"""simple docstring"""




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 							SCREAMING_SNAKE_CASE					(      )	->				Any:
  _lowerCAmelCase						:	str        =					"""https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"""
  _lowerCAmelCase						:	List[str]        =					Image.open(requests.get(_lowerCamelCase      ,stream=_lowerCamelCase     ).raw     ).convert("""RGB"""     )

  return image





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]     )	->				str:
  _lowerCAmelCase						:	Any        =					[]
  # 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 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str      ,_lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	List[Any]     )	->				Any:
  _lowerCAmelCase						:	List[str]        =					dct.pop(_lowerCamelCase     )
  _lowerCAmelCase						:	Optional[int]        =					val





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[Any]      ,_lowerCamelCase						:	Dict     )	->				List[Any]:
  for i in range(config.vision_config.num_hidden_layers     ):
    # read in original q and v biases
    _lowerCAmelCase						:	int        =					state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias"     )
    _lowerCAmelCase						:	Optional[Any]        =					state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias"     )

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





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]      ,_lowerCamelCase						:	int     )	->				Dict:
  _lowerCAmelCase						:	Tuple        =					364 if """coco""" in model_name else 224
  _lowerCAmelCase						:	Optional[int]        =					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:
    _lowerCAmelCase						:	Union[str, Any]        =					OPTConfig.from_pretrained("""facebook/opt-2.7b"""      ,eos_token_id=_lowerCamelCase     ).to_dict()
  elif "opt-6.7b" in model_name:
    _lowerCAmelCase						:	Tuple        =					OPTConfig.from_pretrained("""facebook/opt-6.7b"""      ,eos_token_id=_lowerCamelCase     ).to_dict()
  elif "t5-xl" in model_name:
    _lowerCAmelCase						:	Any        =					TaConfig.from_pretrained("""google/flan-t5-xl"""      ,dense_act_fn="""gelu"""      ,bos_token_id=1     ).to_dict()
  elif "t5-xxl" in model_name:
    _lowerCAmelCase						:	List[str]        =					TaConfig.from_pretrained("""google/flan-t5-xxl"""      ,dense_act_fn="""gelu"""      ,bos_token_id=1     ).to_dict()

  _lowerCAmelCase						:	int        =					BlipaConfig(vision_config=_lowerCamelCase      ,text_config=_lowerCamelCase     )

  return config, image_size





@torch.no_grad()
def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Union[str, Any]      ,_lowerCamelCase						:	Dict=None      ,_lowerCamelCase						:	int=False     )	->				Tuple:
  _lowerCAmelCase						:	Tuple        =					(
      AutoTokenizer.from_pretrained("""facebook/opt-2.7b"""     )
      if """opt""" in model_name
      else AutoTokenizer.from_pretrained("""google/flan-t5-xl"""     )
  )
  _lowerCAmelCase						:	Dict        =					tokenizer("""\n"""      ,add_special_tokens=_lowerCamelCase     ).input_ids[0]
  _lowerCAmelCase      ,     _lowerCAmelCase						:	List[str]        =					get_blipa_config(_lowerCamelCase      ,eos_token_id=_lowerCamelCase     )

  _lowerCAmelCase						:	int        =					BlipaForConditionalGeneration(_lowerCamelCase     ).eval()

  _lowerCAmelCase						:	Tuple        =					{
      """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"""),
  }

  _lowerCAmelCase      ,     _lowerCAmelCase						:	Union[str, Any]        =					model_name_to_original[model_name]

  # load original model
  print("""Loading original model..."""     )
  _lowerCAmelCase						:	Optional[Any]        =					"""cuda""" if torch.cuda.is_available() else """cpu"""
  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	int        =					load_model_and_preprocess(
      name=_lowerCamelCase      ,model_type=_lowerCamelCase      ,is_eval=_lowerCamelCase      ,device=_lowerCamelCase     )
  original_model.eval()
  print("""Done!"""     )

