




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_		(       )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
				__lowerCamelCase		       =    Image.open(requests.get(UpperCamelCase__ ,     stream=UpperCamelCase__      ).raw      ).convert('RGB'      )

				return image







def 			lowerCamelCase_		(       UpperCamelCase__				:      Any      )     ->       Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    []
				# 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_		(       UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      str ,     UpperCamelCase__				:      Optional[int]      )     ->       Optional[int]:





				"""simple docstring"""


				__lowerCamelCase		       =    dct.pop(UpperCamelCase__      )
				__lowerCamelCase		       =    val







def 			lowerCamelCase_		(       UpperCamelCase__				:      Tuple ,     UpperCamelCase__				:      int      )     ->       Dict:





				"""simple docstring"""


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

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







def 			lowerCamelCase_		(       UpperCamelCase__				:      Dict      )     ->       Optional[Any]:





				"""simple docstring"""


				__lowerCamelCase		       =    364 if 'coco' in model_name else 224
				__lowerCamelCase		       =    InstructBlipVisionConfig(image_size=UpperCamelCase__      ).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:
								__lowerCamelCase		       =    TaConfig.from_pretrained('google/flan-t5-xl' ,     dense_act_fn='gelu' ,     bos_token_id=1      ).to_dict()
				elif "t5-xxl" in model_name:
								__lowerCamelCase		       =    TaConfig.from_pretrained('google/flan-t5-xxl' ,     dense_act_fn='gelu' ,     bos_token_id=1      ).to_dict()
				elif "vicuna-7b" in model_name:
								__lowerCamelCase		       =    LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf' ,     vocab_size=3_2001      ).to_dict()
				elif "vicuna-13b" in model_name:
								__lowerCamelCase		       =    LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf' ,     vocab_size=3_2001      ).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
				__lowerCamelCase		       =    InstructBlipQFormerConfig(vocab_size=3_0523      ).to_dict()
				__lowerCamelCase		       =    InstructBlipConfig(vision_config=UpperCamelCase__ ,     text_config=UpperCamelCase__ ,     qformer_config=UpperCamelCase__      )

				return config, image_size







@torch.no_grad()
def 			lowerCamelCase_		(       UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      Optional[int]=None ,     UpperCamelCase__				:      Union[str, Any]=False      )     ->       Optional[Any]:





				"""simple docstring"""


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

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

				__lowerCamelCase       ,						__lowerCamelCase		       =    get_blipa_config(UpperCamelCase__      )
				__lowerCamelCase		       =    InstructBlipForConditionalGeneration(UpperCamelCase__      ).eval()

				__lowerCamelCase		       =    {
				    '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'),
				}

				__lowerCamelCase       ,						__lowerCamelCase		       =    model_name_to_original[model_name]

				# load original model
				print('Loading original model...'      )
				__lowerCamelCase		       =    'cuda:1' if torch.cuda.is_available() else 'cpu'
				__lowerCamelCase		       =    'cuda:2' if torch.cuda.is_available() else 'cpu'
				__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    load_model_and_preprocess(
				    name=UpperCamelCase__ ,     model_type=UpperCamelCase__ ,     is_eval=UpperCamelCase__ ,     device=UpperCamelCase__      )
				original_model.eval()
				print('Done!'      )

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

				# some keys can be renamed efficiently
				for key, val in state_dict.copy().items():
								__lowerCamelCase		       =    state_dict.pop(UpperCamelCase__      )
								if key.startswith('Qformer.bert'      ):
												__lowerCamelCase		       =    key.replace('Qformer.bert' ,     'qformer'      )
								if "attention.self" in key:
												__lowerCamelCase		       =    key.replace('self' ,     'attention'      )
								if "llm_proj" in key:
												__lowerCamelCase		       =    key.replace('llm_proj' ,     'language_projection'      )
								if "t5_proj" in key:
												__lowerCamelCase		       =    key.replace('t5_proj' ,     'language_projection'      )
								if key.startswith('llm_model'      ):
												__lowerCamelCase		       =    key.replace('llm_model' ,     'language_model'      )
								if key.startswith('t5'      ):
												__lowerCamelCase		       =    key.replace('t5' ,     'language'      )
								__lowerCamelCase		       =    val

