


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_   (	_a     ,       _a	):




		"""simple docstring"""






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

		lowerCAmelCase__       :      Optional[int]   					= transforms.Compose(
		    [
		        transforms.Resize((image_size, image_size)     ,       interpolation=InterpolationMode.BICUBIC	),
		        transforms.ToTensor(),
		        transforms.Normalize((0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73)     ,       (0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11)	),
		    ]	)
		lowerCAmelCase__       :      List[Any]   					= transform(_a	).unsqueeze(0	).to(_a	)
		return image






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		if "visual_encoder" in key:
				lowerCAmelCase__       :      Union[str, Any]   					= re.sub('''visual_encoder*'''     ,       '''vision_model.encoder'''     ,       _a	)
		if "blocks" in key:
				lowerCAmelCase__       :      Dict   					= re.sub(R'''blocks'''     ,       '''layers'''     ,       _a	)
		if "attn" in key:
				lowerCAmelCase__       :      Union[str, Any]   					= re.sub(R'''attn'''     ,       '''self_attn'''     ,       _a	)
		if "norm1" in key:
				lowerCAmelCase__       :      Tuple   					= re.sub(R'''norm1'''     ,       '''layer_norm1'''     ,       _a	)
		if "norm2" in key:
				lowerCAmelCase__       :      Dict   					= re.sub(R'''norm2'''     ,       '''layer_norm2'''     ,       _a	)
		if "encoder.norm" in key:
				lowerCAmelCase__       :      List[Any]   					= re.sub(R'''encoder.norm'''     ,       '''post_layernorm'''     ,       _a	)
		if "encoder.patch_embed.proj" in key:
				lowerCAmelCase__       :      Tuple   					= re.sub(R'''encoder.patch_embed.proj'''     ,       '''embeddings.patch_embedding'''     ,       _a	)

		if "encoder.pos_embed" in key:
				lowerCAmelCase__       :      Any   					= re.sub(R'''encoder.pos_embed'''     ,       '''embeddings.position_embedding'''     ,       _a	)
		if "encoder.cls_token" in key:
				lowerCAmelCase__       :      Any   					= re.sub(R'''encoder.cls_token'''     ,       '''embeddings.class_embedding'''     ,       _a	)

		if "self_attn" in key:
				lowerCAmelCase__       :      Union[str, Any]   					= re.sub(R'''self_attn.proj'''     ,       '''self_attn.projection'''     ,       _a	)

		return key






@torch.no_grad()
def 					lowerCamelCase_   (	_a     ,       _a=None	):




		"""simple docstring"""






		if config_path is not None:
				lowerCAmelCase__       :      List[Any]   					= BlipConfig.from_pretrained(_a	)
		else:
				lowerCAmelCase__       :      Optional[int]   					= BlipConfig(projection_dim=512     ,       text_config={}     ,       vision_config={}	)

		lowerCAmelCase__       :      str   					= BlipForConditionalGeneration(_a	).eval()

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

		lowerCAmelCase__       :      int   					= blip_decoder(pretrained=_a     ,       image_size=384     ,       vit='''base'''	)
		lowerCAmelCase__       :      Optional[int]   					= pt_model.eval()

		lowerCAmelCase__       :      List[Any]   					= pt_model.state_dict()
		for key in modified_state_dict.copy():
				lowerCAmelCase__       :      int   					= modified_state_dict.pop(_a	)
				lowerCAmelCase__       :      Optional[int]   					= rename_key(_a	)
				lowerCAmelCase__       :      Union[str, Any]   					= value

		hf_model.load_state_dict(_a	)

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

		lowerCAmelCase__       :      List[str]   					= hf_model.generate(_a     ,       _a	)

		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]

		lowerCAmelCase__       :      List[Any]   					= hf_model.generate(_a	)

		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(_a	)

		# model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
		lowerCAmelCase__       :      Optional[Any]   					= (
		    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
		)

		lowerCAmelCase__       :      int   					= blip_vqa(pretrained=_a     ,       image_size=_a     ,       vit='''base'''	)
		vqa_model.eval()

		lowerCAmelCase__       :      int   					= vqa_model.state_dict()
		for key in modified_state_dict.copy():
				lowerCAmelCase__       :      List[Any]   					= modified_state_dict.pop(_a	)
				lowerCAmelCase__       :      Optional[int]   					= rename_key(_a	)
				lowerCAmelCase__       :      Dict   					= value

		lowerCAmelCase__       :      List[str]   					= BlipForQuestionAnswering(_a	)

		hf_vqa_model.load_state_dict(_a	)

		lowerCAmelCase__       :      int   					= ['''How many dogs are in this image?''']
		lowerCAmelCase__       :      int   					= tokenizer(_a     ,       return_tensors='''pt'''	).input_ids

