase__           =		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]		,   lowerCAmelCase__		,   atol=1E-2							)

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


if __name__ == "__main__":
   _snake_case          =							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."
   )
   _snake_case          =							parser.parse_args()
   convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)







"""simple docstring"""


import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict

import requests
from slack_sdk import WebClient


_snake_case          =							WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])



def   snake_case						(				_a:     Any							)->		Optional[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		test_results.split(' '							)

      lowerCamelCase__           =		0
      lowerCamelCase__           =		0

      # When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
      # When it is too long, those signs are not present.
      lowerCamelCase__           =		expressions[-2] if '=' in expressions[-1] else expressions[-1]

      for i, expression in enumerate(_a							):
            if "failed" in expression:
                  failed += int(expressions[i - 1]							)
            if "passed" in expression:
                  success += int(expressions[i - 1]							)

      return failed, success, time_spent



def   snake_case						(				_a:     Optional[int]							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		{}
      lowerCamelCase__           =		None
      lowerCamelCase__           =		False
      for line in failures_short_lines.split('\n'							):
            if re.search(R'_ \[doctest\]'		,   _a							):
                  lowerCamelCase__           =		True
                  lowerCamelCase__           =		line.split(' '							)[2]
            elif in_error and not line.split(' '							)[0].isdigit():
                  lowerCamelCase__           =		line
                  lowerCamelCase__           =		False

      return failures



class   _a      :
    def __init__( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Dict				):
          lowerCamelCase__           =		title

          lowerCamelCase__           =		doc_test_results['time_spent'].split(','				)[0]
          lowerCamelCase__           =		doc_test_results['success']
          lowerCamelCase__           =		doc_test_results['failures']
          lowerCamelCase__           =		self.n_success + self.n_failures

          # Failures and success of the modeling tests
          lowerCamelCase__           =		doc_test_results
    @property
    def      _UpperCamelCase       ( self							:					List[str]				):
          lowerCamelCase__           =		[self._time_spent]
          lowerCamelCase__           =		0

          for time in time_spent:
                lowerCamelCase__           =		time.split(':'				)

                # Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
                if len(SCREAMING_SNAKE_CASE__				) == 1:
                      lowerCamelCase__           =		[0, 0, time_parts[0]]

                lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		int(time_parts[0]				), int(time_parts[1]				), float(time_parts[2]				)
                total_secs += hours * 36_00 + minutes * 60 + seconds

          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		total_secs // 36_00, (total_secs % 36_00) // 60, total_secs % 60
          return F'{int(SCREAMING_SNAKE_CASE__				)}h{int(SCREAMING_SNAKE_CASE__				)}m{int(SCREAMING_SNAKE_CASE__				)}s'
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          return {
              "type": "section",
              "text": {
                  "type": "plain_text",
                  "text": F'🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.',
                  "emoji": True,
              },
              "accessory": {
                  "type": "button",
                  "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
                  "url": F'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
              },
          }
    @property
    def      _UpperCamelCase       ( self							:					Any				):
          return {
              "type": "section",
              "text": {
                  "type": "plain_text",
                  "text": (
                      F'There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'
                      F' {self.time}.'
                  ),
                  "emoji": True,
              },
              "accessory": {
                  "type": "button",
                  "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
                  "url": F'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
              },
          }
    @property
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		40
          lowerCamelCase__           =		{k: v['failed'] for k, v in doc_test_results.items() if isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)}

          lowerCamelCase__           =		''
          for category, failures in category_failures.items():
                if len(SCREAMING_SNAKE_CASE__				) == 0:
                      continue

                if report != "":
                      report += "\n\n"

                report += F'*{category} failures*:'.ljust(line_length // 2				).rjust(line_length // 2				) + "\n"
                report += "`"
                report += "`\n`".join(SCREAMING_SNAKE_CASE__				)
                report += "`"

          return {
              "type": "section",
              "text": {
                  "type": "mrkdwn",
                  "text": F'The following examples had failures:\n\n\n{report}\n',
              },
          }
    @property
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		[self.header]

          if self.n_failures > 0:
                blocks.append(self.failures				)

          if self.n_failures > 0:
                blocks.extend([self.category_failures]				)

          if self.n_failures == 0:
                blocks.append(self.no_failures				)

          return json.dumps(SCREAMING_SNAKE_CASE__				)
    @staticmethod
    def      _UpperCamelCase       ( ):
          lowerCamelCase__           =		[
              {
                  'type': 'section',
                  'text': {
                      'type': 'plain_text',
                      'text': 'There was an issue running the tests.',
                  },
                  'accessory': {
                      'type': 'button',
                      'text': {'type': 'plain_text', 'text': 'Check Action results', 'emoji': True},
                      'url': F'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
                  },
              }
          ]

          print('Sending the following payload'				)
          print(json.dumps({'blocks': json.loads(SCREAMING_SNAKE_CASE__				)}				)				)

          client.chat_postMessage(
              channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']							,					text='There was an issue running the tests.'							,					blocks=SCREAMING_SNAKE_CASE__							,					)
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          print('Sending the following payload'				)
          print(json.dumps({'blocks': json.loads(self.payload				)}				)				)

          lowerCamelCase__           =		F'{self.n_failures} failures out of {self.n_tests} tests,' if self.n_failures else 'All tests passed.'

