






import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 							__UpperCamelCase     (  _lowerCAmelCase ,      _lowerCAmelCase ,      _lowerCAmelCase ,      unittest.TestCase ):
	__snake_case						:Optional[int] =							StableDiffusionInstructPixaPixPipeline
	__snake_case						:List[Any] =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
	__snake_case						:Any =							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	__snake_case						:int =							IMAGE_TO_IMAGE_IMAGE_PARAMS
	__snake_case						:int =							IMAGE_TO_IMAGE_IMAGE_PARAMS







	def 			_a (	self   :				List[Any]      )		->			Tuple:
		"""simple docstring"""







		torch.manual_seed(0      )
		__lowercase   							=	UNetaDConditionModel(
		    block_out_channels=(32, 64)							,					layers_per_block=2							,					sample_size=32							,					in_channels=8							,					out_channels=4							,					down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")							,					up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")							,					cross_attention_dim=32							,					)
		__lowercase   							=	PNDMScheduler(skip_prk_steps=_lowerCAmelCase      )
		torch.manual_seed(0      )
		__lowercase   							=	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							,					)
		torch.manual_seed(0      )
		__lowercase   							=	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=1000							,					)
		__lowercase   							=	CLIPTextModel(_lowerCAmelCase      )
		__lowercase   							=	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

		__lowercase   							=	{
		    """unet""": unet,
		    """scheduler""": scheduler,
		    """vae""": vae,
		    """text_encoder""": text_encoder,
		    """tokenizer""": tokenizer,
		    """safety_checker""": None,
		    """feature_extractor""": None,
		}
		return components







	def 			_a (	self   :				Optional[int]							,					_lowerCAmelCase   :				str							,					_lowerCAmelCase   :				Optional[Any]=0      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	floats_tensor((1, 3, 32, 32)							,					rng=random.Random(_lowerCAmelCase      )      ).to(_lowerCAmelCase      )
		__lowercase   							=	image.cpu().permute(0							,					2							,					3							,					1      )[0]
		__lowercase   							=	Image.fromarray(np.uinta(_lowerCAmelCase      )      ).convert("""RGB"""      )
		if str(_lowerCAmelCase      ).startswith("""mps"""      ):
			__lowercase   							=	torch.manual_seed(_lowerCAmelCase      )
		else:
			__lowercase   							=	torch.Generator(device=_lowerCAmelCase      ).manual_seed(_lowerCAmelCase      )
		__lowercase   							=	{
		    """prompt""": """A painting of a squirrel eating a burger""",
		    """image""": image,
		    """generator""": generator,
		    """num_inference_steps""": 2,
		    """guidance_scale""": 6.0,
		    """image_guidance_scale""": 1,
		    """output_type""": """numpy""",
		}
		return inputs







	def 			_a (	self   :				str      )		->			Tuple:
		"""simple docstring"""







		__lowercase   							=	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
		__lowercase   							=	self.get_dummy_components()
		__lowercase   							=	StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase      )
		__lowercase   							=	sd_pipe.to(_lowerCAmelCase      )
		sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

		__lowercase   							=	self.get_dummy_inputs(_lowerCAmelCase      )
		__lowercase   							=	sd_pipe(**_lowerCAmelCase      ).images
		__lowercase   							=	image[0, -3:, -3:, -1]
		assert image.shape == (1, 32, 32, 3)
		__lowercase   							=	np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]      )

		assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3







	def 			_a (	self   :				Any      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
		__lowercase   							=	self.get_dummy_components()
		__lowercase   							=	StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase      )
		__lowercase   							=	sd_pipe.to(_lowerCAmelCase      )
		sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

		__lowercase   							=	self.get_dummy_inputs(_lowerCAmelCase      )
		__lowercase   							=	"""french fries"""
		__lowercase   							=	sd_pipe(**_lowerCAmelCase							,					negative_prompt=_lowerCAmelCase      )
		__lowercase   							=	output.images
		__lowercase   							=	image[0, -3:, -3:, -1]

		assert image.shape == (1, 32, 32, 3)
		__lowercase   							=	np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]      )

		assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3







	def 			_a (	self   :				Optional[Any]      )		->			Dict:
		"""simple docstring"""







