






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			(						):






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

				return image






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			[]
				# 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			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			dct.pop(__lowerCAmelCase				)
				lowercase__    =			val






def   a			(						lowerCamelCase_		,   lowerCamelCase_				):






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

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






def   a			(						lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			364 if '''coco''' in model_name else 224
				lowercase__    =			BlipaVisionConfig(image_size=__lowerCAmelCase				).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:
								lowercase__    =			OPTConfig.from_pretrained('''facebook/opt-2.7b'''		,   eos_token_id=__lowerCAmelCase				).to_dict()
				elif "opt-6.7b" in model_name:
								lowercase__    =			OPTConfig.from_pretrained('''facebook/opt-6.7b'''		,   eos_token_id=__lowerCAmelCase				).to_dict()
				elif "t5-xl" in model_name:
								lowercase__    =			TaConfig.from_pretrained('''google/flan-t5-xl'''		,   dense_act_fn='''gelu'''		,   bos_token_id=1				).to_dict()
				elif "t5-xxl" in model_name:
								lowercase__    =			TaConfig.from_pretrained('''google/flan-t5-xxl'''		,   dense_act_fn='''gelu'''		,   bos_token_id=1				).to_dict()

				lowercase__    =			BlipaConfig(vision_config=__lowerCAmelCase		,   text_config=__lowerCAmelCase				)

				return config, image_size






@torch.no_grad()
def   a			(						lowerCamelCase_		,   lowerCamelCase_=None		,   lowerCamelCase_=False				):






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

				lowercase__    =			BlipaForConditionalGeneration(__lowerCAmelCase				).eval()

				lowercase__    =			{
				    '''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'''),
				}

				lowercase__ ,						lowercase__    =			model_name_to_original[model_name]

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

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

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

				# read in qv biases
				read_in_q_v_bias(__lowerCAmelCase		,   __lowerCAmelCase				)

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

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

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

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

				original_model.to(__lowerCAmelCase				)
				hf_model.to(__lowerCAmelCase				)
				with torch.no_grad():
								if "opt" in model_name:
												lowercase__    =			original_model({'''image''': original_pixel_values, '''text_input''': ['''''']}				).logits
												lowercase__    =			hf_model(__lowerCAmelCase		,   __lowerCAmelCase				).logits
								else:
												lowercase__    =			original_model(
												    {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']}				).logits
												lowercase__    =			input_ids.masked_fill(input_ids == tokenizer.pad_token_id		,   -100				)
												lowercase__    =			hf_model(__lowerCAmelCase		,   __lowerCAmelCase		,   labels=__lowerCAmelCase				).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":
								lowercase__    =			torch.tensor(
								    [[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]]		,   device=__lowerCAmelCase				)
								assert torch.allclose(logits[0, :3, :3]		,   __lowerCAmelCase		,   atol=1e-4				)
				elif model_name == "blip2-flan-t5-xl-coco":
								lowercase__    =			torch.tensor(
								    [[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]]		,   device=__lowerCAmelCase				)
				else:
								# cast to same type
								lowercase__    =			logits.dtype
								assert torch.allclose(original_logits.to(__lowerCAmelCase				)		,   __lowerCAmelCase		,   atol=1e-2				)
				print('''Looks ok!'''				)

				print('''Generating a caption...'''				)
				lowercase__    =			''''''
				lowercase__    =			tokenizer(__lowerCAmelCase		,   return_tensors='''pt'''				).input_ids.to(__lowerCAmelCase				)

				lowercase__    =			original_model.generate({'''image''': original_pixel_values}				)
				lowercase__    =			hf_model.generate(
				    __lowerCAmelCase		,   __lowerCAmelCase		,   do_sample=__lowerCAmelCase		,   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:'''		,   __lowerCAmelCase				)
				lowercase__    =			input_ids.shape[1]
				lowercase__    =			processor.batch_decode(outputs[:, prompt_length:]		,   skip_special_tokens=__lowerCAmelCase				)
				lowercase__    =			[text.strip() for text in output_text]
				print('''HF generation:'''		,   __lowerCAmelCase				)

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

				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__	:			List[str]			   = argparse.ArgumentParser()
							A__	:			int			   = [
							    '''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__	:			Dict			   = parser.parse_args()

