


import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu





class  lowerCAmelCase__(    unittest.TestCase						):






    '''simple docstring'''





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





    @property
    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       UpperCAmelCase_							:      str						=   1
       UpperCAmelCase_							:      Any						=   3
       UpperCAmelCase_							:      List[str]						=   (32, 32)

       UpperCAmelCase_							:      Any						=   floats_tensor((batch_size, num_channels) + sizes       ,   rng=random.Random(0	)	).to(__snake_case	)
       return image





    @property
    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       torch.manual_seed(0	)
       UpperCAmelCase_							:      Union[str, Any]						=   UNetaDConditionModel(
           block_out_channels=(32, 64)       ,   layers_per_block=2       ,   sample_size=32       ,   in_channels=4       ,   out_channels=4       ,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,   cross_attention_dim=32       ,   )
       return model





    @property
    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       torch.manual_seed(0	)
       UpperCAmelCase_							:      str						=   AutoencoderKL(
           block_out_channels=[32, 64]       ,   in_channels=3       ,   out_channels=3       ,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,   latent_channels=4       ,   )
       return model





    @property
    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       torch.manual_seed(0	)
       UpperCAmelCase_							:      Any						=   CLIPTextConfig(
           bos_token_id=0       ,   eos_token_id=2       ,   hidden_size=32       ,   intermediate_size=37       ,   layer_norm_eps=1E-05       ,   num_attention_heads=4       ,   num_hidden_layers=5       ,   pad_token_id=1       ,   vocab_size=1_000       ,   )
       return CLIPTextModel(__snake_case	)





    @property
    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       def extract(*__snake_case				:   Any       ,   **__snake_case				:   List[str]	):





          class  lowerCAmelCase__:






              '''simple docstring'''





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





                 '''simple docstring'''
                 UpperCAmelCase_							:      Dict						=   torch.ones([0]	)





              def 					_lowerCamelCase (	self				:   Optional[Any]       ,   __snake_case				:   List[Any]	):





                 '''simple docstring'''
                 self.pixel_values.to(__snake_case	)
                 return self





          return Out()

       return extract





    def 					_lowerCamelCase (	self				:   Optional[Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Tuple						=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_							:      List[str]						=   self.dummy_cond_unet
       UpperCAmelCase_							:      Tuple						=   DDIMScheduler(
           beta_start=0.00_085       ,   beta_end=0.012       ,   beta_schedule='''scaled_linear'''       ,   clip_sample=__snake_case       ,   set_alpha_to_one=__snake_case       ,   )

       UpperCAmelCase_							:      int						=   self.dummy_vae
       UpperCAmelCase_							:      Dict						=   self.dummy_text_encoder
       UpperCAmelCase_							:      Any						=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

       # make sure here that pndm scheduler skips prk
       UpperCAmelCase_							:      int						=   StableDiffusionPipeline(
           unet=__snake_case       ,   scheduler=__snake_case       ,   vae=__snake_case       ,   text_encoder=__snake_case       ,   tokenizer=__snake_case       ,   safety_checker=__snake_case       ,   feature_extractor=self.dummy_extractor       ,   )
       UpperCAmelCase_							:      int						=   sd_pipe.to(__snake_case	)
       sd_pipe.set_progress_bar_config(disable=__snake_case	)

       UpperCAmelCase_							:      int						=   '''A painting of a squirrel eating a burger'''

       UpperCAmelCase_							:      Optional[int]						=   torch.Generator(device=__snake_case	).manual_seed(0	)
       UpperCAmelCase_							:      Any						=   sd_pipe([prompt]       ,   generator=__snake_case       ,   guidance_scale=6.0       ,   num_inference_steps=2       ,   output_type='''np'''	)
       UpperCAmelCase_							:      Tuple						=   output.images

       UpperCAmelCase_							:      str						=   torch.Generator(device=__snake_case	).manual_seed(0	)
       UpperCAmelCase_							:      Optional[Any]						=   sd_pipe(
           [prompt]       ,   generator=__snake_case       ,   guidance_scale=6.0       ,   num_inference_steps=2       ,   output_type='''np'''       ,   return_dict=__snake_case       ,   )[0]

       UpperCAmelCase_							:      Any						=   image[0, -3:, -3:, -1]
       UpperCAmelCase_							:      int						=   image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       UpperCAmelCase_							:      Optional[int]						=   np.array([0.5_756, 0.6_118, 0.5_005, 0.5_041, 0.5_471, 0.4_726, 0.4_976, 0.4_865, 0.4_864]	)