		__lowercase   							=	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
		__lowercase   							=	self.get_dummy_components()
		__lowercase   							=	StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase      )
		__lowercase   							=	sd_pipe.to(_lowerCAmelCase      )
		sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

		__lowercase   							=	self.get_dummy_inputs(_lowerCAmelCase      )
		__lowercase   							=	[inputs["""prompt"""]] * 2

		__lowercase   							=	np.array(inputs["""image"""]      ).astype(np.floataa      ) / 255.0
		__lowercase   							=	torch.from_numpy(_lowerCAmelCase      ).unsqueeze(0      ).to(_lowerCAmelCase      )
		__lowercase   							=	image / 2 + 0.5
		__lowercase   							=	image.permute(0							,					3							,					1							,					2      )
		__lowercase   							=	image.repeat(2							,					1							,					1							,					1      )

		__lowercase   							=	sd_pipe(**_lowerCAmelCase      ).images
		__lowercase   							=	image[-1, -3:, -3:, -1]

		assert image.shape == (2, 32, 32, 3)
		__lowercase   							=	np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]      )

		assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3







	def 			_a (	self   :				int      )		->			List[str]:
		"""simple docstring"""







		__lowercase   							=	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
		__lowercase   							=	self.get_dummy_components()
		__lowercase   							=	EulerAncestralDiscreteScheduler(
		    beta_start=0.00_085							,					beta_end=0.012							,					beta_schedule="""scaled_linear"""      )
		__lowercase   							=	StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase      )
		__lowercase   							=	sd_pipe.to(_lowerCAmelCase      )
		sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

		__lowercase   							=	self.get_dummy_inputs(_lowerCAmelCase      )
		__lowercase   							=	sd_pipe(**_lowerCAmelCase      ).images
		__lowercase   							=	image[0, -3:, -3:, -1]

		__lowercase   							=	[round(_lowerCAmelCase							,					4      ) for x in image_slice.flatten().tolist()]
		print(""",""".join([str(_lowerCAmelCase      ) for x in slice]      )      )

		assert image.shape == (1, 32, 32, 3)
		__lowercase   							=	np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]      )

		assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-3







	def 			_a (	self   :				List[str]      )		->			Dict:
		"""simple docstring"""







		super().test_inference_batch_single_identical(expected_max_diff=3e-3      )







	def 			_a (	self   :				Union[str, Any]      )		->			int:
		"""simple docstring"""







		__lowercase   							=	self.get_dummy_components()
		__lowercase   							=	StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase      )
		__lowercase   							=	VaeImageProcessor(do_resize=_lowerCAmelCase							,					do_normalize=_lowerCAmelCase      )
		__lowercase   							=	pipe.to(_lowerCAmelCase      )
		pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

		__lowercase   							=	pipe(**self.get_dummy_inputs_by_type(_lowerCAmelCase							,					input_image_type="""pt"""      )      )[0]

		__lowercase   							=	components["""vae"""]
		__lowercase   							=	self.get_dummy_inputs_by_type(_lowerCAmelCase							,					input_image_type="""pt"""      )

		for image_param in self.image_latents_params:
			if image_param in inputs.keys():
				__lowercase   							=	vae.encode(inputs[image_param]      ).latent_dist.mode()

		__lowercase   							=	pipe(**_lowerCAmelCase      )[0]

		__lowercase   							=	np.abs(out - out_latents_inputs      ).max()
		self.assertLess(_lowerCAmelCase							,					1e-4							,					"""passing latents as image input generate different result from passing image"""      )


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







	def 			_a (	self   :				Dict      )		->			Optional[int]:
		"""simple docstring"""







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







	def 			_a (	self   :				Dict							,					_lowerCAmelCase   :				str=0      )		->			Optional[int]:
		"""simple docstring"""







