

"""simple docstring"""



import argparse
import json
from pathlib import Path

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						logging.get_logger(__name__)





def  _snake_case     (    UpperCAmelCase_  :					Any					,   UpperCAmelCase_  :					Tuple=False			):
     A__										=  []
     for i in range(config.num_hidden_layers			):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""deit.encoder.layer.{i}.layernorm_before.weight""")			)
          rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""deit.encoder.layer.{i}.layernorm_before.bias""")			)
          rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""deit.encoder.layer.{i}.attention.output.dense.weight""")			)
          rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""deit.encoder.layer.{i}.attention.output.dense.bias""")			)
          rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""deit.encoder.layer.{i}.layernorm_after.weight""")			)
          rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""deit.encoder.layer.{i}.layernorm_after.bias""")			)
          rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""deit.encoder.layer.{i}.intermediate.dense.weight""")			)
          rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""deit.encoder.layer.{i}.intermediate.dense.bias""")			)
          rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""deit.encoder.layer.{i}.output.dense.weight""")			)
          rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""deit.encoder.layer.{i}.output.dense.bias""")			)

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ("""cls_token""", """deit.embeddings.cls_token"""),
             ("""dist_token""", """deit.embeddings.distillation_token"""),
             ("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
             ("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
             ("""pos_embed""", """deit.embeddings.position_embeddings"""),
         ]			)

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ("""norm.weight""", """layernorm.weight"""),
                  ("""norm.bias""", """layernorm.bias"""),
                  ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
                  ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
              ]			)

          # if just the base model, we should remove "deit" from all keys that start with "deit"
          A__										=  [(pair[0], pair[1][4:]) if pair[1].startswith("""deit"""			) else pair for pair in rename_keys]
     else:
          # layernorm + classification heads
          rename_keys.extend(
              [
                  ("""norm.weight""", """deit.layernorm.weight"""),
                  ("""norm.bias""", """deit.layernorm.bias"""),
                  ("""head.weight""", """cls_classifier.weight"""),
                  ("""head.bias""", """cls_classifier.bias"""),
                  ("""head_dist.weight""", """distillation_classifier.weight"""),
                  ("""head_dist.bias""", """distillation_classifier.bias"""),
              ]			)

     return rename_keys





def  _snake_case     (    UpperCAmelCase_  :					Dict					,   UpperCAmelCase_  :					Optional[int]					,   UpperCAmelCase_  :					str=False			):
     for i in range(config.num_hidden_layers			):
          if base_model:
               A__										=  """"""
          else:
               A__										=  """deit."""
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          A__										=  state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""			)
          A__										=  state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""			)
          # next, add query, keys and values (in that order) to the state dict
          A__										=  in_proj_weight[
              : config.hidden_size, :
          ]
          A__										=  in_proj_bias[: config.hidden_size]
          A__										=  in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          A__										=  in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          A__										=  in_proj_weight[
              -config.hidden_size :, :
          ]
          A__										=  in_proj_bias[-config.hidden_size :]





def  _snake_case     (    UpperCAmelCase_  :					Tuple					,   UpperCAmelCase_  :					Any					,   UpperCAmelCase_  :					Tuple			):
     A__										=  dct.pop(UpperCAmelCase_			)
     A__										=  val





def  _snake_case     (    ):
     A__										=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
     A__										=  Image.open(requests.get(UpperCAmelCase_					,   stream=UpperCAmelCase_			).raw			)
     return im





@torch.no_grad()
def  _snake_case     (    UpperCAmelCase_  :					Optional[Any]					,   UpperCAmelCase_  :					Dict			):
     A__										=  DeiTConfig()
     # all deit models have fine-tuned heads
     A__										=  False
     # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
     A__										=  1000
     A__										=  """huggingface/label-files"""
     A__										=  """imagenet-1k-id2label.json"""
     A__										=  json.load(open(hf_hub_download(UpperCAmelCase_					,   UpperCAmelCase_					,   repo_type="""dataset"""			)					,   """r"""			)			)
     A__										=  {int(UpperCAmelCase_			): v for k, v in idalabel.items()}
     A__										=  idalabel
     A__										=  {v: k for k, v in idalabel.items()}
     A__										=  int(deit_name[-6:-4]			)
     A__										=  int(deit_name[-3:]			)
     # size of the architecture
     if deit_name[9:].startswith("""tiny"""			):
          A__										=  192
          A__										=  768
          A__										=  12
          A__										=  3
     elif deit_name[9:].startswith("""small"""			):
          A__										=  384
          A__										=  1536
          A__										=  12
          A__										=  6
     if deit_name[9:].startswith("""base"""			):
          pass
     elif deit_name[4:].startswith("""large"""			):
          A__										=  1024
          A__										=  4096
          A__										=  24
          A__										=  16

     # load original model from timm
     A__										=  timm.create_model(UpperCAmelCase_					,   pretrained=UpperCAmelCase_			)
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     A__										=  timm_model.state_dict()
     A__										=  create_rename_keys(UpperCAmelCase_					,   UpperCAmelCase_			)
     for src, dest in rename_keys:
          rename_key(UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_			)
     read_in_q_k_v(UpperCAmelCase_					,   UpperCAmelCase_					,   UpperCAmelCase_			)

     # load HuggingFace model
     A__										=  DeiTForImageClassificationWithTeacher(UpperCAmelCase_			).eval()
     model.load_state_dict(UpperCAmelCase_			)

     # Check outputs on an image, prepared by DeiTImageProcessor
     A__										=  int(
         (256 / 224) * config.image_size			)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
     A__										=  DeiTImageProcessor(size=UpperCAmelCase_					,   crop_size=config.image_size			)
     A__										=  image_processor(images=prepare_img()					,   return_tensors="""pt"""			)
     A__										=  encoding["""pixel_values"""]
     A__										=  model(UpperCAmelCase_			)

     A__										=  timm_model(UpperCAmelCase_			)
     assert timm_logits.shape == outputs.logits.shape
     assert torch.allclose(UpperCAmelCase_					,   outputs.logits					,   atol=1e-3			)

     Path(UpperCAmelCase_			).mkdir(exist_ok=UpperCAmelCase_			)
     print(F"""Saving model {deit_name} to {pytorch_dump_folder_path}"""			)
     model.save_pretrained(UpperCAmelCase_			)
     print(F"""Saving image processor to {pytorch_dump_folder_path}"""			)
     image_processor.save_pretrained(UpperCAmelCase_			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_					:	List[str]							  =						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--deit_name',
     default='vit_deit_base_distilled_patch16_224',
     type=str,
     help='Name of the DeiT timm model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
 )

 SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						parser.parse_args()
 convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)









"""simple docstring"""



import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_					:	Any							  =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					:	Optional[Any]							  =						{
    'nvidia/segformer-b0-finetuned-ade-512-512': (
        'https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					"segformer"

    def __init__(     self: Optional[Any]    ,						UpperCamelCase: Union[str, Any]=3    ,						UpperCamelCase: int=4    ,						UpperCamelCase: int=[2, 2, 2, 2]    ,						UpperCamelCase: List[str]=[8, 4, 2, 1]    ,						UpperCamelCase: List[Any]=[32, 64, 1_60, 2_56]    ,						UpperCamelCase: Any=[7, 3, 3, 3]    ,						UpperCamelCase: Union[str, Any]=[4, 2, 2, 2]    ,						UpperCamelCase: Tuple=[1, 2, 5, 8]    ,						UpperCamelCase: Optional[int]=[4, 4, 4, 4]    ,						UpperCamelCase: Dict="gelu"    ,						UpperCamelCase: Optional[Any]=0.0    ,						UpperCamelCase: List[str]=0.0    ,						UpperCamelCase: Union[str, Any]=0.1    ,						UpperCamelCase: List[Any]=0.02    ,						UpperCamelCase: int=0.1    ,						UpperCamelCase: Optional[int]=1e-6    ,						UpperCamelCase: int=2_56    ,						UpperCamelCase: Union[str, Any]=2_55    ,						**UpperCamelCase: Optional[Any]    ,						):






         """simple docstring"""


         super().__init__(**UpperCamelCase       )

         if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
              warnings.warn(
                  """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
                  """ removed, as the behaviour will default to that of reshape_last_stage = True."""    ,						UpperCamelCase    ,						)

         A__										=  num_channels
         A__										=  num_encoder_blocks
         A__										=  depths
         A__										=  sr_ratios
         A__										=  hidden_sizes
         A__										=  patch_sizes
         A__										=  strides
         A__										=  mlp_ratios
         A__										=  num_attention_heads
         A__										=  hidden_act
         A__										=  hidden_dropout_prob
         A__										=  attention_probs_dropout_prob
         A__										=  classifier_dropout_prob
         A__										=  initializer_range
         A__										=  drop_path_rate
         A__										=  layer_norm_eps
         A__										=  decoder_hidden_size
         A__										=  kwargs.get("""reshape_last_stage"""    ,						UpperCamelCase       )
         A__										=  semantic_loss_ignore_index



class 					a						(       _lowerCamelCase				):



    """simple docstring"""



    UpperCAmelCase						 =					version.parse("1.11"				)

    @property
    def 				UpperCamelCase	(     self: List[str]       ):






         """simple docstring"""


         return OrderedDict(
             [
                 ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
             ]       )

    @property
    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         return 1e-4





    @property
    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


         return 12






