
"""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 					_snake_case (						snake_case__      :   List[str]		,					snake_case__      :   Optional[int]   ):
	A        =      'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
	A        =      Image.open(requests.get(SCREAMING_SNAKE_CASE_		,					stream=SCREAMING_SNAKE_CASE_   ).raw   ).convert('RGB'   )

	A        =      transforms.Compose(
	    [
	        transforms.Resize((image_size, image_size)		,					interpolation=InterpolationMode.BICUBIC   ),
	        transforms.ToTensor(),
	        transforms.Normalize((0.48145466, 0.4578275, 0.40821073)		,					(0.26862954, 0.26130258, 0.27577711)   ),
	    ]   )
	A        =      transform(SCREAMING_SNAKE_CASE_   ).unsqueeze(0   ).to(SCREAMING_SNAKE_CASE_   )
	return image






def 					_snake_case (						snake_case__      :   List[Any]   ):





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

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

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

	return key






@torch.no_grad()
def 					_snake_case (						snake_case__      :   Any		,					snake_case__      :   List[str]=None   ):





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

	A        =      BlipForConditionalGeneration(SCREAMING_SNAKE_CASE_   ).eval()

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

	A        =      blip_decoder(pretrained=SCREAMING_SNAKE_CASE_		,					image_size=384		,					vit='base'   )
	A        =      pt_model.eval()

	A        =      pt_model.state_dict()
	for key in modified_state_dict.copy():
		A        =      modified_state_dict.pop(SCREAMING_SNAKE_CASE_   )
		A        =      rename_key(SCREAMING_SNAKE_CASE_   )
		A        =      value

	hf_model.load_state_dict(SCREAMING_SNAKE_CASE_   )

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

	A        =      hf_model.generate(SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_   )

	assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

	A        =      hf_model.generate(SCREAMING_SNAKE_CASE_   )

	assert out[0].tolist() == [3_0522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

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

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

	A        =      blip_vqa(pretrained=SCREAMING_SNAKE_CASE_		,					image_size=SCREAMING_SNAKE_CASE_		,					vit='base'   )
	vqa_model.eval()

	A        =      vqa_model.state_dict()
	for key in modified_state_dict.copy():
		A        =      modified_state_dict.pop(SCREAMING_SNAKE_CASE_   )
		A        =      rename_key(SCREAMING_SNAKE_CASE_   )
		A        =      value

	A        =      BlipForQuestionAnswering(SCREAMING_SNAKE_CASE_   )

	hf_vqa_model.load_state_dict(SCREAMING_SNAKE_CASE_   )

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

	A        =      hf_vqa_model.generate(SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_   )
	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'   )

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

	A        =      blip_itm(pretrained=SCREAMING_SNAKE_CASE_		,					image_size=SCREAMING_SNAKE_CASE_		,					vit='base'   )
	itm_model.eval()

	A        =      itm_model.state_dict()
	for key in modified_state_dict.copy():
		A        =      modified_state_dict.pop(SCREAMING_SNAKE_CASE_   )
		A        =      rename_key(SCREAMING_SNAKE_CASE_   )
		A        =      value

	A        =      BlipForImageTextRetrieval(SCREAMING_SNAKE_CASE_   )

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

	hf_itm_model.load_state_dict(SCREAMING_SNAKE_CASE_   )
	hf_itm_model.eval()

	A        =      hf_itm_model(SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					use_itm_head=SCREAMING_SNAKE_CASE_   )
	A        =      hf_itm_model(SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					use_itm_head=SCREAMING_SNAKE_CASE_   )

	assert out[0].item() == 0.2110687494277954
	assert torch.nn.functional.softmax(out_itm[0]		,					dim=1   )[:, 1].item() == 0.45698845386505127

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


if __name__ == "__main__":
		_lowercase							      = 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''')
		_lowercase							      = parser.parse_args()

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


from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

snake_case_			  = TypeVar('''T''')
snake_case_			  = TypeVar('''U''')


class       SCREAMING_SNAKE_CASE__					(Generic[T, U]     ):





    def __init__(      self						,   a						,   a):
         lowercase__    :   List[Any]      =       key
         lowercase__    :   List[Any]      =       val
         lowercase__    :   DoubleLinkedListNode[T, U] | None      =       None
         lowercase__    :   DoubleLinkedListNode[T, U] | None      =       None





    def __repr__(      self):
         return (
             f"""Node: key: {self.key}, val: {self.val}, """
             f"""has next: {bool(self.next)}, has prev: {bool(self.prev)}"""
         )



class       SCREAMING_SNAKE_CASE__					(Generic[T, U]     ):