		__lowercase   							=	torch.manual_seed(_lowerCAmelCase      )
		__lowercase   							=	load_image(
		    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""      )
		__lowercase   							=	{
		    """prompt""": """turn him into a cyborg""",
		    """image""": image,
		    """generator""": generator,
		    """num_inference_steps""": 3,
		    """guidance_scale""": 7.5,
		    """image_guidance_scale""": 1.0,
		    """output_type""": """numpy""",
		}
		return inputs







	def 			_a (	self   :				Tuple      )		->			Optional[Any]:
		"""simple docstring"""







		__lowercase   							=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
		    """timbrooks/instruct-pix2pix"""							,					safety_checker=_lowerCAmelCase      )
		pipe.to(_lowerCAmelCase      )
		pipe.set_progress_bar_config(disable=_lowerCAmelCase      )
		pipe.enable_attention_slicing()

		__lowercase   							=	self.get_inputs()
		__lowercase   							=	pipe(**_lowerCAmelCase      ).images
		__lowercase   							=	image[0, -3:, -3:, -1].flatten()

		assert image.shape == (1, 512, 512, 3)
		__lowercase   							=	np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]      )

		assert np.abs(expected_slice - image_slice      ).max() < 1e-3







	def 			_a (	self   :				Dict      )		->			Union[str, Any]:
		"""simple docstring"""







		__lowercase   							=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
		    """timbrooks/instruct-pix2pix"""							,					safety_checker=_lowerCAmelCase      )
		__lowercase   							=	LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
		pipe.to(_lowerCAmelCase      )
		pipe.set_progress_bar_config(disable=_lowerCAmelCase      )
		pipe.enable_attention_slicing()

		__lowercase   							=	self.get_inputs()
		__lowercase   							=	pipe(**_lowerCAmelCase      ).images
		__lowercase   							=	image[0, -3:, -3:, -1].flatten()

		assert image.shape == (1, 512, 512, 3)
		__lowercase   							=	np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]      )

		assert np.abs(expected_slice - image_slice      ).max() < 1e-3







	def 			_a (	self   :				Any      )		->			Union[str, Any]:
		"""simple docstring"""







		__lowercase   							=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
		    """timbrooks/instruct-pix2pix"""							,					safety_checker=_lowerCAmelCase      )
		__lowercase   							=	DDIMScheduler.from_config(pipe.scheduler.config      )
		pipe.to(_lowerCAmelCase      )
		pipe.set_progress_bar_config(disable=_lowerCAmelCase      )
		pipe.enable_attention_slicing()

		__lowercase   							=	self.get_inputs()
		__lowercase   							=	pipe(**_lowerCAmelCase      ).images
		__lowercase   							=	image[0, -3:, -3:, -1].flatten()

		assert image.shape == (1, 512, 512, 3)
		__lowercase   							=	np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]      )

		assert np.abs(expected_slice - image_slice      ).max() < 1e-3







	def 			_a (	self   :				Union[str, Any]      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	0

		def callback_fn(_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				torch.FloatTensor      ) -> None:
			__lowercase   							=	True
			nonlocal number_of_steps
			number_of_steps += 1
			if step == 1:
				__lowercase   							=	latents.detach().cpu().numpy()
				assert latents.shape == (1, 4, 64, 64)
				__lowercase   							=	latents[0, -3:, -3:, -1]
				__lowercase   							=	np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]      )

				assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2
			elif step == 2:
				__lowercase   							=	latents.detach().cpu().numpy()
				assert latents.shape == (1, 4, 64, 64)
				__lowercase   							=	latents[0, -3:, -3:, -1]
				__lowercase   							=	np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]      )

				assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5e-2

		__lowercase   							=	False

		__lowercase   							=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
		    """timbrooks/instruct-pix2pix"""							,					safety_checker=_lowerCAmelCase							,					torch_dtype=torch.floataa      )
		__lowercase   							=	pipe.to(_lowerCAmelCase      )
		pipe.set_progress_bar_config(disable=_lowerCAmelCase      )
		pipe.enable_attention_slicing()

