
"""simple docstring"""

import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)




def      lowercase__    (       lowerCAmelCase					:       Optional[int]			,					lowerCAmelCase					:       str			)			->   Optional[int]:






    """simple docstring"""




    UpperCAmelCase          =       'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
    UpperCAmelCase          =       Image.open(requests.get(lowerCAmelCase			,					stream=lowerCAmelCase			).raw			).convert('RGB'			)

    UpperCAmelCase          =       transforms.Compose(
        [
            transforms.Resize((image_size, image_size)			,					interpolation=InterpolationMode.BICUBIC			),
            transforms.ToTensor(),
            transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)			,					(0.2686_2954, 0.2613_0258, 0.2757_7711)			),
        ]			)
    UpperCAmelCase          =       transform(lowerCAmelCase			).unsqueeze(0			).to(lowerCAmelCase			)
    return image




def      lowercase__    (       lowerCAmelCase					:       List[str]			)			->   List[Any]:






    """simple docstring"""




    if "visual_encoder" in key:
        UpperCAmelCase          =       re.sub('visual_encoder*'			,					'vision_model.encoder'			,					lowerCAmelCase			)
    if "blocks" in key:
        UpperCAmelCase          =       re.sub(r'blocks'			,					'layers'			,					lowerCAmelCase			)
    if "attn" in key:
        UpperCAmelCase          =       re.sub(r'attn'			,					'self_attn'			,					lowerCAmelCase			)
    if "norm1" in key:
        UpperCAmelCase          =       re.sub(r'norm1'			,					'layer_norm1'			,					lowerCAmelCase			)
    if "norm2" in key:
        UpperCAmelCase          =       re.sub(r'norm2'			,					'layer_norm2'			,					lowerCAmelCase			)
    if "encoder.norm" in key:
        UpperCAmelCase          =       re.sub(r'encoder.norm'			,					'post_layernorm'			,					lowerCAmelCase			)
    if "encoder.patch_embed.proj" in key:
        UpperCAmelCase          =       re.sub(r'encoder.patch_embed.proj'			,					'embeddings.patch_embedding'			,					lowerCAmelCase			)

    if "encoder.pos_embed" in key:
        UpperCAmelCase          =       re.sub(r'encoder.pos_embed'			,					'embeddings.position_embedding'			,					lowerCAmelCase			)
    if "encoder.cls_token" in key:
        UpperCAmelCase          =       re.sub(r'encoder.cls_token'			,					'embeddings.class_embedding'			,					lowerCAmelCase			)

    if "self_attn" in key:
        UpperCAmelCase          =       re.sub(r'self_attn.proj'			,					'self_attn.projection'			,					lowerCAmelCase			)

    return key




@torch.no_grad()
def      lowercase__    (       lowerCAmelCase					:       Tuple			,					lowerCAmelCase					:       Optional[Any]=None			)			->   str:






    """simple docstring"""




    if config_path is not None:
        UpperCAmelCase          =       BlipConfig.from_pretrained(lowerCAmelCase			)
    else:
        UpperCAmelCase          =       BlipConfig(projection_dim=512			,					text_config={}			,					vision_config={}			)

    UpperCAmelCase          =       BlipForConditionalGeneration(lowerCAmelCase			).eval()

    UpperCAmelCase          =       'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'

    UpperCAmelCase          =       blip_decoder(pretrained=lowerCAmelCase			,					image_size=384			,					vit='base'			)
    UpperCAmelCase          =       pt_model.eval()

    UpperCAmelCase          =       pt_model.state_dict()
    for key in modified_state_dict.copy():
        UpperCAmelCase          =       modified_state_dict.pop(lowerCAmelCase			)
        UpperCAmelCase          =       rename_key(lowerCAmelCase			)
        UpperCAmelCase          =       value

    hf_model.load_state_dict(lowerCAmelCase			)

    UpperCAmelCase          =       384
    UpperCAmelCase          =       load_demo_image(image_size=lowerCAmelCase			,					device='cpu'			)
    UpperCAmelCase          =       BertTokenizer.from_pretrained('bert-base-uncased'			)
    UpperCAmelCase          =       tokenizer(['a picture of']			).input_ids

    UpperCAmelCase          =       hf_model.generate(lowerCAmelCase			,					lowerCAmelCase			)

    assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

    UpperCAmelCase          =       hf_model.generate(lowerCAmelCase			)

    assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

    if pytorch_dump_folder_path is not None:
        hf_model.save_pretrained(lowerCAmelCase			)

    # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
    UpperCAmelCase          =       (
        'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
    )

