


'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class      lowerCamelCase_  (      __lowerCAmelCase	,			unittest.TestCase ):
      lowerCAmelCase__							=	DiTPipeline
      lowerCAmelCase__							=	CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
      lowerCAmelCase__							=	PipelineTesterMixin.required_optional_params - {
          '''latents''',
          '''num_images_per_prompt''',
          '''callback''',
          '''callback_steps''',
      }
      lowerCAmelCase__							=	CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
      lowerCAmelCase__							=	False






      def lowercase_				(   self							:       str					):


          '''simple docstring'''


          torch.manual_seed(0					)
          UpperCAmelCase__     :						List[str]						=							TransformeraDModel(
              sample_size=16		,		num_layers=2		,		patch_size=4		,		attention_head_dim=8		,		num_attention_heads=2		,		in_channels=4		,		out_channels=8		,		attention_bias=__SCREAMING_SNAKE_CASE		,		activation_fn='''gelu-approximate'''		,		num_embeds_ada_norm=1_000		,		norm_type='''ada_norm_zero'''		,		norm_elementwise_affine=__SCREAMING_SNAKE_CASE		,		)
          UpperCAmelCase__     :						str						=							AutoencoderKL()
          UpperCAmelCase__     :						Any						=							DDIMScheduler()
          UpperCAmelCase__     :						Tuple						=							{'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
          return components






      def lowercase_				(   self							:       List[str]		,		_A							:       str		,		_A							:       str=0					):


          '''simple docstring'''


          if str(__SCREAMING_SNAKE_CASE					).startswith('''mps'''					):
              UpperCAmelCase__     :						Optional[int]						=							torch.manual_seed(__SCREAMING_SNAKE_CASE					)
          else:
              UpperCAmelCase__     :						List[str]						=							torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(__SCREAMING_SNAKE_CASE					)
          UpperCAmelCase__     :						Dict						=							{
              '''class_labels''': [1],
              '''generator''': generator,
              '''num_inference_steps''': 2,
              '''output_type''': '''numpy''',
          }
          return inputs






      def lowercase_				(   self							:       Optional[int]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Optional[Any]						=							'''cpu'''

          UpperCAmelCase__     :						int						=							self.get_dummy_components()
          UpperCAmelCase__     :						Optional[Any]						=							self.pipeline_class(**__SCREAMING_SNAKE_CASE					)
          pipe.to(__SCREAMING_SNAKE_CASE					)
          pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

          UpperCAmelCase__     :						int						=							self.get_dummy_inputs(__SCREAMING_SNAKE_CASE					)
          UpperCAmelCase__     :						str						=							pipe(**__SCREAMING_SNAKE_CASE					).images
          UpperCAmelCase__     :						Tuple						=							image[0, -3:, -3:, -1]

          self.assertEqual(image.shape		,		(1, 16, 16, 3)					)
          UpperCAmelCase__     :						Optional[int]						=							np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]					)
          UpperCAmelCase__     :						str						=							np.abs(image_slice.flatten() - expected_slice					).max()
          self.assertLessEqual(__SCREAMING_SNAKE_CASE		,		1e-3					)






      def lowercase_				(   self							:       str					):


          '''simple docstring'''


          self._test_inference_batch_single_identical(relax_max_difference=__SCREAMING_SNAKE_CASE		,		expected_max_diff=1e-3					)






      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()		,		reason='''XFormers attention is only available with CUDA and `xformers` installed'''		,		)
      def lowercase_				(   self							:       Tuple					):


          '''simple docstring'''


          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)




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






      def lowercase_				(   self							:       List[Any]					):


          '''simple docstring'''


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






      def lowercase_				(   self							:       List[str]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Union[str, Any]						=							torch.manual_seed(0					)

          UpperCAmelCase__     :						int						=							DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''					)
          pipe.to('''cuda'''					)

          UpperCAmelCase__     :						Optional[int]						=							['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
          UpperCAmelCase__     :						Optional[int]						=							pipe.get_label_ids(__SCREAMING_SNAKE_CASE					)

          UpperCAmelCase__     :						Union[str, Any]						=							pipe(__SCREAMING_SNAKE_CASE		,		generator=__SCREAMING_SNAKE_CASE		,		num_inference_steps=40		,		output_type='''np'''					).images

          for word, image in zip(__SCREAMING_SNAKE_CASE		,		__SCREAMING_SNAKE_CASE					):
              UpperCAmelCase__     :						str						=							load_numpy(
                  f"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"""					)
              assert np.abs((expected_image - image).max()					) < 1e-2






      def lowercase_				(   self							:       str					):


          '''simple docstring'''