		__lowercase   							=	self.get_inputs()
		pipe(**_lowerCAmelCase							,					callback=_lowerCAmelCase							,					callback_steps=1      )
		assert callback_fn.has_been_called
		assert number_of_steps == 3







	def 			_a (	self   :				Optional[int]      )		->			str:
		"""simple docstring"""







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

		__lowercase   							=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
		    """timbrooks/instruct-pix2pix"""							,					safety_checker=_lowerCAmelCase							,					torch_dtype=torch.floataa      )
		__lowercase   							=	pipe.to(_lowerCAmelCase      )
		pipe.set_progress_bar_config(disable=_lowerCAmelCase      )
		pipe.enable_attention_slicing(1      )
		pipe.enable_sequential_cpu_offload()

		__lowercase   							=	self.get_inputs()
		__lowercase   							=	pipe(**_lowerCAmelCase      )

		__lowercase   							=	torch.cuda.max_memory_allocated()
		# make sure that less than 2.2 GB is allocated
		assert mem_bytes < 2.2 * 10**9







	def 			_a (	self   :				List[Any]      )		->			Optional[int]:
		"""simple docstring"""







		__lowercase   							=	self.get_inputs()
		# resize to resolution that is divisible by 8 but not 16 or 32
		__lowercase   							=	inputs["""image"""].resize((504, 504)      )

		__lowercase   							=	"""timbrooks/instruct-pix2pix"""
		__lowercase   							=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
		    _lowerCAmelCase							,					safety_checker=_lowerCAmelCase							,					)
		pipe.to(_lowerCAmelCase      )
		pipe.set_progress_bar_config(disable=_lowerCAmelCase      )
		pipe.enable_attention_slicing()

		__lowercase   							=	pipe(**_lowerCAmelCase      )
		__lowercase   							=	output.images[0]

		__lowercase   							=	image[255:258, 383:386, -1]

		assert image.shape == (504, 504, 3)
		__lowercase   							=	np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]      )

		assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5e-3





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 		_A		(		)					-> int:


     """simple docstring"""


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

     return image






def 		_A		(		_lowercase )					-> int:


     """simple docstring"""


     __UpperCamelCase =			[]
     # 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 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> Optional[int]:


     """simple docstring"""


     __UpperCamelCase =			dct.pop(_lowercase )
     __UpperCamelCase =			val






def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """simple docstring"""


     for i in range(config.vision_config.num_hidden_layers ):
          # read in original q and v biases
          __UpperCamelCase =			state_dict.pop(f'''visual_encoder.blocks.{i}.attn.q_bias''' )
          __UpperCamelCase =			state_dict.pop(f'''visual_encoder.blocks.{i}.attn.v_bias''' )

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






def 		_A		(		_lowercase					,  _lowercase )					-> Any:


     """simple docstring"""


     __UpperCamelCase =			3_64 if 'coco' in model_name else 2_24
     __UpperCamelCase =			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 =			OPTConfig.from_pretrained('facebook/opt-2.7b'					,  eos_token_id=_lowercase ).to_dict()
     elif "opt-6.7b" in model_name:
          __UpperCamelCase =			OPTConfig.from_pretrained('facebook/opt-6.7b'					,  eos_token_id=_lowercase ).to_dict()
     elif "t5-xl" in model_name:
          __UpperCamelCase =			TaConfig.from_pretrained('google/flan-t5-xl'					,  dense_act_fn='gelu'					,  bos_token_id=1 ).to_dict()
     elif "t5-xxl" in model_name:
          __UpperCamelCase =			TaConfig.from_pretrained('google/flan-t5-xxl'					,  dense_act_fn='gelu'					,  bos_token_id=1 ).to_dict()

