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()
UpperCamelCase__											=					logging.get_logger(__name__)




def    lowerCAmelCase_ (     __A,  __A=False							)     ->				List[str]:







      '''simple docstring'''


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

      return new_state_dict




def    lowerCAmelCase_ (     __A,  __A							)     ->				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__								=  state_dict.pop(f"""segformer.encoder.block.{i}.{j}.attention.self.kv.weight"""							)
                  UpperCAmelCase__								=  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__								=  kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  UpperCAmelCase__								=  kv_bias[: config.hidden_sizes[i]]
                  UpperCAmelCase__								=  kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  UpperCAmelCase__								=  kv_bias[
                      config.hidden_sizes[i] :
                  ]




def    lowerCAmelCase_ (     )     ->				List[Any]:







      '''simple docstring'''


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

      return image




@torch.no_grad()
def    lowerCAmelCase_ (     __A,  __A,  __A							)     ->				Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__								=  SegformerConfig()
      UpperCAmelCase__								=  False

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

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

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

      # prepare image
      UpperCAmelCase__								=  prepare_img()
      UpperCAmelCase__								=  image_processor(images=__A,  return_tensors="pt"							).pixel_values

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

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

      # rename keys
      UpperCAmelCase__								=  rename_keys(__A,  encoder_only=__A							)
      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(__A,  __A							)

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

      # forward pass
      UpperCAmelCase__								=  model(__A							)
      UpperCAmelCase__								=  outputs.logits

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

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


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







"""simple docstring"""


import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
	import torch
@is_pipeline_test
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    Union[str, Any]				      =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
							__UpperCAmelCase						:    Union[str, Any]				      =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			TextaTextGenerationPipeline(model=__a    ,   tokenizer=__a	)
										return generator, ["Something to write", "Something else"]

							def 			snake_case				(     self    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			generator("Something there"	)
										self.assertEqual(__a    ,   [{"generated_text": ANY(__a	)}]	)
										# These are encoder decoder, they don't just append to incoming string
										self.assertFalse(outputs[0]["generated_text"].startswith("Something there"	)	)

										__lowerCAmelCase				    =			generator(["This is great !", "Something else"]    ,   num_return_sequences=2    ,   do_sample=__a	)
										self.assertEqual(
										    __a    ,   [
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										    ]    ,   )

										__lowerCAmelCase				    =			generator(
										    ["This is great !", "Something else"]    ,   num_return_sequences=2    ,   batch_size=2    ,   do_sample=__a	)
										self.assertEqual(
										    __a    ,   [
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										        [{"generated_text": ANY(__a	)}, {"generated_text": ANY(__a	)}],
										    ]    ,   )

										with self.assertRaises(__a	):
													generator(4	)

							@require_torch
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pipeline("text2text-generation"    ,   model="patrickvonplaten/t5-tiny-random"    ,   framework="pt"	)
										# do_sample=False necessary for reproducibility
										__lowerCAmelCase				    =			generator("Something there"    ,   do_sample=__a	)
										self.assertEqual(__a    ,   [{"generated_text": ""}]	)

										__lowerCAmelCase				    =			3
										__lowerCAmelCase				    =			generator(
										    "Something there"    ,   num_return_sequences=__a    ,   num_beams=__a    ,   )
										__lowerCAmelCase				    =			[
										    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
										    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
										    {"generated_text": ""},
										]
										self.assertEqual(__a    ,   __a	)

										__lowerCAmelCase				    =			generator("This is a test"    ,   do_sample=__a    ,   num_return_sequences=2    ,   return_tensors=__a	)
										self.assertEqual(
										    __a    ,   [
										        {"generated_token_ids": ANY(torch.Tensor	)},
										        {"generated_token_ids": ANY(torch.Tensor	)},
										    ]    ,   )
										__lowerCAmelCase				    =			generator.model.config.eos_token_id
										__lowerCAmelCase				    =			"<pad>"
										__lowerCAmelCase				    =			generator(
										    ["This is a test", "This is a second test"]    ,   do_sample=__a    ,   num_return_sequences=2    ,   batch_size=2    ,   return_tensors=__a    ,   )
										self.assertEqual(
										    __a    ,   [
										        [
										            {"generated_token_ids": ANY(torch.Tensor	)},
										            {"generated_token_ids": ANY(torch.Tensor	)},
										        ],
										        [
										            {"generated_token_ids": ANY(torch.Tensor	)},
										            {"generated_token_ids": ANY(torch.Tensor	)},
										        ],
										    ]    ,   )



							@require_tf
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			pipeline("text2text-generation"    ,   model="patrickvonplaten/t5-tiny-random"    ,   framework="tf"	)
										# do_sample=False necessary for reproducibility
										__lowerCAmelCase				    =			generator("Something there"    ,   do_sample=__a	)
										self.assertEqual(__a    ,   [{"generated_text": ""}]	)




