


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 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Dict    =				"https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
  lowerCamelCase :					Optional[Any]    =				Image.open(requests.get(SCREAMING_SNAKE_CASE_      ,						stream=SCREAMING_SNAKE_CASE_	).raw	).convert("RGB"	)

  lowerCamelCase :					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)	),
      ]	)
  lowerCamelCase :					Dict    =				transform(SCREAMING_SNAKE_CASE_	).unsqueeze(0	).to(SCREAMING_SNAKE_CASE_	)
  return image






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

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

  if "encoder.pos_embed" in key:
    lowerCamelCase :					int    =				re.sub(r"encoder.pos_embed"      ,						"embeddings.position_embedding"      ,						SCREAMING_SNAKE_CASE_	)
  if "encoder.cls_token" in key:
    lowerCamelCase :					List[Any]    =				re.sub(r"encoder.cls_token"      ,						"embeddings.class_embedding"      ,						SCREAMING_SNAKE_CASE_	)

  if "self_attn" in key:
    lowerCamelCase :					List[str]    =				re.sub(r"self_attn.proj"      ,						"self_attn.projection"      ,						SCREAMING_SNAKE_CASE_	)

  return key






@torch.no_grad()
def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_=None	):


  '''simple docstring'''

  if config_path is not None:
    lowerCamelCase :					Union[str, Any]    =				BlipConfig.from_pretrained(SCREAMING_SNAKE_CASE_	)
  else:
    lowerCamelCase :					int    =				BlipConfig(projection_dim=512      ,						text_config={}      ,						vision_config={}	)

  lowerCamelCase :					Tuple    =				BlipForConditionalGeneration(SCREAMING_SNAKE_CASE_	).eval()

  lowerCamelCase :					Tuple    =				"https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"

  lowerCamelCase :					Optional[Any]    =				blip_decoder(pretrained=SCREAMING_SNAKE_CASE_      ,						image_size=384      ,						vit="base"	)
  lowerCamelCase :					Optional[Any]    =				pt_model.eval()

  lowerCamelCase :					Dict    =				pt_model.state_dict()
  for key in modified_state_dict.copy():
    lowerCamelCase :					Union[str, Any]    =				modified_state_dict.pop(SCREAMING_SNAKE_CASE_	)
    lowerCamelCase :					int    =				rename_key(SCREAMING_SNAKE_CASE_	)
    lowerCamelCase :					int    =				value

  hf_model.load_state_dict(SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					Any    =				384
  lowerCamelCase :					str    =				load_demo_image(image_size=SCREAMING_SNAKE_CASE_      ,						device="cpu"	)
  lowerCamelCase :					int    =				BertTokenizer.from_pretrained("bert-base-uncased"	)
  lowerCamelCase :					str    =				tokenizer(["a picture of"]	).input_ids

  lowerCamelCase :					Union[str, Any]    =				hf_model.generate(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)

  assert out[0].tolist() == [30522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

  lowerCamelCase :					Optional[Any]    =				hf_model.generate(SCREAMING_SNAKE_CASE_	)

  assert out[0].tolist() == [30522, 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'
  lowerCamelCase :					str    =				(
      "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"
  )

  lowerCamelCase :					Any    =				blip_vqa(pretrained=SCREAMING_SNAKE_CASE_      ,						image_size=SCREAMING_SNAKE_CASE_      ,						vit="base"	)
  vqa_model.eval()

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

  lowerCamelCase :					Any    =				BlipForQuestionAnswering(SCREAMING_SNAKE_CASE_	)

  hf_vqa_model.load_state_dict(SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					Any    =				["How many dogs are in this image?"]
  lowerCamelCase :					Union[str, Any]    =				tokenizer(SCREAMING_SNAKE_CASE_      ,						return_tensors="pt"	).input_ids

  lowerCamelCase :					Union[str, Any]    =				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"	)

  lowerCamelCase :					int    =				"https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"

  lowerCamelCase :					str    =				blip_itm(pretrained=SCREAMING_SNAKE_CASE_      ,						image_size=SCREAMING_SNAKE_CASE_      ,						vit="base"	)
  itm_model.eval()

  lowerCamelCase :					Optional[int]    =				itm_model.state_dict()
  for key in modified_state_dict.copy():
    lowerCamelCase :					Union[str, Any]    =				modified_state_dict.pop(SCREAMING_SNAKE_CASE_	)
    lowerCamelCase :					List[str]    =				rename_key(SCREAMING_SNAKE_CASE_	)
    lowerCamelCase :					Optional[int]    =				value

  lowerCamelCase :					Optional[Any]    =				BlipForImageTextRetrieval(SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					Optional[int]    =				["A picture of a woman with a dog sitting in a beach"]
  lowerCamelCase :					List[Any]    =				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()

  lowerCamelCase :					Any    =				hf_itm_model(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						use_itm_head=SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					str    =				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__":
   _snake_case             =     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''')
   _snake_case             =     parser.parse_args()

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







import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class 	UpperCAmelCase_					(					UpperCamelCase		, UpperCamelCase		, UpperCamelCase		, unittest.TestCase    ):

   '''simple docstring'''




   __A :      Tuple     		=		StableUnCLIPPipeline
   __A :      Optional[int]     		=		TEXT_TO_IMAGE_PARAMS
   __A :      str     		=		TEXT_TO_IMAGE_BATCH_PARAMS
   __A :      int     		=		TEXT_TO_IMAGE_IMAGE_PARAMS
   __A :      Tuple     		=		TEXT_TO_IMAGE_IMAGE_PARAMS

   # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
   __A :      Union[str, Any]     		=		False






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				32
     lowerCamelCase :					Dict    =				embedder_hidden_size