          lowerCamelCase__           =		client.chat_postMessage(
              channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']							,					blocks=self.payload							,					text=SCREAMING_SNAKE_CASE__							,					)
    def      _UpperCamelCase       ( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Tuple				):
          lowerCamelCase__           =		''
          for key, value in failures.items():
                lowerCamelCase__           =		value[:2_00] + ' [Truncated]' if len(SCREAMING_SNAKE_CASE__				) > 2_50 else value
                failures_text += F'*{key}*\n_{value}_\n\n'

          lowerCamelCase__           =		job_name
          lowerCamelCase__           =		{'type': 'section', 'text': {'type': 'mrkdwn', 'text': text}}

          if job_link is not None:
                lowerCamelCase__           =		{
                    'type': 'button',
                    'text': {'type': 'plain_text', 'text': 'GitHub Action job', 'emoji': True},
                    'url': job_link,
                }

          return [
              {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
              content,
              {"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
          ]


    def      _UpperCamelCase       ( self							:					Optional[int]				):
          if self.thread_ts is None:
                raise ValueError('Can only post reply if a post has been made.'				)

          lowerCamelCase__           =		self.doc_test_results.pop('job_link'				)
          self.doc_test_results.pop('failures'				)
          self.doc_test_results.pop('success'				)
          self.doc_test_results.pop('time_spent'				)

          lowerCamelCase__           =		sorted(self.doc_test_results.items()							,					key=lambda SCREAMING_SNAKE_CASE__				: t[0]				)
          for job, job_result in sorted_dict:
                if len(job_result['failures']				):
                      lowerCamelCase__           =		F'*Num failures* :{len(job_result["failed"]				)} \n'
                      lowerCamelCase__           =		job_result['failures']
                      lowerCamelCase__           =		self.get_reply_blocks(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					text=SCREAMING_SNAKE_CASE__				)

                      print('Sending the following reply'				)
                      print(json.dumps({'blocks': blocks}				)				)

                      client.chat_postMessage(
                          channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']							,					text=F'Results for {job}'							,					blocks=SCREAMING_SNAKE_CASE__							,					thread_ts=self.thread_ts['ts']							,					)

                      time.sleep(1				)





def   snake_case						(				)->		Optional[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		os.environ['GITHUB_RUN_ID']
      lowerCamelCase__           =		F'https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'
      lowerCamelCase__           =		requests.get(_a							).json()
      lowerCamelCase__           =		{}

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

            for i in range(_a							):
                  lowerCamelCase__           =		requests.get(url + F'&page={i + 2}'							).json()
                  jobs.update({job['name']: job['html_url'] for job in result['jobs']}							)

            return jobs
      except Exception as e:
            print('Unknown error, could not fetch links.'		,   _a							)

      return {}



def   snake_case						(				_a:     str							)->		Dict:
      '''simple docstring'''


      lowerCamelCase__           =		{}

      if os.path.exists(_a							):
            lowerCamelCase__           =		os.listdir(_a							)
            for file in files:
                  try:
                        with open(os.path.join(_a		,   _a							)		,   encoding='utf-8'							) as f:
                              lowerCamelCase__           =		f.read()
                  except UnicodeDecodeError as e:
                        raise ValueError(F'Could not open {os.path.join(_a		,   _a							)}.'							) from e

      return _artifact



def   snake_case						(				)->		Optional[int]:
      '''simple docstring'''


      class   _a      :
          def __init__( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					str				):
                lowerCamelCase__           =		name
                lowerCamelCase__           =		[]
          def __str__( self							:					Dict				):
                return self.name


          def      _UpperCamelCase       ( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					str				):
                self.paths.append({'name': self.name, 'path': path}				)





      lowerCamelCase__           =		{}

      lowerCamelCase__           =		filter(os.path.isdir		,   os.listdir()							)
      for directory in directories:
            lowerCamelCase__           =		directory
            if artifact_name not in _available_artifacts:
                  lowerCamelCase__           =		Artifact(_a							)

                  _available_artifacts[artifact_name].add_path(_a							)

      return _available_artifacts


if __name__ == "__main__":
   _snake_case          =							get_job_links()
   _snake_case          =							retrieve_available_artifacts()

   _snake_case          =							collections.OrderedDict(
       [
           ("*.py", "API Examples"),
           ("*.md", "MD Examples"),
       ]
   )

   # This dict will contain all the information relative to each doc test category:
   # - failed: list of failed tests
   # - failures: dict in the format 'test': 'error_message'
   _snake_case          =							{
       v: {
           "failed": [],
           "failures": {},
       }
       for v in docs.values()
   }

   # Link to the GitHub Action job
   _snake_case          =							github_actions_job_links.get("run_doctests")

   _snake_case          =							available_artifacts["doc_tests_gpu_test_reports"].paths[0]
   _snake_case          =							retrieve_artifact(artifact_path["name"])
   if "stats" in artifact:
      _snake_case      ,					_snake_case      ,					_snake_case          =							handle_test_results(artifact["stats"])
      _snake_case          =							failed
      _snake_case          =							success
      _snake_case          =							time_spent[1:-1] + ", "

      _snake_case          =							extract_first_line_failure(artifact["failures_short"])
      for line in artifact["summary_short"].split("\n"):
         if re.search("FAILED", line):
            _snake_case          =							line.replace("FAILED ", "")
            _snake_case          =							line.split()[0].replace("\n", "")

            if "::" in line:
               _snake_case      ,					_snake_case          =							line.split("::")
            else:
               _snake_case      ,					_snake_case          =							line, line

            for file_regex in docs.keys():
               if fnmatch(file_path, file_regex):
                  _snake_case          =							docs[file_regex]
                  doc_test_results[category]["failed"].append(test)

                  _snake_case          =							all_failures[test] if test in all_failures else "N/A"
                  _snake_case          =							failure
                  break

   _snake_case          =							Message("🤗 Results of the doc tests.", doc_test_results)
   message.post()
   message.post_reply()