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





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       UpperCAmelCase_							:      str						=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_							:      int						=   self.dummy_cond_unet
       UpperCAmelCase_							:      int						=   PNDMScheduler(skip_prk_steps=__snake_case	)
       UpperCAmelCase_							:      List[str]						=   self.dummy_vae
       UpperCAmelCase_							:      Dict						=   self.dummy_text_encoder
       UpperCAmelCase_							:      str						=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

       # make sure here that pndm scheduler skips prk
       UpperCAmelCase_							:      Optional[Any]						=   StableDiffusionPipeline(
           unet=__snake_case       ,   scheduler=__snake_case       ,   vae=__snake_case       ,   text_encoder=__snake_case       ,   tokenizer=__snake_case       ,   safety_checker=__snake_case       ,   feature_extractor=self.dummy_extractor       ,   )
       UpperCAmelCase_							:      Dict						=   sd_pipe.to(__snake_case	)
       sd_pipe.set_progress_bar_config(disable=__snake_case	)

       UpperCAmelCase_							:      Dict						=   '''A painting of a squirrel eating a burger'''
       UpperCAmelCase_							:      Optional[Any]						=   torch.Generator(device=__snake_case	).manual_seed(0	)
       UpperCAmelCase_							:      List[Any]						=   sd_pipe([prompt]       ,   generator=__snake_case       ,   guidance_scale=6.0       ,   num_inference_steps=2       ,   output_type='''np'''	)

       UpperCAmelCase_							:      Optional[int]						=   output.images

       UpperCAmelCase_							:      Tuple						=   torch.Generator(device=__snake_case	).manual_seed(0	)
       UpperCAmelCase_							:      List[Any]						=   sd_pipe(
           [prompt]       ,   generator=__snake_case       ,   guidance_scale=6.0       ,   num_inference_steps=2       ,   output_type='''np'''       ,   return_dict=__snake_case       ,   )[0]

       UpperCAmelCase_							:      int						=   image[0, -3:, -3:, -1]
       UpperCAmelCase_							:      Union[str, Any]						=   image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       UpperCAmelCase_							:      List[Any]						=   np.array([0.5_125, 0.5_716, 0.4_828, 0.5_060, 0.5_650, 0.4_768, 0.5_185, 0.4_895, 0.4_993]	)

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





    def 					_lowerCamelCase (	self				:   str	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[Any]						=   StableDiffusionPipeline.from_pretrained(
           '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''       ,   safety_checker=__snake_case	)
       assert isinstance(__snake_case       ,   __snake_case	)
       assert isinstance(pipe.scheduler       ,   __snake_case	)
       assert pipe.safety_checker is None

       UpperCAmelCase_							:      List[str]						=   pipe('''example prompt'''       ,   num_inference_steps=2	).images[0]
       assert image is not None

       # check that there's no error when saving a pipeline with one of the models being None
       with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(__snake_case	)
          UpperCAmelCase_							:      List[Any]						=   StableDiffusionPipeline.from_pretrained(__snake_case	)

       # sanity check that the pipeline still works
       assert pipe.safety_checker is None
       UpperCAmelCase_							:      int						=   pipe('''example prompt'''       ,   num_inference_steps=2	).images[0]
       assert image is not None





    @unittest.skipIf(torch_device != '''cuda'''       ,   '''This test requires a GPU'''	)
    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   self.dummy_cond_unet
       UpperCAmelCase_							:      Optional[int]						=   PNDMScheduler(skip_prk_steps=__snake_case	)
       UpperCAmelCase_							:      Tuple						=   self.dummy_vae
       UpperCAmelCase_							:      str						=   self.dummy_text_encoder
       UpperCAmelCase_							:      Optional[int]						=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

       # put models in fp16
       UpperCAmelCase_							:      Tuple						=   unet.half()
       UpperCAmelCase_							:      Dict						=   vae.half()
       UpperCAmelCase_							:      Tuple						=   bert.half()

       # make sure here that pndm scheduler skips prk
       UpperCAmelCase_							:      Tuple						=   StableDiffusionPipeline(
           unet=__snake_case       ,   scheduler=__snake_case       ,   vae=__snake_case       ,   text_encoder=__snake_case       ,   tokenizer=__snake_case       ,   safety_checker=__snake_case       ,   feature_extractor=self.dummy_extractor       ,   )
       UpperCAmelCase_							:      Optional[Any]						=   sd_pipe.to(__snake_case	)
       sd_pipe.set_progress_bar_config(disable=__snake_case	)

