



"""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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__		:				str          =	logging.get_logger(__name__)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Tuple=False						)						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				List[Any]								= []

  # fmt: off
  # stem:
  rename_keys.append(('cls_token', 'vit.embeddings.cls_token')						)
  rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')						)

  rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')						)
  rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')						)

  # backbone
  rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')						)
  rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')						)
  rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')						)

  for stage_idx in range(len(config.backbone_config.depths						)						):
    for layer_idx in range(config.backbone_config.depths[stage_idx]						):
      rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")						)
      rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")						)
      rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")						)
      rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")						)
      rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")						)
      rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")						)
      rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")						)
      rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")						)
      rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")						)

    rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")						)
    rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")						)
    rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")						)

  # transformer encoder
  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"vit.encoder.layer.{i}.layernorm_before.weight")						)
    rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")						)
    rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")						)
    rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")						)
    rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")						)
    rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")						)
    rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")						)
    rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")						)
    rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")						)
    rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")						)

  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 "vit" from all keys that start with "vit"
    lowerCAmelCase_   :				Any								= [(pair[0], pair[1][4:]) if pair[1].startswith('vit'						) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ('norm.weight', 'vit.layernorm.weight'),
            ('norm.bias', 'vit.layernorm.bias'),
            ('head.weight', 'classifier.weight'),
            ('head.bias', 'classifier.bias'),
        ]						)
  # fmt: on

  return rename_keys

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				List[str]=False						)						->   List[Any]:

  """simple docstring"""




  for i in range(config.num_hidden_layers						):
    if base_model:
      lowerCAmelCase_   :				Tuple								= ''
    else:
      lowerCAmelCase_   :				Tuple								= 'vit.'
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    lowerCAmelCase_   :				List[Any]								= state_dict.pop(f"blocks.{i}.attn.qkv.weight"						)
    lowerCAmelCase_   :				Optional[Any]								= state_dict.pop(f"blocks.{i}.attn.qkv.bias"						)
    # next, add query, keys and values (in that order) to the state dict
    lowerCAmelCase_   :				str								= in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCAmelCase_   :				List[Any]								= in_proj_bias[: config.hidden_size]
    lowerCAmelCase_   :				Union[str, Any]								= in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCAmelCase_   :				List[str]								= in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    lowerCAmelCase_   :				Optional[Any]								= in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCAmelCase_   :				Any								= in_proj_bias[-config.hidden_size :]

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str						)						->   Union[str, Any]:

  """simple docstring"""




  lowerCAmelCase_   :				int								= ['head.weight', 'head.bias']
  for k in ignore_keys:
    state_dict.pop(lowerCAmelCase__    ,   lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Dict						)						->   str:

  """simple docstring"""




  lowerCAmelCase_   :				Optional[int]								= dct.pop(lowerCAmelCase__						)
  lowerCAmelCase_   :				str								= val

def 				UpperCamelCase_    (    )						->   Dict:

  """simple docstring"""




  lowerCAmelCase_   :				int								= 'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowerCAmelCase_   :				Union[str, Any]								= Image.open(requests.get(lowerCAmelCase__    ,   stream=lowerCAmelCase__						).raw						)
  return im

@torch.no_grad()
def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				Optional[Any]    ,   lowerCAmelCase__      :				Tuple=False						)						->   Union[str, Any]:

  """simple docstring"""




  lowerCAmelCase_   :				Union[str, Any]								= BitConfig(
      global_padding='same'    ,   layer_type='bottleneck'    ,   depths=(3, 4, 9)    ,   out_features=['stage3']    ,   embedding_dynamic_padding=lowerCAmelCase__    ,   )
  lowerCAmelCase_   :				List[Any]								= ViTHybridConfig(backbone_config=lowerCAmelCase__    ,   image_size=384    ,   num_labels=1000						)
  lowerCAmelCase_   :				Dict								= False

  # load original model from timm
  lowerCAmelCase_   :				List[Any]								= timm.create_model(lowerCAmelCase__    ,   pretrained=lowerCAmelCase__						)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  lowerCAmelCase_   :				Tuple								= timm_model.state_dict()
  if base_model:
    remove_classification_head_(lowerCAmelCase__						)
  lowerCAmelCase_   :				List[str]								= create_rename_keys(lowerCAmelCase__    ,   lowerCAmelCase__						)
  for src, dest in rename_keys:
    rename_key(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)
  read_in_q_k_v(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)

  lowerCAmelCase_   :				Union[str, Any]								= 'huggingface/label-files'
  lowerCAmelCase_   :				Any								= 'imagenet-1k-id2label.json'
  lowerCAmelCase_   :				List[Any]								= json.load(open(hf_hub_download(lowerCAmelCase__    ,   lowerCAmelCase__    ,   repo_type='dataset'						)    ,   'r'						)						)
  lowerCAmelCase_   :				Union[str, Any]								= {int(lowerCAmelCase__						): v for k, v in idalabel.items()}
  lowerCAmelCase_   :				str								= idalabel
  lowerCAmelCase_   :				Dict								= {v: k for k, v in idalabel.items()}

