

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

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase							:  List[str]            =						logging.get_logger(__name__)


lowerCAmelCase							:  Tuple            =						{
    """nielsr/canine-s""": 2048,
}

# Unicode defines 1,114,112 total “codepoints”
lowerCAmelCase							:  Any            =						1114112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
lowerCAmelCase							:  Dict            =						0

lowerCAmelCase							:  Dict            =						0xe000
lowerCAmelCase							:  str            =						0xe001
lowerCAmelCase							:  str            =						0xe002
lowerCAmelCase							:  Optional[int]            =						0xe003
lowerCAmelCase							:  List[Any]            =						0xe004

# Maps special codepoints to human-readable names.
lowerCAmelCase							:  Dict[int, str]            =						{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
lowerCAmelCase							:  Dict[str, int]            =						{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							List[str]      							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Tuple=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Tuple=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					str=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					List[Any]=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Optional[int]=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Union[str, Any]=chr(lowerCAmelCase__)       ,						lowerCAmelCase__  :					Union[str, Any]=False       ,						lowerCAmelCase__  :					int=2048       ,						**lowerCAmelCase__  :					Tuple       ,						):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else bos_token
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else eos_token
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else sep_token
									SCREAMING_SNAKE_CASE_:			Any  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else cls_token
									SCREAMING_SNAKE_CASE_:			str  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									SCREAMING_SNAKE_CASE_:			Dict  		=	AddedToken(lowerCAmelCase__       ,						lstrip=lowerCAmelCase__       ,						rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__       ,						lowerCAmelCase__) else mask_token

									super().__init__(
									    bos_token=lowerCAmelCase__       ,						eos_token=lowerCAmelCase__       ,						sep_token=lowerCAmelCase__       ,						cls_token=lowerCAmelCase__       ,						pad_token=lowerCAmelCase__       ,						mask_token=lowerCAmelCase__       ,						add_prefix_space=lowerCAmelCase__       ,						model_max_length=lowerCAmelCase__       ,						**lowerCAmelCase__       ,						)

									# Creates a mapping for looking up the IDs of special symbols.
									SCREAMING_SNAKE_CASE_:			Dict[str, int]  		=	{}
									for codepoint, name in SPECIAL_CODEPOINTS.items():
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	codepoint

									# Creates a mapping for looking up the string forms of special symbol IDs.
									SCREAMING_SNAKE_CASE_:			Dict[int, str]  		=	{
									    codepoint: name for name, codepoint in self._special_codepoints.items()
									}

									SCREAMING_SNAKE_CASE_:			List[str]  		=	UNICODE_VOCAB_SIZE
									SCREAMING_SNAKE_CASE_:			Tuple  		=	len(self._special_codepoints)



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									return self._unicode_vocab_size



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					str):
									return list(lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					str):
									try:
															return ord(lowerCAmelCase__)
									except TypeError:
															raise ValueError(F"invalid token: '{token}'")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					int):
									try:
															if index in SPECIAL_CODEPOINTS:
																					return SPECIAL_CODEPOINTS[index]
															return chr(lowerCAmelCase__)
									except TypeError:
															raise ValueError(F"invalid id: {index}")



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]       ,						lowerCAmelCase__  :					Dict):
									return "".join(lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE_:			Tuple  		=	[self.cls_token_id]

									SCREAMING_SNAKE_CASE_:			int  		=	cls + token_ids_a + sep
									if token_ids_a is not None:
															result += token_ids_a + sep
									return result



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None       ,						lowerCAmelCase__  :					bool = False):
									if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=lowerCAmelCase__       ,						token_ids_a=lowerCAmelCase__       ,						already_has_special_tokens=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Any  		=	[1] + ([0] * len(lowerCAmelCase__)) + [1]
									if token_ids_a is not None:
															result += ([0] * len(lowerCAmelCase__)) + [1]
									return result



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_:			Dict  		=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE_:			int  		=	[self.cls_token_id]

									SCREAMING_SNAKE_CASE_:			List[str]  		=	len(cls + token_ids_a + sep) * [0]
									if token_ids_a is not None:
															result += len(token_ids_a + sep) * [1]
									return result




			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Optional[str] = None):
									return ()