       UpperCAmelCase_							:      Union[str, Any]						=   '''A painting of a squirrel eating a burger'''
       UpperCAmelCase_							:      Optional[Any]						=   sd_pipe([prompt]       ,   num_inference_steps=2       ,   output_type='''np'''	).images

       assert image.shape == (1, 64, 64, 3)





@nightly
@require_torch_gpu
class  lowerCAmelCase__(    unittest.TestCase						):






    '''simple docstring'''





    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





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





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''       ,   safety_checker=__snake_case	)
       UpperCAmelCase_							:      List[Any]						=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
       UpperCAmelCase_							:      List[Any]						=   sd_pipe.to(__snake_case	)
       sd_pipe.set_progress_bar_config(disable=__snake_case	)

       UpperCAmelCase_							:      Any						=   (
           '''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
           ''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
           ''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
           ''' children from bahnhof zoo, detailed '''
       )
       UpperCAmelCase_							:      Optional[Any]						=   4_003_660_346
       UpperCAmelCase_							:      Optional[Any]						=   7

       # without safety guidance (sld_guidance_scale = 0)
       UpperCAmelCase_							:      List[Any]						=   torch.manual_seed(__snake_case	)
       UpperCAmelCase_							:      Optional[int]						=   sd_pipe(
           [prompt]       ,   generator=__snake_case       ,   guidance_scale=__snake_case       ,   num_inference_steps=50       ,   output_type='''np'''       ,   width=512       ,   height=512       ,   sld_guidance_scale=0       ,   )

       UpperCAmelCase_							:      str						=   output.images
       UpperCAmelCase_							:      int						=   image[0, -3:, -3:, -1]
       UpperCAmelCase_							:      str						=   [0.2_278, 0.2_231, 0.2_249, 0.2_333, 0.2_303, 0.1_885, 0.2_273, 0.2_144, 0.2_176]

       assert image.shape == (1, 512, 512, 3)

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

       # without safety guidance (strong configuration)
       UpperCAmelCase_							:      List[str]						=   torch.manual_seed(__snake_case	)
       UpperCAmelCase_							:      Optional[Any]						=   sd_pipe(
           [prompt]       ,   generator=__snake_case       ,   guidance_scale=__snake_case       ,   num_inference_steps=50       ,   output_type='''np'''       ,   width=512       ,   height=512       ,   sld_guidance_scale=2_000       ,   sld_warmup_steps=7       ,   sld_threshold=0.025       ,   sld_momentum_scale=0.5       ,   sld_mom_beta=0.7       ,   )

       UpperCAmelCase_							:      List[str]						=   output.images
       UpperCAmelCase_							:      List[Any]						=   image[0, -3:, -3:, -1]
       UpperCAmelCase_							:      Any						=   [0.2_383, 0.2_276, 0.236, 0.2_192, 0.2_186, 0.2_053, 0.1_971, 0.1_901, 0.1_719]

       assert image.shape == (1, 512, 512, 3)

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





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''       ,   safety_checker=__snake_case	)
       UpperCAmelCase_							:      str						=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
       UpperCAmelCase_							:      Optional[Any]						=   sd_pipe.to(__snake_case	)
       sd_pipe.set_progress_bar_config(disable=__snake_case	)

       UpperCAmelCase_							:      Optional[Any]						=   '''padme amidala taking a bath artwork, safe for work, no nudity'''
       UpperCAmelCase_							:      str						=   2_734_971_755
       UpperCAmelCase_							:      Tuple						=   7

       UpperCAmelCase_							:      Any						=   torch.manual_seed(__snake_case	)
       UpperCAmelCase_							:      List[Any]						=   sd_pipe(
           [prompt]       ,   generator=__snake_case       ,   guidance_scale=__snake_case       ,   num_inference_steps=50       ,   output_type='''np'''       ,   width=512       ,   height=512       ,   sld_guidance_scale=0       ,   )

