




class     lowerCamelCase__:
      def __init__(				self: List[str]    ,       UpperCamelCase_: List[Any]    ,       UpperCamelCase_: Tuple   ):
             __lowerCamelCase   =						name
             __lowerCamelCase   =						val
      def __str__(				self: List[Any]   ):
             return F'{self.__class__.__name__}({self.name}, {self.val})'




      def __lt__(				self: List[Any]    ,       UpperCamelCase_: Optional[Any]   ):
             return self.val < other.val





class     lowerCamelCase__:
      def __init__(				self: Dict    ,       UpperCamelCase_: str   ):
             __lowerCamelCase   =						{}
             __lowerCamelCase   =						{}
             __lowerCamelCase   =						self.build_heap(UpperCamelCase__   )
      def __getitem__(				self: Optional[Any]    ,       UpperCamelCase_: Optional[int]   ):
             return self.get_value(UpperCamelCase__   )
      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: List[str]   ):
             return (idx - 1) // 2
      def 			lowerCAmelCase__						(				self: Any    ,       UpperCamelCase_: Tuple   ):
             return idx * 2 + 1
      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       UpperCamelCase_: Tuple   ):
             return idx * 2 + 2
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: str   ):
             return self.heap_dict[key]
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: Optional[Any]   ):
             __lowerCamelCase   =						len(UpperCamelCase__   ) - 1
             __lowerCamelCase   =						self.get_parent_idx(UpperCamelCase__   )

             for idx, i in enumerate(UpperCamelCase__   ):
                    __lowerCamelCase   =						idx
                    __lowerCamelCase   =						i.val

             for i in range(UpperCamelCase__    ,       -1    ,       -1   ):
                    self.sift_down(UpperCamelCase__    ,       UpperCamelCase__   )
             return array
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: Optional[int]    ,       UpperCamelCase_: List[str]   ):





             while True:
                    __lowerCamelCase   =						self.get_left_child_idx(UpperCamelCase__   )  # noqa: E741
                    __lowerCamelCase   =						self.get_right_child_idx(UpperCamelCase__   )

                    __lowerCamelCase   =						idx
                    if l < len(UpperCamelCase__   ) and array[l] < array[idx]:
                           __lowerCamelCase   =						l
                    if r < len(UpperCamelCase__   ) and array[r] < array[smallest]:
                           __lowerCamelCase   =						r

                    if smallest != idx:
                           __lowerCamelCase   =						array[smallest], array[idx]
                           (
                               __lowerCamelCase  
                           )   =						(
                               self.idx_of_element[array[smallest]],
                               self.idx_of_element[array[idx]],
                           )
                           __lowerCamelCase   =						smallest
                    else:
                           break
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: List[str]   ):
             __lowerCamelCase   =						self.get_parent_idx(UpperCamelCase__   )
             while p >= 0 and self.heap[p] > self.heap[idx]:
                    __lowerCamelCase   =						self.heap[idx], self.heap[p]
                    __lowerCamelCase   =						(
                        self.idx_of_element[self.heap[idx]],
                        self.idx_of_element[self.heap[p]],
                    )
                    __lowerCamelCase   =						p
                    __lowerCamelCase   =						self.get_parent_idx(UpperCamelCase__   )
      def 			lowerCAmelCase__						(				self: str   ):
             return self.heap[0]
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						self.heap[-1], self.heap[0]
             __lowerCamelCase   =						(
                 self.idx_of_element[self.heap[-1]],
                 self.idx_of_element[self.heap[0]],
             )

             __lowerCamelCase   =						self.heap.pop()
             del self.idx_of_element[x]
             self.sift_down(0    ,       self.heap   )
             return x
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: int   ):
             self.heap.append(UpperCamelCase__   )
             __lowerCamelCase   =						len(self.heap   ) - 1
             __lowerCamelCase   =						node.val
             self.sift_up(len(self.heap   ) - 1   )
      def 			lowerCAmelCase__						(				self: int   ):
             return len(self.heap   ) == 0




      def 			lowerCAmelCase__						(				self: Optional[int]    ,       UpperCamelCase_: str    ,       UpperCamelCase_: int   ):
             assert (
                 self.heap[self.idx_of_element[node]].val > new_value
             ), "newValue must be less that current value"
             __lowerCamelCase   =						new_value
             __lowerCamelCase   =						new_value
             self.sift_up(self.idx_of_element[node]   )



UpperCAmelCase_									=	Node('R', -1)
UpperCAmelCase_									=	Node('B', 6)
UpperCAmelCase_									=	Node('A', 3)
UpperCAmelCase_									=	Node('X', 1)
UpperCAmelCase_									=	Node('E', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
UpperCAmelCase_									=	MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
       print(i)

print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
       print(i)

if __name__ == "__main__":
       import doctest

       doctest.testmod()


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       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Any							=							'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							Image.open(requests.get(_lowercase		,  stream=_lowercase						).raw						).convert('''RGB'''						)

