

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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Union[str, Any]	,       SCREAMING_SNAKE_CASE_:							Dict      )			->      Optional[Any]:




				'''simple docstring'''





				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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Union[str, Any]      )			->      int:




				'''simple docstring'''





				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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							Dict=None      )			->      Dict:




				'''simple docstring'''





				if config_path is not None:
								A__        =			BlipConfig.from_pretrained(SCREAMING_SNAKE_CASE_      )
				else:
								A__        =			BlipConfig(projection_dim=5_1_2	,       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=3_8_4	,       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__        =			3_8_4
				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_0_5_2_2, 1_0_3_7, 3_8_6_1, 1_9_9_7, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

				A__        =			hf_model.generate(SCREAMING_SNAKE_CASE_      )

				assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

				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=3_5	,       ).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__":
	lowerCAmelCase__	   = 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""")
	lowerCAmelCase__	   = 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 Iterator
from typing import Any

class 					a__						:
			"""simple docstring"""







			def __init__(    self      ,      lowercase       )  ->						int:
							'''simple docstring'''






							A__        =			data
							A__        =			None

class 					a__						:
			"""simple docstring"""







			def __init__(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			None
							A__        =			None


			def __iter__(    self       )  ->						Iterator[Any]:
							'''simple docstring'''






							A__        =			self.head
							while self.head:
											yield node.data
											A__        =			node.next
											if node == self.head:
															break


			def __len__(    self       )  ->						int:
							'''simple docstring'''






							return sum(1 for _ in self       )


			def __repr__(    self       )  ->						List[Any]:
							'''simple docstring'''






							return "->".join(str(lowercase       ) for item in iter(self       )       )


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						None:
							'''simple docstring'''






							self.insert_nth(len(self       )      ,      lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						None:
							'''simple docstring'''






							self.insert_nth(0      ,      lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						None:
							'''simple docstring'''






							if index < 0 or index > len(self       ):
											raise IndexError("list index out of range."       )
							A__        =			Node(lowercase       )
							if self.head is None:
											A__        =			new_node  # first node points itself
											A__        =			A__        =			new_node
							elif index == 0:  # insert at head
											A__        =			self.head
											A__        =			A__        =			new_node
							else:
											A__        =			self.head
											for _ in range(index - 1       ):
															A__        =			temp.next
											A__        =			temp.next
											A__        =			new_node
											if index == len(self       ) - 1:  # insert at tail
															A__        =			new_node


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							return self.delete_nth(0       )


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							return self.delete_nth(len(self       ) - 1       )


			def      UpperCamelCase		(    self      ,      lowercase = 0       )  ->						Any:
							'''simple docstring'''






							if not 0 <= index < len(self       ):
											raise IndexError("list index out of range."       )
							A__        =			self.head
							if self.head == self.tail:  # just one node
											A__        =			A__        =			None
							elif index == 0:  # delete head node
											A__        =			self.tail.next.next
											A__        =			self.head.next
							else:
											A__        =			self.head
											for _ in range(index - 1       ):
															A__        =			temp.next
											A__        =			temp.next
											A__        =			temp.next.next
											if index == len(self       ) - 1:  # delete at tail
															A__        =			temp
							return delete_node.data


			def      UpperCamelCase		(    self       )  ->						bool:
							'''simple docstring'''






							return len(self       ) == 0



def 					lowerCAmelCase__							(	)			->      None:




				'''simple docstring'''





				A__        =			CircularLinkedList()
				assert len(SCREAMING_SNAKE_CASE_      ) == 0
				assert circular_linked_list.is_empty() is True
				assert str(SCREAMING_SNAKE_CASE_      ) == ""

				try:
								circular_linked_list.delete_front()
								raise AssertionError  # This should not happen
				except IndexError:
								assert True  # This should happen

				try:
								circular_linked_list.delete_tail()
								raise AssertionError  # This should not happen
				except IndexError:
								assert True  # This should happen

				try:
								circular_linked_list.delete_nth(-1      )
								raise AssertionError
				except IndexError:
								assert True

				try:
								circular_linked_list.delete_nth(0      )
								raise AssertionError
				except IndexError:
								assert True

				assert circular_linked_list.is_empty() is True
				for i in range(5      ):
								assert len(SCREAMING_SNAKE_CASE_      ) == i
								circular_linked_list.insert_nth(SCREAMING_SNAKE_CASE_	,       i + 1      )
				assert str(SCREAMING_SNAKE_CASE_      ) == "->".join(str(SCREAMING_SNAKE_CASE_      ) for i in range(1	,       6      )      )

				circular_linked_list.insert_tail(6      )
				assert str(SCREAMING_SNAKE_CASE_      ) == "->".join(str(SCREAMING_SNAKE_CASE_      ) for i in range(1	,       7      )      )
				circular_linked_list.insert_head(0      )
				assert str(SCREAMING_SNAKE_CASE_      ) == "->".join(str(SCREAMING_SNAKE_CASE_      ) for i in range(0	,       7      )      )

				assert circular_linked_list.delete_front() == 0
				assert circular_linked_list.delete_tail() == 6
				assert str(SCREAMING_SNAKE_CASE_      ) == "->".join(str(SCREAMING_SNAKE_CASE_      ) for i in range(1	,       6      )      )
				assert circular_linked_list.delete_nth(2      ) == 3

				circular_linked_list.insert_nth(2	,       3      )
				assert str(SCREAMING_SNAKE_CASE_      ) == "->".join(str(SCREAMING_SNAKE_CASE_      ) for i in range(1	,       6      )      )

				assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
	import doctest

	doctest.testmod()

