

"""simple docstring"""





import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)




def   _A  (  UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	List[str]=False)     ->						List[Any]:
   '''simple docstring'''


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

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

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

      # if just the base model, we should remove "vit" from all keys that start with "vit"
      __lowercase								=						[(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"),
          ])

   return rename_keys




def   _A  (  UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	Tuple,      UpperCamelCase_					:	str=False)     ->						List[str]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	Tuple)     ->						Union[str, Any]:
   '''simple docstring'''


   __lowercase								=						["head.weight", "head.bias"]
   for k in ignore_keys:
      state_dict.pop(UpperCamelCase_,      UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	Optional[Any])     ->						Dict:
   '''simple docstring'''


   __lowercase								=						[
       "module.fc.fc1.weight",
       "module.fc.fc1.bias",
       "module.fc.bn1.weight",
       "module.fc.bn1.bias",
       "module.fc.bn1.running_mean",
       "module.fc.bn1.running_var",
       "module.fc.bn1.num_batches_tracked",
       "module.fc.fc2.weight",
       "module.fc.fc2.bias",
       "module.fc.bn2.weight",
       "module.fc.bn2.bias",
       "module.fc.bn2.running_mean",
       "module.fc.bn2.running_var",
       "module.fc.bn2.num_batches_tracked",
       "module.fc.fc3.weight",
       "module.fc.fc3.bias",
   ]
   for k in ignore_keys:
      state_dict.pop(UpperCamelCase_,      UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	str)     ->						int:
   '''simple docstring'''


   __lowercase								=						dct.pop(UpperCamelCase_)
   __lowercase								=						val




def   _A  (  UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	Any)     ->						Any:
   '''simple docstring'''


   __lowercase								=						ViTMSNConfig()
   __lowercase								=						1000

   __lowercase								=						"datasets/huggingface/label-files"
   __lowercase								=						"imagenet-1k-id2label.json"
   __lowercase								=						json.load(open(hf_hub_download(UpperCamelCase_,      UpperCamelCase_),      "r"))
   __lowercase								=						{int(UpperCamelCase_): v for k, v in idalabel.items()}
   __lowercase								=						idalabel
   __lowercase								=						{v: k for k, v in idalabel.items()}

   if "s16" in checkpoint_url:
      __lowercase								=						384
      __lowercase								=						1536
      __lowercase								=						6
   elif "l16" in checkpoint_url:
      __lowercase								=						1024
      __lowercase								=						4096
      __lowercase								=						24
      __lowercase								=						16
      __lowercase								=						0.1
   elif "b4" in checkpoint_url:
      __lowercase								=						4
   elif "l7" in checkpoint_url:
      __lowercase								=						7
      __lowercase								=						1024
      __lowercase								=						4096
      __lowercase								=						24
      __lowercase								=						16
      __lowercase								=						0.1

   __lowercase								=						ViTMSNModel(UpperCamelCase_)

   __lowercase								=						torch.hub.load_state_dict_from_url(UpperCamelCase_,      map_location="cpu")["target_encoder"]

   __lowercase								=						ViTImageProcessor(size=config.image_size)

   remove_projection_head(UpperCamelCase_)
   __lowercase								=						create_rename_keys(UpperCamelCase_,      base_model=UpperCamelCase_)

   for src, dest in rename_keys:
      rename_key(UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_)
   read_in_q_k_v(UpperCamelCase_,      UpperCamelCase_,      base_model=UpperCamelCase_)

   model.load_state_dict(UpperCamelCase_)
   model.eval()

   __lowercase								=						"http://images.cocodataset.org/val2017/000000039769.jpg"

   __lowercase								=						Image.open(requests.get(UpperCamelCase_,      stream=UpperCamelCase_).raw)
   __lowercase								=						ViTImageProcessor(
       size=config.image_size,      image_mean=UpperCamelCase_,      image_std=UpperCamelCase_)
   __lowercase								=						image_processor(images=UpperCamelCase_,      return_tensors="pt")

   # forward pass
   torch.manual_seed(2)
   __lowercase								=						model(**UpperCamelCase_)
   __lowercase								=						outputs.last_hidden_state

   # The following Colab Notebook was used to generate these outputs:
   # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
   if "s16" in checkpoint_url:
      __lowercase								=						torch.tensor([[-1.0_915, -1.4_876, -1.1_809]])
   elif "b16" in checkpoint_url:
      __lowercase								=						torch.tensor([[14.2_889, -18.9_045, 11.7_281]])
   elif "l16" in checkpoint_url:
      __lowercase								=						torch.tensor([[41.5_028, -22.8_681, 45.6_475]])
   elif "b4" in checkpoint_url:
      __lowercase								=						torch.tensor([[-4.3_868, 5.2_932, -0.4_137]])
   else:
      __lowercase								=						torch.tensor([[-0.1_792, -0.6_465, 2.4_263]])

   # verify logits
   assert torch.allclose(last_hidden_state[:, 0, :3],      UpperCamelCase_,      atol=1E-4)

   print(F"""Saving model 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__":
   _a							=							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--checkpoint_url',
       default='https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar',
       type=str,
       help='URL of the checkpoint you\'d like to convert.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )

   _a							=							parser.parse_args()
   convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







"""simple docstring"""





from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters




def   _A  (  )     ->						Dict:
   '''simple docstring'''


   __lowercase								=						{
       "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
       "path": ["test_1.py", "test_2.py", "unit_test.py"],
       "content": ["a " * 20, "a " * 30, "b " * 7],
   }
   __lowercase								=						Dataset.from_dict(UpperCamelCase_)
   return dataset




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def  _lowercase			(      self							:      int     ):
    __lowercase								=						get_dataset()
    __lowercase								=						make_duplicate_clusters(UpperCAmelCase__, 0.85     )
    self.assertEqual(len(duplicate_clusters[0]     ), 2     )



 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						get_dataset()
    __lowercase		,__lowercase								=						deduplicate_dataset(UpperCAmelCase__     )
    self.assertEqual(len(UpperCAmelCase__     ), 2     )
    print(UpperCAmelCase__     )
    self.assertEqual(duplicate_clusters[0][0]["copies"], 2     )
    self.assertEqual(duplicate_clusters[0][0]["is_extreme"], UpperCAmelCase__     )




