


"""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   __lowerCAmelCase						(						)     ->						Union[str, Any]:


    """simple docstring"""





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

    return image







def   __lowerCAmelCase						(						lowercase  :  List[str]			)     ->						Dict:


    """simple docstring"""





    snake_case   :				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   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  List[str]						,							lowercase  :  Tuple			)     ->						Optional[Any]:


    """simple docstring"""





    snake_case   :				Tuple				 =   dct.pop(lowercase			)
    snake_case   :				Optional[int]				 =   val







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]						,							lowercase  :  Optional[Any]			)     ->						Union[str, Any]:


    """simple docstring"""





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

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







def   __lowerCAmelCase						(						lowercase  :  List[Any]						,							lowercase  :  Optional[int]			)     ->						List[str]:


    """simple docstring"""





    snake_case   :				str				 =   364 if "coco" in model_name else 224
    snake_case   :				Optional[int]				 =   BlipaVisionConfig(image_size=lowercase			).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:
        snake_case   :				int				 =   OPTConfig.from_pretrained("facebook/opt-2.7b"						,							eos_token_id=lowercase			).to_dict()
    elif "opt-6.7b" in model_name:
        snake_case   :				Dict				 =   OPTConfig.from_pretrained("facebook/opt-6.7b"						,							eos_token_id=lowercase			).to_dict()
    elif "t5-xl" in model_name:
        snake_case   :				Dict				 =   TaConfig.from_pretrained("google/flan-t5-xl"						,							dense_act_fn="gelu"						,							bos_token_id=1			).to_dict()
    elif "t5-xxl" in model_name:
        snake_case   :				str				 =   TaConfig.from_pretrained("google/flan-t5-xxl"						,							dense_act_fn="gelu"						,							bos_token_id=1			).to_dict()

    snake_case   :				List[Any]				 =   BlipaConfig(vision_config=lowercase						,							text_config=lowercase			)

    return config, image_size







@torch.no_grad()
def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  Union[str, Any]=None						,							lowercase  :  Optional[Any]=False			)     ->						str:


    """simple docstring"""





    snake_case   :				int				 =   (
        AutoTokenizer.from_pretrained("facebook/opt-2.7b"			)
        if "opt" in model_name
        else AutoTokenizer.from_pretrained("google/flan-t5-xl"			)
    )
    snake_case   :				Any				 =   tokenizer("\n"						,							add_special_tokens=lowercase			).input_ids[0]
    snake_case ,snake_case   :				List[str]				 =   get_blipa_config(lowercase						,							eos_token_id=lowercase			)

    snake_case   :				List[str]				 =   BlipaForConditionalGeneration(lowercase			).eval()

    snake_case   :				str				 =   {
        "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"),
    }

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

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

    # update state dict keys
    snake_case   :				int				 =   original_model.state_dict()
    snake_case   :				Optional[int]				 =   create_rename_keys(lowercase			)
    for src, dest in rename_keys:
        rename_key(lowercase						,							lowercase						,							lowercase			)

    # some keys can be renamed efficiently
    for key, val in state_dict.copy().items():
        snake_case   :				Optional[int]				 =   state_dict.pop(lowercase			)
        if key.startswith("Qformer.bert"			):
            snake_case   :				Union[str, Any]				 =   key.replace("Qformer.bert"						,							"qformer"			)
        if "attention.self" in key:
            snake_case   :				int				 =   key.replace("self"						,							"attention"			)
        if "opt_proj" in key:
            snake_case   :				str				 =   key.replace("opt_proj"						,							"language_projection"			)
        if "t5_proj" in key:
            snake_case   :				Tuple				 =   key.replace("t5_proj"						,							"language_projection"			)
        if key.startswith("opt"			):
            snake_case   :				Optional[Any]				 =   key.replace("opt"						,							"language"			)
        if key.startswith("t5"			):
            snake_case   :				Optional[Any]				 =   key.replace("t5"						,							"language"			)
        snake_case   :				Tuple				 =   val

    # read in qv biases
    read_in_q_v_bias(lowercase						,							lowercase			)

    snake_case ,snake_case   :				Optional[Any]				 =   hf_model.load_state_dict(lowercase						,							strict=lowercase			)
    assert len(lowercase			) == 0
    assert unexpected_keys == ["qformer.embeddings.position_ids"]

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

    # create processor
    snake_case   :				List[Any]				 =   BlipImageProcessor(
        size={"height": image_size, "width": image_size}						,							image_mean=lowercase						,							image_std=lowercase			)
    snake_case   :				Optional[int]				 =   BlipaProcessor(image_processor=lowercase						,							tokenizer=lowercase			)
    snake_case   :				List[str]				 =   processor(images=lowercase						,							return_tensors="pt"			).pixel_values.to(lowercase			)

