




"""simple docstring"""


import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class       lowercase(  __a ,    __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      StableDiffusionDiffEditPipeline
 lowercase__				      =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
 lowercase__				      =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
 lowercase__				      =      frozenset(
     []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 lowercase__				      =      frozenset([]				)


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   torch.manual_seed(0	)
   _snake_case							:	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,   attention_head_dim=(2, 4),   use_linear_projection=a_,   )
   _snake_case							:	Optional[Any]			=						DDIMScheduler(
       beta_start=0.00_085,   beta_end=0.012,   beta_schedule="""scaled_linear""",   clip_sample=a_,   set_alpha_to_one=a_,   )
   _snake_case							:	Dict			=						DDIMInverseScheduler(
       beta_start=0.00_085,   beta_end=0.012,   beta_schedule="""scaled_linear""",   clip_sample=a_,   set_alpha_to_zero=a_,   )
   torch.manual_seed(0	)
   _snake_case							:	List[Any]			=						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,   sample_size=128,   )
   torch.manual_seed(0	)
   _snake_case							:	Optional[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,   hidden_act="""gelu""",   projection_dim=512,   )
   _snake_case							:	Any			=						CLIPTextModel(a_	)
   _snake_case							:	Any			=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""	)

   _snake_case							:	List[Any]			=						{
       """unet""": unet,
       """scheduler""": scheduler,
       """inverse_scheduler""": inverse_scheduler,
       """vae""": vae,
       """text_encoder""": text_encoder,
       """tokenizer""": tokenizer,
       """safety_checker""": None,
       """feature_extractor""": None,
   }

   return components


 def 				UpperCamelCase_     (       self:	Dict,   a_:	Optional[Any],   a_:	Union[str, Any]=0	):




   '''simple docstring'''



   _snake_case							:	str			=						floats_tensor((1, 16, 16),   rng=random.Random(a_	)	).to(a_	)
   _snake_case							:	Dict			=						floats_tensor((1, 2, 4, 16, 16),   rng=random.Random(a_	)	).to(a_	)
   if str(a_	).startswith("""mps"""	):
     _snake_case							:	Union[str, Any]			=						torch.manual_seed(a_	)
   else:
     _snake_case							:	Tuple			=						torch.Generator(device=a_	).manual_seed(a_	)
   _snake_case							:	List[str]			=						{
       """prompt""": """a dog and a newt""",
       """mask_image""": mask,
       """image_latents""": latents,
       """generator""": generator,
       """num_inference_steps""": 2,
       """inpaint_strength""": 1.0,
       """guidance_scale""": 6.0,
       """output_type""": """numpy""",
   }

   return inputs


 def 				UpperCamelCase_     (       self:	Any,   a_:	Tuple,   a_:	str=0	):




   '''simple docstring'''



   _snake_case							:	str			=						floats_tensor((1, 3, 32, 32),   rng=random.Random(a_	)	).to(a_	)
   _snake_case							:	Union[str, Any]			=						image.cpu().permute(0,   2,   3,   1	)[0]
   _snake_case							:	int			=						Image.fromarray(np.uinta(a_	)	).convert("""RGB"""	)
   if str(a_	).startswith("""mps"""	):
     _snake_case							:	List[str]			=						torch.manual_seed(a_	)
   else:
     _snake_case							:	List[str]			=						torch.Generator(device=a_	).manual_seed(a_	)
   _snake_case							:	Union[str, Any]			=						{
       """image""": image,
       """source_prompt""": """a cat and a frog""",
       """target_prompt""": """a dog and a newt""",
       """generator""": generator,
       """num_inference_steps""": 2,
       """num_maps_per_mask""": 2,
       """mask_encode_strength""": 1.0,
       """guidance_scale""": 6.0,
       """output_type""": """numpy""",
   }

