

'''simple docstring'''

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()
A_							    =				logging.get_logger(__name__)





def  A_	(			snake_case   ,							snake_case=False			):
  SCREAMING_SNAKE_CASE: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"),
        ]			)

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    SCREAMING_SNAKE_CASE:List[str]       =       [(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  A_	(			snake_case   ,							snake_case   ,							snake_case=False			):
  for i in range(config.num_hidden_layers			):
    if base_model:
      SCREAMING_SNAKE_CASE:Any       =       ""
    else:
      SCREAMING_SNAKE_CASE:str       =       "vit."
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    SCREAMING_SNAKE_CASE:List[Any]       =       state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''			)
    SCREAMING_SNAKE_CASE:int       =       state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''			)
    # next, add query, keys and values (in that order) to the state dict
    SCREAMING_SNAKE_CASE:Optional[Any]       =       in_proj_weight[
        : config.hidden_size, :
    ]
    SCREAMING_SNAKE_CASE:Tuple       =       in_proj_bias[: config.hidden_size]
    SCREAMING_SNAKE_CASE:Optional[Any]       =       in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    SCREAMING_SNAKE_CASE:Any       =       in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    SCREAMING_SNAKE_CASE:Union[str, Any]       =       in_proj_weight[
        -config.hidden_size :, :
    ]
    SCREAMING_SNAKE_CASE:List[str]       =       in_proj_bias[-config.hidden_size :]





def  A_	(			snake_case			):
  SCREAMING_SNAKE_CASE:List[Any]       =       ["head.weight", "head.bias"]
  for k in ignore_keys:
    state_dict.pop(snake_case   ,							snake_case			)





def  A_	(			snake_case   ,							snake_case   ,							snake_case			):
  SCREAMING_SNAKE_CASE:Union[str, Any]       =       dct.pop(snake_case			)
  SCREAMING_SNAKE_CASE:int       =       val





def  A_	(			):
  SCREAMING_SNAKE_CASE:Tuple       =       "http://images.cocodataset.org/val2017/000000039769.jpg"
  SCREAMING_SNAKE_CASE:int       =       Image.open(requests.get(snake_case   ,							stream=snake_case			).raw			)
  return im





@torch.no_grad()
def  A_	(			snake_case   ,							snake_case   ,							snake_case=True			):
  SCREAMING_SNAKE_CASE:Optional[int]       =       ViTConfig()
  # patch_size
  if model_name[-1] == "8":
    SCREAMING_SNAKE_CASE:Tuple       =       8
  # set labels if required
  if not base_model:
    SCREAMING_SNAKE_CASE:Union[str, Any]       =       1000
    SCREAMING_SNAKE_CASE:str       =       "huggingface/label-files"
    SCREAMING_SNAKE_CASE:Tuple       =       "imagenet-1k-id2label.json"
    SCREAMING_SNAKE_CASE:Any       =       json.load(open(hf_hub_download(snake_case   ,							snake_case   ,							repo_type="dataset"			)   ,							"r"			)			)
    SCREAMING_SNAKE_CASE:Optional[Any]       =       {int(snake_case			): v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE:Union[str, Any]       =       idalabel
    SCREAMING_SNAKE_CASE:Optional[Any]       =       {v: k for k, v in idalabel.items()}
  # size of the architecture
  if model_name in ["dino_vits8", "dino_vits16"]:
    SCREAMING_SNAKE_CASE:str       =       384
    SCREAMING_SNAKE_CASE:Any       =       1536
    SCREAMING_SNAKE_CASE:Union[str, Any]       =       12
    SCREAMING_SNAKE_CASE:str       =       6

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

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

  # load HuggingFace model
  if base_model:
    SCREAMING_SNAKE_CASE:Any       =       ViTModel(snake_case   ,							add_pooling_layer=snake_case			).eval()
  else:
    SCREAMING_SNAKE_CASE:Dict       =       ViTForImageClassification(snake_case			).eval()
  model.load_state_dict(snake_case			)

  # Check outputs on an image, prepared by ViTImageProcessor
  SCREAMING_SNAKE_CASE:List[str]       =       ViTImageProcessor()
  SCREAMING_SNAKE_CASE:Optional[Any]       =       image_processor(images=prepare_img()   ,							return_tensors="pt"			)
  SCREAMING_SNAKE_CASE:str       =       encoding["pixel_values"]
  SCREAMING_SNAKE_CASE:str       =       model(snake_case			)

  if base_model:
    SCREAMING_SNAKE_CASE:Dict       =       original_model(snake_case			)
    assert torch.allclose(snake_case   ,							outputs.last_hidden_state[:, 0, :]   ,							atol=1e-1			)
  else:
    SCREAMING_SNAKE_CASE:str       =       original_model(snake_case			)
    assert logits.shape == outputs.logits.shape
    assert torch.allclose(snake_case   ,							outputs.logits   ,							atol=1e-3			)

  Path(snake_case			).mkdir(exist_ok=snake_case			)
  print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''			)
  model.save_pretrained(snake_case			)
  print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
  image_processor.save_pretrained(snake_case			)


if __name__ == "__main__":
    A_							    =				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)
    A_							    =				parser.parse_args()
    convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase:	List[Any]       			=		logging.get_logger(__name__)

_lowercase:	int       			=		{
    '''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json''',
}



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							="mgp-str"





			def __init__(       self	:	int  ,  lowercase__	:	List[Any]=[32, 1_28]  ,  lowercase__	:	str=4  ,  lowercase__	:	List[str]=3  ,  lowercase__	:	Tuple=27  ,  lowercase__	:	Optional[Any]=38  ,  lowercase__	:	Optional[Any]=5_02_57  ,  lowercase__	:	Dict=3_05_22  ,  lowercase__	:	Dict=7_68  ,  lowercase__	:	Tuple=12  ,  lowercase__	:	int=12  ,  lowercase__	:	int=4.0  ,  lowercase__	:	Tuple=True  ,  lowercase__	:	List[Any]=False  ,  lowercase__	:	int=1e-5  ,  lowercase__	:	int=0.0  ,  lowercase__	:	Optional[Any]=0.0  ,  lowercase__	:	List[Any]=0.0  ,  lowercase__	:	Union[str, Any]=False  ,  lowercase__	:	str=0.0_2  ,  **lowercase__	:	Optional[int]  ,  ):
										super().__init__(**lowercase__						)

										_lowerCAmelCase							=							image_size
										_lowerCAmelCase							=							patch_size
										_lowerCAmelCase							=							num_channels
										_lowerCAmelCase							=							max_token_length
										_lowerCAmelCase							=							num_character_labels
										_lowerCAmelCase							=							num_bpe_labels
										_lowerCAmelCase							=							num_wordpiece_labels
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							mlp_ratio
										_lowerCAmelCase							=							distilled
										_lowerCAmelCase							=							layer_norm_eps
										_lowerCAmelCase							=							drop_rate
										_lowerCAmelCase							=							qkv_bias
										_lowerCAmelCase							=							attn_drop_rate
										_lowerCAmelCase							=							drop_path_rate
										_lowerCAmelCase							=							output_aa_attentions
										_lowerCAmelCase							=							initializer_range






