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 		snake_case__	(		):
   lowerCAmelCase_:							Any       =	'''https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'''
   lowerCAmelCase_:							Tuple       =	Image.open(requests.get(__a							,				stream=__a					).raw					).convert("RGB"					)

   return image
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							Tuple       =	[]
   # 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 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Tuple       =	dct.pop(__a					)
   lowerCAmelCase_:							str       =	val
def 		snake_case__	(		lowercase							,				lowercase					):






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

      # next, set bias in the state dict
      lowerCAmelCase_:							List[Any]       =	torch.cat((q_bias, torch.zeros_like(__a							,				requires_grad=__a					), v_bias)					)
      lowerCAmelCase_:							Dict       =	qkv_bias
def 		snake_case__	(		lowercase							,				lowercase					):
   lowerCAmelCase_:							int       =	364 if '''coco''' in model_name else 224
   lowerCAmelCase_:							Union[str, Any]       =	BlipaVisionConfig(image_size=__a					).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:
      lowerCAmelCase_:							List[str]       =	OPTConfig.from_pretrained("facebook/opt-2.7b"							,				eos_token_id=__a					).to_dict()
   elif "opt-6.7b" in model_name:
      lowerCAmelCase_:							int       =	OPTConfig.from_pretrained("facebook/opt-6.7b"							,				eos_token_id=__a					).to_dict()
   elif "t5-xl" in model_name:
      lowerCAmelCase_:							str       =	TaConfig.from_pretrained("google/flan-t5-xl"							,				dense_act_fn="gelu"							,				bos_token_id=1					).to_dict()
   elif "t5-xxl" in model_name:
      lowerCAmelCase_:							Dict       =	TaConfig.from_pretrained("google/flan-t5-xxl"							,				dense_act_fn="gelu"							,				bos_token_id=1					).to_dict()

   lowerCAmelCase_:							List[str]       =	BlipaConfig(vision_config=__a							,				text_config=__a					)

   return config, image_size
@torch.no_grad()
def 		snake_case__	(		lowercase							,				lowercase=None							,				lowercase=False					):
   lowerCAmelCase_:							Optional[Any]       =	(
       AutoTokenizer.from_pretrained("facebook/opt-2.7b"					)
       if '''opt''' in model_name
       else AutoTokenizer.from_pretrained("google/flan-t5-xl"					)
   )
   lowerCAmelCase_:							int       =	tokenizer("\n"							,				add_special_tokens=__a					).input_ids[0]
   lowerCAmelCase_:							Tuple       =	get_blipa_config(__a							,				eos_token_id=__a					)

   lowerCAmelCase_:							Dict       =	BlipaForConditionalGeneration(__a					).eval()

   lowerCAmelCase_:							Union[str, Any]       =	{
       '''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'''),
   }

   lowerCAmelCase_:							List[Any]       =	model_name_to_original[model_name]

   # load original model
   print("Loading original model..."					)
   lowerCAmelCase_:							Any       =	'''cuda''' if torch.cuda.is_available() else '''cpu'''
   lowerCAmelCase_:							Union[str, Any]       =	load_model_and_preprocess(
       name=__a							,				model_type=__a							,				is_eval=__a							,				device=__a					)
   original_model.eval()
   print("Done!"					)

   # update state dict keys
   lowerCAmelCase_:							Any       =	original_model.state_dict()
   lowerCAmelCase_:							Union[str, Any]       =	create_rename_keys(__a					)
   for src, dest in rename_keys:
      rename_key(__a							,				__a							,				__a					)

   # some keys can be renamed efficiently
   for key, val in state_dict.copy().items():
      lowerCAmelCase_:							Optional[int]       =	state_dict.pop(__a					)
      if key.startswith("Qformer.bert"					):
         lowerCAmelCase_:							Union[str, Any]       =	key.replace("Qformer.bert"							,				"qformer"					)
      if "attention.self" in key:
         lowerCAmelCase_:							Dict       =	key.replace("self"							,				"attention"					)
      if "opt_proj" in key:
         lowerCAmelCase_:							Optional[Any]       =	key.replace("opt_proj"							,				"language_projection"					)
      if "t5_proj" in key:
         lowerCAmelCase_:							Any       =	key.replace("t5_proj"							,				"language_projection"					)
      if key.startswith("opt"					):
         lowerCAmelCase_:							Union[str, Any]       =	key.replace("opt"							,				"language"					)
      if key.startswith("t5"					):
         lowerCAmelCase_:							List[str]       =	key.replace("t5"							,				"language"					)
      lowerCAmelCase_:							Optional[int]       =	val