   return inputs


 def 				UpperCamelCase_     (       self:	Optional[int],   a_:	Optional[Any],   a_:	int=0	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						floats_tensor((1, 3, 32, 32),   rng=random.Random(a_	)	).to(a_	)
   _snake_case							:	Optional[int]			=						image.cpu().permute(0,   2,   3,   1	)[0]
   _snake_case							:	List[Any]			=						Image.fromarray(np.uinta(a_	)	).convert("""RGB"""	)
   if str(a_	).startswith("""mps"""	):
     _snake_case							:	str			=						torch.manual_seed(a_	)
   else:
     _snake_case							:	List[str]			=						torch.Generator(device=a_	).manual_seed(a_	)
   _snake_case							:	Tuple			=						{
       """image""": image,
       """prompt""": """a cat and a frog""",
       """generator""": generator,
       """num_inference_steps""": 2,
       """inpaint_strength""": 1.0,
       """guidance_scale""": 6.0,
       """decode_latents""": True,
       """output_type""": """numpy""",
   }
   return inputs


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   if not hasattr(self.pipeline_class,   """_optional_components"""	):
     return

   _snake_case							:	str			=						self.get_dummy_components()
   _snake_case							:	List[str]			=						self.pipeline_class(**a_	)
   pipe.to(a_	)
   pipe.set_progress_bar_config(disable=a_	)

   # set all optional components to None and update pipeline config accordingly
   for optional_component in pipe._optional_components:
     setattr(a_,   a_,   a_	)
   pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}	)

   _snake_case							:	List[Any]			=						self.get_dummy_inputs(a_	)
   _snake_case							:	Tuple			=						pipe(**a_	)[0]

   with tempfile.TemporaryDirectory() as tmpdir:
     pipe.save_pretrained(a_	)
     _snake_case							:	Dict			=						self.pipeline_class.from_pretrained(a_	)
     pipe_loaded.to(a_	)
     pipe_loaded.set_progress_bar_config(disable=a_	)

   for optional_component in pipe._optional_components:
     self.assertTrue(
         getattr(a_,   a_	) is None,   f"`{optional_component}` did not stay set to None after loading.",   )

   _snake_case							:	int			=						self.get_dummy_inputs(a_	)
   _snake_case							:	Optional[int]			=						pipe_loaded(**a_	)[0]

   _snake_case							:	Any			=						np.abs(output - output_loaded	).max()
   self.assertLess(a_,   1E-4	)


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




   '''simple docstring'''



   _snake_case							:	Tuple			=						"""cpu"""

   _snake_case							:	Dict			=						self.get_dummy_components()
   _snake_case							:	Any			=						self.pipeline_class(**a_	)
   pipe.to(a_	)
   pipe.set_progress_bar_config(disable=a_	)

   _snake_case							:	Tuple			=						self.get_dummy_mask_inputs(a_	)
   _snake_case							:	Union[str, Any]			=						pipe.generate_mask(**a_	)
   _snake_case							:	str			=						mask[0, -3:, -3:]

   self.assertEqual(mask.shape,   (1, 16, 16)	)
   _snake_case							:	Optional[Any]			=						np.array([0] * 9	)
   _snake_case							:	Tuple			=						np.abs(mask_slice.flatten() - expected_slice	).max()
   self.assertLessEqual(a_,   1E-3	)
   self.assertEqual(mask[0, -3, -4],   0	)


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						"""cpu"""

   _snake_case							:	Tuple			=						self.get_dummy_components()
   _snake_case							:	Union[str, Any]			=						self.pipeline_class(**a_	)
   pipe.to(a_	)
   pipe.set_progress_bar_config(disable=a_	)

   _snake_case							:	Union[str, Any]			=						self.get_dummy_inversion_inputs(a_	)
   _snake_case							:	Optional[Any]			=						pipe.invert(**a_	).images
   _snake_case							:	List[Any]			=						image[0, -1, -3:, -3:]

   self.assertEqual(image.shape,   (2, 32, 32, 3)	)
   _snake_case							:	Dict			=						np.array(
       [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799],   )
   _snake_case							:	List[Any]			=						np.abs(image_slice.flatten() - expected_slice	).max()
   self.assertLessEqual(a_,   1E-3	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   super().test_inference_batch_single_identical(expected_max_diff=5E-3	)


