


import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				int:
  # encoder.embeddings are double copied in original FLAVA
  return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items()				)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase				)    ->				List[str]:
  snake_case__						= {}

  for key, value in state_dict.items():
    if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
      continue

    snake_case__						= key.replace('''heads.cmd.mim_head.cls.predictions'''							,   '''mmm_image_head'''				)
    snake_case__						= key.replace('''heads.cmd.mlm_head.cls.predictions'''							,   '''mmm_text_head'''				)
    snake_case__						= key.replace('''heads.cmd.itm_head.cls'''							,   '''itm_head'''				)
    snake_case__						= key.replace('''heads.cmd.itm_head.pooler'''							,   '''itm_head.pooler'''				)
    snake_case__						= key.replace('''heads.cmd.clip_head.logit_scale'''							,   '''flava.logit_scale'''				)
    snake_case__						= key.replace('''heads.fairseq_mlm.cls.predictions'''							,   '''mlm_head'''				)
    snake_case__						= key.replace('''heads.imagenet.mim_head.cls.predictions'''							,   '''mim_head'''				)
    snake_case__						= key.replace('''mm_text_projection'''							,   '''flava.text_to_mm_projection'''				)
    snake_case__						= key.replace('''mm_image_projection'''							,   '''flava.image_to_mm_projection'''				)
    snake_case__						= key.replace('''image_encoder.module'''							,   '''flava.image_model'''				)
    snake_case__						= key.replace('''text_encoder.module'''							,   '''flava.text_model'''				)
    snake_case__						= key.replace('''mm_encoder.module.encoder.cls_token'''							,   '''flava.multimodal_model.cls_token'''				)
    snake_case__						= key.replace('''mm_encoder.module'''							,   '''flava.multimodal_model'''				)
    snake_case__						= key.replace('''text_projection'''							,   '''flava.text_projection'''				)
    snake_case__						= key.replace('''image_projection'''							,   '''flava.image_projection'''				)

    snake_case__						= value.float()

  for key, value in codebook_state_dict.items():
    snake_case__						= value

  return upgrade







@torch.no_grad()
def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase=None				)    ->				Any:
  if config_path is not None:
    snake_case__						= FlavaConfig.from_pretrained(__lowerCAmelCase				)
  else:
    snake_case__						= FlavaConfig()

  snake_case__						= FlavaForPreTraining(__lowerCAmelCase				).eval()

  snake_case__						= convert_dalle_checkpoint(__lowerCAmelCase							,   __lowerCAmelCase							,   save_checkpoint=__lowerCAmelCase				)

  if os.path.exists(__lowerCAmelCase				):
    snake_case__						= torch.load(__lowerCAmelCase							,   map_location='''cpu'''				)
  else:
    snake_case__						= torch.hub.load_state_dict_from_url(__lowerCAmelCase							,   map_location='''cpu'''				)

  snake_case__						= upgrade_state_dict(__lowerCAmelCase							,   __lowerCAmelCase				)
  hf_model.load_state_dict(__lowerCAmelCase				)
  snake_case__						= hf_model.state_dict()
  snake_case__						= count_parameters(__lowerCAmelCase				)
  snake_case__						= count_parameters(__lowerCAmelCase				) + count_parameters(__lowerCAmelCase				)

  assert torch.allclose(__lowerCAmelCase							,   __lowerCAmelCase							,   atol=1e-3				)

  hf_model.save_pretrained(__lowerCAmelCase				)


if __name__ == "__main__":
   lowerCamelCase__							:						Tuple							     =   argparse.ArgumentParser()
   parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
   parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
   parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
   parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
   lowerCamelCase__							:						int							     =   parser.parse_args()

   convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)





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()
lowerCamelCase__							:						int							     =   logging.get_logger(__name__)







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







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Optional[Any]:
  snake_case__						= ['''head.weight''', '''head.bias''']
  for k in ignore_keys:
    state_dict.pop(__lowerCAmelCase							,   __lowerCAmelCase				)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				Union[str, Any]:
  snake_case__						= dct.pop(__lowerCAmelCase				)
  snake_case__						= val







def SCREAMING_SNAKE_CASE	(			)    ->				str:
  snake_case__						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  snake_case__						= Image.open(requests.get(__lowerCAmelCase							,   stream=__lowerCAmelCase				).raw				)
  return im







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

    # load original model from timm
  snake_case__						= timm.create_model(__lowerCAmelCase							,   pretrained=__lowerCAmelCase				)
  timm_model.eval()

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

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

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

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

  Path(__lowerCAmelCase				).mkdir(exist_ok=__lowerCAmelCase				)
  print(F"""Saving model {vit_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__":
   lowerCamelCase__							:						Optional[Any]							     =   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."""
   )

   lowerCamelCase__							:						str							     =   parser.parse_args()
   convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)

