


'''simple docstring'''

import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD





def __snake_case					(      ):
 lowerCamelCase_      =  "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
 lowerCamelCase_      =  Image.open(requests.get(UpperCAmelCase_					,				stream=UpperCAmelCase_		).raw		).convert("RGB"		)

 return image





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  []
 # fmt: off

 # vision encoder
 rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding")		)
 rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding")		)
 rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight")		)
 rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias")		)
 rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight")		)
 rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias")		)

 for i in range(config.vision_config.num_hidden_layers		):
  rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''')		)
  rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''')		)
  rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''')		)
  rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''')		)
  rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')		)
  rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)		)
  rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')		)
  rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')		)
  rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')		)
  rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')		)
  rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')		)

 # QFormer
 rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight")		)
 rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias")		)

 # fmt: on
 return rename_keys





def __snake_case					(      UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						Dict		):
 lowerCamelCase_      =  dct.pop(UpperCAmelCase_		)
 lowerCamelCase_      =  val





def __snake_case					(      UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						Union[str, Any]		):
 for i in range(config.vision_config.num_hidden_layers		):
  # read in original q and v biases
  lowerCamelCase_      =  state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''		)
  lowerCamelCase_      =  state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''		)

  # next, set bias in the state dict
  lowerCamelCase_      =  torch.cat((q_bias, torch.zeros_like(UpperCAmelCase_					,				requires_grad=UpperCAmelCase_		), v_bias)		)
  lowerCamelCase_      =  qkv_bias





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  364 if "coco" in model_name else 224
 lowerCamelCase_      =  BlipaVisionConfig(image_size=UpperCAmelCase_		).to_dict()

 # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
 # seems like flan-T5 models don't have bos_token_id properly set?
 if "opt-2.7b" in model_name:
  lowerCamelCase_      =  OPTConfig.from_pretrained("facebook/opt-2.7b"					,				eos_token_id=UpperCAmelCase_		).to_dict()
 elif "opt-6.7b" in model_name:
  lowerCamelCase_      =  OPTConfig.from_pretrained("facebook/opt-6.7b"					,				eos_token_id=UpperCAmelCase_		).to_dict()
 elif "t5-xl" in model_name:
  lowerCamelCase_      =  TaConfig.from_pretrained("google/flan-t5-xl"					,				dense_act_fn="gelu"					,				bos_token_id=1		).to_dict()
 elif "t5-xxl" in model_name:
  lowerCamelCase_      =  TaConfig.from_pretrained("google/flan-t5-xxl"					,				dense_act_fn="gelu"					,				bos_token_id=1		).to_dict()

 lowerCamelCase_      =  BlipaConfig(vision_config=UpperCAmelCase_					,				text_config=UpperCAmelCase_		)

 return config, image_size





@torch.no_grad()
def __snake_case					(      UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						Tuple=None					,				UpperCAmelCase_						:						Union[str, Any]=False		):
 lowerCamelCase_      =  (
     AutoTokenizer.from_pretrained("facebook/opt-2.7b"		)
     if "opt" in model_name
     else AutoTokenizer.from_pretrained("google/flan-t5-xl"		)
 )
 lowerCamelCase_      =  tokenizer("\n"					,				add_special_tokens=UpperCAmelCase_		).input_ids[0]
 lowerCamelCase_ ,lowerCamelCase_      =  get_blipa_config(UpperCAmelCase_					,				eos_token_id=UpperCAmelCase_		)

 lowerCamelCase_      =  BlipaForConditionalGeneration(UpperCAmelCase_		).eval()

 lowerCamelCase_      =  {
     "blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
     "blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
     "blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
     "blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
     "blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
     "blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"),
     "blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
 }

 lowerCamelCase_ ,lowerCamelCase_      =  model_name_to_original[model_name]

 # load original model
 print("Loading original model..."		)
 lowerCamelCase_      =  "cuda" if torch.cuda.is_available() else "cpu"
 lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  load_model_and_preprocess(
     name=UpperCAmelCase_					,				model_type=UpperCAmelCase_					,				is_eval=UpperCAmelCase_					,				device=UpperCAmelCase_		)
 original_model.eval()
 print("Done!"		)

 # update state dict keys
 lowerCamelCase_      =  original_model.state_dict()
 lowerCamelCase_      =  create_rename_keys(UpperCAmelCase_		)
 for src, dest in rename_keys:
  rename_key(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)

 # some keys can be renamed efficiently
 for key, val in state_dict.copy().items():
  lowerCamelCase_      =  state_dict.pop(UpperCAmelCase_		)
  if key.startswith("Qformer.bert"		):
   lowerCamelCase_      =  key.replace("Qformer.bert"					,				"qformer"		)
  if "attention.self" in key:
   lowerCamelCase_      =  key.replace("self"					,				"attention"		)
  if "opt_proj" in key:
   lowerCamelCase_      =  key.replace("opt_proj"					,				"language_projection"		)
  if "t5_proj" in key:
   lowerCamelCase_      =  key.replace("t5_proj"					,				"language_projection"		)
  if key.startswith("opt"		):
   lowerCamelCase_      =  key.replace("opt"					,				"language"		)
  if key.startswith("t5"		):
   lowerCamelCase_      =  key.replace("t5"					,				"language"		)
  lowerCamelCase_      =  val