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						"""cpu"""

   _snake_case							:	List[Any]			=						self.get_dummy_components()

   _snake_case							:	Optional[Any]			=						{"""beta_start""": 0.00_085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
   _snake_case							:	List[str]			=						DPMSolverMultistepScheduler(**a_	)
   _snake_case							:	List[str]			=						DPMSolverMultistepInverseScheduler(**a_	)

   _snake_case							:	Optional[Any]			=						self.pipeline_class(**a_	)
   pipe.to(a_	)
   pipe.set_progress_bar_config(disable=a_	)

   _snake_case							:	int			=						self.get_dummy_inversion_inputs(a_	)
   _snake_case							:	Union[str, Any]			=						pipe.invert(**a_	).images
   _snake_case							:	Dict			=						image[0, -1, -3:, -3:]

   self.assertEqual(image.shape,   (2, 32, 32, 3)	)
   _snake_case							:	Dict			=						np.array(
       [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799],   )
   _snake_case							:	List[str]			=						np.abs(image_slice.flatten() - expected_slice	).max()
   self.assertLessEqual(a_,   1E-3	)







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







 '''simple docstring'''





 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



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


 @classmethod
 def 				UpperCamelCase_     (       cls:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"""	)

   _snake_case							:	Optional[Any]			=						raw_image.convert("""RGB"""	).resize((768, 768)	)

   _snake_case							:	List[Any]			=						raw_image


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	str			=						torch.manual_seed(0	)

   _snake_case							:	Optional[int]			=						StableDiffusionDiffEditPipeline.from_pretrained(
       """stabilityai/stable-diffusion-2-1""",   safety_checker=a_,   torch_dtype=torch.floataa	)
   _snake_case							:	Optional[Any]			=						DDIMScheduler.from_config(pipe.scheduler.config	)
   _snake_case							:	str			=						DDIMInverseScheduler.from_config(pipe.scheduler.config	)
   pipe.enable_model_cpu_offload()
   pipe.set_progress_bar_config(disable=a_	)

   _snake_case							:	str			=						"""a bowl of fruit"""
   _snake_case							:	str			=						"""a bowl of pears"""

   _snake_case							:	str			=						pipe.generate_mask(
       image=self.raw_image,   source_prompt=a_,   target_prompt=a_,   generator=a_,   )

   _snake_case							:	str			=						pipe.invert(
       prompt=a_,   image=self.raw_image,   inpaint_strength=0.7,   generator=a_	).latents

   _snake_case							:	Optional[Any]			=						pipe(
       prompt=a_,   mask_image=a_,   image_latents=a_,   generator=a_,   negative_prompt=a_,   inpaint_strength=0.7,   output_type="""numpy""",   ).images[0]

   _snake_case							:	str			=						(
       np.array(
           load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/diffedit/pears.png"""	).resize((768, 768)	)	)
       / 255
   )
   assert np.abs((expected_image - image).max()	) < 5E-1


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						torch.manual_seed(0	)

   _snake_case							:	List[Any]			=						StableDiffusionDiffEditPipeline.from_pretrained(
       """stabilityai/stable-diffusion-2-1""",   safety_checker=a_,   torch_dtype=torch.floataa	)
   _snake_case							:	Optional[Any]			=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
   _snake_case							:	str			=						DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config	)
   pipe.enable_model_cpu_offload()
   pipe.set_progress_bar_config(disable=a_	)

   _snake_case							:	Any			=						"""a bowl of fruit"""
   _snake_case							:	Union[str, Any]			=						"""a bowl of pears"""

   _snake_case							:	List[Any]			=						pipe.generate_mask(
       image=self.raw_image,   source_prompt=a_,   target_prompt=a_,   generator=a_,   )

   _snake_case							:	int			=						pipe.invert(
       prompt=a_,   image=self.raw_image,   inpaint_strength=0.7,   generator=a_,   num_inference_steps=25,   ).latents

