
'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin


@flax.struct.dataclass
class 	a_	:
       # setable values
       __lowerCAmelCase     :					List[str]     =   None
       __lowerCAmelCase     :					Optional[int]     =   None
       __lowerCAmelCase     :					Tuple     =   None  # sigma(t_i)




       @classmethod
       def 				__UpperCamelCase							(  cls	):
         return cls()



@dataclass
class 	a_	(__lowercase     ):
       __lowerCAmelCase     :					Tuple     =   4_2
       __lowerCAmelCase     :					int     =   4_2
       __lowerCAmelCase     :					List[str]     =   4_2



class 	a_	(__lowercase				,      __lowercase     ):




       @property
       def 				__UpperCamelCase							(  self	):
         return True




       @register_to_config
       def __init__(  self , snake_case_ = 0.02 , snake_case_ = 1_0_0 , snake_case_ = 1.007 , snake_case_ = 8_0 , snake_case_ = 0.05 , snake_case_ = 5_0 , ):
         pass




       def 				__UpperCamelCase							(  self	):
         return KarrasVeSchedulerState.create()




       def 				__UpperCamelCase							(  self , snake_case_ , snake_case_ , snake_case_ = ()	):
         _lowerCAmelCase							:	Optional[int] 					=       jnp.arange(0 , SCREAMING_SNAKE_CASE_	)[::-1].copy()
         _lowerCAmelCase							:	int 					=       [
             (
                 self.config.sigma_max**2
                 * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
             )
             for i in timesteps
         ]

         return state.replace(
             num_inference_steps=SCREAMING_SNAKE_CASE_ , schedule=jnp.array(SCREAMING_SNAKE_CASE_ , dtype=jnp.floataa	) , timesteps=SCREAMING_SNAKE_CASE_ , )




       def 				__UpperCamelCase							(  self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ):







         if self.config.s_min <= sigma <= self.config.s_max:
           _lowerCAmelCase							:	Optional[Any] 					=       min(self.config.s_churn / state.num_inference_steps , 2**0.5 - 1	)
         else:
           _lowerCAmelCase							:	Tuple 					=       0

         # sample eps ~ N(0, S_noise^2 * I)
         _lowerCAmelCase							:	int 					=       random.split(SCREAMING_SNAKE_CASE_ , num=1	)
         _lowerCAmelCase							:	Dict 					=       self.config.s_noise * random.normal(key=SCREAMING_SNAKE_CASE_ , shape=sample.shape	)
         _lowerCAmelCase							:	Any 					=       sigma + gamma * sigma
         _lowerCAmelCase							:	Union[str, Any] 					=       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

         return sample_hat, sigma_hat




       def 				__UpperCamelCase							(  self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ = True , ):
         _lowerCAmelCase							:	Optional[int] 					=       sample_hat + sigma_hat * model_output
         _lowerCAmelCase							:	Optional[int] 					=       (sample_hat - pred_original_sample) / sigma_hat
         _lowerCAmelCase							:	List[str] 					=       sample_hat + (sigma_prev - sigma_hat) * derivative

         if not return_dict:
           return (sample_prev, derivative, state)

         return FlaxKarrasVeOutput(prev_sample=SCREAMING_SNAKE_CASE_ , derivative=SCREAMING_SNAKE_CASE_ , state=SCREAMING_SNAKE_CASE_	)




       def 				__UpperCamelCase							(  self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ = True , ):
         _lowerCAmelCase							:	List[Any] 					=       sample_prev + sigma_prev * model_output
         _lowerCAmelCase							:	Optional[Any] 					=       (sample_prev - pred_original_sample) / sigma_prev
         _lowerCAmelCase							:	Optional[int] 					=       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

         if not return_dict:
           return (sample_prev, derivative, state)

         return FlaxKarrasVeOutput(prev_sample=SCREAMING_SNAKE_CASE_ , derivative=SCREAMING_SNAKE_CASE_ , state=SCREAMING_SNAKE_CASE_	)







       def 				__UpperCamelCase							(  self , snake_case_ , snake_case_ , snake_case_ , snake_case_	):
         raise NotImplementedError()




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

		SCREAMING_SNAKE_CASE_    			=			transforms.Compose(
		    [
		        transforms.Resize((image_size, image_size),				interpolation=InterpolationMode.BICUBIC     ),
		        transforms.ToTensor(),
		        transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3),				(0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1)     ),
		    ]     )
		SCREAMING_SNAKE_CASE_    			=			transform(__a     ).unsqueeze(0     ).to(__a     )
		return image



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

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

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

		return key



@torch.no_grad()
def 							_lowerCamelCase			(			__a,				__a=None     ):
		if config_path is not None:
				SCREAMING_SNAKE_CASE_    			=			BlipConfig.from_pretrained(__a     )
		else:
				SCREAMING_SNAKE_CASE_    			=			BlipConfig(projection_dim=512,				text_config={},				vision_config={}     )

		SCREAMING_SNAKE_CASE_    			=			BlipForConditionalGeneration(__a     ).eval()

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

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

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

		hf_model.load_state_dict(__a     )

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

		SCREAMING_SNAKE_CASE_    			=			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]

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

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

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

		SCREAMING_SNAKE_CASE_    			=			BlipForQuestionAnswering(__a     )

		hf_vqa_model.load_state_dict(__a     )

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

		SCREAMING_SNAKE_CASE_    			=			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'''     )

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

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

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

		SCREAMING_SNAKE_CASE_    			=			BlipForImageTextRetrieval(__a     )

		SCREAMING_SNAKE_CASE_    			=			['''A picture of a woman with a dog sitting in a beach''']
		SCREAMING_SNAKE_CASE_    			=			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()

		SCREAMING_SNAKE_CASE_    			=			hf_itm_model(__a,				__a,				use_itm_head=__a     )
		SCREAMING_SNAKE_CASE_    			=			hf_itm_model(__a,				__a,				use_itm_head=__a     )

		assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
		assert torch.nn.functional.softmax(out_itm[0],				dim=1     )[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7

		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)