
"""simple docstring"""

import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)






def 							A						(    snake_case				:List[str]    , snake_case				:List[Any]		)				->     int:
 __UpperCamelCase      =	'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
 __UpperCamelCase      =	Image.open(requests.get(snake_case    , stream=snake_case		).raw		).convert('RGB'		)

 __UpperCamelCase      =	transforms.Compose(
     [
         transforms.Resize((image_size, image_size)    , interpolation=InterpolationMode.BICUBIC		),
         transforms.ToTensor(),
         transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073)    , (0.26_862_954, 0.26_130_258, 0.27_577_711)		),
     ]		)
 __UpperCamelCase      =	transform(snake_case		).unsqueeze(0		).to(snake_case		)
 return image






def 							A						(    snake_case				:List[Any]		)				->     List[str]:
 if "visual_encoder" in key:
  __UpperCamelCase      =	re.sub('visual_encoder*'    , 'vision_model.encoder'    , snake_case		)
 if "blocks" in key:
  __UpperCamelCase      =	re.sub(r'blocks'    , 'layers'    , snake_case		)
 if "attn" in key:
  __UpperCamelCase      =	re.sub(r'attn'    , 'self_attn'    , snake_case		)
 if "norm1" in key:
  __UpperCamelCase      =	re.sub(r'norm1'    , 'layer_norm1'    , snake_case		)
 if "norm2" in key:
  __UpperCamelCase      =	re.sub(r'norm2'    , 'layer_norm2'    , snake_case		)
 if "encoder.norm" in key:
  __UpperCamelCase      =	re.sub(r'encoder.norm'    , 'post_layernorm'    , snake_case		)
 if "encoder.patch_embed.proj" in key:
  __UpperCamelCase      =	re.sub(r'encoder.patch_embed.proj'    , 'embeddings.patch_embedding'    , snake_case		)

 if "encoder.pos_embed" in key:
  __UpperCamelCase      =	re.sub(r'encoder.pos_embed'    , 'embeddings.position_embedding'    , snake_case		)
 if "encoder.cls_token" in key:
  __UpperCamelCase      =	re.sub(r'encoder.cls_token'    , 'embeddings.class_embedding'    , snake_case		)

 if "self_attn" in key:
  __UpperCamelCase      =	re.sub(r'self_attn.proj'    , 'self_attn.projection'    , snake_case		)

 return key






@torch.no_grad()
def 							A						(    snake_case				:Any    , snake_case				:Tuple=None		)				->     Optional[Any]:
 if config_path is not None:
  __UpperCamelCase      =	BlipConfig.from_pretrained(snake_case		)
 else:
  __UpperCamelCase      =	BlipConfig(projection_dim=5_1_2    , text_config={}    , vision_config={}		)

 __UpperCamelCase      =	BlipForConditionalGeneration(snake_case		).eval()

 __UpperCamelCase      =	'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'

 __UpperCamelCase      =	blip_decoder(pretrained=snake_case    , image_size=3_8_4    , vit='base'		)
 __UpperCamelCase      =	pt_model.eval()

 __UpperCamelCase      =	pt_model.state_dict()
 for key in modified_state_dict.copy():
  __UpperCamelCase      =	modified_state_dict.pop(snake_case		)
  __UpperCamelCase      =	rename_key(snake_case		)
  __UpperCamelCase      =	value

 hf_model.load_state_dict(snake_case		)

 __UpperCamelCase      =	3_8_4
 __UpperCamelCase      =	load_demo_image(image_size=snake_case    , device='cpu'		)
 __UpperCamelCase      =	BertTokenizer.from_pretrained('bert-base-uncased'		)
 __UpperCamelCase      =	tokenizer(['a picture of']		).input_ids

 __UpperCamelCase      =	hf_model.generate(snake_case    , snake_case		)

 assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 3_8_6_1, 1_9_9_7, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

 __UpperCamelCase      =	hf_model.generate(snake_case		)

 assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

 if pytorch_dump_folder_path is not None:
  hf_model.save_pretrained(snake_case		)

 # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
 __UpperCamelCase      =	(
     'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'
 )

 __UpperCamelCase      =	blip_vqa(pretrained=snake_case    , image_size=snake_case    , vit='base'		)
 vqa_model.eval()

 __UpperCamelCase      =	vqa_model.state_dict()
 for key in modified_state_dict.copy():
  __UpperCamelCase      =	modified_state_dict.pop(snake_case		)
  __UpperCamelCase      =	rename_key(snake_case		)
  __UpperCamelCase      =	value

 __UpperCamelCase      =	BlipForQuestionAnswering(snake_case		)

 hf_vqa_model.load_state_dict(snake_case		)

 __UpperCamelCase      =	['How many dogs are in this image?']
 __UpperCamelCase      =	tokenizer(snake_case    , return_tensors='pt'		).input_ids

 __UpperCamelCase      =	hf_vqa_model.generate(snake_case    , snake_case		)
 print(tokenizer.decode(answer[0]		)		)

 assert tokenizer.decode(answer[0]		) == "[UNK] 1 [SEP]"
 if pytorch_dump_folder_path is not None:
  hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '_vqa'		)