       UpperCAmelCase_							:      int						=   output.images
       UpperCAmelCase_							:      Optional[Any]						=   image[0, -3:, -3:, -1]
       UpperCAmelCase_							:      Optional[Any]						=   [0.3_502, 0.3_622, 0.3_396, 0.3_642, 0.3_478, 0.3_318, 0.35, 0.3_348, 0.3_297]

       assert image.shape == (1, 512, 512, 3)

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

       UpperCAmelCase_							:      Optional[int]						=   torch.manual_seed(__snake_case	)
       UpperCAmelCase_							:      Union[str, Any]						=   sd_pipe(
           [prompt]       ,   generator=__snake_case       ,   guidance_scale=__snake_case       ,   num_inference_steps=50       ,   output_type='''np'''       ,   width=512       ,   height=512       ,   sld_guidance_scale=2_000       ,   sld_warmup_steps=7       ,   sld_threshold=0.025       ,   sld_momentum_scale=0.5       ,   sld_mom_beta=0.7       ,   )

       UpperCAmelCase_							:      Optional[int]						=   output.images
       UpperCAmelCase_							:      Dict						=   image[0, -3:, -3:, -1]
       UpperCAmelCase_							:      int						=   [0.5_531, 0.5_206, 0.4_895, 0.5_156, 0.5_182, 0.4_751, 0.4_802, 0.4_803, 0.4_443]

       assert image.shape == (1, 512, 512, 3)

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





    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       UpperCAmelCase_							:      Any						=   StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''	)
       UpperCAmelCase_							:      int						=   sd_pipe.to(__snake_case	)
       sd_pipe.set_progress_bar_config(disable=__snake_case	)

       UpperCAmelCase_							:      Union[str, Any]						=   (
           '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
           ''' leyendecker'''
       )
       UpperCAmelCase_							:      Optional[Any]						=   1_044_355_234
       UpperCAmelCase_							:      int						=   12

       UpperCAmelCase_							:      List[Any]						=   torch.manual_seed(__snake_case	)
       UpperCAmelCase_							:      Optional[int]						=   sd_pipe(
           [prompt]       ,   generator=__snake_case       ,   guidance_scale=__snake_case       ,   num_inference_steps=50       ,   output_type='''np'''       ,   width=512       ,   height=512       ,   sld_guidance_scale=0       ,   )

       UpperCAmelCase_							:      Optional[int]						=   output.images
       UpperCAmelCase_							:      Optional[int]						=   image[0, -3:, -3:, -1]
       UpperCAmelCase_							:      Tuple						=   np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]	)

       assert image.shape == (1, 512, 512, 3)

       assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-7

       UpperCAmelCase_							:      Dict						=   torch.manual_seed(__snake_case	)
       UpperCAmelCase_							:      List[Any]						=   sd_pipe(
           [prompt]       ,   generator=__snake_case       ,   guidance_scale=__snake_case       ,   num_inference_steps=50       ,   output_type='''np'''       ,   width=512       ,   height=512       ,   sld_guidance_scale=2_000       ,   sld_warmup_steps=7       ,   sld_threshold=0.025       ,   sld_momentum_scale=0.5       ,   sld_mom_beta=0.7       ,   )

       UpperCAmelCase_							:      List[str]						=   output.images
       UpperCAmelCase_							:      List[str]						=   image[0, -3:, -3:, -1]
       UpperCAmelCase_							:      Dict						=   np.array([0.5_818, 0.6_285, 0.6_835, 0.6_019, 0.625, 0.6_754, 0.6_096, 0.6_334, 0.6_561]	)
       assert image.shape == (1, 512, 512, 3)

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



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_						( ):
   UpperCAmelCase_							:      int						=   '''https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'''
   UpperCAmelCase_							:      Dict						=   Image.open(requests.get(__lowercase			,       stream=__lowercase       ).raw       ).convert('''RGB'''       )

   return image
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      List[str]						=   []
   # 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_						( __lowercase			,       __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      Any						=   dct.pop(__lowercase       )
   UpperCAmelCase_							:      Optional[Any]						=   val
def      snake_case_						( __lowercase			,       __lowercase       ):
   for i in range(config.vision_config.num_hidden_layers       ):
      # read in original q and v biases
      UpperCAmelCase_							:      Optional[int]						=   state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''       )
      UpperCAmelCase_							:      Any						=   state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''       )