     # prior components

     torch.manual_seed(0 )
     lowerCamelCase :					Optional[int]    =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

     torch.manual_seed(0 )
     lowerCamelCase :					Optional[int]    =				CLIPTextModelWithProjection(
         CLIPTextConfig(
             bos_token_id=0						,				eos_token_id=2						,				hidden_size=__A						,				projection_dim=__A						,				intermediate_size=37						,				layer_norm_eps=1e-05						,				num_attention_heads=4						,				num_hidden_layers=5						,				pad_token_id=1						,				vocab_size=1000						,				) )

     torch.manual_seed(0 )
     lowerCamelCase :					List[Any]    =				PriorTransformer(
         num_attention_heads=2						,				attention_head_dim=12						,				embedding_dim=__A						,				num_layers=1						,				)

     torch.manual_seed(0 )
     lowerCamelCase :					Dict    =				DDPMScheduler(
         variance_type="fixed_small_log"						,				prediction_type="sample"						,				num_train_timesteps=1000						,				clip_sample=__A						,				clip_sample_range=5.0						,				beta_schedule="squaredcos_cap_v2"						,				)

     # regular denoising components

     torch.manual_seed(0 )
     lowerCamelCase :					Optional[int]    =				StableUnCLIPImageNormalizer(embedding_dim=__A )
     lowerCamelCase :					Tuple    =				DDPMScheduler(beta_schedule="squaredcos_cap_v2" )

     torch.manual_seed(0 )
     lowerCamelCase :					List[Any]    =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

     torch.manual_seed(0 )
     lowerCamelCase :					str    =				CLIPTextModel(
         CLIPTextConfig(
             bos_token_id=0						,				eos_token_id=2						,				hidden_size=__A						,				projection_dim=32						,				intermediate_size=37						,				layer_norm_eps=1e-05						,				num_attention_heads=4						,				num_hidden_layers=5						,				pad_token_id=1						,				vocab_size=1000						,				) )

     torch.manual_seed(0 )
     lowerCamelCase :					Any    =				UNetaDConditionModel(
         sample_size=32						,				in_channels=4						,				out_channels=4						,				down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")						,				up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")						,				block_out_channels=(32, 64)						,				attention_head_dim=(2, 4)						,				class_embed_type="projection"						,				projection_class_embeddings_input_dim=embedder_projection_dim * 2						,				cross_attention_dim=__A						,				layers_per_block=1						,				upcast_attention=__A						,				use_linear_projection=__A						,				)

     torch.manual_seed(0 )
     lowerCamelCase :					int    =				DDIMScheduler(
         beta_schedule="scaled_linear"						,				beta_start=0.00085						,				beta_end=0.012						,				prediction_type="v_prediction"						,				set_alpha_to_one=__A						,				steps_offset=1						,				)

     torch.manual_seed(0 )
     lowerCamelCase :					Optional[Any]    =				AutoencoderKL()

     lowerCamelCase :					Optional[int]    =				{
         # prior components
         "prior_tokenizer": prior_tokenizer,
         "prior_text_encoder": prior_text_encoder,
         "prior": prior,
         "prior_scheduler": prior_scheduler,
         # image noising components
         "image_normalizer": image_normalizer,
         "image_noising_scheduler": image_noising_scheduler,
         # regular denoising components
         "tokenizer": tokenizer,
         "text_encoder": text_encoder,
         "unet": unet,
         "scheduler": scheduler,
         "vae": vae,
     }

     return components






   def _snake_case    (		self						,				__A						,				__A=0 ):






     """simple docstring"""





     if str(__A ).startswith("mps" ):
       lowerCamelCase :					Optional[int]    =				torch.manual_seed(__A )
     else:
       lowerCamelCase :					Optional[Any]    =				torch.Generator(device=__A ).manual_seed(__A )
     lowerCamelCase :					Tuple    =				{
         "prompt": "A painting of a squirrel eating a burger",
         "generator": generator,
         "num_inference_steps": 2,
         "prior_num_inference_steps": 2,
         "output_type": "numpy",
     }
     return inputs






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				torch_device == "cpu"

     self._test_attention_slicing_forward_pass(test_max_difference=__A )






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[Any]    =				torch_device in ["cpu", "mps"]

     self._test_inference_batch_single_identical(test_max_difference=__A )



@slow
@require_torch_gpu
class 	UpperCAmelCase_					(					unittest.TestCase    ):

   '''simple docstring'''




   def _snake_case    (		self ):






     """simple docstring"""





     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Optional[Any]    =				load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy" )

     lowerCamelCase :					str    =				StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"						,				torch_dtype=torch.floataa )
     pipe.to(__A )
     pipe.set_progress_bar_config(disable=__A )
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     lowerCamelCase :					List[Any]    =				torch.Generator(device="cpu" ).manual_seed(0 )
     lowerCamelCase :					Dict    =				pipe("anime turle"						,				generator=__A						,				output_type="np" )

     lowerCamelCase :					Dict    =				output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(__A						,				__A )






   def _snake_case    (		self ):






     """simple docstring"""





     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     lowerCamelCase :					int    =				StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"						,				torch_dtype=torch.floataa )
     lowerCamelCase :					Union[str, Any]    =				pipe.to(__A )
     pipe.set_progress_bar_config(disable=__A )
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     lowerCamelCase :					Any    =				pipe(
         "anime turtle"						,				prior_num_inference_steps=2						,				num_inference_steps=2						,				output_type="np"						,				)

     lowerCamelCase :					List[str]    =				torch.cuda.max_memory_allocated()
     # make sure that less than 7 GB is allocated
     assert mem_bytes < 7 * 10**9



