



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__       =  logging.get_logger(__name__)


def 					__lowerCamelCase					(   lowerCamelCase__     , lowerCamelCase__=False						):

				"""simple docstring"""


				lowercase__     :    Union[str, Any]													=     []
				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__     :    Tuple													=     [(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__     , lowerCamelCase__     , lowerCamelCase__=False						):

				"""simple docstring"""


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


def 					__lowerCamelCase					(   lowerCamelCase__						):

				"""simple docstring"""


				lowercase__     :    Tuple													=     ['''head.weight''', '''head.bias''']
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase__     , lowerCamelCase__						)


def 					__lowerCamelCase					(   lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__						):

				"""simple docstring"""


				lowercase__     :    int													=     dct.pop(lowerCamelCase__						)
				lowercase__     :    Optional[int]													=     val


def 					__lowerCamelCase					(   ):

				"""simple docstring"""


				lowercase__     :    Optional[Any]													=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				lowercase__     :    Optional[int]													=     Image.open(requests.get(lowerCamelCase__     , stream=lowerCamelCase__						).raw						)
				return im


@torch.no_grad()
def 					__lowerCamelCase					(   lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__=True						):

				"""simple docstring"""


				lowercase__     :    List[Any]													=     ViTConfig()
				# patch_size
				if model_name[-1] == "8":
								lowercase__     :    List[str]													=     8
				# set labels if required
				if not base_model:
								lowercase__     :    int													=     1_000
								lowercase__     :    int													=     '''huggingface/label-files'''
								lowercase__     :    List[str]													=     '''imagenet-1k-id2label.json'''
								lowercase__     :    Optional[Any]													=     json.load(open(hf_hub_download(lowerCamelCase__     , lowerCamelCase__     , repo_type="dataset"						)     , "r"						)						)
								lowercase__     :    Dict													=     {int(lowerCamelCase__						): v for k, v in idalabel.items()}
								lowercase__     :    str													=     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__     :    str													=     384
								lowercase__     :    str													=     1_536
								lowercase__     :    List[Any]													=     12
								lowercase__     :    Any													=     6

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

				# load state_dict of original model, remove and rename some keys
				lowercase__     :    List[str]													=     original_model.state_dict()
				if base_model:
								remove_classification_head_(lowerCamelCase__						)
				lowercase__     :    Tuple													=     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:
								lowercase__     :    List[Any]													=     ViTModel(lowerCamelCase__     , add_pooling_layer=lowerCamelCase__						).eval()
				else:
								lowercase__     :    Any													=     ViTForImageClassification(lowerCamelCase__						).eval()
				model.load_state_dict(lowerCamelCase__						)

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

				if base_model:
								lowercase__     :    Optional[Any]													=     original_model(lowerCamelCase__						)
								assert torch.allclose(lowerCamelCase__     , outputs.last_hidden_state[:, 0, :]     , atol=1e-1						)
				else:
								lowercase__     :    Tuple													=     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__       =  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__       =  parser.parse_args()
				convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)





import argparse
import json

from tqdm import tqdm



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		argparse.ArgumentParser()

    # Required parameters
    parser.add_argument(
        '''--src_path'''  ,			type=SCREAMING_SNAKE_CASE  ,			default='''biencoder-nq-dev.json'''  ,			help='''Path to raw DPR training data'''  ,			)
    parser.add_argument(
        '''--evaluation_set'''  ,			type=SCREAMING_SNAKE_CASE  ,			help='''where to store parsed evaluation_set file'''  ,			)
    parser.add_argument(
        '''--gold_data_path'''  ,			type=SCREAMING_SNAKE_CASE  ,			help='''where to store parsed gold_data_path file'''  ,			)
    __UpperCamelCase     :str			  =		parser.parse_args()

    with open(args.src_path  ,			'''r'''	) as src_file, open(args.evaluation_set  ,			'''w'''	) as eval_file, open(
        args.gold_data_path  ,			'''w'''	) as gold_file:
        __UpperCamelCase     :List[str]			  =		json.load(SCREAMING_SNAKE_CASE	)
        for dpr_record in tqdm(SCREAMING_SNAKE_CASE	):
            __UpperCamelCase     :List[str]			  =		dpr_record['''question''']
            __UpperCamelCase     :Tuple			  =		[context['''title'''] for context in dpr_record['''positive_ctxs''']]
            eval_file.write(question + '''\n'''	)
            gold_file.write('''\t'''.join(SCREAMING_SNAKE_CASE	) + '''\n'''	)


if __name__ == "__main__":
    main()