 __UpperCamelCase      =	'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

 __UpperCamelCase      =	blip_itm(pretrained=snake_case    , image_size=snake_case    , vit='base'		)
 itm_model.eval()

 __UpperCamelCase      =	itm_model.state_dict()
 for key in modified_state_dict.copy():
  __UpperCamelCase      =	modified_state_dict.pop(snake_case		)
  __UpperCamelCase      =	rename_key(snake_case		)
  __UpperCamelCase      =	value

 __UpperCamelCase      =	BlipForImageTextRetrieval(snake_case		)

 __UpperCamelCase      =	['A picture of a woman with a dog sitting in a beach']
 __UpperCamelCase      =	tokenizer(
     snake_case    , return_tensors='pt'    , padding='max_length'    , truncation=snake_case    , max_length=3_5    , ).input_ids

 hf_itm_model.load_state_dict(snake_case		)
 hf_itm_model.eval()

 __UpperCamelCase      =	hf_itm_model(snake_case    , snake_case    , use_itm_head=snake_case		)
 __UpperCamelCase      =	hf_itm_model(snake_case    , snake_case    , use_itm_head=snake_case		)

 assert out[0].item() == 0.2_110_687_494_277_954
 assert torch.nn.functional.softmax(out_itm[0]    , dim=1		)[:, 1].item() == 0.45_698_845_386_505_127

 if pytorch_dump_folder_path is not None:
  hf_itm_model.save_pretrained(pytorch_dump_folder_path + '_itm'		)


if __name__ == "__main__":
      UpperCamelCase     :    Dict       =			argparse.ArgumentParser()
      parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
      parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
      UpperCamelCase     :    Dict       =			parser.parse_args()

      convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





"""simple docstring"""

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


UpperCamelCase     :    List[Any]       =			False

@skip_mps
class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  unittest.TestCase      ):
     lowercase						       =						StableDiffusionAttendAndExcitePipeline
     lowercase						       =						False
     lowercase						       =						TEXT_TO_IMAGE_PARAMS
     lowercase						       =						TEXT_TO_IMAGE_BATCH_PARAMS.union({"token_indices"}      )
     lowercase						       =						TEXT_TO_IMAGE_IMAGE_PARAMS
     lowercase						       =						TEXT_TO_IMAGE_IMAGE_PARAMS





     @classmethod
     def 						UpperCAmelCase (     cls				):

      '''simple docstring'''




      super().setUpClass()
      torch.use_deterministic_algorithms(__UpperCAmelCase				)





     @classmethod
     def 						UpperCAmelCase (     cls				):

      '''simple docstring'''




      super().tearDownClass()
      torch.use_deterministic_algorithms(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      torch.manual_seed(0				)
      __UpperCamelCase      =	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=__UpperCAmelCase     ,					)
      __UpperCamelCase      =	DDIMScheduler(
          beta_start=0.0_0_0_8_5     ,					beta_end=0.0_1_2     ,					beta_schedule='scaled_linear'     ,					clip_sample=__UpperCAmelCase     ,					set_alpha_to_one=__UpperCAmelCase     ,					)
      torch.manual_seed(0				)
      __UpperCamelCase      =	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				)
      __UpperCamelCase      =	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     ,					hidden_act='gelu'     ,					projection_dim=512     ,					)
      __UpperCamelCase      =	CLIPTextModel(__UpperCAmelCase				)
      __UpperCamelCase      =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

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

      return components





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=0				):

      '''simple docstring'''




      if str(__UpperCAmelCase				).startswith('mps'				):
       __UpperCamelCase      =	torch.manual_seed(__UpperCAmelCase				)
      else:
       __UpperCamelCase      =	torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)
      __UpperCamelCase      =	__UpperCamelCase      =	{
          '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 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	'cpu'

      __UpperCamelCase      =	self.get_dummy_components()
      __UpperCamelCase      =	self.pipeline_class(**__UpperCAmelCase				)
      pipe.to(__UpperCAmelCase				)
      pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

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

      self.assertEqual(image.shape     ,					(1, 64, 64, 3)				)
      __UpperCamelCase      =	np.array(
          [0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6]				)
      __UpperCamelCase      =	np.abs(image_slice.flatten() - expected_slice				).max()
      self.assertLessEqual(__UpperCAmelCase     ,					1E-3				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_cpu_offload_forward_pass(expected_max_diff=5E-4				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self._test_inference_batch_consistent(batch_sizes=[1, 2]				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self._test_inference_batch_single_identical(batch_size=2     ,					expected_max_diff=7E-4				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_save_load_local(expected_max_difference=5E-4				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      super().test_save_load_optional_components(expected_max_difference=4E-4				)


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





     @classmethod
     def 						UpperCAmelCase (     cls				):

      '''simple docstring'''




      super().setUpClass()
      torch.use_deterministic_algorithms(__UpperCAmelCase				)





     @classmethod
     def 						UpperCAmelCase (     cls				):

      '''simple docstring'''




      super().tearDownClass()
      torch.use_deterministic_algorithms(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




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







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	torch.manual_seed(51				)

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

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

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

      __UpperCamelCase      =	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



