
'''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()
A__   :		int 		=logging.get_logger(__name__)







def        A_     (						__SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  Optional[Any]=False )				->       List[str]:





  """simple docstring"""

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

  return new_state_dict







def        A_     (						__SCREAMING_SNAKE_CASE						:  Tuple			,     __SCREAMING_SNAKE_CASE						:  int )				->       str:





  """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)
      __A		:     Dict												=		state_dict.pop(F"segformer.encoder.block.{i}.{j}.attention.self.kv.weight" )
      __A		:     Union[str, Any]												=		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
      __A		:     int												=		kv_weight[
          : config.hidden_sizes[i], :
      ]
      __A		:     List[Any]												=		kv_bias[: config.hidden_sizes[i]]
      __A		:     Tuple												=		kv_weight[
          config.hidden_sizes[i] :, :
      ]
      __A		:     str												=		kv_bias[
          config.hidden_sizes[i] :
      ]







def        A_     (						)				->       str:





  """simple docstring"""

  __A		:     Union[str, Any]												=		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  __A		:     List[Any]												=		Image.open(requests.get(__SCREAMING_SNAKE_CASE			,     stream=__SCREAMING_SNAKE_CASE ).raw )

  return image







@torch.no_grad()
def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict			,     __SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  Any )				->       str:





  """simple docstring"""

  __A		:     Any												=		SegformerConfig()
  __A		:     Dict												=		False

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

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

  # load image processor (only resize + normalize)
  __A		:     Union[str, Any]												=		SegformerImageProcessor(
      image_scale=(512, 512)			,     keep_ratio=__SCREAMING_SNAKE_CASE			,     align=__SCREAMING_SNAKE_CASE			,     do_random_crop=__SCREAMING_SNAKE_CASE )

  # prepare image
  __A		:     Optional[Any]												=		prepare_img()
  __A		:     str												=		image_processor(images=__SCREAMING_SNAKE_CASE			,     return_tensors="""pt""" ).pixel_values

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

  # load original state dict
  if encoder_only:
    __A		:     List[str]												=		torch.load(__SCREAMING_SNAKE_CASE			,     map_location=torch.device("""cpu""" ) )
  else:
    __A		:     str												=		torch.load(__SCREAMING_SNAKE_CASE			,     map_location=torch.device("""cpu""" ) )["""state_dict"""]

  # rename keys
  __A		:     Union[str, Any]												=		rename_keys(__SCREAMING_SNAKE_CASE			,     encoder_only=__SCREAMING_SNAKE_CASE )
  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(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  # create HuggingFace model and load state dict
  if encoder_only:
    __A		:     Union[str, Any]												=		False
    __A		:     Union[str, Any]												=		SegformerForImageClassification(__SCREAMING_SNAKE_CASE )
  else:
    __A		:     Dict												=		SegformerForSemanticSegmentation(__SCREAMING_SNAKE_CASE )
  model.load_state_dict(__SCREAMING_SNAKE_CASE )
  model.eval()

  # forward pass
  __A		:     Optional[int]												=		model(__SCREAMING_SNAKE_CASE )
  __A		:     Any												=		outputs.logits

  # set expected_slice based on model name
  # ADE20k checkpoints
  if model_name == "segformer.b0.512x512.ade.160k":
    __A		:     Union[str, Any]												=		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":
    __A		:     Dict												=		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":
    __A		:     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":
    __A		:     List[str]												=		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":
    __A		:     Tuple												=		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":
    __A		:     str												=		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":
    __A		:     str												=		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":
    __A		:     List[str]												=		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":
    __A		:     Optional[int]												=		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":
    __A		:     Optional[int]												=		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":
    __A		:     Any												=		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":
    __A		:     Union[str, Any]												=		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":
    __A		:     int												=		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":
    __A		:     str												=		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":
    __A		:     Optional[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:
    __A		:     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]			,     __SCREAMING_SNAKE_CASE			,     atol=1e-2 )

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


if __name__ == "__main__":
     A__   :		str 		=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.'
     )
     A__   :		Union[str, Any] 		=parser.parse_args()
     convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''
import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Tuple:





  """simple docstring"""

  __A		:     Dict												=		{}

  __A		:     Optional[Any]												=		job["""started_at"""]
  __A		:     Tuple												=		job["""completed_at"""]

  __A		:     Optional[Any]												=		date_parser.parse(__SCREAMING_SNAKE_CASE )
  __A		:     List[Any]												=		date_parser.parse(__SCREAMING_SNAKE_CASE )

  __A		:     Union[str, Any]												=		round((end_datetime - start_datetime).total_seconds() / 60.0 )

  __A		:     List[Any]												=		start
  __A		:     Tuple												=		end
  __A		:     int												=		duration_in_min

  return job_info







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  int=None )				->       Dict:





  """simple docstring"""

  __A		:     List[Any]												=		None
  if token is not None:
    __A		:     Dict												=		{"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

  __A		:     Tuple												=		F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
  __A		:     List[str]												=		requests.get(__SCREAMING_SNAKE_CASE			,     headers=__SCREAMING_SNAKE_CASE ).json()
  __A		:     List[str]												=		{}

  try:
    job_time.update({job["""name"""]: extract_time_from_single_job(__SCREAMING_SNAKE_CASE ) for job in result["""jobs"""]} )
    __A		:     Dict												=		math.ceil((result["""total_count"""] - 100) / 100 )

    for i in range(__SCREAMING_SNAKE_CASE ):
      __A		:     int												=		requests.get(url + F"&page={i + 2}"			,     headers=__SCREAMING_SNAKE_CASE ).json()
      job_time.update({job["""name"""]: extract_time_from_single_job(__SCREAMING_SNAKE_CASE ) for job in result["""jobs"""]} )

    return job_time
  except Exception:
    print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}" )

  return {}


if __name__ == "__main__":
     A__   :		Any 		=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
     A__   :		Any 		=parser.parse_args()

     A__   :		Any 		=get_job_time(args.workflow_run_id)
     A__   :		Dict 		=dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

     for k, v in job_time.items():
          print(F"{k}: {v['duration']}")



