

"""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()
lowerCAmelCase							:  str            =						logging.get_logger(__name__)







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase=False   ):
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[]
						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_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase=False   ):
						for i in range(config.num_hidden_layers   ):
												if base_model:
																		SCREAMING_SNAKE_CASE_:			int  		=	""
												else:
																		SCREAMING_SNAKE_CASE_:			Dict  		=	"vit."
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												SCREAMING_SNAKE_CASE_:			Tuple  		=	state_dict.pop(f"blocks.{i}.attn.qkv.weight"   )
												SCREAMING_SNAKE_CASE_:			Any  		=	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_:			Any  		=	in_proj_weight[
												    : config.hidden_size, :
												]
												SCREAMING_SNAKE_CASE_:			str  		=	in_proj_bias[: config.hidden_size]
												SCREAMING_SNAKE_CASE_:			Tuple  		=	in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												SCREAMING_SNAKE_CASE_:			List[str]  		=	in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												SCREAMING_SNAKE_CASE_:			Dict  		=	in_proj_weight[
												    -config.hidden_size :, :
												]
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	in_proj_bias[-config.hidden_size :]







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	["head.weight", "head.bias"]
						for k in ignore_keys:
												state_dict.pop(_UpperCAmelCase    ,     _UpperCAmelCase   )







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Any  		=	dct.pop(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	val







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







@torch.no_grad()
def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase=True   ):
						SCREAMING_SNAKE_CASE_:			int  		=	ViTConfig()
						# patch_size
						if model_name[-1] == "8":
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	8
						# set labels if required
						if not base_model:
												SCREAMING_SNAKE_CASE_:			List[str]  		=	10_00
												SCREAMING_SNAKE_CASE_:			str  		=	"huggingface/label-files"
												SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	"imagenet-1k-id2label.json"
												SCREAMING_SNAKE_CASE_:			Tuple  		=	json.load(open(hf_hub_download(_UpperCAmelCase    ,     _UpperCAmelCase    ,     repo_type="dataset"   )    ,     "r"   )   )
												SCREAMING_SNAKE_CASE_:			Any  		=	{int(_UpperCAmelCase   ): v for k, v in idalabel.items()}
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	idalabel
												SCREAMING_SNAKE_CASE_:			Tuple  		=	{v: k for k, v in idalabel.items()}
						# size of the architecture
						if model_name in ["dino_vits8", "dino_vits16"]:
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	3_84
												SCREAMING_SNAKE_CASE_:			Any  		=	15_36
												SCREAMING_SNAKE_CASE_:			Any  		=	12
												SCREAMING_SNAKE_CASE_:			List[str]  		=	6

						# load original model from torch hub
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.hub.load("facebookresearch/dino:main"    ,     _UpperCAmelCase   )
						original_model.eval()

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

						# load HuggingFace model
						if base_model:
												SCREAMING_SNAKE_CASE_:			List[str]  		=	ViTModel(_UpperCAmelCase    ,     add_pooling_layer=_UpperCAmelCase   ).eval()
						else:
												SCREAMING_SNAKE_CASE_:			Any  		=	ViTForImageClassification(_UpperCAmelCase   ).eval()
						model.load_state_dict(_UpperCAmelCase   )

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

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

						Path(_UpperCAmelCase   ).mkdir(exist_ok=_UpperCAmelCase   )
						print(f"Saving model {model_name} to {pytorch_dump_folder_path}"   )
						model.save_pretrained(_UpperCAmelCase   )
						print(f"Saving image processor to {pytorch_dump_folder_path}"   )
						image_processor.save_pretrained(_UpperCAmelCase   )


if __name__ == "__main__":
						lowerCAmelCase							:  Union[str, Any]            =						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							:  Union[str, Any]            =						parser.parse_args()
						convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)









from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


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



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							Any      							=					['''pixel_values''']



			def __init__(			self  :					List[Any]       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Dict[str, int] = None       ,						lowerCAmelCase__  :					PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Union[int, float] = 1 / 255       ,						lowerCAmelCase__  :					bool = True       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					bool = True       ,						**lowerCAmelCase__  :					Tuple       ,						):
									super().__init__(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	size if size is not None else {"height": 384, "width": 384}
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			int  		=	do_resize
									SCREAMING_SNAKE_CASE_:			Dict  		=	size
									SCREAMING_SNAKE_CASE_:			int  		=	resample
									SCREAMING_SNAKE_CASE_:			str  		=	do_rescale
									SCREAMING_SNAKE_CASE_:			str  		=	rescale_factor
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	do_normalize
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
									SCREAMING_SNAKE_CASE_:			List[str]  		=	image_std if image_std is not None else OPENAI_CLIP_STD
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_convert_rgb



