
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					(    ):
 __a			:  Union[str, Any]		    =  '''https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'''
 __a			:  Any		    =  Image.open(requests.get(lowerCAmelCase__  ,       stream=lowerCAmelCase__  ).raw  ).convert('''RGB'''  )

 return image




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ):
 __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					(    lowerCAmelCase__							:    List[Any]  ,       lowerCAmelCase__							:    Any  ,       lowerCAmelCase__							:    Union[str, Any]  ):
 __a			:  Any		    =  dct.pop(lowerCAmelCase__  )
 __a			:  Optional[Any]		    =  val




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Tuple  ):
 for i in range(config.vision_config.num_hidden_layers  ):
  # read in original q and v biases
  __a			:  int		    =  state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias"  )
  __a			:  Optional[int]		    =  state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias"  )

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




def        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ):
 __a			:  str		    =  3_6_4 if '''coco''' in model_name else 2_2_4
 __a			:  int		    =  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:
  __a			:  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			:  Union[str, Any]		    =  TaConfig.from_pretrained('''google/flan-t5-xxl'''  ,       dense_act_fn='''gelu'''  ,       bos_token_id=1  ).to_dict()
 elif "vicuna-7b" in model_name:
  __a			:  int		    =  LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf'''  ,       vocab_size=3_2_0_0_1  ).to_dict()
 elif "vicuna-13b" in model_name:
  __a			:  Dict		    =  LlamaConfig.from_pretrained('''decapoda-research/llama-13b-hf'''  ,       vocab_size=3_2_0_0_1  ).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			:  List[Any]		    =  InstructBlipQFormerConfig(vocab_size=3_0_5_2_3  ).to_dict()
 __a			:  Union[str, Any]		    =  InstructBlipConfig(vision_config=lowerCAmelCase__  ,       text_config=lowerCAmelCase__  ,       qformer_config=lowerCAmelCase__  )

 return config, image_size




@torch.no_grad()
def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    List[str]=None  ,       lowerCAmelCase__							:    int=False  ):
 __a			:  Optional[int]		    =  AutoTokenizer.from_pretrained('''bert-base-uncased'''  ,       truncation_side='''left'''  )
 qformer_tokenizer.add_special_tokens({'''bos_token''': '''[DEC]'''}  )

 if "t5" in model_name:
  __a			:  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>"})
  __a			:  Tuple		    =  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(lowerCAmelCase__  )
 __a			:  List[Any]		    =  InstructBlipForConditionalGeneration(lowerCAmelCase__  ).eval()

 __a			:  Union[str, 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'''),
 }

 __a    ,	__a			:  Tuple		    =  model_name_to_original[model_name]

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

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

 __a			:  List[str]		    =  load_demo_image()
 __a			:  Tuple		    =  '''What is unusual about this image?'''

 # create processor
 __a			:  Tuple		    =  BlipImageProcessor(
     size={'''height''': image_size, '''width''': image_size}  ,       image_mean=lowerCAmelCase__  ,       image_std=lowerCAmelCase__  )
 __a			:  Dict		    =  InstructBlipProcessor(
     image_processor=lowerCAmelCase__  ,       tokenizer=lowerCAmelCase__  ,       qformer_tokenizer=lowerCAmelCase__  ,       )
 __a			:  List[Any]		    =  processor(images=lowerCAmelCase__  ,       text=lowerCAmelCase__  ,       return_tensors='''pt'''  ).to(lowerCAmelCase__  )

 # make sure processor creates exact same pixel values
 __a			:  Any		    =  vis_processors['''eval'''](lowerCAmelCase__  ).unsqueeze(0  ).to(lowerCAmelCase__  )
 __a			:  str		    =  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:
   __a			:  Dict		    =  original_model({'''image''': original_pixel_values, '''text_input''': [prompt]}  ).logits
   __a			:  str		    =  hf_model(**lowerCAmelCase__  ).logits
  else:
   __a			:  Optional[int]		    =  original_model(
       {'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']}  ).logits
   __a			:  Tuple		    =  tokenizer('''\n'''  ,       return_tensors='''pt'''  ).input_ids.to(lowerCAmelCase__  )
   __a			:  str		    =  label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id  ,       -1_0_0  )
   __a			:  Any		    =  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
 __a			:  Any		    =  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...'''  )
 __a			:  Union[str, Any]		    =  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			:  Optional[Any]		    =  hf_model.generate(
     **lowerCAmelCase__  ,       do_sample=lowerCAmelCase__  ,       num_beams=5  ,       max_length=2_5_6  ,       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			:  List[Any]		    =  2
 print('''Original generation:'''  ,       lowerCAmelCase__  )
 __a			:  Any		    =  processor.batch_decode(lowerCAmelCase__  ,       skip_special_tokens=lowerCAmelCase__  )
 __a			:  Union[str, 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__":
      lowercase__          =argparse.ArgumentParser()
      lowercase__          =[
          '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',
      )

      lowercase__          =parser.parse_args()

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


from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


lowercase__          =logging.get_logger(__name__)

lowercase__          ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__          ={
    'vocab_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
    },
    'merges_file': {
        'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
    },
}

lowercase__          ={'allegro/herbert-base-cased': 514}
lowercase__          ={}



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							str     	=				VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE				:							Dict     	=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				HerbertTokenizer
   def __init__(self			: str ,    snake_case_			: Optional[Any]=None ,    snake_case_			: Tuple=None ,    snake_case_			: Dict=None ,    snake_case_			: List[str]="<s>" ,    snake_case_			: Any="<unk>" ,    snake_case_			: Optional[int]="<pad>" ,    snake_case_			: Optional[Any]="<mask>" ,    snake_case_			: List[Any]="</s>" ,    **snake_case_			: Optional[int] ,    ):
    super().__init__(
        snake_case_ ,    snake_case_ ,    tokenizer_file=snake_case_ ,    cls_token=snake_case_ ,    unk_token=snake_case_ ,    pad_token=snake_case_ ,    mask_token=snake_case_ ,    sep_token=snake_case_ ,    **snake_case_ ,    )
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  int		    =  [self.cls_token_id]
    __a			:  Union[str, Any]		    =  [self.sep_token_id]
    if token_ids_a is None:
     return cls + token_ids_a + sep

    return cls + token_ids_a + sep + token_ids_a + sep
   def 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None ,    snake_case_			: bool = False       ):
    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=snake_case_ ,    token_ids_a=snake_case_ ,    already_has_special_tokens=snake_case_       )

    if token_ids_a is None:
     return [1] + ([0] * len(snake_case_       )) + [1]
    return [1] + ([0] * len(snake_case_       )) + [1] + ([0] * len(snake_case_       )) + [1]
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: List[int] ,    snake_case_			: Optional[List[int]] = None       ):
    __a			:  Optional[int]		    =  [self.sep_token_id]
    __a			:  int		    =  [self.cls_token_id]

    if token_ids_a is None:
     return len(cls + token_ids_a + sep       ) * [0]
    return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]







   def 				lowerCAmelCase      (self			: int ,    snake_case_			: str ,    snake_case_			: Optional[str] = None       ):
    __a			:  Any		    =  self._tokenizer.model.save(snake_case_ ,    name=snake_case_       )
    return tuple(snake_case_       )
