
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()
__lowerCamelCase							=       logging.get_logger(__name__)


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


def 			UpperCamelCase					(    __lowerCamelCase     : Optional[Any]    ):
       snake_case       :   Optional[int]							=      ["head.weight", "head.bias"]
       for k in ignore_keys:
              state_dict.pop(__lowerCamelCase				,					__lowerCamelCase    )


def 			UpperCamelCase					(    __lowerCamelCase     : Dict				,					__lowerCamelCase     : List[str]				,					__lowerCamelCase     : Tuple    ):
       snake_case       :   Optional[int]							=      dct.pop(__lowerCamelCase    )
       snake_case       :   List[Any]							=      val


def 			UpperCamelCase					(    ):
       snake_case       :   List[str]							=      "http://images.cocodataset.org/val2017/000000039769.jpg"
       snake_case       :   str							=      Image.open(requests.get(__lowerCamelCase				,					stream=__lowerCamelCase    ).raw    )
       return im


@torch.no_grad()
def 			UpperCamelCase					(    __lowerCamelCase     : int				,					__lowerCamelCase     : str				,					__lowerCamelCase     : Optional[Any]=False    ):
       snake_case       :   int							=      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=1000    )
       snake_case       :   Optional[Any]							=      False

       # load original model from timm
       snake_case       :   Any							=      timm.create_model(__lowerCamelCase				,					pretrained=__lowerCamelCase    )
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       snake_case       :   Any							=      timm_model.state_dict()
       if base_model:
              remove_classification_head_(__lowerCamelCase    )
       snake_case       :   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    )

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

       # load HuggingFace model
       if vit_name[-5:] == "in21k":
              snake_case       :   Dict							=      ViTHybridModel(__lowerCamelCase    ).eval()
       else:
              snake_case       :   int							=      ViTHybridForImageClassification(__lowerCamelCase    ).eval()
       model.load_state_dict(__lowerCamelCase    )

       # create image processor
       snake_case       :   Union[str, Any]							=      create_transform(**resolve_data_config({}				,					model=__lowerCamelCase    )    )
       snake_case       :   int							=      transform.transforms

       snake_case       :   int							=      {
           "bilinear": PILImageResampling.BILINEAR,
           "bicubic": PILImageResampling.BICUBIC,
           "nearest": PILImageResampling.NEAREST,
       }

       snake_case       :   Optional[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()				,					)

       snake_case       :   int							=      prepare_img()
       snake_case       :   List[str]							=      transform(__lowerCamelCase    ).unsqueeze(0    )
       snake_case       :   Union[str, Any]							=      processor(__lowerCamelCase				,					return_tensors="pt"    ).pixel_values

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

       # verify logits
       with torch.no_grad():
              snake_case       :   Union[str, Any]							=      model(__lowerCamelCase    )
              snake_case       :   Optional[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       :   str							=      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__":
   __lowerCamelCase							=       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."""
   )

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









'''simple docstring'''


from collections import defaultdict
from math import ceil, sqrt







def 			snake_case_   (  SCREAMING_SNAKE_CASE__ = 100_0000							,      SCREAMING_SNAKE_CASE__ = 10     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       defaultdict     		=      defaultdict(SCREAMING_SNAKE_CASE__     )

				for outer_width in range(3							,      (t_limit // 4) + 2     ):
								if outer_width * outer_width > t_limit:
												_SCREAMING_SNAKE_CASE :       int     		=      max(
												    ceil(sqrt(outer_width * outer_width - t_limit     )     )							,      1     )
								else:
												_SCREAMING_SNAKE_CASE :       List[str]     		=      1

								hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

								for hole_width in range(SCREAMING_SNAKE_CASE__							,      outer_width - 1							,      2     ):
												count[outer_width * outer_width - hole_width * hole_width] += 1

				return sum(1 for n in count.values() if 1 <= n <= 10     )


if __name__ == "__main__":
				print(F"{solution() = }")