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Dict[str, int]       ,						lowerCAmelCase__  :					PILImageResampling = PILImageResampling.BICUBIC       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					Tuple       ,						):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)
									if "height" not in size or "width" not in size:
															raise ValueError(F"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
									SCREAMING_SNAKE_CASE_:			int  		=	(size["height"], size["width"])
									return resize(lowerCAmelCase__       ,						size=lowerCAmelCase__       ,						resample=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Union[int, float]       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					List[Any]       ,						):
									return rescale(lowerCAmelCase__       ,						scale=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					np.ndarray       ,						lowerCAmelCase__  :					Union[float, List[float]]       ,						lowerCAmelCase__  :					Union[float, List[float]]       ,						lowerCAmelCase__  :					Optional[Union[str, ChannelDimension]] = None       ,						**lowerCAmelCase__  :					List[Any]       ,						):
									return normalize(lowerCAmelCase__       ,						mean=lowerCAmelCase__       ,						std=lowerCAmelCase__       ,						data_format=lowerCAmelCase__       ,						**lowerCAmelCase__)




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					ImageInput       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[Dict[str, int]] = None       ,						lowerCAmelCase__  :					PILImageResampling = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[float] = None       ,						lowerCAmelCase__  :					Optional[bool] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[float, List[float]]] = None       ,						lowerCAmelCase__  :					Optional[Union[str, TensorType]] = None       ,						lowerCAmelCase__  :					bool = None       ,						lowerCAmelCase__  :					ChannelDimension = ChannelDimension.FIRST       ,						**lowerCAmelCase__  :					Dict       ,						):
									SCREAMING_SNAKE_CASE_:			Any  		=	do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE_:			Dict  		=	do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE_:			Any  		=	rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image_std if image_std is not None else self.image_std
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									SCREAMING_SNAKE_CASE_:			str  		=	size if size is not None else self.size
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	get_size_dict(lowerCAmelCase__       ,						default_to_square=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	make_list_of_images(lowerCAmelCase__)

									if not valid_images(lowerCAmelCase__):
															raise ValueError(
															    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
															    "torch.Tensor, tf.Tensor or jax.ndarray.")

									if do_resize and size is None or resample is None:
															raise ValueError("Size and resample must be specified if do_resize is True.")

									if do_rescale and rescale_factor is None:
															raise ValueError("Rescale factor must be specified if do_rescale is True.")

									if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("Image mean and std must be specified if do_normalize is True.")

									# PIL RGBA images are converted to RGB
									if do_convert_rgb:
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	[convert_to_rgb(lowerCAmelCase__) for image in images]

									# All transformations expect numpy arrays.
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	[to_numpy_array(lowerCAmelCase__) for image in images]

									if do_resize:
															SCREAMING_SNAKE_CASE_:			int  		=	[self.resize(image=lowerCAmelCase__       ,						size=lowerCAmelCase__       ,						resample=lowerCAmelCase__) for image in images]

									if do_rescale:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[self.rescale(image=lowerCAmelCase__       ,						scale=lowerCAmelCase__) for image in images]

									if do_normalize:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	[self.normalize(image=lowerCAmelCase__       ,						mean=lowerCAmelCase__       ,						std=lowerCAmelCase__) for image in images]

									SCREAMING_SNAKE_CASE_:			str  		=	[to_channel_dimension_format(lowerCAmelCase__       ,						lowerCAmelCase__) for image in images]

									SCREAMING_SNAKE_CASE_:			List[str]  		=	BatchFeature(data={"pixel_values": images}       ,						tensor_type=lowerCAmelCase__)

									return encoded_outputs






