

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 		UpperCamelCase   ()	-> List[str]:
	A__				:       List[str]	  =						"""https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"""
	A__				:       List[Any]	  =						Image.open(requests.get(lowercase_       , stream=lowercase_   ).raw   ).convert("""RGB"""   )

	return image

def 		UpperCamelCase   (lowercase_:					int   )	-> List[str]:
	A__				:       List[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 		UpperCamelCase   (lowercase_:					Union[str, Any]       , lowercase_:					Tuple       , lowercase_:					Union[str, Any]   )	-> int:
	A__				:       str	  =						dct.pop(lowercase_   )
	A__				:       List[Any]	  =						val

def 		UpperCamelCase   (lowercase_:					List[Any]       , lowercase_:					List[Any]   )	-> Union[str, Any]:
	for i in range(config.vision_config.num_hidden_layers   ):
		# read in original q and v biases
		A__				:       Dict	  =						state_dict.pop(f"""visual_encoder.blocks.{i}.attn.q_bias"""   )
		A__				:       Any	  =						state_dict.pop(f"""visual_encoder.blocks.{i}.attn.v_bias"""   )

		# next, set bias in the state dict
		A__				:       Optional[int]	  =						torch.cat((q_bias, torch.zeros_like(lowercase_       , requires_grad=lowercase_   ), v_bias)   )
		A__				:       List[str]	  =						qkv_bias

def 		UpperCamelCase   (lowercase_:					Optional[Any]   )	-> List[Any]:
	A__				:       Tuple	  =						364 if """coco""" in model_name else 224
	A__				:       Dict	  =						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:
		A__				:       List[Any]	  =						TaConfig.from_pretrained("""google/flan-t5-xl"""       , dense_act_fn="""gelu"""       , bos_token_id=1   ).to_dict()
	elif "t5-xxl" in model_name:
		A__				:       Tuple	  =						TaConfig.from_pretrained("""google/flan-t5-xxl"""       , dense_act_fn="""gelu"""       , bos_token_id=1   ).to_dict()
	elif "vicuna-7b" in model_name:
		A__				:       Optional[int]	  =						LlamaConfig.from_pretrained("""decapoda-research/llama-7b-hf"""       , vocab_size=32001   ).to_dict()
	elif "vicuna-13b" in model_name:
		A__				:       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
	A__				:       Optional[Any]	  =						InstructBlipQFormerConfig(vocab_size=30523   ).to_dict()
	A__				:       Tuple	  =						InstructBlipConfig(vision_config=lowercase_       , text_config=lowercase_       , qformer_config=lowercase_   )

	return config, image_size

@torch.no_grad()
def 		UpperCamelCase   (lowercase_:					int       , lowercase_:					List[Any]=None       , lowercase_:					Optional[int]=False   )	-> Optional[int]:
	A__				:       Optional[Any]	  =						AutoTokenizer.from_pretrained("""bert-base-uncased"""       , truncation_side="""left"""   )
	qformer_tokenizer.add_special_tokens({"""bos_token""": """[DEC]"""}   )

	if "t5" in model_name:
		A__				:       Union[str, Any]	  =						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>"})
		A__				:       Dict	  =						LlamaTokenizerFast.from_pretrained(
		    """huggyllama/llama-7b"""       , truncation_side="""left"""       , bos_token="""</s>"""       , unk_token="""</s>"""   )
		tokenizer.add_special_tokens({"""pad_token""": """[PAD]"""}   )

	A__  ,  A__				:       Dict	  =						get_blipa_config(lowercase_   )
	A__				:       List[Any]	  =						InstructBlipForConditionalGeneration(lowercase_   ).eval()

	A__				:       Dict	  =						{
	    """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"""),
	}

	A__  ,  A__				:       Any	  =						model_name_to_original[model_name]

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

	# update state dict keys
	A__				:       str	  =						original_model.state_dict()
	A__				:       Dict	  =						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():
		A__				:       List[Any]	  =						state_dict.pop(lowercase_   )
		if key.startswith("""Qformer.bert"""   ):
			A__				:       int	  =						key.replace("""Qformer.bert"""       , """qformer"""   )
		if "attention.self" in key:
			A__				:       Union[str, Any]	  =						key.replace("""self"""       , """attention"""   )
		if "llm_proj" in key:
			A__				:       int	  =						key.replace("""llm_proj"""       , """language_projection"""   )
		if "t5_proj" in key:
			A__				:       Dict	  =						key.replace("""t5_proj"""       , """language_projection"""   )
		if key.startswith("""llm_model"""   ):
			A__				:       Tuple	  =						key.replace("""llm_model"""       , """language_model"""   )
		if key.startswith("""t5"""   ):
			A__				:       Dict	  =						key.replace("""t5"""       , """language"""   )
		A__				:       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_   )

	A__				:       List[str]	  =						load_demo_image()
	A__				:       Optional[Any]	  =						"""What is unusual about this image?"""

