




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 lowercase    (  )			->							Optional[Any]:


			'''simple docstring'''







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

			return image

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Any:


			'''simple docstring'''







			SCREAMING_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 lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Tuple:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						dct.pop(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						val

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							List[str]:


			'''simple docstring'''







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

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

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







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

			SCREAMING_SNAKE_CASE_								=						BlipaConfig(vision_config=SCREAMING_SNAKE_CASE       ,    text_config=SCREAMING_SNAKE_CASE )

			return config, image_size

@torch.no_grad()
def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE=None       ,    SCREAMING_SNAKE_CASE=False )			->							Dict:


			'''simple docstring'''







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

			SCREAMING_SNAKE_CASE_								=						BlipaForConditionalGeneration(SCREAMING_SNAKE_CASE ).eval()

			SCREAMING_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'),
			}

			SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						model_name_to_original[model_name]

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

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

			# some keys can be renamed efficiently
			for key, val in state_dict.copy().items():
						SCREAMING_SNAKE_CASE_								=						state_dict.pop(SCREAMING_SNAKE_CASE )
						if key.startswith('Qformer.bert' ):
									SCREAMING_SNAKE_CASE_								=						key.replace('Qformer.bert'       ,    'qformer' )
						if "attention.self" in key:
									SCREAMING_SNAKE_CASE_								=						key.replace('self'       ,    'attention' )
						if "opt_proj" in key:
									SCREAMING_SNAKE_CASE_								=						key.replace('opt_proj'       ,    'language_projection' )
						if "t5_proj" in key:
									SCREAMING_SNAKE_CASE_								=						key.replace('t5_proj'       ,    'language_projection' )
						if key.startswith('opt' ):
									SCREAMING_SNAKE_CASE_								=						key.replace('opt'       ,    'language' )
						if key.startswith('t5' ):
									SCREAMING_SNAKE_CASE_								=						key.replace('t5'       ,    'language' )
						SCREAMING_SNAKE_CASE_								=						val

			# read in qv biases
			read_in_q_v_bias(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

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

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

			# create processor
			SCREAMING_SNAKE_CASE_								=						BlipImageProcessor(
			    size={'height': image_size, 'width': image_size}       ,    image_mean=SCREAMING_SNAKE_CASE       ,    image_std=SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						BlipaProcessor(image_processor=SCREAMING_SNAKE_CASE       ,    tokenizer=SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						processor(images=SCREAMING_SNAKE_CASE       ,    return_tensors='pt' ).pixel_values.to(SCREAMING_SNAKE_CASE )

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

			original_model.to(SCREAMING_SNAKE_CASE )
			hf_model.to(SCREAMING_SNAKE_CASE )
			with torch.no_grad():
						if "opt" in model_name:
									SCREAMING_SNAKE_CASE_								=						original_model({'image': original_pixel_values, 'text_input': ['']} ).logits
									SCREAMING_SNAKE_CASE_								=						hf_model(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ).logits
						else:
									SCREAMING_SNAKE_CASE_								=						original_model(
									    {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']} ).logits
									SCREAMING_SNAKE_CASE_								=						input_ids.masked_fill(input_ids == tokenizer.pad_token_id       ,    -1_00 )
									SCREAMING_SNAKE_CASE_								=						hf_model(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    labels=SCREAMING_SNAKE_CASE ).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":
						SCREAMING_SNAKE_CASE_								=						torch.tensor(
						    [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]       ,    device=SCREAMING_SNAKE_CASE )
						assert torch.allclose(logits[0, :3, :3]       ,    SCREAMING_SNAKE_CASE       ,    atol=1E-4 )
			elif model_name == "blip2-flan-t5-xl-coco":
						SCREAMING_SNAKE_CASE_								=						torch.tensor(
						    [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]       ,    device=SCREAMING_SNAKE_CASE )
			else:
						# cast to same type
						SCREAMING_SNAKE_CASE_								=						logits.dtype
						assert torch.allclose(original_logits.to(SCREAMING_SNAKE_CASE )       ,    SCREAMING_SNAKE_CASE       ,    atol=1E-2 )
			print('Looks ok!' )

			print('Generating a caption...' )
			SCREAMING_SNAKE_CASE_								=						''
			SCREAMING_SNAKE_CASE_								=						tokenizer(SCREAMING_SNAKE_CASE       ,    return_tensors='pt' ).input_ids.to(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE_								=						original_model.generate({'image': original_pixel_values} )
			SCREAMING_SNAKE_CASE_								=						hf_model.generate(
			    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    do_sample=SCREAMING_SNAKE_CASE       ,    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:'       ,    SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						input_ids.shape[1]
			SCREAMING_SNAKE_CASE_								=						processor.batch_decode(outputs[:, prompt_length:]       ,    skip_special_tokens=SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						[text.strip() for text in output_text]
			print('HF generation:'       ,    SCREAMING_SNAKE_CASE )

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

			if push_to_hub:
						processor.push_to_hub(F'nielsr/{model_name}' )
						hf_model.push_to_hub(F'nielsr/{model_name}' )


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     str		    =       argparse.ArgumentParser()
				SCREAMING_SNAKE_CASE__     :     str		    =       [
				    "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",
				)

				SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       parser.parse_args()

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






def lowercase    (  SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						len(SCREAMING_SNAKE_CASE )
			while cur > 1:
						# Find the maximum number in arr
						SCREAMING_SNAKE_CASE_								=						arr.index(max(arr[0:cur] ) )
						# Reverse from 0 to mi
						SCREAMING_SNAKE_CASE_								=						arr[mi::-1] + arr[mi + 1 : len(SCREAMING_SNAKE_CASE )]
						# Reverse whole list
						SCREAMING_SNAKE_CASE_								=						arr[cur - 1 :: -1] + arr[cur : len(SCREAMING_SNAKE_CASE )]
						cur -= 1
			return arr


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     List[str]		    =       input("Enter numbers separated by a comma:\n").strip()
				SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       [int(item) for item in user_input.split(",")]
				print(pancake_sort(unsorted))
