
'''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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_ :  str     =					logging.get_logger(__name__)



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       List[str]=False):
   UpperCamelCase       =  []

   # fmt: off
   # stem:
   rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token'''))
   rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings'''))

   rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''))
   rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''))

   # backbone
   rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight'''))
   rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight'''))
   rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias'''))

   for stage_idx in range(len(config.backbone_config.depths)):
      for layer_idx in range(config.backbone_config.depths[stage_idx]):
         rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight'))
         rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight'))
         rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias'))
         rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight'))
         rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight'))
         rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias'))
         rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight'))
         rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight'))
         rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias'))

      rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight'))
      rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight'))
      rename_keys.append((f'patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias', f'vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias'))

   # transformer encoder
   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'))

   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"
      UpperCamelCase       =  [(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'''),
          ])
   # fmt: on

   return rename_keys



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



def  __snake_case     (    _UpperCAmelCase					:       int):
   UpperCamelCase       =  ['''head.weight''', '''head.bias''']
   for k in ignore_keys:
      state_dict.pop(_UpperCAmelCase,      _UpperCAmelCase)



def  __snake_case     (    _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Tuple):
   UpperCamelCase       =  dct.pop(_UpperCAmelCase)
   UpperCamelCase       =  val



def  __snake_case     (    ):
   UpperCamelCase       =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   UpperCamelCase       =  Image.open(requests.get(_UpperCAmelCase,      stream=_UpperCAmelCase).raw)
   return im



@torch.no_grad()
def  __snake_case     (    _UpperCAmelCase					:       List[str],      _UpperCAmelCase					:       Optional[Any],      _UpperCAmelCase					:       str=False):
   UpperCamelCase       =  BitConfig(
       global_padding='''same''',      layer_type='''bottleneck''',      depths=(3, 4, 9),      out_features=['''stage3'''],      embedding_dynamic_padding=_UpperCAmelCase,      )
   UpperCamelCase       =  ViTHybridConfig(backbone_config=_UpperCAmelCase,      image_size=384,      num_labels=1000)
   UpperCamelCase       =  False

   # load original model from timm
   UpperCamelCase       =  timm.create_model(_UpperCAmelCase,      pretrained=_UpperCAmelCase)
   timm_model.eval()

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

   UpperCamelCase       =  '''huggingface/label-files'''
   UpperCamelCase       =  '''imagenet-1k-id2label.json'''
   UpperCamelCase       =  json.load(open(hf_hub_download(_UpperCAmelCase,      _UpperCAmelCase,      repo_type='''dataset'''),      '''r'''))
   UpperCamelCase       =  {int(_UpperCAmelCase): v for k, v in idalabel.items()}
   UpperCamelCase       =  idalabel
   UpperCamelCase       =  {v: k for k, v in idalabel.items()}

   # load HuggingFace model
   if vit_name[-5:] == "in21k":
      UpperCamelCase       =  ViTHybridModel(_UpperCAmelCase).eval()
   else:
      UpperCamelCase       =  ViTHybridForImageClassification(_UpperCAmelCase).eval()
   model.load_state_dict(_UpperCAmelCase)

   # create image processor
   UpperCamelCase       =  create_transform(**resolve_data_config({},      model=_UpperCAmelCase))
   UpperCamelCase       =  transform.transforms

   UpperCamelCase       =  {
       '''bilinear''': PILImageResampling.BILINEAR,
       '''bicubic''': PILImageResampling.BICUBIC,
       '''nearest''': PILImageResampling.NEAREST,
   }

   UpperCamelCase       =  ViTHybridImageProcessor(
       do_resize=_UpperCAmelCase,      size={'''shortest_edge''': timm_transforms[0].size},      resample=pillow_resamplings[timm_transforms[0].interpolation.value],      do_center_crop=_UpperCAmelCase,      crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]},      do_normalize=_UpperCAmelCase,      image_mean=timm_transforms[-1].mean.tolist(),      image_std=timm_transforms[-1].std.tolist(),      )

   UpperCamelCase       =  prepare_img()
   UpperCamelCase       =  transform(_UpperCAmelCase).unsqueeze(0)
   UpperCamelCase       =  processor(_UpperCAmelCase,      return_tensors='''pt''').pixel_values

   # verify pixel values
   assert torch.allclose(_UpperCAmelCase,      _UpperCAmelCase)

   # verify logits
   with torch.no_grad():
      UpperCamelCase       =  model(_UpperCAmelCase)
      UpperCamelCase       =  outputs.logits

   print('''Predicted class:''',      logits.argmax(-1).item())
   if base_model:
      UpperCamelCase       =  timm_model.forward_features(_UpperCAmelCase)
      assert timm_pooled_output.shape == outputs.pooler_output.shape
      assert torch.allclose(_UpperCAmelCase,      outputs.pooler_output,      atol=1E-3)
   else:
      UpperCamelCase       =  timm_model(_UpperCAmelCase)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(_UpperCAmelCase,      outputs.logits,      atol=1E-3)
   print('''Looks ok!''')

