



'''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	:		Tuple				=   logging.get_logger(__name__)



def   __lowerCAmelCase					(							lowerCamelCase   :						Any				,		lowerCamelCase   :						Dict=False					):






	'''simple docstring'''




	__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					(							lowerCamelCase   :						Optional[int]				,		lowerCamelCase   :						int				,		lowerCamelCase   :						Tuple=False					):






	'''simple docstring'''




	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					(							lowerCamelCase   :						Any					):






	'''simple docstring'''




	__lowerCAmelCase												=  ["head.weight", "head.bias"]
	for k in ignore_keys:
		state_dict.pop(lowerCamelCase				,		lowerCamelCase					)



def   __lowerCAmelCase					(							lowerCamelCase   :						List[str]				,		lowerCamelCase   :						Union[str, Any]				,		lowerCamelCase   :						Union[str, Any]					):






	'''simple docstring'''




	__lowerCAmelCase												=  dct.pop(lowerCamelCase					)
	__lowerCAmelCase												=  val



def   __lowerCAmelCase					(							):






	'''simple docstring'''




	__lowerCAmelCase												=  "http://images.cocodataset.org/val2017/000000039769.jpg"
	__lowerCAmelCase												=  Image.open(requests.get(lowerCamelCase				,		stream=lowerCamelCase					).raw					)
	return im



@torch.no_grad()
def   __lowerCAmelCase					(							lowerCamelCase   :						List[str]				,		lowerCamelCase   :						Optional[Any]				,		lowerCamelCase   :						Optional[int]=True					):






	'''simple docstring'''




	__lowerCAmelCase												=  ViTConfig()
	# patch_size
	if model_name[-1] == "8":
		__lowerCAmelCase												=  8
	# set labels if required
	if not base_model:
		__lowerCAmelCase												=  10_00
		__lowerCAmelCase												=  "huggingface/label-files"
		__lowerCAmelCase												=  "imagenet-1k-id2label.json"
		__lowerCAmelCase												=  json.load(open(hf_hub_download(lowerCamelCase				,		lowerCamelCase				,		repo_type="dataset"					)				,		"r"					)					)
		__lowerCAmelCase												=  {int(lowerCamelCase					): 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_84
		__lowerCAmelCase												=  15_36
		__lowerCAmelCase												=  12
		__lowerCAmelCase												=  6

	# load original model from torch hub
	__lowerCAmelCase												=  torch.hub.load("facebookresearch/dino:main"				,		lowerCamelCase					)
	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_(lowerCamelCase					)
	__lowerCAmelCase												=  create_rename_keys(lowerCamelCase				,		base_model=lowerCamelCase					)
	for src, dest in rename_keys:
		rename_key(lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase					)
	read_in_q_k_v(lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase					)

	# load HuggingFace model
	if base_model:
		__lowerCAmelCase												=  ViTModel(lowerCamelCase				,		add_pooling_layer=lowerCamelCase					).eval()
	else:
		__lowerCAmelCase												=  ViTForImageClassification(lowerCamelCase					).eval()
	model.load_state_dict(lowerCamelCase					)

	# 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(lowerCamelCase					)

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

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


if __name__ == "__main__":
	lowerCAmelCase	:		Optional[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	:		int				=   parser.parse_args()
	convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)




'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


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

lowerCAmelCase	:		Optional[Any]				=   {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase	:		str				=   {
    '''vocab_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'''
        ),
        '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt''',
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCAmelCase	:		Optional[Any]				=   {
    '''squeezebert/squeezebert-uncased''': 5_1_2,
    '''squeezebert/squeezebert-mnli''': 5_1_2,
    '''squeezebert/squeezebert-mnli-headless''': 5_1_2,
}


lowerCAmelCase	:		Tuple				=   {
    '''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     Dict        =							VOCAB_FILES_NAMES
					a      :     Any        =							PRETRAINED_VOCAB_FILES_MAP
					a      :     Dict        =							PRETRAINED_INIT_CONFIGURATION
					a      :     str        =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a      :     Optional[Any]        =							SqueezeBertTokenizer



					def __init__(    self			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=True			,				UpperCamelCase="[UNK]"			,				UpperCamelCase="[SEP]"			,				UpperCamelCase="[PAD]"			,				UpperCamelCase="[CLS]"			,				UpperCamelCase="[MASK]"			,				UpperCamelCase=True			,				UpperCamelCase=None			,				**UpperCamelCase			,				)     ->			List[Any]:
						super().__init__(
						    UpperCamelCase			,				tokenizer_file=UpperCamelCase			,				do_lower_case=UpperCamelCase			,				unk_token=UpperCamelCase			,				sep_token=UpperCamelCase			,				pad_token=UpperCamelCase			,				cls_token=UpperCamelCase			,				mask_token=UpperCamelCase			,				tokenize_chinese_chars=UpperCamelCase			,				strip_accents=UpperCamelCase			,				**UpperCamelCase			,				)

						__lowerCAmelCase												=  json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
						if (
						    normalizer_state.get("lowercase"			,				UpperCamelCase     ) != do_lower_case
						    or normalizer_state.get("strip_accents"			,				UpperCamelCase     ) != strip_accents
						    or normalizer_state.get("handle_chinese_chars"			,				UpperCamelCase     ) != tokenize_chinese_chars
						):
							__lowerCAmelCase												=  getattr(UpperCamelCase			,				normalizer_state.pop("type"     )     )
							__lowerCAmelCase												=  do_lower_case
							__lowerCAmelCase												=  strip_accents
							__lowerCAmelCase												=  tokenize_chinese_chars
							__lowerCAmelCase												=  normalizer_class(**UpperCamelCase     )

						__lowerCAmelCase												=  do_lower_case



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase=None     )     ->			str:
						__lowerCAmelCase												=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

						return output



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = None     )     ->			List[int]:
						__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     ) * [0] + len(token_ids_a + sep     ) * [1]



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = None     )     ->			Tuple[str]:
						__lowerCAmelCase												=  self._tokenizer.model.save(UpperCamelCase			,				name=UpperCamelCase     )
						return tuple(UpperCamelCase     )