




import argparse
import json
from pathlib import Path

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

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


logging.set_verbosity_info()
_lowerCAmelCase						:   Optional[Any]		=   logging.get_logger(__name__)



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Tuple						,							_lowerCAmelCase							:   Any=False	):




 """simple docstring"""







 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'''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"),
      ]	)

  # 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"),
      ]	)

 return rename_keys



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Tuple						,							_lowerCAmelCase							:   List[Any]						,							_lowerCAmelCase							:   int=False	):




 """simple docstring"""







 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 						lowerCAmelCase		(		_lowerCAmelCase							:   Any	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Any						,							_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   List[Any]	):




 """simple docstring"""







 UpperCAmelCase__							 =						dct.pop(_lowerCAmelCase	)
 UpperCAmelCase__							 =						val



def 						lowerCAmelCase		(		):




 """simple docstring"""







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



@torch.no_grad()
def 						lowerCAmelCase		(		_lowerCAmelCase							:   Dict						,							_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   Tuple=True	):




 """simple docstring"""







 UpperCAmelCase__							 =						ViTConfig()
 # patch_size
 if model_name[-1] == "8":
  UpperCAmelCase__							 =						8
 # set labels if required
 if not base_model:
  UpperCAmelCase__							 =						1000
  UpperCAmelCase__							 =						"huggingface/label-files"
  UpperCAmelCase__							 =						"imagenet-1k-id2label.json"
  UpperCAmelCase__							 =						json.load(open(hf_hub_download(_lowerCAmelCase						,							_lowerCAmelCase						,							repo_type="dataset"	)						,							"r"	)	)
  UpperCAmelCase__							 =						{int(_lowerCAmelCase	): v for k, v in idalabel.items()}
  UpperCAmelCase__							 =						idalabel
  UpperCAmelCase__							 =						{v: k for k, v in idalabel.items()}
 # size of the architecture
 if model_name in ["dino_vits8", "dino_vits16"]:
  UpperCAmelCase__							 =						384
  UpperCAmelCase__							 =						1536
  UpperCAmelCase__							 =						12
  UpperCAmelCase__							 =						6

 # load original model from torch hub
 UpperCAmelCase__							 =						torch.hub.load("facebookresearch/dino:main"						,							_lowerCAmelCase	)
 original_model.eval()

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

 # load HuggingFace model
 if base_model:
  UpperCAmelCase__							 =						ViTModel(_lowerCAmelCase						,							add_pooling_layer=_lowerCAmelCase	).eval()
 else:
  UpperCAmelCase__							 =						ViTForImageClassification(_lowerCAmelCase	).eval()
 model.load_state_dict(_lowerCAmelCase	)

 # Check outputs on an image, prepared by ViTImageProcessor
 UpperCAmelCase__							 =						ViTImageProcessor()
 UpperCAmelCase__							 =						image_processor(images=prepare_img()						,							return_tensors="pt"	)
 UpperCAmelCase__							 =						encoding["pixel_values"]
 UpperCAmelCase__							 =						model(_lowerCAmelCase	)

 if base_model:
  UpperCAmelCase__							 =						original_model(_lowerCAmelCase	)
  assert torch.allclose(_lowerCAmelCase						,							outputs.last_hidden_state[:, 0, :]						,							atol=1E-1	)
 else:
  UpperCAmelCase__							 =						original_model(_lowerCAmelCase	)
  assert logits.shape == outputs.logits.shape
  assert torch.allclose(_lowerCAmelCase						,							outputs.logits						,							atol=1E-3	)

 Path(_lowerCAmelCase	).mkdir(exist_ok=_lowerCAmelCase	)
 print(F'''Saving model {model_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						:   int		=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="dino_vitb16",
      type=str,
      help="Name of the model trained with DINO 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(
      "--base_model",
      action="store_true",
      help="Whether to only convert the base model (no projection head weights).",
  )

  parser.set_defaults(base_model=True)
  _lowerCAmelCase						:   Any		=   parser.parse_args()
  convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)











# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_lowerCAmelCase						:   int		=   "facebook/wmt19-en-de"
_lowerCAmelCase						:   int		=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCAmelCase						:   Dict		=   FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_lowerCAmelCase						:   List[Any]		=   FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCAmelCase						:   Optional[Any]		=   tokenizer(["Making tiny model"], return_tensors="pt")
_lowerCAmelCase						:   Optional[Any]		=   tiny_model(**batch)

print("test output:", len(outputs.logits[0]))

# Save
_lowerCAmelCase						:   Optional[Any]		=   "tiny-wmt19-en-de"
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-de