	# create processor
	A__				:       Optional[int]	  =						BlipImageProcessor(
	    size={"""height""": image_size, """width""": image_size}       , image_mean=lowercase_       , image_std=lowercase_   )
	A__				:       int	  =						InstructBlipProcessor(
	    image_processor=lowercase_       , tokenizer=lowercase_       , qformer_tokenizer=lowercase_       , )
	A__				:       Dict	  =						processor(images=lowercase_       , text=lowercase_       , return_tensors="""pt"""   ).to(lowercase_   )

	# make sure processor creates exact same pixel values
	A__				:       Any	  =						vis_processors["""eval"""](lowercase_   ).unsqueeze(0   ).to(lowercase_   )
	A__				:       Dict	  =						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:
			A__				:       Any	  =						original_model({"""image""": original_pixel_values, """text_input""": [prompt]}   ).logits
			A__				:       Dict	  =						hf_model(**lowercase_   ).logits
		else:
			A__				:       Tuple	  =						original_model(
			    {"""image""": original_pixel_values, """text_input""": [prompt], """text_output""": ["""\n"""]}   ).logits
			A__				:       Tuple	  =						tokenizer("""\n"""       , return_tensors="""pt"""   ).input_ids.to(lowercase_   )
			A__				:       List[str]	  =						label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id       , -100   )
			A__				:       Dict	  =						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
	A__				:       int	  =						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..."""   )
	A__				:       List[str]	  =						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..."""   )
	A__				:       Any	  =						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?
		A__				:       Any	  =						2
	print("""Original generation:"""       , lowercase_   )
	A__				:       Optional[Any]	  =						processor.batch_decode(lowercase_       , skip_special_tokens=lowercase_   )
	A__				:       List[str]	  =						[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_			:					Optional[Any]    			=     argparse.ArgumentParser()
	A_			:					str    			=     [
	    '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_			:					Any    			=     parser.parse_args()

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






import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


A_			:					Any    			=     logging.get_logger(__name__)



class        _a		:


					'''simple docstring'''






					UpperCAmelCase__:  str    				=			None



@experimental
def 		UpperCamelCase   (lowercase_:					int       , lowercase_:					Any       , lowercase_:					str       , lowercase_:					str       , lowercase_:					List[str]       , lowercase_:					List[Any]       , lowercase_:					Union[str, Any]   )	-> Union[str, Any]:
	if ParallelBackendConfig.backend_name is None:
		return _map_with_multiprocessing_pool(
		    lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )

	return _map_with_joblib(lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )

def 		UpperCamelCase   (lowercase_:					Tuple       , lowercase_:					List[Any]       , lowercase_:					Tuple       , lowercase_:					Tuple       , lowercase_:					List[str]       , lowercase_:					Dict       , lowercase_:					Tuple   )	-> Tuple:
	A__				:       Union[str, Any]	  =						num_proc if num_proc <= len(lowercase_   ) else len(lowercase_   )
	A__				:       str	  =						[]  # We organize the splits ourselve (contiguous splits)
	for index in range(lowercase_   ):
		A__				:       Tuple	  =						len(lowercase_   ) // num_proc
		A__				:       Tuple	  =						len(lowercase_   ) % num_proc
		A__				:       Optional[Any]	  =						div * index + min(lowercase_       , lowercase_   )
		A__				:       Union[str, Any]	  =						start + div + (1 if index < mod else 0)
		split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)   )

	if len(lowercase_   ) != sum(len(i[1]   ) for i in split_kwds   ):
		raise ValueError(
		    f"""Error dividing inputs iterable among processes. """
		    f"""Total number of objects {len(lowercase_   )}, """
		    f"""length: {sum(len(i[1]   ) for i in split_kwds   )}"""   )

	logger.info(
	    f"""Spawning {num_proc} processes for {len(lowercase_   )} objects in slices of {[len(i[1]   ) for i in split_kwds]}"""   )
	A__  ,  A__				:       Optional[int]	  =						None, None
	if not disable_tqdm:
		A__  ,  A__				:       List[str]	  =						(RLock(),), tqdm.set_lock
	with Pool(lowercase_       , initargs=lowercase_       , initializer=lowercase_   ) as pool:
		A__				:       Optional[Any]	  =						pool.map(lowercase_       , lowercase_   )
	logger.info(f"""Finished {num_proc} processes"""   )
	A__				:       Tuple	  =						[obj for proc_res in mapped for obj in proc_res]
	logger.info(f"""Unpacked {len(lowercase_   )} objects"""   )

	return mapped

def 		UpperCamelCase   (lowercase_:					Union[str, Any]       , lowercase_:					Dict       , lowercase_:					str       , lowercase_:					Union[str, Any]       , lowercase_:					List[Any]       , lowercase_:					Dict       , lowercase_:					Optional[Any]   )	-> Union[str, Any]:
	# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
	# and it requires monkey-patching joblib internal classes which is subject to change
	import joblib

	with joblib.parallel_backend(ParallelBackendConfig.backend_name       , n_jobs=lowercase_   ):
		return joblib.Parallel()(
		    joblib.delayed(lowercase_   )((function, obj, types, None, True, None)   ) for obj in iterable   )


@experimental
@contextlib.contextmanager
def 		UpperCamelCase   (lowercase_:					str   )	-> str:
	A__				:       int	  =						backend_name

	if backend_name == "spark":
		from joblibspark import register_spark

		register_spark()

		# TODO: call create_cache_and_write_probe if "download" in steps
		# TODO: raise NotImplementedError when Dataset.map etc is called

	try:
		yield
	finally:
		A__				:       Optional[Any]	  =						None



