

'''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()
UpperCamelCase_							=					logging.get_logger(__name__)






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






def 							_lowerCAmelCase (      __magic_name__	:    Any	)							->     int:
						lowercase   :       Any								=['''head.weight''', '''head.bias''']
						for k in ignore_keys:
												state_dict.pop(__magic_name__      ,   __magic_name__	)






def 							_lowerCAmelCase (      __magic_name__	:    Tuple      ,   __magic_name__	:    List[Any]      ,   __magic_name__	:    str	)							->     Union[str, Any]:
						lowercase   :       Optional[Any]								=dct.pop(__magic_name__	)
						lowercase   :       Optional[Any]								=val






def 							_lowerCAmelCase (      )							->     Dict:
						lowercase   :       List[Any]								='''http://images.cocodataset.org/val2017/000000039769.jpg'''
						lowercase   :       List[str]								=Image.open(requests.get(__magic_name__      ,   stream=__magic_name__	).raw	)
						return im






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

						# load original model from torch hub
						lowercase   :       List[str]								=torch.hub.load('''facebookresearch/dino:main'''      ,   __magic_name__	)
						original_model.eval()

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

						# load HuggingFace model
						if base_model:
												lowercase   :       List[str]								=ViTModel(__magic_name__      ,   add_pooling_layer=__magic_name__	).eval()
						else:
												lowercase   :       Dict								=ViTForImageClassification(__magic_name__	).eval()
						model.load_state_dict(__magic_name__	)

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

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

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


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








'''simple docstring'''





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase_							=					logging.get_logger(__name__)

UpperCamelCase_							=					"""▁"""

UpperCamelCase_							=					{"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCamelCase_							=					{
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

UpperCamelCase_							=					{
    """facebook/xglm-564M""": 2048,
}





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				lowerCamelCase_							=						VOCAB_FILES_NAMES
				lowerCamelCase_							=						PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase_							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase_							=						['input_ids', 'attention_mask']







				def __init__(					self    :		Dict		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		List[Any]="<s>"		,				UpperCAmelCase__    :		int="</s>"		,				UpperCAmelCase__    :		Optional[Any]="</s>"		,				UpperCAmelCase__    :		Optional[Any]="<s>"		,				UpperCAmelCase__    :		Any="<unk>"		,				UpperCAmelCase__    :		Any="<pad>"		,				UpperCAmelCase__    :		Optional[Dict[str, Any]] = None		,				**UpperCAmelCase__    :		Optional[int]		,				):
										'''simple docstring'''
										lowercase   :       int								={} if sp_model_kwargs is None else sp_model_kwargs

										# Compatibility with the original tokenizer
										lowercase   :       Optional[Any]								=7
										lowercase   :       Optional[int]								=[F'''<madeupword{i}>''' for i in range(self.num_madeup_words      )]

										lowercase   :       List[Any]								=kwargs.get('''additional_special_tokens'''		,				[]      )
										kwargs["additional_special_tokens"] += [
										    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
										]

										super().__init__(
										    bos_token=UpperCAmelCase__		,				eos_token=UpperCAmelCase__		,				unk_token=UpperCAmelCase__		,				sep_token=UpperCAmelCase__		,				cls_token=UpperCAmelCase__		,				pad_token=UpperCAmelCase__		,				sp_model_kwargs=self.sp_model_kwargs		,				**UpperCAmelCase__		,				)

										lowercase   :       List[str]								=spm.SentencePieceProcessor(**self.sp_model_kwargs      )
										self.sp_model.Load(str(UpperCAmelCase__      )      )
										lowercase   :       List[Any]								=vocab_file

										# Original fairseq vocab and spm vocab must be "aligned":
										# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
										# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
										# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
										# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

										# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
										lowercase   :       Union[str, Any]								=1