      # next, set bias in the state dict
      UpperCAmelCase_							:      int						=   torch.cat((q_bias, torch.zeros_like(__lowercase			,       requires_grad=__lowercase       ), v_bias)       )
      UpperCAmelCase_							:      List[str]						=   qkv_bias
def      snake_case_						( __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      Optional[Any]						=   3_6_4 if '''coco''' in model_name else 2_2_4
   UpperCAmelCase_							:      Any						=   BlipaVisionConfig(image_size=__lowercase       ).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:
      UpperCAmelCase_							:      Any						=   OPTConfig.from_pretrained('''facebook/opt-2.7b'''			,       eos_token_id=__lowercase       ).to_dict()
   elif "opt-6.7b" in model_name:
      UpperCAmelCase_							:      List[str]						=   OPTConfig.from_pretrained('''facebook/opt-6.7b'''			,       eos_token_id=__lowercase       ).to_dict()
   elif "t5-xl" in model_name:
      UpperCAmelCase_							:      List[str]						=   TaConfig.from_pretrained('''google/flan-t5-xl'''			,       dense_act_fn='''gelu'''			,       bos_token_id=1       ).to_dict()
   elif "t5-xxl" in model_name:
      UpperCAmelCase_							:      Any						=   TaConfig.from_pretrained('''google/flan-t5-xxl'''			,       dense_act_fn='''gelu'''			,       bos_token_id=1       ).to_dict()

   UpperCAmelCase_							:      List[Any]						=   BlipaConfig(vision_config=__lowercase			,       text_config=__lowercase       )

   return config, image_size
@torch.no_grad()
def      snake_case_						( __lowercase			,       __lowercase=None			,       __lowercase=False       ):
   UpperCAmelCase_							:      List[Any]						=   (
       AutoTokenizer.from_pretrained('''facebook/opt-2.7b'''       )
       if '''opt''' in model_name
       else AutoTokenizer.from_pretrained('''google/flan-t5-xl'''       )
   )
   UpperCAmelCase_							:      List[str]						=   tokenizer('''\n'''			,       add_special_tokens=__lowercase       ).input_ids[0]
   UpperCAmelCase_		,      UpperCAmelCase_							:      str						=   get_blipa_config(__lowercase			,       eos_token_id=__lowercase       )

   UpperCAmelCase_							:      List[Any]						=   BlipaForConditionalGeneration(__lowercase       ).eval()

   UpperCAmelCase_							:      Tuple						=   {
       '''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'''),
   }

   UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[Any]						=   model_name_to_original[model_name]

   # load original model
   print('''Loading original model...'''       )
   UpperCAmelCase_							:      int						=   '''cuda''' if torch.cuda.is_available() else '''cpu'''
   UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[Any]						=   load_model_and_preprocess(
       name=__lowercase			,       model_type=__lowercase			,       is_eval=__lowercase			,       device=__lowercase       )
   original_model.eval()
   print('''Done!'''       )

   # update state dict keys
   UpperCAmelCase_							:      Optional[Any]						=   original_model.state_dict()
   UpperCAmelCase_							:      List[Any]						=   create_rename_keys(__lowercase       )
   for src, dest in rename_keys:
      rename_key(__lowercase			,       __lowercase			,       __lowercase       )

   # some keys can be renamed efficiently
   for key, val in state_dict.copy().items():
      UpperCAmelCase_							:      Union[str, Any]						=   state_dict.pop(__lowercase       )
      if key.startswith('''Qformer.bert'''       ):
         UpperCAmelCase_							:      Tuple						=   key.replace('''Qformer.bert'''			,       '''qformer'''       )
      if "attention.self" in key:
         UpperCAmelCase_							:      Optional[Any]						=   key.replace('''self'''			,       '''attention'''       )
      if "opt_proj" in key:
         UpperCAmelCase_							:      Any						=   key.replace('''opt_proj'''			,       '''language_projection'''       )
      if "t5_proj" in key:
         UpperCAmelCase_							:      Tuple						=   key.replace('''t5_proj'''			,       '''language_projection'''       )
      if key.startswith('''opt'''       ):
         UpperCAmelCase_							:      Any						=   key.replace('''opt'''			,       '''language'''       )
      if key.startswith('''t5'''       ):
         UpperCAmelCase_							:      Optional[Any]						=   key.replace('''t5'''			,       '''language'''       )
      UpperCAmelCase_							:      List[str]						=   val

