

'''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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_a					:			Any							=    logging.get_logger(__name__)


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Dict							, SCREAMING_SNAKE_CASE   : str=False    ):
  UpperCAmelCase    				=						[]
  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'''deit.encoder.layer.{i}.layernorm_before.weight''')    )
    rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''deit.encoder.layer.{i}.layernorm_before.bias''')    )
    rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''deit.encoder.layer.{i}.attention.output.dense.weight''')    )
    rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''deit.encoder.layer.{i}.attention.output.dense.bias''')    )
    rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''deit.encoder.layer.{i}.layernorm_after.weight''')    )
    rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''deit.encoder.layer.{i}.layernorm_after.bias''')    )
    rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''deit.encoder.layer.{i}.intermediate.dense.weight''')    )
    rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''deit.encoder.layer.{i}.intermediate.dense.bias''')    )
    rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''deit.encoder.layer.{i}.output.dense.weight''')    )
    rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''deit.encoder.layer.{i}.output.dense.bias''')    )

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ('cls_token', 'deit.embeddings.cls_token'),
          ('dist_token', 'deit.embeddings.distillation_token'),
          ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
          ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
          ('pos_embed', 'deit.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 "deit" from all keys that start with "deit"
    UpperCAmelCase    				=						[(pair[0], pair[1][4:]) if pair[1].startswith('deit'    ) else pair for pair in rename_keys]
  else:
    # layernorm + classification heads
    rename_keys.extend(
        [
            ('norm.weight', 'deit.layernorm.weight'),
            ('norm.bias', 'deit.layernorm.bias'),
            ('head.weight', 'cls_classifier.weight'),
            ('head.bias', 'cls_classifier.bias'),
            ('head_dist.weight', 'distillation_classifier.weight'),
            ('head_dist.bias', 'distillation_classifier.bias'),
        ]    )

  return rename_keys


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int							, SCREAMING_SNAKE_CASE   : int							, SCREAMING_SNAKE_CASE   : Union[str, Any]=False    ):
  for i in range(config.num_hidden_layers    ):
    if base_model:
      UpperCAmelCase    				=						''
    else:
      UpperCAmelCase    				=						'deit.'
    # 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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[Any]							, SCREAMING_SNAKE_CASE   : List[str]							, SCREAMING_SNAKE_CASE   : int    ):
  UpperCAmelCase    				=						dct.pop(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						val


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


@torch.no_grad()
def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Any							, SCREAMING_SNAKE_CASE   : Dict    ):
  UpperCAmelCase    				=						DeiTConfig()
  # all deit models have fine-tuned heads
  UpperCAmelCase    				=						False
  # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
  UpperCAmelCase    				=						1000
  UpperCAmelCase    				=						'huggingface/label-files'
  UpperCAmelCase    				=						'imagenet-1k-id2label.json'
  UpperCAmelCase    				=						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, repo_type='dataset'    )							, 'r'    )    )
  UpperCAmelCase    				=						{int(SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}
  UpperCAmelCase    				=						idalabel
  UpperCAmelCase    				=						{v: k for k, v in idalabel.items()}
  UpperCAmelCase    				=						int(deit_name[-6:-4]    )
  UpperCAmelCase    				=						int(deit_name[-3:]    )
  # size of the architecture
  if deit_name[9:].startswith('tiny'    ):
    UpperCAmelCase    				=						192
    UpperCAmelCase    				=						768
    UpperCAmelCase    				=						12
    UpperCAmelCase    				=						3
  elif deit_name[9:].startswith('small'    ):
    UpperCAmelCase    				=						384
    UpperCAmelCase    				=						1536
    UpperCAmelCase    				=						12
    UpperCAmelCase    				=						6
  if deit_name[9:].startswith('base'    ):
    pass
  elif deit_name[4:].startswith('large'    ):
    UpperCAmelCase    				=						1024
    UpperCAmelCase    				=						4096
    UpperCAmelCase    				=						24
    UpperCAmelCase    				=						16

  # load original model from timm
  UpperCAmelCase    				=						timm.create_model(SCREAMING_SNAKE_CASE							, pretrained=SCREAMING_SNAKE_CASE    )
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  UpperCAmelCase    				=						timm_model.state_dict()
  UpperCAmelCase    				=						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
  UpperCAmelCase    				=						DeiTForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE    ).eval()
  model.load_state_dict(SCREAMING_SNAKE_CASE    )

  # Check outputs on an image, prepared by DeiTImageProcessor
  UpperCAmelCase    				=						int(
      (256 / 224) * config.image_size    )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
  UpperCAmelCase    				=						DeiTImageProcessor(size=SCREAMING_SNAKE_CASE							, crop_size=config.image_size    )
  UpperCAmelCase    				=						image_processor(images=prepare_img()							, return_tensors='pt'    )
  UpperCAmelCase    				=						encoding['pixel_values']
  UpperCAmelCase    				=						model(SCREAMING_SNAKE_CASE    )

  UpperCAmelCase    				=						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 {deit_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__":
  _a					:			int							=    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--deit_name',
      default='vit_deit_base_distilled_patch16_224',
      type=str,
      help='Name of the DeiT 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.'
  )

  _a					:			Any							=    parser.parse_args()
  convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)





'''simple docstring'''




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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str    ):
  # 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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : int							, SCREAMING_SNAKE_CASE   : Optional[int]    ):
  UpperCAmelCase    				=						{}

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

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

    UpperCAmelCase    				=						value.float()

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

  return upgrade


@torch.no_grad()
def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[str]							, SCREAMING_SNAKE_CASE   : Optional[Any]							, SCREAMING_SNAKE_CASE   : Optional[int]							, SCREAMING_SNAKE_CASE   : Any=None    ):
  if config_path is not None:
    UpperCAmelCase    				=						FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE    )
  else:
    UpperCAmelCase    				=						FlavaConfig()

  UpperCAmelCase    				=						FlavaForPreTraining(SCREAMING_SNAKE_CASE    ).eval()

  UpperCAmelCase    				=						convert_dalle_checkpoint(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, save_checkpoint=SCREAMING_SNAKE_CASE    )

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

  UpperCAmelCase    				=						upgrade_state_dict(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  hf_model.load_state_dict(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						hf_model.state_dict()
  UpperCAmelCase    				=						count_parameters(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						count_parameters(SCREAMING_SNAKE_CASE    ) + count_parameters(SCREAMING_SNAKE_CASE    )

  assert torch.allclose(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, atol=1E-3    )

  hf_model.save_pretrained(SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
  _a					:			str							=    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')
  _a					:			str							=    parser.parse_args()

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


