






'''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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




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

   lowercase_	:    Any           =     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)							),
       ]							)
   lowercase_	:    Optional[int]           =     transform(_UpperCamelCase							).unsqueeze(0							).to(_UpperCamelCase							)
   return image




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




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

   if "encoder.pos_embed" in key:
      lowercase_	:    int           =     re.sub(R"encoder.pos_embed"						,		"embeddings.position_embedding"						,		_UpperCamelCase							)
   if "encoder.cls_token" in key:
      lowercase_	:    Union[str, Any]           =     re.sub(R"encoder.cls_token"						,		"embeddings.class_embedding"						,		_UpperCamelCase							)

   if "self_attn" in key:
      lowercase_	:    Dict           =     re.sub(R"self_attn.proj"						,		"self_attn.projection"						,		_UpperCamelCase							)

   return key




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=None							):


   """simple docstring"""




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

   lowercase_	:    List[str]           =     BlipForConditionalGeneration(_UpperCamelCase							).eval()

   lowercase_	:    Union[str, Any]           =     "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"

   lowercase_	:    Tuple           =     blip_decoder(pretrained=_UpperCamelCase						,		image_size=384						,		vit="base"							)
   lowercase_	:    List[str]           =     pt_model.eval()

   lowercase_	:    List[str]           =     pt_model.state_dict()
   for key in modified_state_dict.copy():
      lowercase_	:    Dict           =     modified_state_dict.pop(_UpperCamelCase							)
      lowercase_	:    Tuple           =     rename_key(_UpperCamelCase							)
      lowercase_	:    List[str]           =     value

   hf_model.load_state_dict(_UpperCamelCase							)

   lowercase_	:    Tuple           =     384
   lowercase_	:    str           =     load_demo_image(image_size=_UpperCamelCase						,		device="cpu"							)
   lowercase_	:    Any           =     BertTokenizer.from_pretrained("bert-base-uncased"							)
   lowercase_	:    Dict           =     tokenizer(["a picture of"]							).input_ids

   lowercase_	:    Optional[Any]           =     hf_model.generate(_UpperCamelCase						,		_UpperCamelCase							)

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

   lowercase_	:    int           =     hf_model.generate(_UpperCamelCase							)

   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(_UpperCamelCase							)

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

   lowercase_	:    str           =     blip_vqa(pretrained=_UpperCamelCase						,		image_size=_UpperCamelCase						,		vit="base"							)
   vqa_model.eval()

   lowercase_	:    List[str]           =     vqa_model.state_dict()
   for key in modified_state_dict.copy():
      lowercase_	:    str           =     modified_state_dict.pop(_UpperCamelCase							)
      lowercase_	:    int           =     rename_key(_UpperCamelCase							)
      lowercase_	:    Any           =     value

   lowercase_	:    List[Any]           =     BlipForQuestionAnswering(_UpperCamelCase							)

   hf_vqa_model.load_state_dict(_UpperCamelCase							)

   lowercase_	:    str           =     ["How many dogs are in this image?"]
   lowercase_	:    Tuple           =     tokenizer(_UpperCamelCase						,		return_tensors="pt"							).input_ids

   lowercase_	:    Dict           =     hf_vqa_model.generate(_UpperCamelCase						,		_UpperCamelCase							)
   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"							)

   lowercase_	:    List[str]           =     "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"

   lowercase_	:    int           =     blip_itm(pretrained=_UpperCamelCase						,		image_size=_UpperCamelCase						,		vit="base"							)
   itm_model.eval()

   lowercase_	:    Any           =     itm_model.state_dict()
   for key in modified_state_dict.copy():
      lowercase_	:    str           =     modified_state_dict.pop(_UpperCamelCase							)
      lowercase_	:    Union[str, Any]           =     rename_key(_UpperCamelCase							)
      lowercase_	:    Union[str, Any]           =     value

   lowercase_	:    List[Any]           =     BlipForImageTextRetrieval(_UpperCamelCase							)

   lowercase_	:    Dict           =     ["A picture of a woman with a dog sitting in a beach"]
   lowercase_	:    int           =     tokenizer(
       _UpperCamelCase						,		return_tensors="pt"						,		padding="max_length"						,		truncation=_UpperCamelCase						,		max_length=35						,		).input_ids

   hf_itm_model.load_state_dict(_UpperCamelCase							)
   hf_itm_model.eval()

   lowercase_	:    int           =     hf_itm_model(_UpperCamelCase						,		_UpperCamelCase						,		use_itm_head=_UpperCamelCase							)
   lowercase_	:    Optional[Any]           =     hf_itm_model(_UpperCamelCase						,		_UpperCamelCase						,		use_itm_head=_UpperCamelCase							)

   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__":
 UpperCamelCase__ =			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__ =			parser.parse_args()

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












'''simple docstring'''
import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class        _UpperCAmelCase						(		snake_case				,					unittest.TestCase			):
    __lowerCamelCase:   Dict		       =    KandinskyVaaPriorPipeline
    __lowerCamelCase:   Optional[int]		       =    ['prompt']
    __lowerCamelCase:   Any		       =    ['prompt', 'negative_prompt']
    __lowerCamelCase:   List[Any]		       =    [
        'num_images_per_prompt',
        'generator',
        'num_inference_steps',
        'latents',
        'negative_prompt',
        'guidance_scale',
        'output_type',
        'return_dict',
    ]
    __lowerCamelCase:   List[Any]		       =    False