  # update state dict keys
  _lowerCAmelCase						:	str        =					original_model.state_dict()
  _lowerCAmelCase						:	Optional[int]        =					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():
    _lowerCAmelCase						:	Any        =					state_dict.pop(_lowerCamelCase     )
    if key.startswith("""Qformer.bert"""     ):
      _lowerCAmelCase						:	Optional[Any]        =					key.replace("""Qformer.bert"""      ,"""qformer"""     )
    if "attention.self" in key:
      _lowerCAmelCase						:	Union[str, Any]        =					key.replace("""self"""      ,"""attention"""     )
    if "opt_proj" in key:
      _lowerCAmelCase						:	str        =					key.replace("""opt_proj"""      ,"""language_projection"""     )
    if "t5_proj" in key:
      _lowerCAmelCase						:	Union[str, Any]        =					key.replace("""t5_proj"""      ,"""language_projection"""     )
    if key.startswith("""opt"""     ):
      _lowerCAmelCase						:	Optional[int]        =					key.replace("""opt"""      ,"""language"""     )
    if key.startswith("""t5"""     ):
      _lowerCAmelCase						:	List[str]        =					key.replace("""t5"""      ,"""language"""     )
    _lowerCAmelCase						:	List[Any]        =					val

  # read in qv biases
  read_in_q_v_bias(_lowerCamelCase      ,_lowerCamelCase     )

  _lowerCAmelCase      ,     _lowerCAmelCase						:	Union[str, Any]        =					hf_model.load_state_dict(_lowerCamelCase      ,strict=_lowerCamelCase     )
  assert len(_lowerCamelCase     ) == 0
  assert unexpected_keys == ["qformer.embeddings.position_ids"]

  _lowerCAmelCase						:	Optional[Any]        =					load_demo_image()
  _lowerCAmelCase						:	List[str]        =					vis_processors["""eval"""](_lowerCamelCase     ).unsqueeze(0     ).to(_lowerCamelCase     )
  _lowerCAmelCase						:	Dict        =					tokenizer(["""\n"""]      ,return_tensors="""pt"""     ).input_ids.to(_lowerCamelCase     )

  # create processor
  _lowerCAmelCase						:	Union[str, Any]        =					BlipImageProcessor(
      size={"""height""": image_size, """width""": image_size}      ,image_mean=_lowerCamelCase      ,image_std=_lowerCamelCase     )
  _lowerCAmelCase						:	List[str]        =					BlipaProcessor(image_processor=_lowerCamelCase      ,tokenizer=_lowerCamelCase     )
  _lowerCAmelCase						:	Tuple        =					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:
      _lowerCAmelCase						:	List[str]        =					original_model({"""image""": original_pixel_values, """text_input""": [""""""]}     ).logits
      _lowerCAmelCase						:	List[Any]        =					hf_model(_lowerCamelCase      ,_lowerCamelCase     ).logits
    else:
      _lowerCAmelCase						:	List[Any]        =					original_model(
          {"""image""": original_pixel_values, """text_input""": ["""\n"""], """text_output""": ["""\n"""]}     ).logits
      _lowerCAmelCase						:	Dict        =					input_ids.masked_fill(input_ids == tokenizer.pad_token_id      ,-100     )
      _lowerCAmelCase						:	Any        =					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":
    _lowerCAmelCase						:	Optional[Any]        =					torch.tensor(
        [[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]]      ,device=_lowerCamelCase     )
    assert torch.allclose(logits[0, :3, :3]      ,_lowerCamelCase      ,atol=1e-4     )
  elif model_name == "blip2-flan-t5-xl-coco":
    _lowerCAmelCase						:	List[str]        =					torch.tensor(
        [[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]]      ,device=_lowerCamelCase     )
  else:
    # cast to same type
    _lowerCAmelCase						:	Optional[int]        =					logits.dtype
    assert torch.allclose(original_logits.to(_lowerCamelCase     )      ,_lowerCamelCase      ,atol=1e-2     )
  print("""Looks ok!"""     )

  print("""Generating a caption..."""     )
  _lowerCAmelCase						:	Optional[int]        =					""""""
  _lowerCAmelCase						:	Union[str, Any]        =					tokenizer(_lowerCamelCase      ,return_tensors="""pt"""     ).input_ids.to(_lowerCamelCase     )