     __UpperCamelCase =			BlipaConfig(vision_config=_lowercase					,  text_config=_lowercase )

     return config, image_size






@torch.no_grad()
def 		_A		(		_lowercase					,  _lowercase=None					,  _lowercase=False )					-> Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase =			(
         AutoTokenizer.from_pretrained('facebook/opt-2.7b' )
         if 'opt' in model_name
         else AutoTokenizer.from_pretrained('google/flan-t5-xl' )
     )
     __UpperCamelCase =			tokenizer('\n'					,  add_special_tokens=_lowercase ).input_ids[0]
     __UpperCamelCase,    __UpperCamelCase =			get_blipa_config(_lowercase					,  eos_token_id=_lowercase )

     __UpperCamelCase =			BlipaForConditionalGeneration(_lowercase ).eval()

     __UpperCamelCase =			{
         '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 =			model_name_to_original[model_name]

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

     # update state dict keys
     __UpperCamelCase =			original_model.state_dict()
     __UpperCamelCase =			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 =			state_dict.pop(_lowercase )
          if key.startswith('Qformer.bert' ):
               __UpperCamelCase =			key.replace('Qformer.bert'					,  'qformer' )
          if "attention.self" in key:
               __UpperCamelCase =			key.replace('self'					,  'attention' )
          if "opt_proj" in key:
               __UpperCamelCase =			key.replace('opt_proj'					,  'language_projection' )
          if "t5_proj" in key:
               __UpperCamelCase =			key.replace('t5_proj'					,  'language_projection' )
          if key.startswith('opt' ):
               __UpperCamelCase =			key.replace('opt'					,  'language' )
          if key.startswith('t5' ):
               __UpperCamelCase =			key.replace('t5'					,  'language' )
          __UpperCamelCase =			val

     # read in qv biases
     read_in_q_v_bias(_lowercase					,  _lowercase )

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

     __UpperCamelCase =			load_demo_image()
     __UpperCamelCase =			vis_processors['eval'](_lowercase ).unsqueeze(0 ).to(_lowercase )
     __UpperCamelCase =			tokenizer(['\n']					,  return_tensors='pt' ).input_ids.to(_lowercase )

     # create processor
     __UpperCamelCase =			BlipImageProcessor(
         size={'height': image_size, 'width': image_size}					,  image_mean=_lowercase					,  image_std=_lowercase )
     __UpperCamelCase =			BlipaProcessor(image_processor=_lowercase					,  tokenizer=_lowercase )
     __UpperCamelCase =			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 =			original_model({'image': original_pixel_values, 'text_input': ['']} ).logits
               __UpperCamelCase =			hf_model(_lowercase					,  _lowercase ).logits
          else:
               __UpperCamelCase =			original_model(
                   {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']} ).logits
               __UpperCamelCase =			input_ids.masked_fill(input_ids == tokenizer.pad_token_id					,  -1_00 )
               __UpperCamelCase =			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 =			torch.tensor(
              [[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]]					,  device=_lowercase )
          assert torch.allclose(logits[0, :3, :3]					,  _lowercase					,  atol=1e-4 )
     elif model_name == "blip2-flan-t5-xl-coco":
          __UpperCamelCase =			torch.tensor(
              [[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]]					,  device=_lowercase )
     else:
          # cast to same type
          __UpperCamelCase =			logits.dtype
          assert torch.allclose(original_logits.to(_lowercase )					,  _lowercase					,  atol=1e-2 )
     print('Looks ok!' )

     print('Generating a caption...' )
     __UpperCamelCase =			''
     __UpperCamelCase =			tokenizer(_lowercase					,  return_tensors='pt' ).input_ids.to(_lowercase )

     __UpperCamelCase =			original_model.generate({'image': original_pixel_values} )
     __UpperCamelCase =			hf_model.generate(
         _lowercase					,  _lowercase					,  do_sample=_lowercase					,  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:'					,  _lowercase )
     __UpperCamelCase =			input_ids.shape[1]
     __UpperCamelCase =			processor.batch_decode(outputs[:, prompt_length:]					,  skip_special_tokens=_lowercase )
     __UpperCamelCase =			[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__":
    __snake_case   							=						argparse.ArgumentParser()
    __snake_case   							=						[
        '''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''',
    )

    __snake_case   							=						parser.parse_args()

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


