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







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					Any			     =    ["head.weight", "head.bias"]
   for k in ignore_keys:
      state_dict.pop(__lowerCamelCase,		__lowerCamelCase  )







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Optional[Any]			     =    dct.pop(__lowerCamelCase  )
   UpperCAmelCase_					:					List[str]			     =    val







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







@torch.no_grad()
def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase=False  ):
   UpperCAmelCase_					:					int			     =    BitConfig(
       global_padding="same",		layer_type="bottleneck",		depths=(3, 4, 9),		out_features=["stage3"],		embedding_dynamic_padding=__lowerCamelCase,		)
   UpperCAmelCase_					:					List[str]			     =    ViTHybridConfig(backbone_config=__lowerCamelCase,		image_size=384,		num_labels=1000  )
   UpperCAmelCase_					:					int			     =    False

   # load original model from timm
   UpperCAmelCase_					:					List[str]			     =    timm.create_model(__lowerCamelCase,		pretrained=__lowerCamelCase  )
   timm_model.eval()

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

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

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

   # create image processor
   UpperCAmelCase_					:					str			     =    create_transform(**resolve_data_config({},		model=__lowerCamelCase  )  )
   UpperCAmelCase_					:					Any			     =    transform.transforms

   UpperCAmelCase_					:					Optional[Any]			     =    {
       "bilinear": PILImageResampling.BILINEAR,
       "bicubic": PILImageResampling.BICUBIC,
       "nearest": PILImageResampling.NEAREST,
   }

   UpperCAmelCase_					:					List[str]			     =    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(),		)

   UpperCAmelCase_					:					Any			     =    prepare_img()
   UpperCAmelCase_					:					Optional[int]			     =    transform(__lowerCamelCase  ).unsqueeze(0  )
   UpperCAmelCase_					:					int			     =    processor(__lowerCamelCase,		return_tensors="pt"  ).pixel_values

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

   # verify logits
   with torch.no_grad():
      UpperCAmelCase_					:					Optional[int]			     =    model(__lowerCamelCase  )
      UpperCAmelCase_					:					Any			     =    outputs.logits

   print("Predicted class:",		logits.argmax(-1  ).item()  )
   if base_model:
      UpperCAmelCase_					:					int			     =    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:
      UpperCAmelCase_					:					Dict			     =    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"""





def 	__a     (		__lowerCamelCase = 3,		__lowerCamelCase = 7,		__lowerCamelCase = 100_0000  ):
   UpperCAmelCase_					:					Dict			     =    0
   UpperCAmelCase_					:					List[Any]			     =    1

   for current_denominator in range(1,		limit + 1  ):
      UpperCAmelCase_					:					Dict			     =    current_denominator * numerator // denominator
      if current_denominator % denominator == 0:
         current_numerator -= 1
      if current_numerator * max_denominator > current_denominator * max_numerator:
         UpperCAmelCase_					:					List[Any]			     =    current_numerator
         UpperCAmelCase_					:					Optional[int]			     =    current_denominator
   return max_numerator


if __name__ == "__main__":
    print(solution(numerator=3, denominator=7, limit=1_000_000))


