



"""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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def    A_  (			):







						'''simple docstring'''


						snake_case_ :Optional[int]						=			"""https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"""
						snake_case_ :Optional[Any]						=			Image.open(requests.get(_lowercase,					stream=_lowercase	).raw	).convert("""RGB"""	)

						return image
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :str						=			[]
						# 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.embeddings.layernorm.weight""")	)
						rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.bias""", """qformer.embeddings.layernorm.bias""")	)

						# fmt: on
						return rename_keys
def    A_  (			_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :str						=			dct.pop(_lowercase	)
						snake_case_ :Dict						=			val
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


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

												# next, set bias in the state dict
												snake_case_ :Union[str, Any]						=			torch.cat((q_bias, torch.zeros_like(_lowercase,					requires_grad=_lowercase	), v_bias)	)
												snake_case_ :int						=			qkv_bias
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :List[str]						=			364 if """coco""" in model_name else 224
						snake_case_ :Tuple						=			InstructBlipVisionConfig(image_size=_lowercase	).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 "t5-xl" in model_name:
												snake_case_ :Optional[Any]						=			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_ :List[Any]						=			TaConfig.from_pretrained("""google/flan-t5-xxl""",					dense_act_fn="""gelu""",					bos_token_id=1	).to_dict()
						elif "vicuna-7b" in model_name:
												snake_case_ :Optional[int]						=			LlamaConfig.from_pretrained("""decapoda-research/llama-7b-hf""",					vocab_size=32001	).to_dict()
						elif "vicuna-13b" in model_name:
												snake_case_ :Optional[Any]						=			LlamaConfig.from_pretrained("""decapoda-research/llama-13b-hf""",					vocab_size=32001	).to_dict()
						else:
												raise ValueError("""Model name not supported"""	)

						# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
						snake_case_ :Optional[int]						=			InstructBlipQFormerConfig(vocab_size=30523	).to_dict()
						snake_case_ :List[str]						=			InstructBlipConfig(vision_config=_lowercase,					text_config=_lowercase,					qformer_config=_lowercase	)

						return config, image_size
@torch.no_grad()
def    A_  (			_lowercase,					_lowercase=None,					_lowercase=False	):







						'''simple docstring'''


						snake_case_ :int						=			AutoTokenizer.from_pretrained("""bert-base-uncased""",					truncation_side="""left"""	)
						qformer_tokenizer.add_special_tokens({"""bos_token""": """[DEC]"""}	)

						if "t5" in model_name:
												snake_case_ :Optional[int]						=			TaTokenizerFast.from_pretrained("""google/flan-t5-xl""",					truncation_side="""left"""	)
						elif "vicuna" in model_name:
												# the following was used in the original implementation:
												# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
												# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
												# tokenizer.add_special_tokens({"bos_token": "</s>"})
												# tokenizer.add_special_tokens({"eos_token": "</s>"})
												# tokenizer.add_special_tokens({"unk_token": "</s>"})
												snake_case_ :str						=			LlamaTokenizerFast.from_pretrained(
												    """huggyllama/llama-7b""",					truncation_side="""left""",					bos_token="""</s>""",					unk_token="""</s>"""	)
												tokenizer.add_special_tokens({"""pad_token""": """[PAD]"""}	)

						snake_case_,       snake_case_ :Union[str, Any]						=			get_blipa_config(_lowercase	)
						snake_case_ :Union[str, Any]						=			InstructBlipForConditionalGeneration(_lowercase	).eval()

						snake_case_ :Union[str, Any]						=			{
						    """instructblip-vicuna-7b""": ("""blip2_vicuna_instruct""", """vicuna7b"""),
						    """instructblip-vicuna-13b""": ("""blip2_vicuna_instruct""", """vicuna13b"""),
						    """instructblip-flan-t5-xl""": ("""blip2_t5_instruct""", """flant5xl"""),
						    """instructblip-flan-t5-xxl""": ("""blip2_t5_instruct""", """flant5xxl"""),
						}

						snake_case_,       snake_case_ :List[Any]						=			model_name_to_original[model_name]

						# load original model
						print("""Loading original model..."""	)
						snake_case_ :Optional[Any]						=			"""cuda:1""" if torch.cuda.is_available() else """cpu"""
						snake_case_ :Any						=			"""cuda:2""" if torch.cuda.is_available() else """cpu"""
						snake_case_,       snake_case_,       snake_case_ :int						=			load_model_and_preprocess(
						    name=_lowercase,					model_type=_lowercase,					is_eval=_lowercase,					device=_lowercase	)
						original_model.eval()
						print("""Done!"""	)

						# update state dict keys
						snake_case_ :int						=			original_model.state_dict()
						snake_case_ :int						=			create_rename_keys(_lowercase	)
						for src, dest in rename_keys:
												rename_key(_lowercase,					_lowercase,					_lowercase	)

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

						# read in qv biases
						read_in_q_v_bias(_lowercase,					_lowercase	)

						# note: weights get loaded in torch.float32 by default
						hf_model.load_state_dict(_lowercase,					strict=_lowercase	)

						snake_case_ :Optional[Any]						=			load_demo_image()
						snake_case_ :Tuple						=			"""What is unusual about this image?"""