   if pytorch_dump_folder_path is not None:
      Path(_UpperCAmelCase).mkdir(exist_ok=_UpperCAmelCase)
      print(f'Saving model {vit_name} to {pytorch_dump_folder_path}')
      model.save_pretrained(_UpperCAmelCase)
      print(f'Saving processor to {pytorch_dump_folder_path}')
      processor.save_pretrained(_UpperCAmelCase)

   if push_to_hub:
      print(f'Pushing model and processor to the hub {vit_name}')
      model.push_to_hub(f'ybelkada/{vit_name}')
      processor.push_to_hub(f'ybelkada/{vit_name}')


if __name__ == "__main__":
   snake_case_ :  List[str]     =					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--vit_name',
       default='vit_base_r50_s16_384',
       type=str,
       help='Name of the hybrid 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.'
   )
   parser.add_argument(
       '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
   )

   snake_case_ :  Optional[int]     =					parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''


import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							['''image_processor''', '''tokenizer''']
   _snake_case		   =							'''BlipImageProcessor'''
   _snake_case		   =							'''AutoTokenizer'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      super().__init__(lowerCamelCase__ ,   lowerCamelCase__   )

      # add QFormer tokenizer
      UpperCamelCase       =  qformer_tokenizer







   def __call__(			self ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = True ,   lowerCamelCase__ = False ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = 0 ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = True ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      if images is None and text is None:
         raise ValueError('''You have to specify at least images or text.'''   )

      UpperCamelCase       =  BatchFeature()

      if text is not None:
         UpperCamelCase       =  self.tokenizer(
             text=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__ ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   max_length=lowerCamelCase__ ,   stride=lowerCamelCase__ ,   pad_to_multiple_of=lowerCamelCase__ ,   return_attention_mask=lowerCamelCase__ ,   return_overflowing_tokens=lowerCamelCase__ ,   return_special_tokens_mask=lowerCamelCase__ ,   return_offsets_mapping=lowerCamelCase__ ,   return_token_type_ids=lowerCamelCase__ ,   return_length=lowerCamelCase__ ,   verbose=lowerCamelCase__ ,   return_tensors=lowerCamelCase__ ,   **lowerCamelCase__ ,   )
         encoding.update(lowerCamelCase__   )
         UpperCamelCase       =  self.qformer_tokenizer(
             text=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__ ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   max_length=lowerCamelCase__ ,   stride=lowerCamelCase__ ,   pad_to_multiple_of=lowerCamelCase__ ,   return_attention_mask=lowerCamelCase__ ,   return_overflowing_tokens=lowerCamelCase__ ,   return_special_tokens_mask=lowerCamelCase__ ,   return_offsets_mapping=lowerCamelCase__ ,   return_token_type_ids=lowerCamelCase__ ,   return_length=lowerCamelCase__ ,   verbose=lowerCamelCase__ ,   return_tensors=lowerCamelCase__ ,   **lowerCamelCase__ ,   )
         UpperCamelCase       =  qformer_text_encoding.pop('''input_ids'''   )
         UpperCamelCase       =  qformer_text_encoding.pop('''attention_mask'''   )

      if images is not None:
         UpperCamelCase       =  self.image_processor(lowerCamelCase__ ,   return_tensors=lowerCamelCase__   )
         encoding.update(lowerCamelCase__   )

      return encoding







   def       UpperCAmelCase				(			self ,   *lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      return self.tokenizer.batch_decode(*lowerCamelCase__ ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   *lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      return self.tokenizer.decode(*lowerCamelCase__ ,   **lowerCamelCase__   )







   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.tokenizer.model_input_names
      UpperCamelCase       =  self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      if os.path.isfile(lowerCamelCase__   ):
         raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file'   )
      os.makedirs(lowerCamelCase__ ,   exist_ok=lowerCamelCase__   )
      UpperCamelCase       =  os.path.join(lowerCamelCase__ ,   '''qformer_tokenizer'''   )
      self.qformer_tokenizer.save_pretrained(lowerCamelCase__   )
      return super().save_pretrained(lowerCamelCase__ ,   **lowerCamelCase__   )







   @classmethod
   def       UpperCAmelCase				(			cls ,   lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  AutoTokenizer.from_pretrained(lowerCamelCase__ ,   subfolder='''qformer_tokenizer'''   )
      UpperCamelCase       =  cls._get_arguments_from_pretrained(lowerCamelCase__ ,   **lowerCamelCase__   )
      args.append(lowerCamelCase__   )
      return cls(*lowerCamelCase__   )





