




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 		lowerCAmelCase_ (						):
				__magic_name__						:						List[str]										="""https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"""
				__magic_name__						:						Dict										=Image.open(requests.get(lowerCamelCase						,  stream=lowerCamelCase ).raw ).convert("""RGB""" )

				return image




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						int										=[]
				# 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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Optional[int]										=dct.pop(lowerCamelCase )
				__magic_name__						:						int										=val




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				for i in range(config.vision_config.num_hidden_layers ):
								# read in original q and v biases
								__magic_name__						:						List[Any]										=state_dict.pop(F"visual_encoder.blocks.{i}.attn.q_bias" )
								__magic_name__						:						Tuple										=state_dict.pop(F"visual_encoder.blocks.{i}.attn.v_bias" )

								# next, set bias in the state dict
								__magic_name__						:						Tuple										=torch.cat((q_bias, torch.zeros_like(lowerCamelCase						,  requires_grad=lowerCamelCase ), v_bias) )
								__magic_name__						:						Dict										=qkv_bias




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

				return config, image_size




@torch.no_grad()
def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=None						,  lowerCamelCase=False ):
				__magic_name__						:						Dict										=AutoTokenizer.from_pretrained("""bert-base-uncased"""						,  truncation_side="""left""" )
				qformer_tokenizer.add_special_tokens({"""bos_token""": """[DEC]"""} )

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

				__magic_name__					,			__magic_name__						:						Union[str, Any]										=get_blipa_config(lowerCamelCase )
				__magic_name__						:						List[Any]										=InstructBlipForConditionalGeneration(lowerCamelCase ).eval()

				__magic_name__						:						List[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"""),
				}

				__magic_name__					,			__magic_name__						:						List[str]										=model_name_to_original[model_name]

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

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

				# read in qv biases
				read_in_q_v_bias(lowerCamelCase						,  lowerCamelCase )

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

				__magic_name__						:						Tuple										=load_demo_image()
				__magic_name__						:						Any										="""What is unusual about this image?"""

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

				# make sure processor creates exact same pixel values
				__magic_name__						:						Optional[int]										=vis_processors["""eval"""](lowerCamelCase ).unsqueeze(0 ).to(lowerCamelCase )
				__magic_name__						:						int										=inputs.pixel_values
				assert torch.allclose(original_pixel_values.to(pixel_values.device )						,  lowerCamelCase )

				original_model.to(lowerCamelCase )
				hf_model.to(lowerCamelCase )
				with torch.no_grad():
								if "vicuna" in model_name:
												__magic_name__						:						Dict										=original_model({"""image""": original_pixel_values, """text_input""": [prompt]} ).logits
												__magic_name__						:						Tuple										=hf_model(**lowerCamelCase ).logits
								else:
												__magic_name__						:						int										=original_model(
												    {"""image""": original_pixel_values, """text_input""": [prompt], """text_output""": ["""\n"""]} ).logits
												__magic_name__						:						Tuple										=tokenizer("""\n"""						,  return_tensors="""pt""" ).input_ids.to(lowerCamelCase )
												__magic_name__						:						Union[str, Any]										=label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id						,  -100 )
												__magic_name__						:						Tuple										=hf_model(**lowerCamelCase						,  labels=lowerCamelCase ).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
				__magic_name__						:						Optional[int]										=1E-4 if """vicuna""" in model_name else 1E-5
				assert torch.allclose(original_logits.to(logits.device )						,  lowerCamelCase						,  atol=lowerCamelCase )
				print("""Looks ok!""" )

				print("""Generating with original model...""" )
				__magic_name__						:						Optional[int]										=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...""" )
				__magic_name__						:						List[Any]										=hf_model.generate(
				    **lowerCamelCase						,  do_sample=lowerCamelCase						,  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?
								__magic_name__						:						Optional[Any]										=2
				print("""Original generation:"""						,  lowerCamelCase )
				__magic_name__						:						int										=processor.batch_decode(lowerCamelCase						,  skip_special_tokens=lowerCamelCase )
				__magic_name__						:						Any										=[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"Salesforce/{model_name}" )
								hf_model.push_to_hub(F"Salesforce/{model_name}" )


if __name__ == "__main__":
							UpperCAmelCase_		:					Tuple          =  argparse.ArgumentParser()
							UpperCAmelCase_		:					Dict          =  [
							    "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",
							)

							UpperCAmelCase_		:					Union[str, Any]          =  parser.parse_args()

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








'''simple docstring'''
def 	_A  (       A							)  ->				list:

	if len(A							) <= 1:
		return [tuple(A							)]

	lowercase						:		Dict   					=		[]

	def generate(A							,A							):
		lowercase						:		List[Any]   					=		[0] * n
		res.append(tuple(A							)							)

		lowercase						:		Tuple   					=		0
		while i < n:
			if c[i] < i:
				if i % 2 == 0:
					lowercase					,		lowercase						:		List[Any]   					=		arr[i], arr[0]
				else:
					lowercase					,		lowercase						:		Optional[Any]   					=		arr[i], arr[c[i]]
				res.append(tuple(A							)							)
				c[i] += 1
				lowercase						:		Optional[Any]   					=		0
			else:
				lowercase						:		Any   					=		0
				i += 1

	generate(len(A							)							,A							)
	return res


if __name__ == "__main__":
					lowerCAmelCase   : Any             =				input("""Enter numbers separated by a comma:\n""").strip()
					lowerCAmelCase   : List[str]             =				[int(item) for item in user_input.split(""",""")]
					print(heaps(arr))