          UpperCAmelCase__     :						List[Any]						=							DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''					)
          UpperCAmelCase__     :						Any						=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
          pipe.to('''cuda'''					)

          UpperCAmelCase__     :						Union[str, Any]						=							['''vase''', '''umbrella''']
          UpperCAmelCase__     :						str						=							pipe.get_label_ids(__SCREAMING_SNAKE_CASE					)

          UpperCAmelCase__     :						str						=							torch.manual_seed(0					)
          UpperCAmelCase__     :						List[str]						=							pipe(__SCREAMING_SNAKE_CASE		,		generator=__SCREAMING_SNAKE_CASE		,		num_inference_steps=25		,		output_type='''np'''					).images

          for word, image in zip(__SCREAMING_SNAKE_CASE		,		__SCREAMING_SNAKE_CASE					):
              UpperCAmelCase__     :						Dict						=							load_numpy(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                  f"""/dit/{word}_512.npy"""					)

              assert np.abs((expected_image - image).max()					) < 1e-1






'''simple docstring'''



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       _UpperCamelCase			()-> int:




     '''simple docstring'''





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

     return image







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, Any]						)-> List[Any]:




     '''simple docstring'''





     __snake_case            =	[]
     # 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       _UpperCamelCase			(_lowerCamelCase				:  Dict ,						_lowerCamelCase				:  List[str] ,						_lowerCamelCase				:  Optional[int]						)-> Tuple:




     '''simple docstring'''





     __snake_case            =	dct.pop(_lowerCamelCase						)
     __snake_case            =	val







def       _UpperCamelCase			(_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  Tuple						)-> str:




     '''simple docstring'''





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

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







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  Tuple						)-> Dict:




     '''simple docstring'''





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

     __snake_case            =	BlipaConfig(vision_config=_lowerCamelCase ,						text_config=_lowerCamelCase						)

     return config, image_size







@torch.no_grad()
def       _UpperCamelCase			(_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  Union[str, Any]=None ,						_lowerCamelCase				:  Any=False						)-> Dict:




     '''simple docstring'''





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

     __snake_case            =	BlipaForConditionalGeneration(_lowerCamelCase						).eval()

     __snake_case            =	{
         '''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'''),
     }

     __snake_case       ,     __snake_case            =	model_name_to_original[model_name]

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

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

     # read in qv biases
     read_in_q_v_bias(_lowerCamelCase ,						_lowerCamelCase						)

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

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

     # create processor
     __snake_case            =	BlipImageProcessor(
         size={'''height''': image_size, '''width''': image_size} ,						image_mean=_lowerCamelCase ,						image_std=_lowerCamelCase						)
     __snake_case            =	BlipaProcessor(image_processor=_lowerCamelCase ,						tokenizer=_lowerCamelCase						)
     __snake_case            =	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:
               __snake_case            =	original_model({'''image''': original_pixel_values, '''text_input''': ['''''']}						).logits
               __snake_case            =	hf_model(_lowerCamelCase ,						_lowerCamelCase						).logits
          else:
               __snake_case            =	original_model(
                   {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']}						).logits
               __snake_case            =	input_ids.masked_fill(input_ids == tokenizer.pad_token_id ,						-1_00						)
               __snake_case            =	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":
          __snake_case            =	torch.tensor(
              [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]] ,						device=_lowerCamelCase						)
          assert torch.allclose(logits[0, :3, :3] ,						_lowerCamelCase ,						atol=1E-4						)
     elif model_name == "blip2-flan-t5-xl-coco":
          __snake_case            =	torch.tensor(
              [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]] ,						device=_lowerCamelCase						)
     else:
          # cast to same type
          __snake_case            =	logits.dtype
          assert torch.allclose(original_logits.to(_lowerCamelCase						) ,						_lowerCamelCase ,						atol=1E-2						)
     print('''Looks ok!'''						)

     print('''Generating a caption...'''						)
     __snake_case            =	''''''
     __snake_case            =	tokenizer(_lowerCamelCase ,						return_tensors='''pt'''						).input_ids.to(_lowerCamelCase						)

     __snake_case            =	original_model.generate({'''image''': original_pixel_values}						)
     __snake_case            =	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						)
     __snake_case            =	input_ids.shape[1]
     __snake_case            =	processor.batch_decode(outputs[:, prompt_length:] ,						skip_special_tokens=_lowerCamelCase						)
     __snake_case            =	[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__":
     UpperCAmelCase_							:	Any	     =		argparse.ArgumentParser()
     UpperCAmelCase_							:	Tuple	     =		[
         '''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''',
     )

     UpperCAmelCase_							:	List[str]	     =		parser.parse_args()

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






