
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

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


lowercase   =	logging.get_logger(__name__)

lowercase   =	{
    '''vocab_file''': '''vocab.txt''',
    '''merges_file''': '''bpe.codes''',
}

lowercase   =	{
    '''vocab_file''': {
        '''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt''',
        '''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt''',
    },
    '''merges_file''': {
        '''vinai/phobert-base''': '''https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes''',
        '''vinai/phobert-large''': '''https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes''',
    },
}

lowercase   =	{
    '''vinai/phobert-base''': 2_5_6,
    '''vinai/phobert-large''': 2_5_6,
}



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[str, Any]							)	->     Any:
							lowerCamelCase_					=       set()
							lowerCamelCase_					=       word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)							)
														lowerCamelCase_					=       char

							lowerCamelCase_					=       set(UpperCAmelCase__							)
							return pairs





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE           =     PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

					def __init__(						self       :  Optional[Any] ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  Any="<s>" ,       __UpperCamelCase       :  Tuple="</s>" ,       __UpperCamelCase       :  Any="</s>" ,       __UpperCamelCase       :  Any="<s>" ,       __UpperCamelCase       :  str="<unk>" ,       __UpperCamelCase       :  Optional[int]="<pad>" ,       __UpperCamelCase       :  Tuple="<mask>" ,       **__UpperCamelCase       :  int ,       ):
												super().__init__(
												    bos_token=__UpperCamelCase ,       eos_token=__UpperCamelCase ,       unk_token=__UpperCamelCase ,       sep_token=__UpperCamelCase ,       cls_token=__UpperCamelCase ,       pad_token=__UpperCamelCase ,       mask_token=__UpperCamelCase ,       **__UpperCamelCase ,       )

												lowerCamelCase_					=       vocab_file
												lowerCamelCase_					=       merges_file

												lowerCamelCase_					=       {}
												lowerCamelCase_					=       0
												lowerCamelCase_					=       1
												lowerCamelCase_					=       2
												lowerCamelCase_					=       3

												self.add_from_file(__UpperCamelCase )

												lowerCamelCase_					=       {v: k for k, v in self.encoder.items()}

												with open(__UpperCamelCase ,       encoding="""utf-8""" ) as merges_handle:
																			lowerCamelCase_					=       merges_handle.read().split("""\n""" )[:-1]
												lowerCamelCase_					=       [tuple(merge.split()[:-1] ) for merge in merges]
												lowerCamelCase_					=       dict(zip(__UpperCamelCase ,       range(len(__UpperCamelCase ) ) ) )
												lowerCamelCase_					=       {}

					def  lowercase__   (						self       :  int ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ):

												if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												lowerCamelCase_					=       [self.cls_token_id]
												lowerCamelCase_					=       [self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep

					def  lowercase__   (						self       :  str ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ,       __UpperCamelCase       :  bool = False ):

												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 )) + [1]
												return [1] + ([0] * len(__UpperCamelCase )) + [1, 1] + ([0] * len(__UpperCamelCase )) + [1]

					def  lowercase__   (						self       :  Any ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ):
												lowerCamelCase_					=       [self.sep_token_id]
												lowerCamelCase_					=       [self.cls_token_id]

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

					@property
					def  lowercase__   (						self       :  Union[str, Any] ):
												return len(self.encoder )

					def  lowercase__   (						self       :  Optional[Any] ):
												return dict(self.encoder ,       **self.added_tokens_encoder )

					def  lowercase__   (						self       :  int ,       __UpperCamelCase       :  Optional[int] ):
												if token in self.cache:
																			return self.cache[token]
												lowerCamelCase_					=       tuple(__UpperCamelCase )
												lowerCamelCase_					=       tuple(list(word[:-1] ) + [word[-1] + """</w>"""] )
												lowerCamelCase_					=       get_pairs(__UpperCamelCase )

												if not pairs:
																			return token

												while True:
																			lowerCamelCase_					=       min(__UpperCamelCase ,       key=lambda __UpperCamelCase : self.bpe_ranks.get(__UpperCamelCase ,       float("""inf""" ) ) )
																			if bigram not in self.bpe_ranks:
																										break
																			lowerCamelCase_	, lowerCamelCase_					=       bigram
																			lowerCamelCase_					=       []
																			lowerCamelCase_					=       0
																			while i < len(__UpperCamelCase ):
																										try:
																																	lowerCamelCase_					=       word.index(__UpperCamelCase ,       __UpperCamelCase )
																										except ValueError:
																																	new_word.extend(word[i:] )
																																	break
																										else:
																																	new_word.extend(word[i:j] )
																																	lowerCamelCase_					=       j