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

    original_model.to(lowercase			)
    hf_model.to(lowercase			)
    with torch.no_grad():
        if "opt" in model_name:
            snake_case   :				List[str]				 =   original_model({"image": original_pixel_values, "text_input": [""]}			).logits
            snake_case   :				List[str]				 =   hf_model(lowercase						,							lowercase			).logits
        else:
            snake_case   :				int				 =   original_model(
                {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}			).logits
            snake_case   :				Tuple				 =   input_ids.masked_fill(input_ids == tokenizer.pad_token_id						,							-100			)
            snake_case   :				Dict				 =   hf_model(lowercase						,							lowercase						,							labels=lowercase			).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":
        snake_case   :				str				 =   torch.tensor(
            [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]						,							device=lowercase			)
        assert torch.allclose(logits[0, :3, :3]						,							lowercase						,							atol=1e-4			)
    elif model_name == "blip2-flan-t5-xl-coco":
        snake_case   :				int				 =   torch.tensor(
            [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]						,							device=lowercase			)
    else:
        # cast to same type
        snake_case   :				Union[str, Any]				 =   logits.dtype
        assert torch.allclose(original_logits.to(lowercase			)						,							lowercase						,							atol=1e-2			)
    print("Looks ok!"			)

    print("Generating a caption..."			)
    snake_case   :				Dict				 =   ""
    snake_case   :				List[Any]				 =   tokenizer(lowercase						,							return_tensors="pt"			).input_ids.to(lowercase			)

    snake_case   :				Any				 =   original_model.generate({"image": original_pixel_values}			)
    snake_case   :				Dict				 =   hf_model.generate(
        lowercase						,							lowercase						,							do_sample=lowercase						,							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:"						,							lowercase			)
    snake_case   :				Any				 =   input_ids.shape[1]
    snake_case   :				List[Any]				 =   processor.batch_decode(outputs[:, prompt_length:]						,							skip_special_tokens=lowercase			)
    snake_case   :				str				 =   [text.strip() for text in output_text]
    print("HF generation:"						,							lowercase			)

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

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


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

  __snake_case             =   parser.parse_args()

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









"""simple docstring"""



import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__snake_case             =   logging.getLogger()







@unittest.skip('''Temporarily disable the doc tests.'''     )
@require_torch
@require_tf
@slow
class _lowerCAmelCase    (				unittest.TestCase     ):







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = True						,     )     ->		Tuple:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   [file for file in os.listdir(UpperCamelCase__						) if os.path.isfile(os.path.join(UpperCamelCase__						,     UpperCamelCase__						)						)]

       if identifier is not None:
           snake_case   :				Optional[Any]				 =   [file for file in files if identifier in file]

       if n_identifier is not None:
           if isinstance(UpperCamelCase__						,     UpperCamelCase__						):
               for n_ in n_identifier:
                   snake_case   :				str				 =   [file for file in files if n_ not in file]
           else:
               snake_case   :				str				 =   [file for file in files if n_identifier not in file]

       snake_case   :				Tuple				 =   ignore_files or []
       ignore_files.append("__init__.py"						)
       snake_case   :				Optional[Any]				 =   [file for file in files if file not in ignore_files]

       for file in files:
           # Open all files
           print("Testing"						,     UpperCamelCase__						)

           if only_modules:
               snake_case   :				str				 =   file.split("."						)[0]
               try:
                   snake_case   :				Optional[int]				 =   getattr(UpperCamelCase__						,     UpperCamelCase__						)
                   snake_case   :				str				 =   doctest.DocTestSuite(UpperCamelCase__						)
                   snake_case   :				Optional[Any]				 =   unittest.TextTestRunner().run(UpperCamelCase__						)
                   self.assertIs(len(result.failures						)						,     0						)
               except AttributeError:
                   logger.info(F'{module_identifier} is not a module.'						)
           else:
               snake_case   :				Tuple				 =   doctest.testfile(str(".." / directory / file						)						,     optionflags=doctest.ELLIPSIS						)
               self.assertIs(result.failed						,     0						)







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				Tuple				 =   Path("src/transformers"						)
       snake_case   :				List[Any]				 =   "modeling"
       snake_case   :				Dict				 =   [
           "modeling_ctrl.py",
           "modeling_tf_ctrl.py",
       ]
       self.analyze_directory(UpperCamelCase__						,     identifier=UpperCamelCase__						,     ignore_files=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   Path("src/transformers"						)
       snake_case   :				Optional[Any]				 =   "tokenization"
       self.analyze_directory(UpperCamelCase__						,     identifier=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		List[str]:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   Path("src/transformers"						)
       snake_case   :				Optional[Any]				 =   "configuration"
       self.analyze_directory(UpperCamelCase__						,     identifier=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				List[str]				 =   Path("src/transformers"						)
       snake_case   :				List[str]				 =   ["configuration", "modeling", "tokenization"]
       self.analyze_directory(UpperCamelCase__						,     n_identifier=UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				Any				 =   Path("docs/source"						)
       snake_case   :				Tuple				 =   ["favicon.ico"]
       self.analyze_directory(UpperCamelCase__						,     ignore_files=UpperCamelCase__						,     only_modules=UpperCamelCase__						)