   _snake_case							:	Optional[Any]			=						pipe(
       prompt=a_,   mask_image=a_,   image_latents=a_,   generator=a_,   negative_prompt=a_,   inpaint_strength=0.7,   num_inference_steps=25,   output_type="""numpy""",   ).images[0]

   _snake_case							:	Dict			=						(
       np.array(
           load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/diffedit/pears.png"""	).resize((768, 768)	)	)
       / 255
   )
   assert np.abs((expected_image - image).max()	) < 5E-1








"""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       UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	Optional[Any]			=						"""https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"""
  _snake_case							:	Union[str, Any]			=						Image.open(requests.get(snake_case__	,     stream=snake_case__					).raw					).convert("""RGB"""					)

  return image


def       UpperCAmelCase__ (snake_case__							:      Any					):
  """simple docstring"""

  _snake_case							:	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       UpperCAmelCase__ (snake_case__							:      Optional[int]	,     snake_case__							:      List[Any]	,     snake_case__							:      Tuple					):
  """simple docstring"""

  _snake_case							:	Optional[Any]			=						dct.pop(snake_case__					)
  _snake_case							:	Optional[int]			=						val


def       UpperCAmelCase__ (snake_case__							:      List[str]	,     snake_case__							:      str					):
  """simple docstring"""

  for i in range(config.vision_config.num_hidden_layers					):
    # read in original q and v biases
    _snake_case							:	Optional[int]			=						state_dict.pop(F"visual_encoder.blocks.{i}.attn.q_bias"					)
    _snake_case							:	Tuple			=						state_dict.pop(F"visual_encoder.blocks.{i}.attn.v_bias"					)

    # next, set bias in the state dict
    _snake_case							:	List[str]			=						torch.cat((q_bias, torch.zeros_like(snake_case__	,     requires_grad=snake_case__					), v_bias)					)
    _snake_case							:	Dict			=						qkv_bias


def       UpperCAmelCase__ (snake_case__							:      List[Any]	,     snake_case__							:      Union[str, Any]					):
  """simple docstring"""

  _snake_case							:	List[Any]			=						3_64 if """coco""" in model_name else 2_24
  _snake_case							:	List[str]			=						BlipaVisionConfig(image_size=snake_case__					).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:
    _snake_case							:	List[str]			=						OPTConfig.from_pretrained("""facebook/opt-2.7b"""	,     eos_token_id=snake_case__					).to_dict()
  elif "opt-6.7b" in model_name:
    _snake_case							:	List[str]			=						OPTConfig.from_pretrained("""facebook/opt-6.7b"""	,     eos_token_id=snake_case__					).to_dict()
  elif "t5-xl" in model_name:
    _snake_case							:	Tuple			=						TaConfig.from_pretrained("""google/flan-t5-xl"""	,     dense_act_fn="""gelu"""	,     bos_token_id=1					).to_dict()
  elif "t5-xxl" in model_name:
    _snake_case							:	List[Any]			=						TaConfig.from_pretrained("""google/flan-t5-xxl"""	,     dense_act_fn="""gelu"""	,     bos_token_id=1					).to_dict()

  _snake_case							:	int			=						BlipaConfig(vision_config=snake_case__	,     text_config=snake_case__					)

  return config, image_size


@torch.no_grad()
def       UpperCAmelCase__ (snake_case__							:      int	,     snake_case__							:      int=None	,     snake_case__							:      str=False					):
  """simple docstring"""

  _snake_case							:	List[str]			=						(
      AutoTokenizer.from_pretrained("""facebook/opt-2.7b"""					)
      if """opt""" in model_name
      else AutoTokenizer.from_pretrained("""google/flan-t5-xl"""					)
  )
  _snake_case							:	str			=						tokenizer("""\n"""	,     add_special_tokens=snake_case__					).input_ids[0]
  _snake_case	,      _snake_case							:	Dict			=						get_blipa_config(snake_case__	,     eos_token_id=snake_case__					)

  _snake_case							:	str			=						BlipaForConditionalGeneration(snake_case__					).eval()