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








import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

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


snake_case							:       int     			=      False







@skip_mps
class        snake_case_   (lowerCamelCase_	,						lowerCamelCase_	,						lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Union[str, Any]          =			StableDiffusionAttendAndExcitePipeline
					UpperCAmelCase__					:    List[str]          =			False
					UpperCAmelCase__					:    int          =			TEXT_TO_IMAGE_PARAMS
					UpperCAmelCase__					:    List[Any]          =			TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''}      )
					UpperCAmelCase__					:    List[str]          =			TEXT_TO_IMAGE_IMAGE_PARAMS
					UpperCAmelCase__					:    str          =			TEXT_TO_IMAGE_IMAGE_PARAMS
					@classmethod
					def  lowerCamelCase__(      cls   :int      )					->    int:
												super().setUpClass()
												torch.use_deterministic_algorithms(__snake_case      )
					@classmethod
					def  lowerCamelCase__(      cls   :Optional[Any]      )					->    int:
												super().tearDownClass()
												torch.use_deterministic_algorithms(__snake_case      )
					def  lowerCamelCase__(      self   :Dict      )					->    Optional[Any]:
												torch.manual_seed(0      )
												a__             =     UNetaDConditionModel(
												    block_out_channels=(32, 64)			,layers_per_block=1			,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=__snake_case			,)
												a__             =     DDIMScheduler(
												    beta_start=0.0_00_85			,beta_end=0.0_12			,beta_schedule='scaled_linear'			,clip_sample=__snake_case			,set_alpha_to_one=__snake_case			,)
												torch.manual_seed(0      )
												a__             =     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=1_28			,)
												torch.manual_seed(0      )
												a__             =     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=10_00			,hidden_act='gelu'			,projection_dim=5_12			,)
												a__             =     CLIPTextModel(__snake_case      )
												a__             =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

												a__             =     {
												    'unet': unet,
												    'scheduler': scheduler,
												    'vae': vae,
												    'text_encoder': text_encoder,
												    'tokenizer': tokenizer,
												    'safety_checker': None,
												    'feature_extractor': None,
												}

												return components
					def  lowerCamelCase__(      self   :Any			,__snake_case   :Union[str, Any]			,__snake_case   :Tuple=0      )					->    Any:
												if str(__snake_case      ).startswith('mps'      ):
																			a__             =     torch.manual_seed(__snake_case      )
												else:
																			a__             =     torch.Generator(device=__snake_case      ).manual_seed(__snake_case      )
												a__             =     a__             =     {
												    'prompt': 'a cat and a frog',
												    'token_indices': [2, 5],
												    'generator': generator,
												    'num_inference_steps': 1,
												    'guidance_scale': 6.0,
												    'output_type': 'numpy',
												    'max_iter_to_alter': 2,
												    'thresholds': {0: 0.7},
												}
												return inputs
					def  lowerCamelCase__(      self   :Optional[int]      )					->    Any:
												a__             =     'cpu'

												a__             =     self.get_dummy_components()
												a__             =     self.pipeline_class(**__snake_case      )
												pipe.to(__snake_case      )
												pipe.set_progress_bar_config(disable=__snake_case      )

												a__             =     self.get_dummy_inputs(__snake_case      )
												a__             =     pipe(**__snake_case      ).images
												a__             =     image[0, -3:, -3:, -1]

												self.assertEqual(image.shape			,(1, 64, 64, 3)      )
												a__             =     np.array(
												    [0.63_90_53_64, 0.62_89_73_07, 0.48_59_90_17, 0.5_13_36_24, 0.5_55_00_48, 0.45_76_95_16, 0.50_32_69_73, 0.5_02_31_39, 0.45_38_44_96]      )
												a__             =     np.abs(image_slice.flatten() - expected_slice      ).max()
												self.assertLessEqual(__snake_case			,1E-3      )
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    List[str]:
												super().test_cpu_offload_forward_pass(expected_max_diff=5E-4      )
					def  lowerCamelCase__(      self   :Dict      )					->    List[str]:
												# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
												self._test_inference_batch_consistent(batch_sizes=[1, 2]      )
					def  lowerCamelCase__(      self   :List[str]      )					->    Optional[int]:
												self._test_inference_batch_single_identical(batch_size=2			,expected_max_diff=7E-4      )
					def  lowerCamelCase__(      self   :Dict      )					->    Dict:
												super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3      )
					def  lowerCamelCase__(      self   :Dict      )					->    Union[str, Any]:
												super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4      )
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Dict:
												super().test_save_load_local(expected_max_difference=5E-4      )



					def  lowerCamelCase__(      self   :List[Any]      )					->    List[Any]:
												super().test_save_load_optional_components(expected_max_difference=4E-4      )







@require_torch_gpu
@slow
class        snake_case_   (unittest.TestCase      ):
					@classmethod
					def  lowerCamelCase__(      cls   :Optional[Any]      )					->    Tuple:
												super().setUpClass()
												torch.use_deterministic_algorithms(__snake_case      )
					@classmethod
					def  lowerCamelCase__(      cls   :Optional[int]      )					->    Tuple:
												super().tearDownClass()
												torch.use_deterministic_algorithms(__snake_case      )
					def  lowerCamelCase__(      self   :Tuple      )					->    Union[str, Any]:
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()



					def  lowerCamelCase__(      self   :Optional[Any]      )					->    Tuple:
												a__             =     torch.manual_seed(51      )

												a__             =     StableDiffusionAttendAndExcitePipeline.from_pretrained(
												    'CompVis/stable-diffusion-v1-4'			,safety_checker=__snake_case			,torch_dtype=torch.floataa      )
												pipe.to('cuda'      )

												a__             =     'a painting of an elephant with glasses'
												a__             =     [5, 7]

												a__             =     pipe(
												    prompt=__snake_case			,token_indices=__snake_case			,guidance_scale=7.5			,generator=__snake_case			,num_inference_steps=5			,max_iter_to_alter=5			,output_type='numpy'			,).images[0]

												a__             =     load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'      )
												assert np.abs((expected_image - image).max()      ) < 5E-1




