



"""simple docstring"""
import argparse
import json
from pathlib import Path

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

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase						:		Any			= logging.get_logger(__name__)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase=False							) ->						str:







  '''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'''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''')							)

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

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ("""norm.weight""", """layernorm.weight"""),
            ("""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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase=False							) ->						Any:







  '''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'''blocks.{i}.attn.qkv.weight'''							)
    lowercase_								= state_dict.pop(F'''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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						str:







  '''simple docstring'''


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


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						Union[str, Any]:







  '''simple docstring'''


  lowercase_								= dct.pop(__lowerCAmelCase							)
  lowercase_								= val


def 			_SCREAMING_SNAKE_CASE						() ->						Any:







  '''simple docstring'''


  lowercase_								= """http://images.cocodataset.org/val2017/000000039769.jpg"""
  lowercase_								= Image.open(requests.get(__lowerCAmelCase ,  stream=__lowerCAmelCase							).raw							)
  return im


@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase=True							) ->						Optional[int]:







  '''simple docstring'''


  lowercase_								= ViTConfig()
  # patch_size
  if model_name[-1] == "8":
    lowercase_								= 8
  # set labels if required
  if not base_model:
    lowercase_								= 10_00
    lowercase_								= """huggingface/label-files"""
    lowercase_								= """imagenet-1k-id2label.json"""
    lowercase_								= json.load(open(hf_hub_download(__lowerCAmelCase ,  __lowerCAmelCase ,  repo_type="""dataset"""							) ,  """r"""							)							)
    lowercase_								= {int(__lowerCAmelCase							): v for k, v in idalabel.items()}
    lowercase_								= idalabel
    lowercase_								= {v: k for k, v in idalabel.items()}
  # size of the architecture
  if model_name in ["dino_vits8", "dino_vits16"]:
    lowercase_								= 3_84
    lowercase_								= 15_36
    lowercase_								= 12
    lowercase_								= 6

  # load original model from torch hub
  lowercase_								= torch.hub.load("""facebookresearch/dino:main""" ,  __lowerCAmelCase							)
  original_model.eval()

  # load state_dict of original model, remove and rename some keys
  lowercase_								= original_model.state_dict()
  if base_model:
    remove_classification_head_(__lowerCAmelCase							)
  lowercase_								= create_rename_keys(__lowerCAmelCase ,  base_model=__lowerCAmelCase							)
  for src, dest in rename_keys:
    rename_key(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							)
  read_in_q_k_v(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							)

  # load HuggingFace model
  if base_model:
    lowercase_								= ViTModel(__lowerCAmelCase ,  add_pooling_layer=__lowerCAmelCase							).eval()
  else:
    lowercase_								= ViTForImageClassification(__lowerCAmelCase							).eval()
  model.load_state_dict(__lowerCAmelCase							)

  # Check outputs on an image, prepared by ViTImageProcessor
  lowercase_								= ViTImageProcessor()
  lowercase_								= image_processor(images=prepare_img() ,  return_tensors="""pt"""							)
  lowercase_								= encoding["""pixel_values"""]
  lowercase_								= model(__lowerCAmelCase							)

  if base_model:
    lowercase_								= original_model(__lowerCAmelCase							)
    assert torch.allclose(__lowerCAmelCase ,  outputs.last_hidden_state[:, 0, :] ,  atol=1E-1							)
  else:
    lowercase_								= original_model(__lowerCAmelCase							)
    assert logits.shape == outputs.logits.shape
    assert torch.allclose(__lowerCAmelCase ,  outputs.logits ,  atol=1E-3							)

  Path(__lowerCAmelCase							).mkdir(exist_ok=__lowerCAmelCase							)
  print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''							)
  model.save_pretrained(__lowerCAmelCase							)
  print(F'''Saving image processor to {pytorch_dump_folder_path}'''							)
  image_processor.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
       UpperCAmelCase						:		str			= argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="dino_vitb16",
           type=str,
           help="Name of the model trained with DINO 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(
           "--base_model",
           action="store_true",
           help="Whether to only convert the base model (no projection head weights).",
       )

       parser.set_defaults(base_model=True)
       UpperCAmelCase						:		List[Any]			= parser.parse_args()
       convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase						:		Union[str, Any]			= {
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[Any]			= ["LongformerTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Tuple			= [
           "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "LongformerForMaskedLM",
           "LongformerForMultipleChoice",
           "LongformerForQuestionAnswering",
           "LongformerForSequenceClassification",
           "LongformerForTokenClassification",
           "LongformerModel",
           "LongformerPreTrainedModel",
           "LongformerSelfAttention",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		List[Any]			= [
           "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFLongformerForMaskedLM",
           "TFLongformerForMultipleChoice",
           "TFLongformerForQuestionAnswering",
           "TFLongformerForSequenceClassification",
           "TFLongformerForTokenClassification",
           "TFLongformerModel",
           "TFLongformerPreTrainedModel",
           "TFLongformerSelfAttention",
       ]


if TYPE_CHECKING:
       from .configuration_longformer import (
           LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LongformerConfig,
           LongformerOnnxConfig,
       )
       from .tokenization_longformer import LongformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_longformer_fast import LongformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_longformer import (
                  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LongformerForMaskedLM,
                  LongformerForMultipleChoice,
                  LongformerForQuestionAnswering,
                  LongformerForSequenceClassification,
                  LongformerForTokenClassification,
                  LongformerModel,
                  LongformerPreTrainedModel,
                  LongformerSelfAttention,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_longformer import (
                  TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLongformerForMaskedLM,
                  TFLongformerForMultipleChoice,
                  TFLongformerForQuestionAnswering,
                  TFLongformerForSequenceClassification,
                  TFLongformerForTokenClassification,
                  TFLongformerModel,
                  TFLongformerPreTrainedModel,
                  TFLongformerSelfAttention,
              )

else:
       import sys

       UpperCAmelCase						:		Optional[Any]			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

