






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()
lowerCamelCase__							=     logging.get_logger(__name__)






def   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   Optional[Any]=False  ):




     '''simple docstring'''






     __snake_case			:Union[str, Any]          =						[]
     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"
          __snake_case			:Optional[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__     :   str	,snake_case__     :   List[str]	,snake_case__     :   List[Any]=False  ):




     '''simple docstring'''






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






def   UpperCamelCase  (       snake_case__     :   Dict  ):




     '''simple docstring'''






     __snake_case			:Optional[int]          =						["""head.weight""", """head.bias"""]
     for k in ignore_keys:
          state_dict.pop(snake_case__	,snake_case__  )






def   UpperCamelCase  (       snake_case__     :   Union[str, Any]	,snake_case__     :   List[Any]	,snake_case__     :   Union[str, Any]  ):




     '''simple docstring'''






     __snake_case			:List[Any]          =						dct.pop(snake_case__  )
     __snake_case			:Dict          =						val






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:Dict          =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
     __snake_case			:Any          =						Image.open(requests.get(snake_case__	,stream=snake_case__  ).raw  )
     return im






@torch.no_grad()
def   UpperCamelCase  (       snake_case__     :   Union[str, Any]	,snake_case__     :   Any	,snake_case__     :   Dict=True  ):




     '''simple docstring'''






     __snake_case			:Optional[int]          =						ViTConfig()
     # patch_size
     if model_name[-1] == "8":
          __snake_case			:Optional[int]          =						8
     # set labels if required
     if not base_model:
          __snake_case			:Any          =						1000
          __snake_case			:Dict          =						"""huggingface/label-files"""
          __snake_case			:Optional[int]          =						"""imagenet-1k-id2label.json"""
          __snake_case			:int          =						json.load(open(hf_hub_download(snake_case__	,snake_case__	,repo_type="""dataset"""  )	,"""r"""  )  )
          __snake_case			:Union[str, Any]          =						{int(snake_case__  ): v for k, v in idalabel.items()}
          __snake_case			:List[Any]          =						idalabel
          __snake_case			:int          =						{v: k for k, v in idalabel.items()}
     # size of the architecture
     if model_name in ["dino_vits8", "dino_vits16"]:
          __snake_case			:str          =						384
          __snake_case			:Optional[Any]          =						1536
          __snake_case			:Dict          =						12
          __snake_case			:int          =						6

     # load original model from torch hub
     __snake_case			:List[str]          =						torch.hub.load("""facebookresearch/dino:main"""	,snake_case__  )
     original_model.eval()

     # load state_dict of original model, remove and rename some keys
     __snake_case			:int          =						original_model.state_dict()
     if base_model:
          remove_classification_head_(snake_case__  )
     __snake_case			:Tuple          =						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__	,snake_case__  )

     # load HuggingFace model
     if base_model:
          __snake_case			:List[Any]          =						ViTModel(snake_case__	,add_pooling_layer=snake_case__  ).eval()
     else:
          __snake_case			:Optional[int]          =						ViTForImageClassification(snake_case__  ).eval()
     model.load_state_dict(snake_case__  )

     # Check outputs on an image, prepared by ViTImageProcessor
     __snake_case			:int          =						ViTImageProcessor()
     __snake_case			:Optional[int]          =						image_processor(images=prepare_img()	,return_tensors="""pt"""  )
     __snake_case			:Optional[int]          =						encoding["""pixel_values"""]
     __snake_case			:Optional[Any]          =						model(snake_case__  )

     if base_model:
          __snake_case			:List[Any]          =						original_model(snake_case__  )
          assert torch.allclose(snake_case__	,outputs.last_hidden_state[:, 0, :]	,atol=1e-1  )
     else:
          __snake_case			:List[str]          =						original_model(snake_case__  )
          assert logits.shape == outputs.logits.shape
          assert torch.allclose(snake_case__	,outputs.logits	,atol=1e-3  )

     Path(snake_case__  ).mkdir(exist_ok=snake_case__  )
     print(f'''Saving model {model_name} 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__":
     lowerCamelCase__							=     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)
     lowerCamelCase__							=     parser.parse_args()
     convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)










# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


lowerCamelCase__							=     """pytorch_model.bin"""
lowerCamelCase__							=     """pytorch_model.bin.index.json"""
lowerCamelCase__							=     """adapter_config.json"""
lowerCamelCase__							=     """adapter_model.bin"""
lowerCamelCase__							=     """adapter_model.safetensors"""
lowerCamelCase__							=     """tf_model.h5"""
lowerCamelCase__							=     """tf_model.h5.index.json"""
lowerCamelCase__							=     """model.ckpt"""
lowerCamelCase__							=     """flax_model.msgpack"""
lowerCamelCase__							=     """flax_model.msgpack.index.json"""
lowerCamelCase__							=     """model.safetensors"""
lowerCamelCase__							=     """model.safetensors.index.json"""
lowerCamelCase__							=     """config.json"""
lowerCamelCase__							=     """preprocessor_config.json"""
lowerCamelCase__							=     FEATURE_EXTRACTOR_NAME
lowerCamelCase__							=     """generation_config.json"""
lowerCamelCase__							=     """modelcard.json"""

lowerCamelCase__							=     """▁"""
lowerCamelCase__							=     SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

lowerCamelCase__							=     [
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
lowerCamelCase__							=     [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
lowerCamelCase__							=     [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]






def   UpperCamelCase  (       snake_case__     :   Dict  ):




     '''simple docstring'''






     if version.parse(snake_case__  ) < version.parse(snake_case__  ):
          if "dev" in min_version:
               __snake_case			:Tuple          =						(
                   """This example requires a source install from HuggingFace Transformers (see """
                   """`https://huggingface.co/docs/transformers/installation#install-from-source`),"""
               )
          else:
               __snake_case			:List[str]          =						f'''This example requires a minimum version of {min_version},'''
          error_message += f''' but the version found is {__version__}.\n'''
          raise ImportError(
              error_message
              + """Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other """
              """versions of HuggingFace Transformers."""  )