  _lowerCAmelCase						:	Dict        =					original_model.generate({"""image""": original_pixel_values}     )
  _lowerCAmelCase						:	Dict        =					hf_model.generate(
      _lowerCamelCase      ,_lowerCamelCase      ,do_sample=_lowerCamelCase      ,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:"""      ,_lowerCamelCase     )
  _lowerCAmelCase						:	Union[str, Any]        =					input_ids.shape[1]
  _lowerCAmelCase						:	Dict        =					processor.batch_decode(outputs[:, prompt_length:]      ,skip_special_tokens=_lowerCamelCase     )
  _lowerCAmelCase						:	Dict        =					[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__":
     _a				:		Dict	   =       argparse.ArgumentParser()
     _a				:		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',
     )

     _a				:		Tuple	   =       parser.parse_args()

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





"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[torch.FloatTensor]		 =		None
     _UpperCamelCase							:      torch.FloatTensor		 =		None
     _UpperCamelCase							:      Optional[Tuple[torch.FloatTensor]]		 =		None
     _UpperCamelCase							:      Optional[Tuple[torch.FloatTensor]]		 =		None







class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def __init__(		self    ,					a__=1    ,					a__=0    ,					a__=2    ,					a__=512    ,					a__="cls"    ,					a__=False    ,					a__=True    ,					**a__    ,					):
       super().__init__(pad_token_id=a__    ,					bos_token_id=a__    ,					eos_token_id=a__    ,					**a__  )
       _lowerCAmelCase						:	Optional[Any]        =					project_dim
       _lowerCAmelCase						:	List[str]        =					pooler_fn
       _lowerCAmelCase						:	Any        =					learn_encoder
       _lowerCAmelCase						:	Optional[int]        =					use_attention_mask







class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[int]		 =		[R"pooler", R"logit_scale"]
     _UpperCamelCase							:      List[Any]		 =		[R"position_ids", R"predictions.decoder.bias"]
     _UpperCamelCase							:      List[Any]		 =		"roberta"
     _UpperCamelCase							:      Optional[int]		 =		RobertaSeriesConfig






     def __init__(		self    ,					a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:	str        =					XLMRobertaModel(a__  )
       _lowerCAmelCase						:	Optional[Any]        =					nn.Linear(config.hidden_size    ,					config.project_dim  )
       _lowerCAmelCase						:	List[Any]        =					getattr(a__    ,					"""has_pre_transformation"""    ,					a__  )
       if self.has_pre_transformation:
         _lowerCAmelCase						:	List[str]        =					nn.Linear(config.hidden_size    ,					config.project_dim  )
         _lowerCAmelCase						:	Optional[int]        =					nn.LayerNorm(config.hidden_size    ,					eps=config.layer_norm_eps  )
       self.post_init()






     def    __A  (		self    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					):
       _lowerCAmelCase						:	List[str]        =					return_dict if return_dict is not None else self.config.use_return_dict

       _lowerCAmelCase						:	Optional[int]        =					self.base_model(
           input_ids=a__    ,					attention_mask=a__    ,					token_type_ids=a__    ,					position_ids=a__    ,					head_mask=a__    ,					inputs_embeds=a__    ,					encoder_hidden_states=a__    ,					encoder_attention_mask=a__    ,					output_attentions=a__    ,					output_hidden_states=True if self.has_pre_transformation else output_hidden_states    ,					return_dict=a__    ,					)

       if self.has_pre_transformation:
         _lowerCAmelCase						:	Optional[Any]        =					outputs["""hidden_states"""][-2]
         _lowerCAmelCase						:	Optional[Any]        =					self.pre_LN(a__  )
         _lowerCAmelCase						:	int        =					self.transformation_pre(a__  )

         return TransformationModelOutput(
             projection_state=a__    ,					last_hidden_state=outputs.last_hidden_state    ,					hidden_states=outputs.hidden_states    ,					attentions=outputs.attentions    ,					)
       else:
         _lowerCAmelCase						:	Union[str, Any]        =					self.transformation(outputs.last_hidden_state  )
         return TransformationModelOutput(
             projection_state=a__    ,					last_hidden_state=outputs.last_hidden_state    ,					hidden_states=outputs.hidden_states    ,					attentions=outputs.attentions    ,					)