		lowerCAmelCase__       :      List[str]   					= hf_vqa_model.generate(_a     ,       _a	)
		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'''	)

		lowerCAmelCase__       :      Optional[int]   					= '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

		lowerCAmelCase__       :      Optional[int]   					= blip_itm(pretrained=_a     ,       image_size=_a     ,       vit='''base'''	)
		itm_model.eval()

		lowerCAmelCase__       :      Dict   					= itm_model.state_dict()
		for key in modified_state_dict.copy():
				lowerCAmelCase__       :      Dict   					= modified_state_dict.pop(_a	)
				lowerCAmelCase__       :      str   					= rename_key(_a	)
				lowerCAmelCase__       :      List[str]   					= value

		lowerCAmelCase__       :      Any   					= BlipForImageTextRetrieval(_a	)

		lowerCAmelCase__       :      List[Any]   					= ['''A picture of a woman with a dog sitting in a beach''']
		lowerCAmelCase__       :      int   					= tokenizer(
		    _a     ,       return_tensors='''pt'''     ,       padding='''max_length'''     ,       truncation=_a     ,       max_length=35     ,       ).input_ids

		hf_itm_model.load_state_dict(_a	)
		hf_itm_model.eval()

		lowerCAmelCase__       :      str   					= hf_itm_model(_a     ,       _a     ,       use_itm_head=_a	)
		lowerCAmelCase__       :      Union[str, Any]   					= hf_itm_model(_a     ,       _a     ,       use_itm_head=_a	)

		assert out[0].item() == 0.21_10_68_74_94_27_79_54
		assert torch.nn.functional.softmax(out_itm[0]     ,       dim=1	)[:, 1].item() == 0.4_56_98_84_53_86_50_51_27

		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)








class  _a							:




	def __init__( self						:		str		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		List[Any]		)->	Tuple:
			lowerCAmelCase__       :      List[Any]   					= None
			lowerCAmelCase__       :      int   					= None
			lowerCAmelCase__       :      Union[str, Any]   					= graph

			self._normalize_graph(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Tuple   					= len(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Optional[int]   					= None




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		)->	Union[str, Any]:
			if sources is int:
					lowerCAmelCase__       :      List[Any]   					= [sources]
			if sinks is int:
					lowerCAmelCase__       :      Optional[Any]   					= [sinks]

			if len(_SCREAMING_SNAKE_CASE		) == 0 or len(_SCREAMING_SNAKE_CASE		) == 0:
					return

			lowerCAmelCase__       :      Union[str, Any]   					= sources[0]
			lowerCAmelCase__       :      Dict   					= sinks[0]

			# make fake vertex if there are more
			# than one source or sink
			if len(_SCREAMING_SNAKE_CASE		) > 1 or len(_SCREAMING_SNAKE_CASE		) > 1:
					lowerCAmelCase__       :      List[Any]   					= 0
					for i in sources:
							max_input_flow += sum(self.graph[i]		)

					lowerCAmelCase__       :      Any   					= len(self.graph		) + 1
					for room in self.graph:
							room.insert(0		, 0		)
					self.graph.insert(0		, [0] * size		)
					for i in sources:
							lowerCAmelCase__       :      Optional[Any]   					= max_input_flow
					lowerCAmelCase__       :      List[str]   					= 0

					lowerCAmelCase__       :      List[Any]   					= len(self.graph		) + 1
					for room in self.graph:
							room.append(0		)
					self.graph.append([0] * size		)
					for i in sinks:
							lowerCAmelCase__       :      str   					= max_input_flow
					lowerCAmelCase__       :      Tuple   					= size - 1




	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Union[str, Any]:
			if self.maximum_flow_algorithm is None:
					raise Exception('''You need to set maximum flow algorithm before.'''		)
			if self.source_index is None or self.sink_index is None:
					return 0

			self.maximum_flow_algorithm.execute()
			return self.maximum_flow_algorithm.getMaximumFlow()




