


import gc
import unittest

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

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference

class 		__a				(    UpperCAmelCase   ,				unittest.TestCase      ):
		_a     :  Optional[Any]           =     ShapEPipeline
		_a     :  Dict           =     ['prompt']
		_a     :  str           =     ['prompt']
		_a     :  Optional[int]           =     [
		    'num_images_per_prompt',
		    'num_inference_steps',
		    'generator',
		    'latents',
		    'guidance_scale',
		    'frame_size',
		    'output_type',
		    'return_dict',
		]
		_a     :  Dict           =     False
		@property
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									return 32
		@property
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									return 32
		@property
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									return self.time_input_dim * 4
		@property
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									return 8
		@property
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)
									return tokenizer
		@property
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									torch.manual_seed(0						)
									_UpperCAmelCase					=      CLIPTextConfig(
									    bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=self.text_embedder_hidden_size      ,   projection_dim=self.text_embedder_hidden_size      ,   intermediate_size=37      ,   layer_norm_eps=1e-0_5      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   pad_token_id=1      ,   vocab_size=1000      ,   )
									return CLIPTextModelWithProjection(_SCREAMING_SNAKE_CASE						)
		@property
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									torch.manual_seed(0						)

									_UpperCAmelCase					=      {
									    'num_attention_heads': 2,
									    'attention_head_dim': 16,
									    'embedding_dim': self.time_input_dim,
									    'num_embeddings': 32,
									    'embedding_proj_dim': self.text_embedder_hidden_size,
									    'time_embed_dim': self.time_embed_dim,
									    'num_layers': 1,
									    'clip_embed_dim': self.time_input_dim * 2,
									    'additional_embeddings': 0,
									    'time_embed_act_fn': 'gelu',
									    'norm_in_type': 'layer',
									    'encoder_hid_proj_type': None,
									    'added_emb_type': None,
									}

									_UpperCAmelCase					=      PriorTransformer(**_SCREAMING_SNAKE_CASE						)
									return model
		@property
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									torch.manual_seed(0						)

									_UpperCAmelCase					=      {
									    'param_shapes': (
									        (self.renderer_dim, 93),
									        (self.renderer_dim, 8),
									        (self.renderer_dim, 8),
									        (self.renderer_dim, 8),
									    ),
									    'd_latent': self.time_input_dim,
									    'd_hidden': self.renderer_dim,
									    'n_output': 12,
									    'background': (
									        0.1,
									        0.1,
									        0.1,
									    ),
									}
									_UpperCAmelCase					=      ShapERenderer(**_SCREAMING_SNAKE_CASE						)
									return model
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.dummy_prior
									_UpperCAmelCase					=      self.dummy_text_encoder
									_UpperCAmelCase					=      self.dummy_tokenizer
									_UpperCAmelCase					=      self.dummy_renderer

									_UpperCAmelCase					=      HeunDiscreteScheduler(
									    beta_schedule='exp'      ,   num_train_timesteps=1024      ,   prediction_type='sample'      ,   use_karras_sigmas=_SCREAMING_SNAKE_CASE      ,   clip_sample=_SCREAMING_SNAKE_CASE      ,   clip_sample_range=1.0      ,   )
									_UpperCAmelCase					=      {
									    'prior': prior,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									    'renderer': renderer,
									    'scheduler': scheduler,
									}

									return components
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=0						)			->	Dict:




									"""simple docstring"""





									if str(_SCREAMING_SNAKE_CASE						).startswith('mps'						):
																_UpperCAmelCase					=      torch.manual_seed(_SCREAMING_SNAKE_CASE						)
									else:
																_UpperCAmelCase					=      torch.Generator(device=_SCREAMING_SNAKE_CASE						).manual_seed(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      {
									    'prompt': 'horse',
									    'generator': generator,
									    'num_inference_steps': 1,
									    'frame_size': 32,
									    'output_type': 'np',
									}
									return inputs
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      'cpu'

									_UpperCAmelCase					=      self.get_dummy_components()

									_UpperCAmelCase					=      self.pipeline_class(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      pipe.to(_SCREAMING_SNAKE_CASE						)

									pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE						)						)
									_UpperCAmelCase					=      output.images[0]
									_UpperCAmelCase					=      image[0, -3:, -3:, -1]

									assert image.shape == (20, 32, 32, 3)

									_UpperCAmelCase					=      np.array(
									    [
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									        0.00039216,
									    ]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									self._test_inference_batch_consistent(batch_sizes=[1, 2]						)
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      torch_device == 'cpu'
									_UpperCAmelCase					=      True

									self._test_inference_batch_single_identical(
									    batch_size=2      ,   test_max_difference=_SCREAMING_SNAKE_CASE      ,   relax_max_difference=_SCREAMING_SNAKE_CASE      ,   )






		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_dummy_components()
									_UpperCAmelCase					=      self.pipeline_class(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      pipe.to(_SCREAMING_SNAKE_CASE						)
									pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      1
									_UpperCAmelCase					=      2

									_UpperCAmelCase					=      self.get_dummy_inputs(_SCREAMING_SNAKE_CASE						)

									for key in inputs.keys():
																if key in self.batch_params:
																							_UpperCAmelCase					=      batch_size * [inputs[key]]

									_UpperCAmelCase					=      pipe(**_SCREAMING_SNAKE_CASE      ,   num_images_per_prompt=_SCREAMING_SNAKE_CASE						)[0]

									assert images.shape[0] == batch_size * num_images_per_prompt



@slow
@require_torch_gpu
class 		__a				(    unittest.TestCase      ):
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