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							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)						),
						    ]						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							transform(_lowercase						).unsqueeze(0						).to(_lowercase						)
						return image
def       A	(    _lowercase						):
						if "visual_encoder" in key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							re.sub('''visual_encoder*'''		,  '''vision_model.encoder'''		,  _lowercase						)
						if "blocks" in key:
												SCREAMING_SNAKE_CASE							:    str							=							re.sub(R'''blocks'''		,  '''layers'''		,  _lowercase						)
						if "attn" in key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							re.sub(R'''attn'''		,  '''self_attn'''		,  _lowercase						)
						if "norm1" in key:
												SCREAMING_SNAKE_CASE							:    List[str]							=							re.sub(R'''norm1'''		,  '''layer_norm1'''		,  _lowercase						)
						if "norm2" in key:
												SCREAMING_SNAKE_CASE							:    str							=							re.sub(R'''norm2'''		,  '''layer_norm2'''		,  _lowercase						)
						if "encoder.norm" in key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							re.sub(R'''encoder.norm'''		,  '''post_layernorm'''		,  _lowercase						)
						if "encoder.patch_embed.proj" in key:
												SCREAMING_SNAKE_CASE							:    Dict							=							re.sub(R'''encoder.patch_embed.proj'''		,  '''embeddings.patch_embedding'''		,  _lowercase						)

						if "encoder.pos_embed" in key:
												SCREAMING_SNAKE_CASE							:    int							=							re.sub(R'''encoder.pos_embed'''		,  '''embeddings.position_embedding'''		,  _lowercase						)
						if "encoder.cls_token" in key:
												SCREAMING_SNAKE_CASE							:    List[str]							=							re.sub(R'''encoder.cls_token'''		,  '''embeddings.class_embedding'''		,  _lowercase						)

						if "self_attn" in key:
												SCREAMING_SNAKE_CASE							:    int							=							re.sub(R'''self_attn.proj'''		,  '''self_attn.projection'''		,  _lowercase						)

						return key
@torch.no_grad()
def       A	(    _lowercase		,  _lowercase=None						):
						if config_path is not None:
												SCREAMING_SNAKE_CASE							:    Any							=							BlipConfig.from_pretrained(_lowercase						)
						else:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							BlipConfig(projection_dim=512		,  text_config={}		,  vision_config={}						)

						SCREAMING_SNAKE_CASE							:    str							=							BlipForConditionalGeneration(_lowercase						).eval()

						SCREAMING_SNAKE_CASE							:    List[Any]							=							'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

						SCREAMING_SNAKE_CASE							:    Tuple							=							blip_decoder(pretrained=_lowercase		,  image_size=384		,  vit='''base'''						)
						SCREAMING_SNAKE_CASE							:    Any							=							pt_model.eval()

						SCREAMING_SNAKE_CASE							:    Dict							=							pt_model.state_dict()
						for key in modified_state_dict.copy():
												SCREAMING_SNAKE_CASE							:    Tuple							=							modified_state_dict.pop(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							rename_key(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Any							=							value

						hf_model.load_state_dict(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							384
						SCREAMING_SNAKE_CASE							:    Tuple							=							load_demo_image(image_size=_lowercase		,  device='''cpu'''						)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							BertTokenizer.from_pretrained('''bert-base-uncased'''						)
						SCREAMING_SNAKE_CASE							:    int							=							tokenizer(['''a picture of''']						).input_ids

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							hf_model.generate(_lowercase		,  _lowercase						)

						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]

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							hf_model.generate(_lowercase						)

						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(_lowercase						)

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

						SCREAMING_SNAKE_CASE							:    Tuple							=							blip_vqa(pretrained=_lowercase		,  image_size=_lowercase		,  vit='''base'''						)
						vqa_model.eval()

						SCREAMING_SNAKE_CASE							:    List[str]							=							vqa_model.state_dict()
						for key in modified_state_dict.copy():
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							modified_state_dict.pop(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							rename_key(_lowercase						)
												SCREAMING_SNAKE_CASE							:    str							=							value

						SCREAMING_SNAKE_CASE							:    Dict							=							BlipForQuestionAnswering(_lowercase						)

						hf_vqa_model.load_state_dict(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Dict							=							['''How many dogs are in this image?''']
						SCREAMING_SNAKE_CASE							:    Tuple							=							tokenizer(_lowercase		,  return_tensors='''pt'''						).input_ids

						SCREAMING_SNAKE_CASE							:    Dict							=							hf_vqa_model.generate(_lowercase		,  _lowercase						)
						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'''						)

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

						SCREAMING_SNAKE_CASE							:    int							=							blip_itm(pretrained=_lowercase		,  image_size=_lowercase		,  vit='''base'''						)
						itm_model.eval()

						SCREAMING_SNAKE_CASE							:    List[Any]							=							itm_model.state_dict()
						for key in modified_state_dict.copy():
												SCREAMING_SNAKE_CASE							:    int							=							modified_state_dict.pop(_lowercase						)
												SCREAMING_SNAKE_CASE							:    Dict							=							rename_key(_lowercase						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							value

						SCREAMING_SNAKE_CASE							:    int							=							BlipForImageTextRetrieval(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Tuple							=							['''A picture of a woman with a dog sitting in a beach''']
						SCREAMING_SNAKE_CASE							:    str							=							tokenizer(
						    _lowercase		,  return_tensors='''pt'''		,  padding='''max_length'''		,  truncation=_lowercase		,  max_length=35		,  ).input_ids

						hf_itm_model.load_state_dict(_lowercase						)
						hf_itm_model.eval()

						SCREAMING_SNAKE_CASE							:    int							=							hf_itm_model(_lowercase		,  _lowercase		,  use_itm_head=_lowercase						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							hf_itm_model(_lowercase		,  _lowercase		,  use_itm_head=_lowercase						)

						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__":
					__UpperCamelCase		:       Optional[int]									=     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')
					__UpperCamelCase		:       int									=     parser.parse_args()

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