   # read in qv biases
   read_in_q_v_bias(__lowercase			,       __lowercase       )

   UpperCAmelCase_		,      UpperCAmelCase_							:      Optional[int]						=   hf_model.load_state_dict(__lowercase			,       strict=__lowercase       )
   assert len(__lowercase       ) == 0
   assert unexpected_keys == ["qformer.embeddings.position_ids"]

   UpperCAmelCase_							:      str						=   load_demo_image()
   UpperCAmelCase_							:      Any						=   vis_processors['''eval'''](__lowercase       ).unsqueeze(0       ).to(__lowercase       )
   UpperCAmelCase_							:      Optional[Any]						=   tokenizer(['''\n''']			,       return_tensors='''pt'''       ).input_ids.to(__lowercase       )

   # create processor
   UpperCAmelCase_							:      Optional[int]						=   BlipImageProcessor(
       size={'''height''': image_size, '''width''': image_size}			,       image_mean=__lowercase			,       image_std=__lowercase       )
   UpperCAmelCase_							:      Tuple						=   BlipaProcessor(image_processor=__lowercase			,       tokenizer=__lowercase       )
   UpperCAmelCase_							:      str						=   processor(images=__lowercase			,       return_tensors='''pt'''       ).pixel_values.to(__lowercase       )

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

   original_model.to(__lowercase       )
   hf_model.to(__lowercase       )
   with torch.no_grad():
      if "opt" in model_name:
         UpperCAmelCase_							:      Tuple						=   original_model({'''image''': original_pixel_values, '''text_input''': ['''''']}       ).logits
         UpperCAmelCase_							:      Optional[int]						=   hf_model(__lowercase			,       __lowercase       ).logits
      else:
         UpperCAmelCase_							:      int						=   original_model(
             {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']}       ).logits
         UpperCAmelCase_							:      Optional[int]						=   input_ids.masked_fill(input_ids == tokenizer.pad_token_id			,       -1_0_0       )
         UpperCAmelCase_							:      int						=   hf_model(__lowercase			,       __lowercase			,       labels=__lowercase       ).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":
      UpperCAmelCase_							:      Tuple						=   torch.tensor(
          [[-4_1.5_8_5_0, -4.4_4_4_0, -8.9_9_2_2], [-4_7.4_3_2_2, -5.9_1_4_3, -1.7_3_4_0]]			,       device=__lowercase       )
      assert torch.allclose(logits[0, :3, :3]			,       __lowercase			,       atol=1e-4       )
   elif model_name == "blip2-flan-t5-xl-coco":
      UpperCAmelCase_							:      Tuple						=   torch.tensor(
          [[-5_7.0_1_0_9, -9.8_9_6_7, -1_2.6_2_8_0], [-6_8.6_5_7_8, -1_2.7_1_9_1, -1_0.5_0_6_5]]			,       device=__lowercase       )
   else:
      # cast to same type
      UpperCAmelCase_							:      Optional[int]						=   logits.dtype
      assert torch.allclose(original_logits.to(__lowercase       )			,       __lowercase			,       atol=1e-2       )
   print('''Looks ok!'''       )

   print('''Generating a caption...'''       )
   UpperCAmelCase_							:      Union[str, Any]						=   ''''''
   UpperCAmelCase_							:      Optional[Any]						=   tokenizer(__lowercase			,       return_tensors='''pt'''       ).input_ids.to(__lowercase       )

   UpperCAmelCase_							:      int						=   original_model.generate({'''image''': original_pixel_values}       )
   UpperCAmelCase_							:      Optional[int]						=   hf_model.generate(
       __lowercase			,       __lowercase			,       do_sample=__lowercase			,       num_beams=5			,       max_length=3_0			,       min_length=1			,       top_p=0.9			,       repetition_penalty=1.0			,       length_penalty=1.0			,       temperature=1			,       )
   print('''Original generation:'''			,       __lowercase       )
   UpperCAmelCase_							:      Tuple						=   input_ids.shape[1]
   UpperCAmelCase_							:      str						=   processor.batch_decode(outputs[:, prompt_length:]			,       skip_special_tokens=__lowercase       )
   UpperCAmelCase_							:      Optional[int]						=   [text.strip() for text in output_text]
   print('''HF generation:'''			,       __lowercase       )

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

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


if __name__ == "__main__":
       __UpperCamelCase				:  List[Any]      					=							argparse.ArgumentParser()
       __UpperCamelCase				:  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',
       )

       __UpperCamelCase				:  int      					=							parser.parse_args()

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