  _snake_case							:	int			=						{
      """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"""),
  }

  _snake_case	,      _snake_case							:	List[Any]			=						model_name_to_original[model_name]

  # load original model
  print("""Loading original model..."""					)
  _snake_case							:	int			=						"""cuda""" if torch.cuda.is_available() else """cpu"""
  _snake_case	,      _snake_case	,      _snake_case							:	Any			=						load_model_and_preprocess(
      name=snake_case__	,     model_type=snake_case__	,     is_eval=snake_case__	,     device=snake_case__					)
  original_model.eval()
  print("""Done!"""					)

  # update state dict keys
  _snake_case							:	Any			=						original_model.state_dict()
  _snake_case							:	Dict			=						create_rename_keys(snake_case__					)
  for src, dest in rename_keys:
    rename_key(snake_case__	,     snake_case__	,     snake_case__					)

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

  # read in qv biases
  read_in_q_v_bias(snake_case__	,     snake_case__					)

  _snake_case	,      _snake_case							:	List[str]			=						hf_model.load_state_dict(snake_case__	,     strict=snake_case__					)
  assert len(snake_case__					) == 0
  assert unexpected_keys == ["qformer.embeddings.position_ids"]

  _snake_case							:	Any			=						load_demo_image()
  _snake_case							:	str			=						vis_processors["""eval"""](snake_case__					).unsqueeze(0					).to(snake_case__					)
  _snake_case							:	List[Any]			=						tokenizer(["""\n"""]	,     return_tensors="""pt"""					).input_ids.to(snake_case__					)

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

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

  original_model.to(snake_case__					)
  hf_model.to(snake_case__					)
  with torch.no_grad():
    if "opt" in model_name:
      _snake_case							:	str			=						original_model({"""image""": original_pixel_values, """text_input""": [""""""]}					).logits
      _snake_case							:	int			=						hf_model(snake_case__	,     snake_case__					).logits
    else:
      _snake_case							:	str			=						original_model(
          {"""image""": original_pixel_values, """text_input""": ["""\n"""], """text_output""": ["""\n"""]}					).logits
      _snake_case							:	Optional[int]			=						input_ids.masked_fill(input_ids == tokenizer.pad_token_id	,     -1_00					)
      _snake_case							:	Union[str, Any]			=						hf_model(snake_case__	,     snake_case__	,     labels=snake_case__					).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":
    _snake_case							:	List[str]			=						torch.tensor(
        [[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]]	,     device=snake_case__					)
    assert torch.allclose(logits[0, :3, :3]	,     snake_case__	,     atol=1e-4					)
  elif model_name == "blip2-flan-t5-xl-coco":
    _snake_case							:	Union[str, Any]			=						torch.tensor(
        [[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]]	,     device=snake_case__					)
  else:
    # cast to same type
    _snake_case							:	int			=						logits.dtype
    assert torch.allclose(original_logits.to(snake_case__					)	,     snake_case__	,     atol=1e-2					)
  print("""Looks ok!"""					)

  print("""Generating a caption..."""					)
  _snake_case							:	Any			=						""""""
  _snake_case							:	str			=						tokenizer(snake_case__	,     return_tensors="""pt"""					).input_ids.to(snake_case__					)

  _snake_case							:	Union[str, Any]			=						original_model.generate({"""image""": original_pixel_values}					)
  _snake_case							:	Tuple			=						hf_model.generate(
      snake_case__	,     snake_case__	,     do_sample=snake_case__	,     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:"""	,     snake_case__					)
  _snake_case							:	Optional[Any]			=						input_ids.shape[1]
  _snake_case							:	int			=						processor.batch_decode(outputs[:, prompt_length:]	,     skip_special_tokens=snake_case__					)
  _snake_case							:	Optional[Any]			=						[text.strip() for text in output_text]
  print("""HF generation:"""	,     snake_case__					)

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

  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_      			=       argparse.ArgumentParser()
     A_      			=       [
         '''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_      			=       parser.parse_args()

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


