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()
__lowercase							 =					logging.get_logger(__name__)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=False	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, 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"
        __UpperCamelCase     :Tuple			  =		[(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  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=False	):
    '''simple docstring'''







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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :int			  =		dct.pop(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Optional[int]			  =		val



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __UpperCamelCase     :Optional[int]			  =		Image.open(requests.get(SCREAMING_SNAKE_CASE  ,			stream=SCREAMING_SNAKE_CASE	).raw	)
    return im



@torch.no_grad()
def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=True	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		ViTConfig()
    # patch_size
    if model_name[-1] == "8":
        __UpperCamelCase     :List[str]			  =		8
    # set labels if required
    if not base_model:
        __UpperCamelCase     :int			  =		1_000
        __UpperCamelCase     :int			  =		'''huggingface/label-files'''
        __UpperCamelCase     :List[str]			  =		'''imagenet-1k-id2label.json'''
        __UpperCamelCase     :Optional[Any]			  =		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			repo_type='''dataset'''	)  ,			'''r'''	)	)
        __UpperCamelCase     :Dict			  =		{int(SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
        __UpperCamelCase     :str			  =		idalabel
        __UpperCamelCase     :Union[str, Any]			  =		{v: k for k, v in idalabel.items()}
    # size of the architecture
    if model_name in ["dino_vits8", "dino_vits16"]:
        __UpperCamelCase     :str			  =		384
        __UpperCamelCase     :str			  =		1_536
        __UpperCamelCase     :List[Any]			  =		12
        __UpperCamelCase     :Any			  =		6

    # load original model from torch hub
    __UpperCamelCase     :Dict			  =		torch.hub.load('''facebookresearch/dino:main'''  ,			SCREAMING_SNAKE_CASE	)
    original_model.eval()

    # load state_dict of original model, remove and rename some keys
    __UpperCamelCase     :List[str]			  =		original_model.state_dict()
    if base_model:
        remove_classification_head_(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Tuple			  =		create_rename_keys(SCREAMING_SNAKE_CASE  ,			base_model=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
    if base_model:
        __UpperCamelCase     :List[Any]			  =		ViTModel(SCREAMING_SNAKE_CASE  ,			add_pooling_layer=SCREAMING_SNAKE_CASE	).eval()
    else:
        __UpperCamelCase     :Any			  =		ViTForImageClassification(SCREAMING_SNAKE_CASE	).eval()
    model.load_state_dict(SCREAMING_SNAKE_CASE	)

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

    if base_model:
        __UpperCamelCase     :Optional[Any]			  =		original_model(SCREAMING_SNAKE_CASE	)
        assert torch.allclose(SCREAMING_SNAKE_CASE  ,			outputs.last_hidden_state[:, 0, :]  ,			atol=1e-1	)
    else:
        __UpperCamelCase     :Tuple			  =		original_model(SCREAMING_SNAKE_CASE	)
        assert 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 {model_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__":
    __lowercase							 =					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)
    __lowercase							 =					parser.parse_args()
    convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)






def lowerCamelCase  (   SCREAMING_SNAKE_CASE = 1  ,			SCREAMING_SNAKE_CASE = 1_000	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		1
    __UpperCamelCase     :Any			  =		0

    for divide_by_number in range(SCREAMING_SNAKE_CASE  ,			digit + 1	):
        __UpperCamelCase     :list[int]			  =		[]
        __UpperCamelCase     :Optional[int]			  =		numerator
        for _ in range(1  ,			digit + 1	):
            if now_divide in has_been_divided:
                if longest_list_length < len(SCREAMING_SNAKE_CASE	):
                    __UpperCamelCase     :Optional[Any]			  =		len(SCREAMING_SNAKE_CASE	)
                    __UpperCamelCase     :int			  =		divide_by_number
            else:
                has_been_divided.append(SCREAMING_SNAKE_CASE	)
                __UpperCamelCase     :Any			  =		now_divide * 10 % divide_by_number

    return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()





