


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       :		int         =				logging.get_logger(__name__)




def     UpperCamelCase    (      _a       ,		_a=False		)					->							Tuple:
     '''simple docstring'''





     lowercase_       :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'''),
                  ('''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"
          lowercase_       :int     =       [(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		)					->							List[str]:
     '''simple docstring'''





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




def     UpperCamelCase    (      _a		)					->							List[Any]:
     '''simple docstring'''





     lowercase_       :List[Any]     =       ['''head.weight''', '''head.bias''']
     for k in ignore_keys:
          state_dict.pop(_a       ,		_a		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Union[str, Any]:
     '''simple docstring'''





     lowercase_       :Any     =       dct.pop(_a		)
     lowercase_       :List[Any]     =       val




def     UpperCamelCase    (      )					->							Optional[int]:
     '''simple docstring'''





     lowercase_       :int     =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     lowercase_       :Optional[Any]     =       Image.open(requests.get(_a       ,		stream=_a		).raw		)
     return im




@torch.no_grad()
def     UpperCamelCase    (      _a       ,		_a		)					->							Union[str, Any]:
     '''simple docstring'''





     lowercase_       :Union[str, Any]     =       ViTConfig()
     lowercase_       :Any     =       False
     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     if vit_name[-5:] == "in21k":
          lowercase_       :Dict     =       True
          lowercase_       :Optional[int]     =       int(vit_name[-1_2:-1_0]		)
          lowercase_       :List[Any]     =       int(vit_name[-9:-6]		)
     else:
          lowercase_       :Tuple     =       1_0_0_0
          lowercase_       :Optional[Any]     =       '''huggingface/label-files'''
          lowercase_       :int     =       '''imagenet-1k-id2label.json'''
          lowercase_       :int     =       json.load(open(hf_hub_download(_a       ,		_a       ,		repo_type='''dataset'''		)       ,		'''r'''		)		)
          lowercase_       :str     =       {int(_a		): v for k, v in idalabel.items()}
          lowercase_       :Any     =       idalabel
          lowercase_       :Dict     =       {v: k for k, v in idalabel.items()}
          lowercase_       :int     =       int(vit_name[-6:-4]		)
          lowercase_       :List[Any]     =       int(vit_name[-3:]		)
     # size of the architecture
     if "deit" in vit_name:
          if vit_name[9:].startswith('''tiny'''		):
               lowercase_       :Any     =       1_9_2
               lowercase_       :List[Any]     =       7_6_8
               lowercase_       :Any     =       1_2
               lowercase_       :Dict     =       3
          elif vit_name[9:].startswith('''small'''		):
               lowercase_       :List[str]     =       3_8_4
               lowercase_       :List[Any]     =       1_5_3_6
               lowercase_       :Optional[int]     =       1_2
               lowercase_       :Optional[int]     =       6
          else:
               pass
     else:
          if vit_name[4:].startswith('''small'''		):
               lowercase_       :Optional[Any]     =       7_6_8
               lowercase_       :Tuple     =       2_3_0_4
               lowercase_       :Union[str, Any]     =       8
               lowercase_       :Optional[Any]     =       8
          elif vit_name[4:].startswith('''base'''		):
               pass
          elif vit_name[4:].startswith('''large'''		):
               lowercase_       :List[str]     =       1_0_2_4
               lowercase_       :List[str]     =       4_0_9_6
               lowercase_       :Any     =       2_4
               lowercase_       :List[Any]     =       1_6
          elif vit_name[4:].startswith('''huge'''		):
               lowercase_       :Any     =       1_2_8_0
               lowercase_       :Optional[int]     =       5_1_2_0
               lowercase_       :Any     =       3_2
               lowercase_       :List[Any]     =       1_6

    # load original model from timm
     lowercase_       :Optional[Any]     =       timm.create_model(_a       ,		pretrained=_a		)
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     lowercase_       :Union[str, Any]     =       timm_model.state_dict()
     if base_model:
          remove_classification_head_(_a		)
     lowercase_       :List[Any]     =       create_rename_keys(_a       ,		_a		)
     for src, dest in rename_keys:
          rename_key(_a       ,		_a       ,		_a		)
     read_in_q_k_v(_a       ,		_a       ,		_a		)

     # load HuggingFace model
     if vit_name[-5:] == "in21k":
          lowercase_       :Optional[Any]     =       ViTModel(_a		).eval()
     else:
          lowercase_       :Any     =       ViTForImageClassification(_a		).eval()
     model.load_state_dict(_a		)

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

     if base_model:
          lowercase_       :Any     =       timm_model.forward_features(_a		)
          assert timm_pooled_output.shape == outputs.pooler_output.shape
          assert torch.allclose(_a       ,		outputs.pooler_output       ,		atol=1E-3		)
     else:
          lowercase_       :Union[str, Any]     =       timm_model(_a		)
          assert timm_logits.shape == outputs.logits.shape
          assert torch.allclose(_a       ,		outputs.logits       ,		atol=1E-3		)

     Path(_a		).mkdir(exist_ok=_a		)
     print(f"Saving model {vit_name} 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__":
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				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       :		Optional[Any]         =				parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE       :		str         =				{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		Dict         =				[
       "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TapasForMaskedLM",
       "TapasForQuestionAnswering",
       "TapasForSequenceClassification",
       "TapasModel",
       "TapasPreTrainedModel",
       "load_tf_weights_in_tapas",
   ]
try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		str         =				[
       "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFTapasForMaskedLM",
       "TFTapasForQuestionAnswering",
       "TFTapasForSequenceClassification",
       "TFTapasModel",
       "TFTapasPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
   from .tokenization_tapas import TapasTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tapas import (
          TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
          TapasForMaskedLM,
          TapasForQuestionAnswering,
          TapasForSequenceClassification,
          TapasModel,
          TapasPreTrainedModel,
          load_tf_weights_in_tapas,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_tapas import (
          TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFTapasForMaskedLM,
          TFTapasForQuestionAnswering,
          TFTapasForSequenceClassification,
          TFTapasModel,
          TFTapasPreTrainedModel,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