 # read in qv biases
 read_in_q_v_bias(UpperCAmelCase_					,				UpperCAmelCase_		)

 lowerCamelCase_ ,lowerCamelCase_      =  hf_model.load_state_dict(UpperCAmelCase_					,				strict=UpperCAmelCase_		)
 assert len(UpperCAmelCase_		) == 0
 assert unexpected_keys == ["qformer.embeddings.position_ids"]

 lowerCamelCase_      =  load_demo_image()
 lowerCamelCase_      =  vis_processors["eval"](UpperCAmelCase_		).unsqueeze(0		).to(UpperCAmelCase_		)
 lowerCamelCase_      =  tokenizer(["\n"]					,				return_tensors="pt"		).input_ids.to(UpperCAmelCase_		)

 # create processor
 lowerCamelCase_      =  BlipImageProcessor(
     size={"height": image_size, "width": image_size}					,				image_mean=UpperCAmelCase_					,				image_std=UpperCAmelCase_		)
 lowerCamelCase_      =  BlipaProcessor(image_processor=UpperCAmelCase_					,				tokenizer=UpperCAmelCase_		)
 lowerCamelCase_      =  processor(images=UpperCAmelCase_					,				return_tensors="pt"		).pixel_values.to(UpperCAmelCase_		)

 # make sure processor creates exact same pixel values
 assert torch.allclose(UpperCAmelCase_					,				UpperCAmelCase_		)

 original_model.to(UpperCAmelCase_		)
 hf_model.to(UpperCAmelCase_		)
 with torch.no_grad():
  if "opt" in model_name:
   lowerCamelCase_      =  original_model({"image": original_pixel_values, "text_input": [""]}		).logits
   lowerCamelCase_      =  hf_model(UpperCAmelCase_					,				UpperCAmelCase_		).logits
  else:
   lowerCamelCase_      =  original_model(
       {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}		).logits
   lowerCamelCase_      =  input_ids.masked_fill(input_ids == tokenizer.pad_token_id					,				-100		)
   lowerCamelCase_      =  hf_model(UpperCAmelCase_					,				UpperCAmelCase_					,				labels=UpperCAmelCase_		).logits

 assert original_logits.shape == logits.shape
 print("First values of original logits:"					,				original_logits[0, :3, :3]		)
 print("First values of HF logits:"					,				logits[0, :3, :3]		)

 # assert values
 if model_name == "blip2-flan-t5-xl":
  lowerCamelCase_      =  torch.tensor(
      [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]					,				device=UpperCAmelCase_		)
  assert torch.allclose(logits[0, :3, :3]					,				UpperCAmelCase_					,				atol=1E-4		)
 elif model_name == "blip2-flan-t5-xl-coco":
  lowerCamelCase_      =  torch.tensor(
      [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]					,				device=UpperCAmelCase_		)
 else:
  # cast to same type
  lowerCamelCase_      =  logits.dtype
  assert torch.allclose(original_logits.to(UpperCAmelCase_		)					,				UpperCAmelCase_					,				atol=1E-2		)
 print("Looks ok!"		)

 print("Generating a caption..."		)
 lowerCamelCase_      =  ""
 lowerCamelCase_      =  tokenizer(UpperCAmelCase_					,				return_tensors="pt"		).input_ids.to(UpperCAmelCase_		)

 lowerCamelCase_      =  original_model.generate({"image": original_pixel_values}		)
 lowerCamelCase_      =  hf_model.generate(
     UpperCAmelCase_					,				UpperCAmelCase_					,				do_sample=UpperCAmelCase_					,				num_beams=5					,				max_length=30					,				min_length=1					,				top_p=0.9					,				repetition_penalty=1.0					,				length_penalty=1.0					,				temperature=1					,				)
 print("Original generation:"					,				UpperCAmelCase_		)
 lowerCamelCase_      =  input_ids.shape[1]
 lowerCamelCase_      =  processor.batch_decode(outputs[:, prompt_length:]					,				skip_special_tokens=UpperCAmelCase_		)
 lowerCamelCase_      =  [text.strip() for text in output_text]
 print("HF generation:"					,				UpperCAmelCase_		)

 if pytorch_dump_folder_path is not None:
  processor.save_pretrained(UpperCAmelCase_		)
  hf_model.save_pretrained(UpperCAmelCase_		)

 if push_to_hub:
  processor.push_to_hub(F'''nielsr/{model_name}'''		)
  hf_model.push_to_hub(F'''nielsr/{model_name}'''		)


if __name__ == "__main__":
     a_  :				Union[str, Any]						= argparse.ArgumentParser()
     a_  :				Optional[Any]						= [
         """blip2-opt-2.7b""",
         """blip2-opt-6.7b""",
         """blip2-opt-2.7b-coco""",
         """blip2-opt-6.7b-coco""",
         """blip2-flan-t5-xl""",
         """blip2-flan-t5-xl-coco""",
         """blip2-flan-t5-xxl""",
     ]
     parser.add_argument(
         """--model_name""",
         default="""blip2-opt-2.7b""",
         choices=choices,
         type=str,
         help="""Path to hf config.json of model to convert""",
     )
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument(
         """--push_to_hub""",
         action="""store_true""",
         help="""Whether to push the model and processor to the hub after converting""",
     )

     a_  :				Any						= parser.parse_args()

     convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''