						# create processor
						snake_case_ :Tuple						=			BlipImageProcessor(
						    size={"""height""": image_size, """width""": image_size},					image_mean=_lowercase,					image_std=_lowercase	)
						snake_case_ :Tuple						=			InstructBlipProcessor(
						    image_processor=_lowercase,					tokenizer=_lowercase,					qformer_tokenizer=_lowercase,					)
						snake_case_ :Tuple						=			processor(images=_lowercase,					text=_lowercase,					return_tensors="""pt"""	).to(_lowercase	)

						# make sure processor creates exact same pixel values
						snake_case_ :Optional[Any]						=			vis_processors["""eval"""](_lowercase	).unsqueeze(0	).to(_lowercase	)
						snake_case_ :List[str]						=			inputs.pixel_values
						assert torch.allclose(original_pixel_values.to(pixel_values.device	),					_lowercase	)

						original_model.to(_lowercase	)
						hf_model.to(_lowercase	)
						with torch.no_grad():
												if "vicuna" in model_name:
																		snake_case_ :List[Any]						=			original_model({"""image""": original_pixel_values, """text_input""": [prompt]}	).logits
																		snake_case_ :Union[str, Any]						=			hf_model(**_lowercase	).logits
												else:
																		snake_case_ :Dict						=			original_model(
																		    {"""image""": original_pixel_values, """text_input""": [prompt], """text_output""": ["""\n"""]}	).logits
																		snake_case_ :Dict						=			tokenizer("""\n""",					return_tensors="""pt"""	).input_ids.to(_lowercase	)
																		snake_case_ :List[str]						=			label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id,					-100	)
																		snake_case_ :List[Any]						=			hf_model(**_lowercase,					labels=_lowercase	).logits

						print("""First values of original logits:""",					original_logits[0, :3, :3]	)
						print("""First values of HF logits:""",					logits[0, :3, :3]	)

						# assert values
						assert original_logits.shape == logits.shape
						snake_case_ :Any						=			1e-4 if """vicuna""" in model_name else 1e-5
						assert torch.allclose(original_logits.to(logits.device	),					_lowercase,					atol=_lowercase	)
						print("""Looks ok!"""	)

						print("""Generating with original model..."""	)
						snake_case_ :Dict						=			original_model.generate({"""image""": original_pixel_values, """prompt""": prompt},					num_beams=5	)

						# important: we need to cast the weights of the HF model to the appropriate type
						print("""Generating with HF model..."""	)
						snake_case_ :str						=			hf_model.generate(
						    **_lowercase,					do_sample=_lowercase,					num_beams=5,					max_length=256,					min_length=1,					top_p=0.9,					repetition_penalty=1.5,					length_penalty=1.0,					temperature=1,					)
						if "vicuna" in model_name:
												# convert output id 0 to 2 (eos_token_id)
												# TODO add this in the generate method?
												snake_case_ :Optional[int]						=			2
						print("""Original generation:""",					_lowercase	)
						snake_case_ :str						=			processor.batch_decode(_lowercase,					skip_special_tokens=_lowercase	)
						snake_case_ :Any						=			[text.strip() for text in output_text]
						print("""HF generation:""",					_lowercase	)

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

						if push_to_hub:
												processor.push_to_hub(f"""Salesforce/{model_name}"""	)
												hf_model.push_to_hub(f"""Salesforce/{model_name}"""	)


if __name__ == "__main__":
						__a      =				argparse.ArgumentParser()
						__a      =				[
						    "instructblip-vicuna-7b",
						    "instructblip-vicuna-13b",
						    "instructblip-flan-t5-xl",
						    "instructblip-flan-t5-xxl",
						]
						parser.add_argument(
						    "--model_name",
						    default="instructblip-flan-t5-xl",
						    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      =				parser.parse_args()

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







def    __lowercase   (      _SCREAMING_SNAKE_CASE = 10	) ->   str:


      '''simple docstring'''


      if not isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) or n < 0:
            raise ValueError("""Invalid input"""	)
      SCREAMING_SNAKE_CASE		=						10**n
      SCREAMING_SNAKE_CASE		=						2_84_33 * (pow(2 ,						7_83_04_57 ,						_SCREAMING_SNAKE_CASE	)) + 1
      return str(number % modulus	)


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      print(F'''{solution(1_0) = }''')