    @property
    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       return 3_2




    @property
    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       return 3_2




    @property
    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       return self.time_input_dim




    @property
    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       return self.time_input_dim * 4




    @property
    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       return 1_0_0




    @property
    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    Any           =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)
       return tokenizer




    @property
    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       torch.manual_seed(0			)
       lowercase_	:    Tuple           =     CLIPTextConfig(
           bos_token_id=0  ,     eos_token_id=2  ,     hidden_size=self.text_embedder_hidden_size  ,     projection_dim=self.text_embedder_hidden_size  ,     intermediate_size=3_7  ,     layer_norm_eps=1e-05  ,     num_attention_heads=4  ,     num_hidden_layers=5  ,     pad_token_id=1  ,     vocab_size=1_0_0_0  ,     )
       return CLIPTextModelWithProjection(a			)




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       torch.manual_seed(0			)

       lowercase_	:    List[str]           =     {
           "num_attention_heads": 2,
           "attention_head_dim": 1_2,
           "embedding_dim": self.text_embedder_hidden_size,
           "num_layers": 1,
       }

       lowercase_	:    Union[str, Any]           =     PriorTransformer(**a			)
       # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
       lowercase_	:    List[Any]           =     nn.Parameter(torch.ones(model.clip_std.shape			)			)
       return model




    @property
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       torch.manual_seed(0			)
       lowercase_	:    Dict           =     CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size  ,     image_size=2_2_4  ,     projection_dim=self.text_embedder_hidden_size  ,     intermediate_size=3_7  ,     num_attention_heads=4  ,     num_channels=3  ,     num_hidden_layers=5  ,     patch_size=1_4  ,     )

       lowercase_	:    Optional[Any]           =     CLIPVisionModelWithProjection(a			)
       return model




    @property
    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     CLIPImageProcessor(
           crop_size=2_2_4  ,     do_center_crop=a  ,     do_normalize=a  ,     do_resize=a  ,     image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]  ,     image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]  ,     resample=3  ,     size=2_2_4  ,     )

       return image_processor




    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    Any           =     self.dummy_prior
       lowercase_	:    Optional[Any]           =     self.dummy_image_encoder
       lowercase_	:    List[Any]           =     self.dummy_text_encoder
       lowercase_	:    Any           =     self.dummy_tokenizer
       lowercase_	:    Optional[Any]           =     self.dummy_image_processor

       lowercase_	:    List[str]           =     UnCLIPScheduler(
           variance_type="fixed_small_log"  ,     prediction_type="sample"  ,     num_train_timesteps=1_0_0_0  ,     clip_sample=a  ,     clip_sample_range=10.0  ,     )

       lowercase_	:    List[Any]           =     {
           "prior": prior,
           "image_encoder": image_encoder,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "scheduler": scheduler,
           "image_processor": image_processor,
       }

       return components




    def 				lowerCAmelCase__				(			self  :       Any  ,     a  :       Dict  ,     a  :       Dict=0			):






       '''simple docstring'''






       if str(a			).startswith("mps"			):
          lowercase_	:    int           =     torch.manual_seed(a			)
       else:
          lowercase_	:    Optional[Any]           =     torch.Generator(device=a			).manual_seed(a			)
       lowercase_	:    Any           =     {
           "prompt": "horse",
           "generator": generator,
           "guidance_scale": 4.0,
           "num_inference_steps": 2,
           "output_type": "np",
       }
       return inputs




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    str           =     "cpu"

       lowercase_	:    Any           =     self.get_dummy_components()

       lowercase_	:    int           =     self.pipeline_class(**a			)
       lowercase_	:    Any           =     pipe.to(a			)

       pipe.set_progress_bar_config(disable=a			)

       lowercase_	:    Any           =     pipe(**self.get_dummy_inputs(a			)			)
       lowercase_	:    List[Any]           =     output.image_embeds

       lowercase_	:    str           =     pipe(
           **self.get_dummy_inputs(a			)  ,     return_dict=a  ,     )[0]

       lowercase_	:    Any           =     image[0, -1_0:]
       lowercase_	:    Dict           =     image_from_tuple[0, -1_0:]

       assert image.shape == (1, 3_2)

       lowercase_	:    int           =     np.array(
           [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]			)

       assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2




    @skip_mps
    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_	:    int           =     torch_device == "cpu"
       lowercase_	:    Tuple           =     True
       lowercase_	:    str           =     False

       self._test_inference_batch_single_identical(
           test_max_difference=a  ,     relax_max_difference=a  ,     test_mean_pixel_difference=a  ,     )





    @skip_mps
    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_	:    Any           =     torch_device == "cpu"
       lowercase_	:    int           =     False

       self._test_attention_slicing_forward_pass(
           test_max_difference=a  ,     test_mean_pixel_difference=a  ,     )