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






		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      load_numpy(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/shap_e/test_shap_e_np_out.npy'						)
									_UpperCAmelCase					=      ShapEPipeline.from_pretrained('openai/shap-e'						)
									_UpperCAmelCase					=      pipe.to(_SCREAMING_SNAKE_CASE						)
									pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      torch.Generator(device=_SCREAMING_SNAKE_CASE						).manual_seed(0						)

									_UpperCAmelCase					=      pipe(
									    'a shark'      ,   generator=_SCREAMING_SNAKE_CASE      ,   guidance_scale=15.0      ,   num_inference_steps=64      ,   frame_size=64      ,   output_type='np'      ,   ).images[0]

									assert images.shape == (20, 64, 64, 3)

									assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)




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 	lowerCAmelCase__			(      a__:		str      ,  a__:		List[Any]	)  ->		Optional[int]:


							'''simple docstring'''





							_UpperCAmelCase					=      'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
							_UpperCAmelCase					=      Image.open(requests.get(a__      ,  stream=a__	).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(a__	).unsqueeze(0	).to(a__	)
							return image

def 	lowerCAmelCase__			(      a__:		Optional[int]	)  ->		int:


							'''simple docstring'''





							if "visual_encoder" in key:
														_UpperCAmelCase					=      re.sub('visual_encoder*'      ,  'vision_model.encoder'      ,  a__	)
							if "blocks" in key:
														_UpperCAmelCase					=      re.sub(R'blocks'      ,  'layers'      ,  a__	)
							if "attn" in key:
														_UpperCAmelCase					=      re.sub(R'attn'      ,  'self_attn'      ,  a__	)
							if "norm1" in key:
														_UpperCAmelCase					=      re.sub(R'norm1'      ,  'layer_norm1'      ,  a__	)
							if "norm2" in key:
														_UpperCAmelCase					=      re.sub(R'norm2'      ,  'layer_norm2'      ,  a__	)
							if "encoder.norm" in key:
														_UpperCAmelCase					=      re.sub(R'encoder.norm'      ,  'post_layernorm'      ,  a__	)
							if "encoder.patch_embed.proj" in key:
														_UpperCAmelCase					=      re.sub(R'encoder.patch_embed.proj'      ,  'embeddings.patch_embedding'      ,  a__	)

							if "encoder.pos_embed" in key:
														_UpperCAmelCase					=      re.sub(R'encoder.pos_embed'      ,  'embeddings.position_embedding'      ,  a__	)
							if "encoder.cls_token" in key:
														_UpperCAmelCase					=      re.sub(R'encoder.cls_token'      ,  'embeddings.class_embedding'      ,  a__	)

							if "self_attn" in key:
														_UpperCAmelCase					=      re.sub(R'self_attn.proj'      ,  'self_attn.projection'      ,  a__	)

							return key

@torch.no_grad()
def 	lowerCAmelCase__			(      a__:		Optional[Any]      ,  a__:		List[str]=None	)  ->		Optional[Any]:


							'''simple docstring'''





							if config_path is not None:
														_UpperCAmelCase					=      BlipConfig.from_pretrained(a__	)
							else:
														_UpperCAmelCase					=      BlipConfig(projection_dim=5_1_2      ,  text_config={}      ,  vision_config={}	)

							_UpperCAmelCase					=      BlipForConditionalGeneration(a__	).eval()

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

							_UpperCAmelCase					=      blip_decoder(pretrained=a__      ,  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(a__	)
														_UpperCAmelCase					=      rename_key(a__	)
														_UpperCAmelCase					=      value

							hf_model.load_state_dict(a__	)

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

							_UpperCAmelCase					=      hf_model.generate(a__      ,  a__	)

							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(a__	)

							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(a__	)

							# 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=a__      ,  image_size=a__      ,  vit='base'	)
							vqa_model.eval()

							_UpperCAmelCase					=      vqa_model.state_dict()
							for key in modified_state_dict.copy():
														_UpperCAmelCase					=      modified_state_dict.pop(a__	)
														_UpperCAmelCase					=      rename_key(a__	)
														_UpperCAmelCase					=      value

							_UpperCAmelCase					=      BlipForQuestionAnswering(a__	)

							hf_vqa_model.load_state_dict(a__	)

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

							_UpperCAmelCase					=      hf_vqa_model.generate(a__      ,  a__	)
							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=a__      ,  image_size=a__      ,  vit='base'	)
							itm_model.eval()

							_UpperCAmelCase					=      itm_model.state_dict()
							for key in modified_state_dict.copy():
														_UpperCAmelCase					=      modified_state_dict.pop(a__	)
														_UpperCAmelCase					=      rename_key(a__	)
														_UpperCAmelCase					=      value

							_UpperCAmelCase					=      BlipForImageTextRetrieval(a__	)

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

							hf_itm_model.load_state_dict(a__	)
							hf_itm_model.eval()

							_UpperCAmelCase					=      hf_itm_model(a__      ,  a__      ,  use_itm_head=a__	)
							_UpperCAmelCase					=      hf_itm_model(a__      ,  a__      ,  use_itm_head=a__	)

							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__":
		lowerCAmelCase__  :int             =							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''')
		lowerCAmelCase__  :List[Any]             =							parser.parse_args()

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