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		List[Any]		)->	int:
			lowerCAmelCase__       :      int   					= algorithm(self		)





class  _a							:




	def __init__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Tuple		)->	Union[str, Any]:
			lowerCAmelCase__       :      Tuple   					= flow_network
			lowerCAmelCase__       :      Dict   					= flow_network.verticesCount
			lowerCAmelCase__       :      Optional[Any]   					= flow_network.sourceIndex
			lowerCAmelCase__       :      Optional[Any]   					= flow_network.sinkIndex
			# it's just a reference, so you shouldn't change
			# it in your algorithms, use deep copy before doing that
			lowerCAmelCase__       :      str   					= flow_network.graph
			lowerCAmelCase__       :      Optional[int]   					= False




	def 							UpperCAmelCase__( self						:		List[str]		)->	Dict:
			if not self.executed:
					self._algorithm()
					lowerCAmelCase__       :      Any   					= True




	def 							UpperCAmelCase__( self						:		Optional[Any]		)->	int:
			pass





class  _a							(    _lowercase):




	def __init__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		List[Any]		)->	Union[str, Any]:
			super().__init__(_SCREAMING_SNAKE_CASE		)
			# use this to save your result
			lowerCAmelCase__       :      Dict   					= -1




	def 							UpperCAmelCase__( self						:		Any		)->	Optional[Any]:
			if not self.executed:
					raise Exception('''You should execute algorithm before using its result!'''		)

			return self.maximum_flow





class  _a							(    _lowercase):




	def __init__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		List[str]		)->	List[str]:
			super().__init__(_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      Any   					= [[0] * self.verticies_count for i in range(self.verticies_count		)]

			lowerCAmelCase__       :      Optional[Any]   					= [0] * self.verticies_count
			lowerCAmelCase__       :      str   					= [0] * self.verticies_count




	def 							UpperCAmelCase__( self						:		Any		)->	List[Any]:
			lowerCAmelCase__       :      Optional[Any]   					= self.verticies_count

			# push some substance to graph
			for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]		):
					self.preflow[self.source_index][nextvertex_index] += bandwidth
					self.preflow[nextvertex_index][self.source_index] -= bandwidth
					self.excesses[nextvertex_index] += bandwidth

			# Relabel-to-front selection rule
			lowerCAmelCase__       :      Any   					= [
			    i
			    for i in range(self.verticies_count		)
			    if i != self.source_index and i != self.sink_index
			]

			# move through list
			lowerCAmelCase__       :      str   					= 0
			while i < len(_SCREAMING_SNAKE_CASE		):
					lowerCAmelCase__       :      Union[str, Any]   					= vertices_list[i]
					lowerCAmelCase__       :      Any   					= self.heights[vertex_index]
					self.process_vertex(_SCREAMING_SNAKE_CASE		)
					if self.heights[vertex_index] > previous_height:
							# if it was relabeled, swap elements
							# and start from 0 index
							vertices_list.insert(0		, vertices_list.pop(_SCREAMING_SNAKE_CASE		)		)
							lowerCAmelCase__       :      Optional[Any]   					= 0
					else:
							i += 1

			lowerCAmelCase__       :      Optional[Any]   					= sum(self.preflow[self.source_index]		)




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		)->	Optional[int]:
			while self.excesses[vertex_index] > 0:
					for neighbour_index in range(self.verticies_count		):
							# if it's neighbour and current vertex is higher
							if (
							    self.graph[vertex_index][neighbour_index]
							    - self.preflow[vertex_index][neighbour_index]
							    > 0
							    and self.heights[vertex_index] > self.heights[neighbour_index]
							):
									self.push(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

					self.relabel(_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		)->	Union[str, Any]:
			lowerCAmelCase__       :      Union[str, Any]   					= min(
			    self.excesses[from_index]		, self.graph[from_index][to_index] - self.preflow[from_index][to_index]		, )
			self.preflow[from_index][to_index] += preflow_delta
			self.preflow[to_index][from_index] -= preflow_delta
			self.excesses[from_index] -= preflow_delta
			self.excesses[to_index] += preflow_delta




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Any		)->	Optional[int]:
			lowerCAmelCase__       :      Optional[int]   					= None
			for to_index in range(self.verticies_count		):
					if (
					    self.graph[vertex_index][to_index]
					    - self.preflow[vertex_index][to_index]
					    > 0
					) and (min_height is None or self.heights[to_index] < min_height):
							lowerCAmelCase__       :      List[Any]   					= self.heights[to_index]

			if min_height is not None:
					lowerCAmelCase__       :      Optional[Any]   					= min_height + 1


if __name__ == "__main__":
		lowerCamelCase						=		[0]
		lowerCamelCase						=		[3]
		# graph = [
		#     [0, 0, 4, 6, 0, 0],
		#     [0, 0, 5, 2, 0, 0],
		#     [0, 0, 0, 0, 4, 4],
		#     [0, 0, 0, 0, 6, 6],
		#     [0, 0, 0, 0, 0, 0],
		#     [0, 0, 0, 0, 0, 0],
		# ]
		lowerCamelCase						=		[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

		# prepare our network
		lowerCamelCase						=		FlowNetwork(graph, entrances, exits)
		# set algorithm
		flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
		# and calculate
		lowerCamelCase						=		flow_network.find_maximum_flow()

		print(f'''maximum flow is {maximum_flow}''')




