






'''simple docstring'''







import argparse
import json
from pathlib import Path

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

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_      =			logging.get_logger(__name__)




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE=False		)				->					int:
     """simple docstring"""




     __a       =   []
     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"""),
                  ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
                  ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
              ]		)

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          __a       =   [(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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE=False		)				->					Optional[Any]:
     """simple docstring"""




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




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Dict:
     """simple docstring"""




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




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Dict:
     """simple docstring"""




     __a       =   dct.pop(__SCREAMING_SNAKE_CASE		)
     __a       =   val




def        __lowercase						(      )				->					str:
     """simple docstring"""




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




@torch.no_grad()
def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					int:
     """simple docstring"""




     __a       =   ViTConfig()
     __a       =   False
     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     if vit_name[-5:] == "in21k":
          __a       =   True
          __a       =   int(vit_name[-12:-10]		)
          __a       =   int(vit_name[-9:-6]		)
     else:
          __a       =   1000
          __a       =   """huggingface/label-files"""
          __a       =   """imagenet-1k-id2label.json"""
          __a       =   json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  repo_type="""dataset"""		)				,  """r"""		)		)
          __a       =   {int(__SCREAMING_SNAKE_CASE		): v for k, v in idalabel.items()}
          __a       =   idalabel
          __a       =   {v: k for k, v in idalabel.items()}
          __a       =   int(vit_name[-6:-4]		)
          __a       =   int(vit_name[-3:]		)
     # size of the architecture
     if "deit" in vit_name:
          if vit_name[9:].startswith("""tiny"""		):
               __a       =   192
               __a       =   768
               __a       =   12
               __a       =   3
          elif vit_name[9:].startswith("""small"""		):
               __a       =   384
               __a       =   1536
               __a       =   12
               __a       =   6
          else:
               pass
     else:
          if vit_name[4:].startswith("""small"""		):
               __a       =   768
               __a       =   2304
               __a       =   8
               __a       =   8
          elif vit_name[4:].startswith("""base"""		):
               pass
          elif vit_name[4:].startswith("""large"""		):
               __a       =   1024
               __a       =   4096
               __a       =   24
               __a       =   16
          elif vit_name[4:].startswith("""huge"""		):
               __a       =   1280
               __a       =   5120
               __a       =   32
               __a       =   16

    # load original model from timm
     __a       =   timm.create_model(__SCREAMING_SNAKE_CASE				,  pretrained=__SCREAMING_SNAKE_CASE		)
     timm_model.eval()

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

     # load HuggingFace model
     if vit_name[-5:] == "in21k":
          __a       =   ViTModel(__SCREAMING_SNAKE_CASE		).eval()
     else:
          __a       =   ViTForImageClassification(__SCREAMING_SNAKE_CASE		).eval()
     model.load_state_dict(__SCREAMING_SNAKE_CASE		)

     # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
     if "deit" in vit_name:
          __a       =   DeiTImageProcessor(size=config.image_size		)
     else:
          __a       =   ViTImageProcessor(size=config.image_size		)
     __a       =   image_processor(images=prepare_img()				,  return_tensors="""pt"""		)
     __a       =   encoding["""pixel_values"""]
     __a       =   model(__SCREAMING_SNAKE_CASE		)

     if base_model:
          __a       =   timm_model.forward_features(__SCREAMING_SNAKE_CASE		)
          assert timm_pooled_output.shape == outputs.pooler_output.shape
          assert torch.allclose(__SCREAMING_SNAKE_CASE				,  outputs.pooler_output				,  atol=1E-3		)
     else:
          __a       =   timm_model(__SCREAMING_SNAKE_CASE		)
          assert timm_logits.shape == outputs.logits.shape
          assert torch.allclose(__SCREAMING_SNAKE_CASE				,  outputs.logits				,  atol=1E-3		)

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_      =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--vit_name',
     default='vit_base_patch16_224',
     type=str,
     help='Name of the ViT timm model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
 )

 SCREAMING_SNAKE_CASE_      =			parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)










'''simple docstring'''







from ...processing_utils import ProcessorMixin


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :Dict												=["""image_processor""", """feature_extractor"""]
  a_       :str												="""TvltImageProcessor"""
  a_       :str												="""TvltFeatureExtractor"""

  def __init__(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]						):





       '''simple docstring'''

       super().__init__(image_processor=SCREAMING_SNAKE_CASE__				,							feature_extractor=SCREAMING_SNAKE_CASE__						)

       __a       =   image_processor
       __a       =   feature_extractor

  def __call__(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							str=None				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=None				,							SCREAMING_SNAKE_CASE__     :							Tuple=None				,							SCREAMING_SNAKE_CASE__     :							List[Any]=None				,							SCREAMING_SNAKE_CASE__     :							Any=False				,							SCREAMING_SNAKE_CASE__     :							List[str]=False				,							*SCREAMING_SNAKE_CASE__     :							str				,							**SCREAMING_SNAKE_CASE__     :							Optional[int]				,							):





       '''simple docstring'''

       if images is None and audio is None:
            raise ValueError("""You need to specify either an `images` or `audio` input to process."""						)

       __a       =   None
       if images is not None:
            __a       =   self.image_processor(SCREAMING_SNAKE_CASE__				,							mask_pixel=SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
       if images_mixed is not None:
            __a       =   self.image_processor(SCREAMING_SNAKE_CASE__				,							is_mixed=SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)
       if audio is not None:
            __a       =   self.feature_extractor(
                SCREAMING_SNAKE_CASE__				,							*SCREAMING_SNAKE_CASE__				,							sampling_rate=SCREAMING_SNAKE_CASE__				,							mask_audio=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)

       __a       =   {}
       if audio is not None:
            output_dict.update(SCREAMING_SNAKE_CASE__						)
       if images is not None:
            output_dict.update(SCREAMING_SNAKE_CASE__						)
       if images_mixed_dict is not None:
            output_dict.update(SCREAMING_SNAKE_CASE__						)
       return output_dict


  @property
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   self.image_processor.model_input_names
       __a       =   self.feature_extractor.model_input_names
       return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names						)						)