																										if word[i] == first and i < len(__UpperCamelCase ) - 1 and word[i + 1] == second:
																																	new_word.append(first + second )
																																	i += 2
																										else:
																																	new_word.append(word[i] )
																																	i += 1
																			lowerCamelCase_					=       tuple(__UpperCamelCase )
																			lowerCamelCase_					=       new_word
																			if len(__UpperCamelCase ) == 1:
																										break
																			else:
																										lowerCamelCase_					=       get_pairs(__UpperCamelCase )
												lowerCamelCase_					=       """@@ """.join(__UpperCamelCase )
												lowerCamelCase_					=       word[:-4]
												lowerCamelCase_					=       word
												return word

					def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  Dict ):
												lowerCamelCase_					=       []

												lowerCamelCase_					=       re.findall(R"""\S+\n?""" ,       __UpperCamelCase )

												for token in words:
																			split_tokens.extend(list(self.bpe(__UpperCamelCase ).split(""" """ ) ) )
												return split_tokens

					def  lowercase__   (						self       :  Union[str, Any] ,       __UpperCamelCase       :  Any ):
												return self.encoder.get(__UpperCamelCase ,       self.encoder.get(self.unk_token ) )

					def  lowercase__   (						self       :  Optional[Any] ,       __UpperCamelCase       :  Optional[int] ):
												return self.decoder.get(__UpperCamelCase ,       self.unk_token )

					def  lowercase__   (						self       :  Optional[Any] ,       __UpperCamelCase       :  Optional[int] ):
												lowerCamelCase_					=       """ """.join(__UpperCamelCase ).replace("""@@ """ ,       """""" ).strip()
												return out_string

					def  lowercase__   (						self       :  List[str] ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Optional[str] = None ):
												if not os.path.isdir(__UpperCamelCase ):
																			logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
																			return
												lowerCamelCase_					=       os.path.join(
												    __UpperCamelCase ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
												lowerCamelCase_					=       os.path.join(
												    __UpperCamelCase ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )

												if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCamelCase ):
																			copyfile(self.vocab_file ,       __UpperCamelCase )

												if os.path.abspath(self.merges_file ) != os.path.abspath(__UpperCamelCase ):
																			copyfile(self.merges_file ,       __UpperCamelCase )

												return out_vocab_file, out_merge_file



					def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  int ):
												if isinstance(__UpperCamelCase ,       __UpperCamelCase ):
																			try:
																										with open(__UpperCamelCase ,       """r""" ,       encoding="""utf-8""" ) as fd:
																																	self.add_from_file(__UpperCamelCase )
																			except FileNotFoundError as fnfe:
																										raise fnfe
																			except UnicodeError:
																										raise Exception(F'''Incorrect encoding detected in {f}, please rebuild the dataset''' )
																			return

												lowerCamelCase_					=       f.readlines()
												for lineTmp in lines:
																			lowerCamelCase_					=       lineTmp.strip()
																			lowerCamelCase_					=       line.rfind(""" """ )
																			if idx == -1:
																										raise ValueError("""Incorrect dictionary format, expected '<token> <cnt>'""" )
																			lowerCamelCase_					=       line[:idx]
																			lowerCamelCase_					=       len(self.encoder )







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



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str							)	->     str:
							lowerCamelCase_					=       ["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(UpperCAmelCase__      ,					UpperCAmelCase__							)



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Dict      ,					UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   int							)	->     Tuple:
							lowerCamelCase_					=       dct.pop(UpperCAmelCase__							)
							lowerCamelCase_					=       val



def   __lowerCAmelCase    (				)	->     Any:
							lowerCamelCase_					=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase_					=       Image.open(requests.get(UpperCAmelCase__      ,					stream=UpperCAmelCase__							).raw							)
							return im



@torch.no_grad()
def   __lowerCAmelCase    (				UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   Tuple=True							)	->     Dict:
							lowerCamelCase_					=       ViTConfig()
							# patch_size
							if model_name[-1] == "8":
														lowerCamelCase_					=       8
							# set labels if required
							if not base_model:
														lowerCamelCase_					=       1_0_0_0
														lowerCamelCase_					=       """huggingface/label-files"""
														lowerCamelCase_					=       """imagenet-1k-id2label.json"""
														lowerCamelCase_					=       json.load(open(hf_hub_download(UpperCAmelCase__      ,					UpperCAmelCase__      ,					repo_type="""dataset"""							)      ,					"""r"""							)							)
														lowerCamelCase_					=       {int(UpperCAmelCase__							): v for k, v in idalabel.items()}
														lowerCamelCase_					=       idalabel
														lowerCamelCase_					=       {v: k for k, v in idalabel.items()}
							# size of the architecture
							if model_name in ["dino_vits8", "dino_vits16"]:
														lowerCamelCase_					=       3_8_4
														lowerCamelCase_					=       1_5_3_6
														lowerCamelCase_					=       1_2
														lowerCamelCase_					=       6

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

							# load state_dict of original model, remove and rename some keys
							lowerCamelCase_					=       original_model.state_dict()
							if base_model:
														remove_classification_head_(UpperCAmelCase__							)
							lowerCamelCase_					=       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:
														lowerCamelCase_					=       ViTModel(UpperCAmelCase__      ,					add_pooling_layer=UpperCAmelCase__							).eval()
							else:
														lowerCamelCase_					=       ViTForImageClassification(UpperCAmelCase__							).eval()
							model.load_state_dict(UpperCAmelCase__							)

							# Check outputs on an image, prepared by ViTImageProcessor
							lowerCamelCase_					=       ViTImageProcessor()
							lowerCamelCase_					=       image_processor(images=prepare_img()      ,					return_tensors="""pt"""							)
							lowerCamelCase_					=       encoding["""pixel_values"""]
							lowerCamelCase_					=       model(UpperCAmelCase__							)

							if base_model:
														lowerCamelCase_					=       original_model(UpperCAmelCase__							)
														assert torch.allclose(UpperCAmelCase__      ,					outputs.last_hidden_state[:, 0, :]      ,					atol=1e-1							)
							else:
														lowerCamelCase_					=       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__":
		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)





