



'''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()
__a          =		logging.get_logger(__name__)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase=False		)		-> Optional[Any]:
      snake_case__  :    str    						=      []

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





def 						__snake_case(				_lowerCAmelCase		)		-> List[Any]:
      snake_case__  :    int    						=      ["""head.weight""", """head.bias"""]
      for k in ignore_keys:
            state_dict.pop(_lowerCAmelCase     ,   _lowerCAmelCase		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> List[str]:
      snake_case__  :    List[Any]    						=      dct.pop(_lowerCAmelCase		)
      snake_case__  :    List[str]    						=      val





def 						__snake_case(				)		-> int:
      snake_case__  :    Tuple    						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
      snake_case__  :    Optional[Any]    						=      Image.open(requests.get(_lowerCAmelCase     ,   stream=_lowerCAmelCase		).raw		)
      return im





@torch.no_grad()
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase=False		)		-> Tuple:
      snake_case__  :    List[str]    						=      BitConfig(
          global_padding="""same"""     ,   layer_type="""bottleneck"""     ,   depths=(3, 4, 9)     ,   out_features=["""stage3"""]     ,   embedding_dynamic_padding=_lowerCAmelCase     ,   )
      snake_case__  :    Optional[Any]    						=      ViTHybridConfig(backbone_config=_lowerCAmelCase     ,   image_size=384     ,   num_labels=1_000		)
      snake_case__  :    Union[str, Any]    						=      False

      # load original model from timm
      snake_case__  :    Union[str, Any]    						=      timm.create_model(_lowerCAmelCase     ,   pretrained=_lowerCAmelCase		)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      snake_case__  :    Optional[int]    						=      timm_model.state_dict()
      if base_model:
            remove_classification_head_(_lowerCAmelCase		)
      snake_case__  :    Any    						=      create_rename_keys(_lowerCAmelCase     ,   _lowerCAmelCase		)
      for src, dest in rename_keys:
            rename_key(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
      read_in_q_k_v(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)

      snake_case__  :    str    						=      """huggingface/label-files"""
      snake_case__  :    str    						=      """imagenet-1k-id2label.json"""
      snake_case__  :    List[str]    						=      json.load(open(hf_hub_download(_lowerCAmelCase     ,   _lowerCAmelCase     ,   repo_type="""dataset"""		)     ,   """r"""		)		)
      snake_case__  :    Dict    						=      {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
      snake_case__  :    Tuple    						=      idalabel
      snake_case__  :    List[str]    						=      {v: k for k, v in idalabel.items()}

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

      # create image processor
      snake_case__  :    int    						=      create_transform(**resolve_data_config({}     ,   model=_lowerCAmelCase		)		)
      snake_case__  :    List[str]    						=      transform.transforms

      snake_case__  :    List[str]    						=      {
          """bilinear""": PILImageResampling.BILINEAR,
          """bicubic""": PILImageResampling.BICUBIC,
          """nearest""": PILImageResampling.NEAREST,
      }

      snake_case__  :    Optional[int]    						=      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()     ,   )

      snake_case__  :    Union[str, Any]    						=      prepare_img()
      snake_case__  :    Dict    						=      transform(_lowerCAmelCase		).unsqueeze(0		)
      snake_case__  :    int    						=      processor(_lowerCAmelCase     ,   return_tensors="""pt"""		).pixel_values

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

      # verify logits
      with torch.no_grad():
            snake_case__  :    Optional[Any]    						=      model(_lowerCAmelCase		)
            snake_case__  :    List[Any]    						=      outputs.logits

      print("""Predicted class:"""     ,   logits.argmax(-1		).item()		)
      if base_model:
            snake_case__  :    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:
            snake_case__  :    Any    						=      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__":
       __a          =		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."
       )

       __a          =		parser.parse_args()
       convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""



import logging

from transformers.configuration_utils import PretrainedConfig


a_ =			logging.getLogger(__name__)






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """masked_bert"""

 def __init__(    self					,						__lowerCamelCase=3_0522					,						__lowerCamelCase=768					,						__lowerCamelCase=12					,						__lowerCamelCase=12					,						__lowerCamelCase=3072					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=512					,						__lowerCamelCase=2					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-1_2					,						__lowerCamelCase=0					,						__lowerCamelCase="topK"					,						__lowerCamelCase="constant"					,						__lowerCamelCase=0.0					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(pad_token_id=__lowerCamelCase					,						**__lowerCamelCase  )

     __A    :						Dict						 =     vocab_size
     __A    :						Union[str, Any]						 =     hidden_size
     __A    :						Tuple						 =     num_hidden_layers
     __A    :						Tuple						 =     num_attention_heads
     __A    :						Optional[Any]						 =     hidden_act
     __A    :						List[str]						 =     intermediate_size
     __A    :						Any						 =     hidden_dropout_prob
     __A    :						Optional[Any]						 =     attention_probs_dropout_prob
     __A    :						Any						 =     max_position_embeddings
     __A    :						str						 =     type_vocab_size
     __A    :						List[Any]						 =     initializer_range
     __A    :						str						 =     layer_norm_eps
     __A    :						Optional[int]						 =     pruning_method
     __A    :						str						 =     mask_init
     __A    :						Any						 =     mask_scale




