

"""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       =							logging.get_logger(__name__)




def   _A  (  UpperCamelCase_     :       Optional[int],			UpperCamelCase_     :       str=False)	->						Optional[int]:
   '''simple docstring'''


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

   return new_state_dict




def   _A  (  UpperCamelCase_     :       Optional[int],			UpperCamelCase_     :       Union[str, Any])	->						Dict:
   '''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)
         __lowercase								=						state_dict.pop(F"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight""")
         __lowercase								=						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
         __lowercase								=						kv_weight[
             : config.hidden_sizes[i], :
         ]
         __lowercase								=						kv_bias[: config.hidden_sizes[i]]
         __lowercase								=						kv_weight[
             config.hidden_sizes[i] :, :
         ]
         __lowercase								=						kv_bias[
             config.hidden_sizes[i] :
         ]




def   _A  (  )	->						List[Any]:
   '''simple docstring'''


   __lowercase								=						"http://images.cocodataset.org/val2017/000000039769.jpg"
   __lowercase								=						Image.open(requests.get(UpperCamelCase_,			stream=UpperCamelCase_).raw)

   return image




@torch.no_grad()
def   _A  (  UpperCamelCase_     :       Union[str, Any],			UpperCamelCase_     :       int,			UpperCamelCase_     :       Tuple)	->						List[Any]:
   '''simple docstring'''


   __lowercase								=						SegformerConfig()
   __lowercase								=						False

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

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

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

   # prepare image
   __lowercase								=						prepare_img()
   __lowercase								=						image_processor(images=UpperCamelCase_,			return_tensors="pt").pixel_values

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

   # load original state dict
   if encoder_only:
      __lowercase								=						torch.load(UpperCamelCase_,			map_location=torch.device("cpu"))
   else:
      __lowercase								=						torch.load(UpperCamelCase_,			map_location=torch.device("cpu"))["state_dict"]

   # rename keys
   __lowercase								=						rename_keys(UpperCamelCase_,			encoder_only=UpperCamelCase_)
   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(UpperCamelCase_,			UpperCamelCase_)

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

   # forward pass
   __lowercase								=						model(UpperCamelCase_)
   __lowercase								=						outputs.logits

   # set expected_slice based on model name
   # ADE20k checkpoints
   if model_name == "segformer.b0.512x512.ade.160k":
      __lowercase								=						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":
      __lowercase								=						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":
      __lowercase								=						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":
      __lowercase								=						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":
      __lowercase								=						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":
      __lowercase								=						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":
      __lowercase								=						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":
      __lowercase								=						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":
      __lowercase								=						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":
      __lowercase								=						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":
      __lowercase								=						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":
      __lowercase								=						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":
      __lowercase								=						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":
      __lowercase								=						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":
      __lowercase								=						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:
      __lowercase								=						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],			UpperCamelCase_,			atol=1E-2)

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


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







"""simple docstring"""





import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class 					_lowerCAmelCase  (	pl.LightningModule   ):







 """simple docstring"""







 def __init__(			self							:			Optional[Any],					UpperCAmelCase__							:			str	):
    super().__init__()
    __lowercase								=						model
    __lowercase								=						2
    __lowercase								=						nn.Linear(self.model.config.hidden_size,					self.num_labels	)



 def 					_lowercase (			self							:			Optional[int]	):
    pass







def   _A  (  UpperCamelCase_     :       str,			UpperCamelCase_     :       str,			UpperCamelCase_     :       str)	->						str:
   '''simple docstring'''


   __lowercase								=						LongformerModel.from_pretrained(UpperCamelCase_)
   __lowercase								=						LightningModel(UpperCamelCase_)

   __lowercase								=						torch.load(UpperCamelCase_,			map_location=torch.device("cpu"))
   lightning_model.load_state_dict(ckpt["state_dict"])

   # init longformer question answering model
   __lowercase								=						LongformerForQuestionAnswering.from_pretrained(UpperCamelCase_)

   # transfer weights
   longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict())
   longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict())
   longformer_for_qa.eval()

   # save model
   longformer_for_qa.save_pretrained(UpperCamelCase_)

   print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}""")


if __name__ == "__main__":
   _a       =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--longformer_model',
       default=None,
       type=str,
       required=True,
       help='model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.',
   )
   parser.add_argument(
       '--longformer_question_answering_ckpt_path',
       default=None,
       type=str,
       required=True,
       help='Path the official PyTorch Lightning Checkpoint.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _a       =							parser.parse_args()
   convert_longformer_qa_checkpoint_to_pytorch(
       args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
   )




