


'''simple docstring'''
from datetime import datetime as dt
import os

from github import Github


__snake_case							      =     [
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]


def 			a      (			)  ->	List[str]:



					'''simple docstring'''






					UpperCamelCase__   :List[str]												= Github(os.environ['''GITHUB_TOKEN''']   )
					UpperCamelCase__   :Tuple												= g.get_repo('''huggingface/transformers'''   )
					UpperCamelCase__   :Union[str, Any]												= repo.get_issues(state='''open'''   )

					for issue in open_issues:
										UpperCamelCase__   :List[Any]												= sorted([comment for comment in issue.get_comments()] ,					key=lambda __a   : i.created_at ,					reverse=__a   )
										UpperCamelCase__   :List[Any]												= comments[0] if len(__a   ) > 0 else None
										if (
										    last_comment is not None
										    and last_comment.user.login == "github-actions[bot]"
										    and (dt.utcnow() - issue.updated_at).days > 7
										    and (dt.utcnow() - issue.created_at).days >= 30
										    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()   )
										):
															# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
															issue.edit(state='''closed'''   )
										elif (
										    (dt.utcnow() - issue.updated_at).days > 23
										    and (dt.utcnow() - issue.created_at).days >= 30
										    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()   )
										):
															# print(f"Would add stale comment to {issue.number}")
															issue.create_comment(
															    '''This issue has been automatically marked as stale because it has not had '''
															    '''recent activity. If you think this still needs to be addressed '''
															    '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
															    '''[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '''
															    '''are likely to be ignored.'''   )


if __name__ == "__main__":
		main()





"""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 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					List[str]=False    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  []
  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"
    _snake_case							:		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"""),
        ]    )

  return rename_keys


def 					UpperCAmelCase__       (snake_case__							:					Dict		,   snake_case__							:					Dict		,   snake_case__							:					List[str]=False    ):
  """simple docstring"""

  for i in range(config.num_hidden_layers    ):
    if base_model:
      _snake_case							:		List[Any]								=  """"""
    else:
      _snake_case							:		List[Any]								=  """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"module.blocks.{i}.attn.qkv.weight"    )
    _snake_case							:		Optional[Any]								=  state_dict.pop(F"module.blocks.{i}.attn.qkv.bias"    )
    # next, add query, keys and values (in that order) to the state dict
    _snake_case							:		Optional[Any]								=  in_proj_weight[
        : config.hidden_size, :
    ]
    _snake_case							:		Union[str, Any]								=  in_proj_bias[: config.hidden_size]
    _snake_case							:		Union[str, Any]								=  in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _snake_case							:		Optional[Any]								=  in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _snake_case							:		Union[str, Any]								=  in_proj_weight[
        -config.hidden_size :, :
    ]
    _snake_case							:		List[str]								=  in_proj_bias[-config.hidden_size :]


def 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		Tuple								=  ["""head.weight""", """head.bias"""]
  for k in ignore_keys:
    state_dict.pop(snake_case__		,   snake_case__    )


def 					UpperCAmelCase__       (snake_case__							:					int    ):
  """simple docstring"""

  _snake_case							:		List[str]								=  [
      """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(snake_case__		,   snake_case__    )


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					Tuple		,   snake_case__							:					int    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  dct.pop(snake_case__    )
  _snake_case							:		Union[str, Any]								=  val


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		str								=  ViTMSNConfig()
  _snake_case							:		Any								=  10_00

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

  if "s16" in checkpoint_url:
    _snake_case							:		Tuple								=  3_84
    _snake_case							:		Dict								=  15_36
    _snake_case							:		Tuple								=  6
  elif "l16" in checkpoint_url:
    _snake_case							:		Any								=  10_24
    _snake_case							:		int								=  40_96
    _snake_case							:		str								=  24
    _snake_case							:		Optional[int]								=  16
    _snake_case							:		List[Any]								=  0.1
  elif "b4" in checkpoint_url:
    _snake_case							:		Tuple								=  4
  elif "l7" in checkpoint_url:
    _snake_case							:		int								=  7
    _snake_case							:		Dict								=  10_24
    _snake_case							:		Optional[Any]								=  40_96
    _snake_case							:		Any								=  24
    _snake_case							:		Union[str, Any]								=  16
    _snake_case							:		Optional[int]								=  0.1

  _snake_case							:		int								=  ViTMSNModel(snake_case__    )

  _snake_case							:		Optional[int]								=  torch.hub.load_state_dict_from_url(snake_case__		,   map_location="""cpu"""    )["""target_encoder"""]

  _snake_case							:		List[str]								=  ViTImageProcessor(size=config.image_size    )

  remove_projection_head(snake_case__    )
  _snake_case							:		List[str]								=  create_rename_keys(snake_case__		,   base_model=snake_case__    )

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

  model.load_state_dict(snake_case__    )
  model.eval()

  _snake_case							:		Union[str, Any]								=  """http://images.cocodataset.org/val2017/000000039769.jpg"""

  _snake_case							:		Tuple								=  Image.open(requests.get(snake_case__		,   stream=snake_case__    ).raw    )
  _snake_case							:		str								=  ViTImageProcessor(
      size=config.image_size		,   image_mean=snake_case__		,   image_std=snake_case__    )
  _snake_case							:		Any								=  image_processor(images=snake_case__		,   return_tensors="""pt"""    )

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

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

  print(F"Saving model to {pytorch_dump_folder_path}"    )
  model.save_pretrained(snake_case__    )

  print(F"Saving image processor to {pytorch_dump_folder_path}"    )
  image_processor.save_pretrained(snake_case__    )


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)