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    lowerCAmelCase_   :				str								= ViTHybridModel(lowerCAmelCase__						).eval()
  else:
    lowerCAmelCase_   :				str								= ViTHybridForImageClassification(lowerCAmelCase__						).eval()
  model.load_state_dict(lowerCAmelCase__						)

  # create image processor
  lowerCAmelCase_   :				Any								= create_transform(**resolve_data_config({}    ,   model=lowerCAmelCase__						)						)
  lowerCAmelCase_   :				int								= transform.transforms

  lowerCAmelCase_   :				Dict								= {
      'bilinear': PILImageResampling.BILINEAR,
      'bicubic': PILImageResampling.BICUBIC,
      'nearest': PILImageResampling.NEAREST,
  }

  lowerCAmelCase_   :				List[Any]								= ViTHybridImageProcessor(
      do_resize=lowerCAmelCase__    ,   size={'shortest_edge': timm_transforms[0].size}    ,   resample=pillow_resamplings[timm_transforms[0].interpolation.value]    ,   do_center_crop=lowerCAmelCase__    ,   crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}    ,   do_normalize=lowerCAmelCase__    ,   image_mean=timm_transforms[-1].mean.tolist()    ,   image_std=timm_transforms[-1].std.tolist()    ,   )

  lowerCAmelCase_   :				int								= prepare_img()
  lowerCAmelCase_   :				Optional[Any]								= transform(lowerCAmelCase__						).unsqueeze(0						)
  lowerCAmelCase_   :				Optional[Any]								= processor(lowerCAmelCase__    ,   return_tensors='pt'						).pixel_values

  # verify pixel values
  assert torch.allclose(lowerCAmelCase__    ,   lowerCAmelCase__						)

  # verify logits
  with torch.no_grad():
    lowerCAmelCase_   :				Union[str, Any]								= model(lowerCAmelCase__						)
    lowerCAmelCase_   :				Dict								= outputs.logits

  print('Predicted class:'    ,   logits.argmax(-1						).item()						)
  if base_model:
    lowerCAmelCase_   :				List[str]								= timm_model.forward_features(lowerCAmelCase__						)
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(lowerCAmelCase__    ,   outputs.pooler_output    ,   atol=1e-3						)
  else:
    lowerCAmelCase_   :				int								= timm_model(lowerCAmelCase__						)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(lowerCAmelCase__    ,   outputs.logits    ,   atol=1e-3						)
  print('Looks ok!'						)

  if pytorch_dump_folder_path is not None:
    Path(lowerCAmelCase__						).mkdir(exist_ok=lowerCAmelCase__						)
    print(f"Saving model {vit_name} to {pytorch_dump_folder_path}"						)
    model.save_pretrained(lowerCAmelCase__						)
    print(f"Saving processor to {pytorch_dump_folder_path}"						)
    processor.save_pretrained(lowerCAmelCase__						)

  if push_to_hub:
    print(f"Pushing model and processor to the hub {vit_name}"						)
    model.push_to_hub(f"ybelkada/{vit_name}"						)
    processor.push_to_hub(f"ybelkada/{vit_name}"						)


if __name__ == "__main__":
     lowercase__		:				List[Any]          =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--vit_name""",
         default="""vit_base_r50_s16_384""",
         type=str,
         help="""Name of the hybrid ViT 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."""
     )
     parser.add_argument(
         """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
     )

     lowercase__		:				Any          =	parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__		:				Tuple          =	logging.get_logger(__name__)

lowercase__		:				Any          =	{
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """dpr"""



      def __init__(      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple=3_0_5_2_2    ,			SCREAMING_SNAKE_CASE_				:      Any=7_6_8    ,			SCREAMING_SNAKE_CASE_				:      Dict=1_2    ,			SCREAMING_SNAKE_CASE_				:      List[str]=1_2    ,			SCREAMING_SNAKE_CASE_				:      int=3_0_7_2    ,			SCREAMING_SNAKE_CASE_				:      List[Any]="gelu"    ,			SCREAMING_SNAKE_CASE_				:      int=0.1    ,			SCREAMING_SNAKE_CASE_				:      int=0.1    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=5_1_2    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=2    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=0.02    ,			SCREAMING_SNAKE_CASE_				:      List[str]=1E-12    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=0    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="absolute"    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			**SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			):
        super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Optional[int]								= vocab_size
        lowerCAmelCase_   :				Tuple								= hidden_size
        lowerCAmelCase_   :				int								= num_hidden_layers
        lowerCAmelCase_   :				str								= num_attention_heads
        lowerCAmelCase_   :				Any								= hidden_act
        lowerCAmelCase_   :				List[str]								= intermediate_size
        lowerCAmelCase_   :				int								= hidden_dropout_prob
        lowerCAmelCase_   :				Union[str, Any]								= attention_probs_dropout_prob
        lowerCAmelCase_   :				Tuple								= max_position_embeddings
        lowerCAmelCase_   :				List[str]								= type_vocab_size
        lowerCAmelCase_   :				str								= initializer_range
        lowerCAmelCase_   :				str								= layer_norm_eps
        lowerCAmelCase_   :				List[str]								= projection_dim
        lowerCAmelCase_   :				List[str]								= position_embedding_type