   # read in qv biases
   read_in_q_v_bias(__a							,				__a					)

   lowerCAmelCase_:							str       =	hf_model.load_state_dict(__a							,				strict=__a					)
   assert len(__a					) == 0
   assert unexpected_keys == ["qformer.embeddings.position_ids"]

   lowerCAmelCase_:							Optional[Any]       =	load_demo_image()
   lowerCAmelCase_:							int       =	vis_processors['''eval'''](__a					).unsqueeze(0					).to(__a					)
   lowerCAmelCase_:							List[str]       =	tokenizer(["\n"]							,				return_tensors="pt"					).input_ids.to(__a					)

   # create processor
   lowerCAmelCase_:							Tuple       =	BlipImageProcessor(
       size={"height": image_size, "width": image_size}							,				image_mean=__a							,				image_std=__a					)
   lowerCAmelCase_:							Optional[Any]       =	BlipaProcessor(image_processor=__a							,				tokenizer=__a					)
   lowerCAmelCase_:							int       =	processor(images=__a							,				return_tensors="pt"					).pixel_values.to(__a					)

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

   original_model.to(__a					)
   hf_model.to(__a					)
   with torch.no_grad():
      if "opt" in model_name:
         lowerCAmelCase_:							Dict       =	original_model({"image": original_pixel_values, "text_input": [""]}					).logits
         lowerCAmelCase_:							Union[str, Any]       =	hf_model(__a							,				__a					).logits
      else:
         lowerCAmelCase_:							Tuple       =	original_model(
             {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}					).logits
         lowerCAmelCase_:							Any       =	input_ids.masked_fill(input_ids == tokenizer.pad_token_id							,				-100					)
         lowerCAmelCase_:							Optional[int]       =	hf_model(__a							,				__a							,				labels=__a					).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":
      lowerCAmelCase_:							Dict       =	torch.tensor(
          [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]							,				device=__a					)
      assert torch.allclose(logits[0, :3, :3]							,				__a							,				atol=1E-4					)
   elif model_name == "blip2-flan-t5-xl-coco":
      lowerCAmelCase_:							Union[str, Any]       =	torch.tensor(
          [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]							,				device=__a					)
   else:
      # cast to same type
      lowerCAmelCase_:							int       =	logits.dtype
      assert torch.allclose(original_logits.to(__a					)							,				__a							,				atol=1E-2					)
   print("Looks ok!"					)

   print("Generating a caption..."					)
   lowerCAmelCase_:							Optional[Any]       =	''''''
   lowerCAmelCase_:							List[Any]       =	tokenizer(__a							,				return_tensors="pt"					).input_ids.to(__a					)

   lowerCAmelCase_:							List[Any]       =	original_model.generate({"image": original_pixel_values}					)
   lowerCAmelCase_:							List[Any]       =	hf_model.generate(
       __a							,				__a							,				do_sample=__a							,				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:"							,				__a					)
   lowerCAmelCase_:							int       =	input_ids.shape[1]
   lowerCAmelCase_:							List[Any]       =	processor.batch_decode(outputs[:, prompt_length:]							,				skip_special_tokens=__a					)
   lowerCAmelCase_:							Tuple       =	[text.strip() for text in output_text]
   print("HF generation:"							,				__a					)

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

   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  :      Optional[Any]            =    parser.parse_args()

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



'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							['image_processor', 'tokenizer']
		_a       					=							'ChineseCLIPImageProcessor'
		_a       					=							('BertTokenizer', 'BertTokenizerFast')




		def __init__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  **UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''       ,  UpperCamelCase_       ,  )
												UpperCamelCase__  :List[Any]				   =					kwargs.pop('''feature_extractor'''				)

							UpperCamelCase__  :List[str]				   =					image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
												raise ValueError('''You need to specify an `image_processor`.'''				)
							if tokenizer is None:
												raise ValueError('''You need to specify a `tokenizer`.'''				)

							super().__init__(UpperCamelCase_       ,  UpperCamelCase_				)
							UpperCamelCase__  :Any				   =					self.image_processor




		def __call__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  **UpperCamelCase_				):

							'''simple docstring'''


							if text is None and images is None:
												raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

							if text is not None:
												UpperCamelCase__  :Tuple				   =					self.tokenizer(UpperCamelCase_       ,  return_tensors=UpperCamelCase_       ,  **UpperCamelCase_				)

							if images is not None:
												UpperCamelCase__  :Any				   =					self.image_processor(UpperCamelCase_       ,  return_tensors=UpperCamelCase_       ,  **UpperCamelCase_				)

							if text is not None and images is not None:
												UpperCamelCase__  :Optional[int]				   =					image_features.pixel_values
												return encoding
							elif text is not None:
												return encoding
							else:
												return BatchEncoding(data=dict(**UpperCamelCase_				)       ,  tensor_type=UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


							return self.tokenizer.batch_decode(*UpperCamelCase_       ,  **UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


							return self.tokenizer.decode(*UpperCamelCase_       ,  **UpperCamelCase_				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					self.tokenizer.model_input_names
							UpperCamelCase__  :Any				   =					self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''       ,  UpperCamelCase_       ,  )
							return self.image_processor_class