


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

							return image






def 							__lowercase   ( __lowerCAmelCase							:				str   ):
							a__             =     []
							# 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 							__lowercase   ( __lowerCAmelCase							:				int					,    __lowerCAmelCase							:				Union[str, Any]					,    __lowerCAmelCase							:				Optional[Any]   ):
							a__             =     dct.pop(__lowerCAmelCase   )
							a__             =     val






def 							__lowercase   ( __lowerCAmelCase							:				List[Any]					,    __lowerCAmelCase							:				Dict   ):
							for i in range(config.vision_config.num_hidden_layers   ):
														# read in original q and v biases
														a__             =     state_dict.pop(F'visual_encoder.blocks.{i}.attn.q_bias'   )
														a__             =     state_dict.pop(F'visual_encoder.blocks.{i}.attn.v_bias'   )

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






def 							__lowercase   ( __lowerCAmelCase							:				Any					,    __lowerCAmelCase							:				Tuple   ):
							a__             =     3_6_4 if 'coco' in model_name else 2_2_4
							a__             =     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:
														a__             =     OPTConfig.from_pretrained('facebook/opt-2.7b'					,    eos_token_id=__lowerCAmelCase   ).to_dict()
							elif "opt-6.7b" in model_name:
														a__             =     OPTConfig.from_pretrained('facebook/opt-6.7b'					,    eos_token_id=__lowerCAmelCase   ).to_dict()
							elif "t5-xl" in model_name:
														a__             =     TaConfig.from_pretrained('google/flan-t5-xl'					,    dense_act_fn='gelu'					,    bos_token_id=1   ).to_dict()
							elif "t5-xxl" in model_name:
														a__             =     TaConfig.from_pretrained('google/flan-t5-xxl'					,    dense_act_fn='gelu'					,    bos_token_id=1   ).to_dict()

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

							return config, image_size






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

							a__             =     BlipaForConditionalGeneration(__lowerCAmelCase   ).eval()

							a__             =     {
							    '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'),
							}

							a__      ,					a__             =     model_name_to_original[model_name]

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

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

							# read in qv biases
							read_in_q_v_bias(__lowerCAmelCase					,    __lowerCAmelCase   )

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

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

							# create processor
							a__             =     BlipImageProcessor(
							    size={'height': image_size, 'width': image_size}					,    image_mean=__lowerCAmelCase					,    image_std=__lowerCAmelCase   )
							a__             =     BlipaProcessor(image_processor=__lowerCAmelCase					,    tokenizer=__lowerCAmelCase   )
							a__             =     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:
																					a__             =     original_model({'image': original_pixel_values, 'text_input': ['']}   ).logits
																					a__             =     hf_model(__lowerCAmelCase					,    __lowerCAmelCase   ).logits
														else:
																					a__             =     original_model(
																					    {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}   ).logits
																					a__             =     input_ids.masked_fill(input_ids == tokenizer.pad_token_id					,    -1_0_0   )
																					a__             =     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":
														a__             =     torch.tensor(
														    [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]]					,    device=__lowerCAmelCase   )
														assert torch.allclose(logits[0, :3, :3]					,    __lowerCAmelCase					,    atol=1E-4   )
							elif model_name == "blip2-flan-t5-xl-coco":
														a__             =     torch.tensor(
														    [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]]					,    device=__lowerCAmelCase   )
							else:
														# cast to same type
														a__             =     logits.dtype
														assert torch.allclose(original_logits.to(__lowerCAmelCase   )					,    __lowerCAmelCase					,    atol=1E-2   )
							print('Looks ok!'   )

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

							a__             =     original_model.generate({'image': original_pixel_values}   )
							a__             =     hf_model.generate(
							    __lowerCAmelCase					,    __lowerCAmelCase					,    do_sample=__lowerCAmelCase					,    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:'					,    __lowerCAmelCase   )
							a__             =     input_ids.shape[1]
							a__             =     processor.batch_decode(outputs[:, prompt_length:]					,    skip_special_tokens=__lowerCAmelCase   )
							a__             =     [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__":
						snake_case							:       List[str]     			=      argparse.ArgumentParser()
						snake_case							:       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''',
						)

						snake_case							:       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