    def __init__(      self):
         lowercase__    :   DoubleLinkedListNode[T, U]      =       DoubleLinkedListNode(a						,   a)
         lowercase__    :   DoubleLinkedListNode[T, U]      =       DoubleLinkedListNode(a						,   a)
         lowercase__      , lowercase__    :   Union[str, Any]      =       self.rear, self.head





    def __repr__(      self):
         lowercase__    :   Any      =       ['DoubleLinkedList']
         lowercase__    :   List[str]      =       self.head
         while node.next is not None:
              rep.append(str(a))
              lowercase__    :   Tuple      =       node.next
         rep.append(str(self.rear))
         return ",\n    ".join(a)





    def       snake_case_				(      self						,   a):
         lowercase__    :   Optional[Any]      =       self.rear.prev

         # All nodes other than self.head are guaranteed to have non-None previous
         assert previous is not None

         lowercase__    :   Dict      =       node
         lowercase__    :   int      =       previous
         lowercase__    :   Union[str, Any]      =       node
         lowercase__    :   Optional[int]      =       self.rear





    def       snake_case_				(      self						,   a):

         if node.prev is None or node.next is None:
              return None

         lowercase__    :   Union[str, Any]      =       node.next
         lowercase__    :   Tuple      =       node.prev
         lowercase__    :   Union[str, Any]      =       None
         lowercase__    :   List[Any]      =       None
         return node



class       SCREAMING_SNAKE_CASE__					(Generic[T, U]     ):
    __lowerCamelCase						:       dict[Callable[[T], U], LRUCache[T, U]]														=     {}





    def __init__(      self						,   a):
         lowercase__    :   DoubleLinkedList[T, U]      =       DoubleLinkedList()
         lowercase__    :   Optional[Any]      =       capacity
         lowercase__    :   Union[str, Any]      =       0
         lowercase__    :   Tuple      =       0
         lowercase__    :   int      =       0
         lowercase__    :   dict[T, DoubleLinkedListNode[T, U]]      =       {}





    def __repr__(      self):
         return (
             f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
             f"""capacity={self.capacity}, current size={self.num_keys})"""
         )





    def __contains__(      self						,   a):
         return key in self.cache





    def       snake_case_				(      self						,   a):
         # Note: pythonic interface would throw KeyError rather than return None

         if key in self.cache:
              self.hits += 1
              lowercase__    :   DoubleLinkedListNode[T, U]      =       self.cache[key]
              lowercase__    :   str      =       self.list.remove(self.cache[key])
              assert node == value_node

              # node is guaranteed not None because it is in self.cache
              assert node is not None
              self.list.add(a)
              return node.val
         self.miss += 1
         return None





    def       snake_case_				(      self						,   a						,   a):

         if key not in self.cache:
              if self.num_keys >= self.capacity:
                   # delete first node (oldest) when over capacity
                   lowercase__    :   Optional[int]      =       self.list.head.next

                   # guaranteed to have a non-None first node when num_keys > 0
                   # explain to type checker via assertions
                   assert first_node is not None
                   assert first_node.key is not None
                   assert (
                       self.list.remove(a) is not None
                   )  # node guaranteed to be in list assert node.key is not None

                   del self.cache[first_node.key]
                   self.num_keys -= 1
              lowercase__    :   Optional[Any]      =       DoubleLinkedListNode(a						,   a)
              self.list.add(self.cache[key])
              self.num_keys += 1

         else:
              # bump node to the end of the list, update value
              lowercase__    :   Any      =       self.list.remove(self.cache[key])
              assert node is not None  # node guaranteed to be in list
              lowercase__    :   Union[str, Any]      =       value
              self.list.add(a)





    @classmethod
    def       snake_case_				(      cls						,   a = 128):

         def cache_decorator_inner(a) -> Callable[..., U]:
              def cache_decorator_wrapper(*a) -> U:
                   if func not in cls.decorator_function_to_instance_map:
                        lowercase__    :   Dict      =       LRUCache(a)

                   lowercase__    :   str      =       cls.decorator_function_to_instance_map[func].get(args[0])
                   if result is None:
                        lowercase__    :   str      =       func(*a)
                        cls.decorator_function_to_instance_map[func].put(args[0]						,   a)
                   return result

              def cache_info() -> LRUCache[T, U]:
                   return cls.decorator_function_to_instance_map[func]

              setattr(a						,   'cache_info'						,   a)  # noqa: B010

              return cache_decorator_wrapper

         return cache_decorator_inner


if __name__ == "__main__":
       import doctest

       doctest.testmod()