										# Mimic fairseq token-to-id alignment for the first 4 token
										lowercase   :       List[str]								={'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

										lowercase   :       str								=len(self.sp_model      )
										lowercase   :       List[Any]								={F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words      )}
										self.fairseq_tokens_to_ids.update(UpperCAmelCase__      )

										lowercase   :       int								={v: k for k, v in self.fairseq_tokens_to_ids.items()}







				def __getstate__(					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=self.__dict__.copy()
										lowercase   :       List[Any]								=None
										lowercase   :       Tuple								=self.sp_model.serialized_model_proto()
										return state







				def __setstate__(					self    :		Optional[Any]		,				UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										lowercase   :       int								=d

										# for backward compatibility
										if not hasattr(self		,				'''sp_model_kwargs'''      ):
																lowercase   :       Optional[int]								={}

										lowercase   :       List[Any]								=spm.SentencePieceProcessor(**self.sp_model_kwargs      )
										self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )







				def    lowerCamelCase_  (					self    :		str		,				UpperCAmelCase__    :		List[int]		,				UpperCAmelCase__    :		Optional[List[int]] = None      ):
										'''simple docstring'''

										if token_ids_a is None:
																return [self.sep_token_id] + token_ids_a
										lowercase   :       List[Any]								=[self.sep_token_id]
										return sep + token_ids_a + sep + sep + token_ids_a







				def    lowerCamelCase_  (					self    :		Any		,				UpperCAmelCase__    :		List[int]		,				UpperCAmelCase__    :		Optional[List[int]] = None		,				UpperCAmelCase__    :		bool = False      ):
										'''simple docstring'''

										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=UpperCAmelCase__		,				token_ids_a=UpperCAmelCase__		,				already_has_special_tokens=UpperCAmelCase__      )

										if token_ids_a is None:
																return [1] + ([0] * len(UpperCAmelCase__      ))
										return [1] + ([0] * len(UpperCAmelCase__      )) + [1, 1] + ([0] * len(UpperCAmelCase__      ))







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		List[int]		,				UpperCAmelCase__    :		Optional[List[int]] = None      ):
										'''simple docstring'''
										lowercase   :       int								=[self.sep_token_id]

										if token_ids_a is None:
																return len(sep + token_ids_a      ) * [0]
										return len(sep + token_ids_a + sep + sep + token_ids_a      ) * [0]







				@property
				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										return len(self.sp_model      ) + self.fairseq_offset + self.num_madeup_words







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       int								={self.convert_ids_to_tokens(UpperCAmelCase__      ): i for i in range(self.vocab_size      )}
										vocab.update(self.added_tokens_encoder      )
										return vocab







				def    lowerCamelCase_  (					self    :		Tuple		,				UpperCAmelCase__    :		str      ):
										'''simple docstring'''
										return self.sp_model.encode(UpperCAmelCase__		,				out_type=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		Optional[Any]      ):
										'''simple docstring'''
										if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
										lowercase   :       List[str]								=self.sp_model.PieceToId(UpperCAmelCase__      )

										# Need to return unknown token if the SP model returned 0
										return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







				def    lowerCamelCase_  (					self    :		Optional[Any]		,				UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										if index in self.fairseq_ids_to_tokens:
																return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(index - self.fairseq_offset      )







				def    lowerCamelCase_  (					self    :		List[str]		,				UpperCAmelCase__    :		Optional[Any]      ):
										'''simple docstring'''
										lowercase   :       Dict								=''''''.join(UpperCAmelCase__      ).replace(UpperCAmelCase__		,				''' '''      ).strip()
										return out_string







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		Optional[str] = None      ):
										'''simple docstring'''
										if not os.path.isdir(UpperCAmelCase__      ):
																logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
																return
										lowercase   :       Dict								=os.path.join(
										    UpperCAmelCase__		,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

										if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCAmelCase__      ) and os.path.isfile(self.vocab_file      ):
																copyfile(self.vocab_file		,				UpperCAmelCase__      )
										elif not os.path.isfile(self.vocab_file      ):
																with open(UpperCAmelCase__		,				'''wb'''      ) as fi:
																						lowercase   :       Optional[int]								=self.sp_model.serialized_model_proto()
																						fi.write(UpperCAmelCase__      )

										return (out_vocab_file,)