    UpperCAmelCase          =       blip_vqa(pretrained=lowerCAmelCase			,					image_size=lowerCAmelCase			,					vit='base'			)
    vqa_model.eval()

    UpperCAmelCase          =       vqa_model.state_dict()
    for key in modified_state_dict.copy():
        UpperCAmelCase          =       modified_state_dict.pop(lowerCAmelCase			)
        UpperCAmelCase          =       rename_key(lowerCAmelCase			)
        UpperCAmelCase          =       value

    UpperCAmelCase          =       BlipForQuestionAnswering(lowerCAmelCase			)

    hf_vqa_model.load_state_dict(lowerCAmelCase			)

    UpperCAmelCase          =       ['How many dogs are in this image?']
    UpperCAmelCase          =       tokenizer(lowerCAmelCase			,					return_tensors='pt'			).input_ids

    UpperCAmelCase          =       hf_vqa_model.generate(lowerCAmelCase			,					lowerCAmelCase			)
    print(tokenizer.decode(answer[0]			)			)

    assert tokenizer.decode(answer[0]			) == "[UNK] 1 [SEP]"
    if pytorch_dump_folder_path is not None:
        hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '_vqa'			)

    UpperCAmelCase          =       'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

    UpperCAmelCase          =       blip_itm(pretrained=lowerCAmelCase			,					image_size=lowerCAmelCase			,					vit='base'			)
    itm_model.eval()

    UpperCAmelCase          =       itm_model.state_dict()
    for key in modified_state_dict.copy():
        UpperCAmelCase          =       modified_state_dict.pop(lowerCAmelCase			)
        UpperCAmelCase          =       rename_key(lowerCAmelCase			)
        UpperCAmelCase          =       value

    UpperCAmelCase          =       BlipForImageTextRetrieval(lowerCAmelCase			)

    UpperCAmelCase          =       ['A picture of a woman with a dog sitting in a beach']
    UpperCAmelCase          =       tokenizer(
        lowerCAmelCase			,					return_tensors='pt'			,					padding='max_length'			,					truncation=lowerCAmelCase			,					max_length=35			,					).input_ids

    hf_itm_model.load_state_dict(lowerCAmelCase			)
    hf_itm_model.eval()

    UpperCAmelCase          =       hf_itm_model(lowerCAmelCase			,					lowerCAmelCase			,					use_itm_head=lowerCAmelCase			)
    UpperCAmelCase          =       hf_itm_model(lowerCAmelCase			,					lowerCAmelCase			,					use_itm_head=lowerCAmelCase			)

    assert out[0].item() == 0.2110_6874_9427_7954
    assert torch.nn.functional.softmax(out_itm[0]			,					dim=1			)[:, 1].item() == 0.4_5698_8453_8650_5127

    if pytorch_dump_folder_path is not None:
        hf_itm_model.save_pretrained(pytorch_dump_folder_path + '_itm'			)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_            =    argparse.ArgumentParser()
   parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
   parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
   SCREAMING_SNAKE_CASE_            =    parser.parse_args()

   convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





"""simple docstring"""

from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

SCREAMING_SNAKE_CASE_            =    datasets.load_iris()

SCREAMING_SNAKE_CASE_            =    np.array(data['''data'''])
SCREAMING_SNAKE_CASE_            =    np.array(data['''target'''])
SCREAMING_SNAKE_CASE_            =    data['''target_names''']

SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_            =    train_test_split(X, y)




def      lowercase__    (       lowerCAmelCase					:       Optional[int]			,					lowerCAmelCase					:       Any			)			->   Optional[Any]:






    """simple docstring"""




    return np.linalg.norm(np.array(lowerCAmelCase			) - np.array(lowerCAmelCase			)			)




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Any			,					lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Tuple			,					lowerCAmelCase					:       Dict=5			)			->   str:






    """simple docstring"""




    UpperCAmelCase          =       zip(lowerCAmelCase			,					lowerCAmelCase			)
    # List of distances of all points from the point to be classified
    UpperCAmelCase          =       []
    for data_point in data:
        UpperCAmelCase          =       euclidean_distance(data_point[0]			,					lowerCAmelCase			)
        distances.append((distance, data_point[1])			)
    # Choosing 'k' points with the least distances.
    UpperCAmelCase          =       [i[1] for i in sorted(lowerCAmelCase			)[:k]]
    # Most commonly occurring class among them
    # is the class into which the point is classified
    UpperCAmelCase          =       Counter(lowerCAmelCase			).most_common(1			)[0][0]
    return classes[result]


if __name__ == "__main__":
   print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



