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

				return image



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							[]
				# 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 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							dct.pop(lowerCamelCase )
				UpperCAmelCase__    =							val



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				for i in range(config.vision_config.num_hidden_layers ):
								# read in original q and v biases
								UpperCAmelCase__    =							state_dict.pop(f'''visual_encoder.blocks.{i}.attn.q_bias''' )
								UpperCAmelCase__    =							state_dict.pop(f'''visual_encoder.blocks.{i}.attn.v_bias''' )

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



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							3_6_4 if 'coco' in model_name else 2_2_4
				UpperCAmelCase__    =							BlipaVisionConfig(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 "opt-2.7b" in model_name:
								UpperCAmelCase__    =							OPTConfig.from_pretrained('facebook/opt-2.7b'      ,    eos_token_id=lowerCamelCase ).to_dict()
				elif "opt-6.7b" in model_name:
								UpperCAmelCase__    =							OPTConfig.from_pretrained('facebook/opt-6.7b'      ,    eos_token_id=lowerCamelCase ).to_dict()
				elif "t5-xl" in model_name:
								UpperCAmelCase__    =							TaConfig.from_pretrained('google/flan-t5-xl'      ,    dense_act_fn='gelu'      ,    bos_token_id=1 ).to_dict()
				elif "t5-xxl" in model_name:
								UpperCAmelCase__    =							TaConfig.from_pretrained('google/flan-t5-xxl'      ,    dense_act_fn='gelu'      ,    bos_token_id=1 ).to_dict()

				UpperCAmelCase__    =							BlipaConfig(vision_config=lowerCamelCase      ,    text_config=lowerCamelCase )

				return config, image_size



@torch.no_grad()
def 						a_		(      lowerCamelCase      ,    lowerCamelCase=None      ,    lowerCamelCase=False ):
				UpperCAmelCase__    =							(
				    AutoTokenizer.from_pretrained('facebook/opt-2.7b' )
				    if 'opt' in model_name
				    else AutoTokenizer.from_pretrained('google/flan-t5-xl' )
				)
				UpperCAmelCase__    =							tokenizer('\n'      ,    add_special_tokens=lowerCamelCase ).input_ids[0]
				UpperCAmelCase__   ,			UpperCAmelCase__    =							get_blipa_config(lowerCamelCase      ,    eos_token_id=lowerCamelCase )

				UpperCAmelCase__    =							BlipaForConditionalGeneration(lowerCamelCase ).eval()

				UpperCAmelCase__    =							{
				    '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'),
				}

				UpperCAmelCase__   ,			UpperCAmelCase__    =							model_name_to_original[model_name]

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

				# update state dict keys
				UpperCAmelCase__    =							original_model.state_dict()
				UpperCAmelCase__    =							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():
								UpperCAmelCase__    =							state_dict.pop(lowerCamelCase )
								if key.startswith('Qformer.bert' ):
												UpperCAmelCase__    =							key.replace('Qformer.bert'      ,    'qformer' )
								if "attention.self" in key:
												UpperCAmelCase__    =							key.replace('self'      ,    'attention' )
								if "opt_proj" in key:
												UpperCAmelCase__    =							key.replace('opt_proj'      ,    'language_projection' )
								if "t5_proj" in key:
												UpperCAmelCase__    =							key.replace('t5_proj'      ,    'language_projection' )
								if key.startswith('opt' ):
												UpperCAmelCase__    =							key.replace('opt'      ,    'language' )
								if key.startswith('t5' ):
												UpperCAmelCase__    =							key.replace('t5'      ,    'language' )
								UpperCAmelCase__    =							val

				# read in qv biases
				read_in_q_v_bias(lowerCamelCase      ,    lowerCamelCase )

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

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

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

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

				original_model.to(lowerCamelCase )
				hf_model.to(lowerCamelCase )
				with torch.no_grad():
								if "opt" in model_name:
												UpperCAmelCase__    =							original_model({'image': original_pixel_values, 'text_input': ['']} ).logits
												UpperCAmelCase__    =							hf_model(lowerCamelCase      ,    lowerCamelCase ).logits
								else:
												UpperCAmelCase__    =							original_model(
												    {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']} ).logits
												UpperCAmelCase__    =							input_ids.masked_fill(input_ids == tokenizer.pad_token_id      ,    -1_0_0 )
												UpperCAmelCase__    =							hf_model(lowerCamelCase      ,    lowerCamelCase      ,    labels=lowerCamelCase ).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":
								UpperCAmelCase__    =							torch.tensor(
								    [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]      ,    device=lowerCamelCase )
								assert torch.allclose(logits[0, :3, :3]      ,    lowerCamelCase      ,    atol=1e-4 )
				elif model_name == "blip2-flan-t5-xl-coco":
								UpperCAmelCase__    =							torch.tensor(
								    [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]      ,    device=lowerCamelCase )
				else:
								# cast to same type
								UpperCAmelCase__    =							logits.dtype
								assert torch.allclose(original_logits.to(lowerCamelCase )      ,    lowerCamelCase      ,    atol=1e-2 )
				print('Looks ok!' )

				print('Generating a caption...' )
				UpperCAmelCase__    =							''
				UpperCAmelCase__    =							tokenizer(lowerCamelCase      ,    return_tensors='pt' ).input_ids.to(lowerCamelCase )

				UpperCAmelCase__    =							original_model.generate({'image': original_pixel_values} )
				UpperCAmelCase__    =							hf_model.generate(
				    lowerCamelCase      ,    lowerCamelCase      ,    do_sample=lowerCamelCase      ,    num_beams=5      ,    max_length=3_0      ,    min_length=1      ,    top_p=0.9      ,    repetition_penalty=1.0      ,    length_penalty=1.0      ,    temperature=1      ,    )
				print('Original generation:'      ,    lowerCamelCase )
				UpperCAmelCase__    =							input_ids.shape[1]
				UpperCAmelCase__    =							processor.batch_decode(outputs[:, prompt_length:]      ,    skip_special_tokens=lowerCamelCase )
				UpperCAmelCase__    =							[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'''nielsr/{model_name}''' )
								hf_model.push_to_hub(f'''nielsr/{model_name}''' )


if __name__ == "__main__":
						lowerCAmelCase__						:						Union[str, Any]		  =			argparse.ArgumentParser()
						lowerCAmelCase__						:						List[Any]		  =			[
						    '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',
						)

						lowerCAmelCase__						:						List[Any]		  =			parser.parse_args()

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




"""simple docstring"""






import socket



def 						a_		(      ):
				UpperCAmelCase__    =							socket.socket(socket.AF_INET      ,    socket.SOCK_STREAM )
				UpperCAmelCase__    =							socket.gethostname()
				UpperCAmelCase__    =							1_2_3_1_2

				sock.connect((host, port) )
				sock.send(b'Hello server!' )

				with open('Received_file'      ,    'wb' ) as out_file:
								print('File opened' )
								print('Receiving data...' )
								while True:
												UpperCAmelCase__    =							sock.recv(1_0_2_4 )
												if not data:
																break
												out_file.write(lowerCamelCase )

				print('Successfully received the file' )
				sock.close()
				print('Connection closed' )


if __name__ == "__main__":
						main()