				# read in qv biases
				read_in_q_v_bias(UpperCamelCase__ ,     UpperCamelCase__      )

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

				__lowerCamelCase		       =    load_demo_image()
				__lowerCamelCase		       =    'What is unusual about this image?'

				# create processor
				__lowerCamelCase		       =    BlipImageProcessor(
				    size={'height': image_size, 'width': image_size} ,     image_mean=UpperCamelCase__ ,     image_std=UpperCamelCase__      )
				__lowerCamelCase		       =    InstructBlipProcessor(
				    image_processor=UpperCamelCase__ ,     tokenizer=UpperCamelCase__ ,     qformer_tokenizer=UpperCamelCase__ ,     )
				__lowerCamelCase		       =    processor(images=UpperCamelCase__ ,     text=UpperCamelCase__ ,     return_tensors='pt'      ).to(UpperCamelCase__      )

				# make sure processor creates exact same pixel values
				__lowerCamelCase		       =    vis_processors['eval'](UpperCamelCase__      ).unsqueeze(0      ).to(UpperCamelCase__      )
				__lowerCamelCase		       =    inputs.pixel_values
				assert torch.allclose(original_pixel_values.to(pixel_values.device      ) ,     UpperCamelCase__      )

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

				print('Generating with original model...'      )
				__lowerCamelCase		       =    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...'      )
				__lowerCamelCase		       =    hf_model.generate(
				    **UpperCamelCase__ ,     do_sample=UpperCamelCase__ ,     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?
								__lowerCamelCase		       =    2
				print('Original generation:' ,     UpperCamelCase__      )
				__lowerCamelCase		       =    processor.batch_decode(UpperCamelCase__ ,     skip_special_tokens=UpperCamelCase__      )
				__lowerCamelCase		       =    [text.strip() for text in output_text]
				print('HF generation:' ,     UpperCamelCase__      )

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

				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)







"""simple docstring"""






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							['image_processor', 'tokenizer']
					_a   			=							'BlipImageProcessor'
					_a   			=							'AutoTokenizer'



					def __init__(       self							:  Tuple,  lowerCamelCase							:  List[str],  lowerCamelCase							:  Dict     )->					str:
								lowerCamelCase__		:		Any			=False
								super().__init__(lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=self.image_processor



					def __call__(       self							:  Union[str, Any],  lowerCamelCase							:  ImageInput = None,  lowerCamelCase							:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Union[bool, str, PaddingStrategy] = False,  lowerCamelCase							:  Union[bool, str, TruncationStrategy] = None,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  int = 0,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  Optional[bool] = None,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Optional[Union[str, TensorType]] = None,  **lowerCamelCase							:  List[str],  )->					BatchEncoding:
								if images is None and text is None:
											raise ValueError('''You have to specify either images or text.'''     )

								# Get only text
								if images is None:
											lowerCamelCase__		:		str			=self.tokenizer
											lowerCamelCase__		:		str			=self.tokenizer(
											    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
											return text_encoding

								# add pixel_values
								lowerCamelCase__		:		Optional[int]			=self.image_processor(lowerCamelCase,  return_tensors=lowerCamelCase     )

								if text is not None:
											lowerCamelCase__		:		Union[str, Any]			=self.tokenizer(
											    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
								else:
											lowerCamelCase__		:		Optional[Any]			=None

								if text_encoding is not None:
											encoding_image_processor.update(lowerCamelCase     )

								return encoding_image_processor



					def 		snake_case       (       self							:  str,  *lowerCamelCase							:  Any,  **lowerCamelCase							:  List[str]     )->					Union[str, Any]:
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase     )



					def 		snake_case       (       self							:  Dict,  *lowerCamelCase							:  str,  **lowerCamelCase							:  str     )->					Union[str, Any]:
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase     )




					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def 		snake_case       (       self							:  List[str]     )->					List[str]:
								lowerCamelCase__		:		Union[str, Any]			=self.tokenizer.model_input_names
								lowerCamelCase__		:		List[str]			=self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )