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      snake_case    (    lowercase			,						lowercase			,						lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  StableUnCLIPPipeline
  _lowerCamelCase		    =  TEXT_TO_IMAGE_PARAMS
  _lowerCamelCase		    =  TEXT_TO_IMAGE_BATCH_PARAMS
  _lowerCamelCase		    =  TEXT_TO_IMAGE_IMAGE_PARAMS
  _lowerCamelCase		    =  TEXT_TO_IMAGE_IMAGE_PARAMS

  # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  32
   lowerCamelCase_      =  embedder_hidden_size

   # prior components

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTextModelWithProjection(
       CLIPTextConfig(
           bos_token_id=0			,  eos_token_id=2			,  hidden_size=UpperCamelCase			,  projection_dim=UpperCamelCase			,  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_      =  PriorTransformer(
       num_attention_heads=2			,  attention_head_dim=12			,  embedding_dim=UpperCamelCase			,  num_layers=1			,  )

   torch.manual_seed(0     )
   lowerCamelCase_      =  DDPMScheduler(
       variance_type="fixed_small_log"			,  prediction_type="sample"			,  num_train_timesteps=1000			,  clip_sample=UpperCamelCase			,  clip_sample_range=5.0			,  beta_schedule="squaredcos_cap_v2"			,  )

   # regular denoising components

   torch.manual_seed(0     )
   lowerCamelCase_      =  StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase     )
   lowerCamelCase_      =  DDPMScheduler(beta_schedule="squaredcos_cap_v2"     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

   torch.manual_seed(0     )
   lowerCamelCase_      =  CLIPTextModel(
       CLIPTextConfig(
           bos_token_id=0			,  eos_token_id=2			,  hidden_size=UpperCamelCase			,  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_      =  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=UpperCamelCase			,  layers_per_block=1			,  upcast_attention=UpperCamelCase			,  use_linear_projection=UpperCamelCase			,  )

   torch.manual_seed(0     )
   lowerCamelCase_      =  DDIMScheduler(
       beta_schedule="scaled_linear"			,  beta_start=0.00_085			,  beta_end=0.012			,  prediction_type="v_prediction"			,  set_alpha_to_one=UpperCamelCase			,  steps_offset=1			,  )

   torch.manual_seed(0     )
   lowerCamelCase_      =  AutoencoderKL()

   lowerCamelCase_      =  {
       # 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			,  UpperCamelCase			,  UpperCamelCase=0     ):







   """simple docstring"""
   if str(UpperCamelCase     ).startswith("mps"     ):
    lowerCamelCase_      =  torch.manual_seed(UpperCamelCase     )
   else:
    lowerCamelCase_      =  torch.Generator(device=UpperCamelCase     ).manual_seed(UpperCamelCase     )
   lowerCamelCase_      =  {
       "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_      =  torch_device == "cpu"

   self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase     )







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  torch_device in ["cpu", "mps"]

   self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase     )





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





  """simple docstring"""




  def   snake_case		(    self     ):







   """simple docstring"""
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  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_      =  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"			,  torch_dtype=torch.floataa     )
   pipe.to(UpperCamelCase     )
   pipe.set_progress_bar_config(disable=UpperCamelCase     )
   # 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_      =  torch.Generator(device="cpu"     ).manual_seed(0     )
   lowerCamelCase_      =  pipe("anime turle"			,  generator=UpperCamelCase			,  output_type="np"     )

   lowerCamelCase_      =  output.images[0]

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

   assert_mean_pixel_difference(UpperCamelCase			,  UpperCamelCase     )







  def   snake_case		(    self     ):







   """simple docstring"""
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()

   lowerCamelCase_      =  StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l"			,  torch_dtype=torch.floataa     )
   lowerCamelCase_      =  pipe.to(UpperCamelCase     )
   pipe.set_progress_bar_config(disable=UpperCamelCase     )
   pipe.enable_attention_slicing()
   pipe.enable_sequential_cpu_offload()

   lowerCamelCase_      =  pipe(
       "anime turtle"			,  prior_num_inference_steps=2			,  num_inference_steps=2			,  output_type="np"			,  )

   lowerCamelCase_      =  torch.cuda.max_memory_allocated()
   # make sure that less than 7 GB is allocated
   assert mem_bytes < 7 * 10**9



