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







def  __UpperCamelCase    (						A  ):
     UpperCamelCase__										=      ['''head.weight''', '''head.bias''']
     for k in ignore_keys:
          state_dict.pop(A ,							A  )







def  __UpperCamelCase    (						A  ):
     # projection head is used in the self-supervised pre-training in MSN,
     # for downstream task it's not needed.
     UpperCamelCase__										=      [
         '''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(A ,							A  )







def  __UpperCamelCase    (						A ,							A ,							A  ):
     UpperCamelCase__										=      dct.pop(A  )
     UpperCamelCase__										=      val







def  __UpperCamelCase    (						A ,							A  ):
     UpperCamelCase__										=      ViTMSNConfig()
     UpperCamelCase__										=      1000

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

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

     UpperCamelCase__										=      ViTMSNModel(A  )

     UpperCamelCase__										=      torch.hub.load_state_dict_from_url(A ,							map_location='''cpu'''  )['''target_encoder''']

     UpperCamelCase__										=      ViTImageProcessor(size=config.image_size  )

     remove_projection_head(A  )
     UpperCamelCase__										=      create_rename_keys(A ,							base_model=A  )

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

     model.load_state_dict(A  )
     model.eval()

     UpperCamelCase__										=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''

     UpperCamelCase__										=      Image.open(requests.get(A ,							stream=A  ).raw  )
     UpperCamelCase__										=      ViTImageProcessor(
         size=config.image_size ,							image_mean=A ,							image_std=A  )
     UpperCamelCase__										=      image_processor(images=A ,							return_tensors='''pt'''  )

     # forward pass
     torch.manual_seed(2  )
     UpperCamelCase__										=      model(**A  )
     UpperCamelCase__										=      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:
          UpperCamelCase__										=      torch.tensor([[-1.09_15, -1.48_76, -1.18_09]]  )
     elif "b16" in checkpoint_url:
          UpperCamelCase__										=      torch.tensor([[14.28_89, -18.90_45, 11.72_81]]  )
     elif "l16" in checkpoint_url:
          UpperCamelCase__										=      torch.tensor([[41.50_28, -22.86_81, 45.64_75]]  )
     elif "b4" in checkpoint_url:
          UpperCamelCase__										=      torch.tensor([[-4.38_68, 5.29_32, -0.41_37]]  )
     else:
          UpperCamelCase__										=      torch.tensor([[-0.17_92, -0.64_65, 2.42_63]]  )

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

     print(f"Saving model to {pytorch_dump_folder_path}"  )
     model.save_pretrained(A  )

     print(f"Saving image processor to {pytorch_dump_folder_path}"  )
     image_processor.save_pretrained(A  )


if __name__ == "__main__":
     __magic_name__								=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.'''
     )

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




# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__magic_name__								='''2.13.1'''

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('''3.7'''):
     raise ImportWarning(
         '''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
     )

if version.parse(pyarrow.__version__).major < 8:
     raise ImportWarning(
         '''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
         '''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
     )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__magic_name__								=concatenate_datasets
__magic_name__								=DownloadConfig
__magic_name__								=DownloadManager
__magic_name__								=DownloadMode
__magic_name__								=DownloadConfig
__magic_name__								=DownloadMode
__magic_name__								=DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager



