




'''simple docstring'''



import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


UpperCAmelCase_							:	str	     =		(
    '''https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'''
)


UpperCAmelCase_							:	str	     =		logging.get_logger(__name__)  # pylint: disable=invalid-name







def       _UpperCamelCase			()-> int:




     '''simple docstring'''





     __snake_case            =	'''https://pypi.org/pypi/diffusers/json'''
     __snake_case            =	json.loads(request.urlopen(_lowerCamelCase						).read()						)['''releases'''].keys()
     return sorted(_lowerCamelCase ,						key=lambda _lowerCamelCase						: version.Version(_lowerCamelCase						)						)







def       _UpperCamelCase			()-> Tuple:




     '''simple docstring'''





     if HF_MODULES_CACHE in sys.path:
          return

     sys.path.append(_lowerCamelCase						)
     os.makedirs(_lowerCamelCase ,						exist_ok=_lowerCamelCase						)
     __snake_case            =	Path(_lowerCamelCase						) / '''__init__.py'''
     if not init_path.exists():
          init_path.touch()







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, os.PathLike]						)-> Optional[int]:




     '''simple docstring'''





     init_hf_modules()
     __snake_case            =	Path(_lowerCamelCase						) / name
     # If the parent module does not exist yet, recursively create it.
     if not dynamic_module_path.parent.exists():
          create_dynamic_module(dynamic_module_path.parent						)
     os.makedirs(_lowerCamelCase ,						exist_ok=_lowerCamelCase						)
     __snake_case            =	dynamic_module_path / '''__init__.py'''
     if not init_path.exists():
          init_path.touch()







def       _UpperCamelCase			(_lowerCamelCase				:  Dict						)-> Any:




     '''simple docstring'''





     with open(_lowerCamelCase ,						'''r''' ,						encoding='''utf-8'''						) as f:
          __snake_case            =	f.read()

     # Imports of the form `import .xxx`
     __snake_case            =	re.findall('''^\s*import\s+\.(\S+)\s*$''' ,						_lowerCamelCase ,						flags=re.MULTILINE						)
     # Imports of the form `from .xxx import yyy`
     relative_imports += re.findall('''^\s*from\s+\.(\S+)\s+import''' ,						_lowerCamelCase ,						flags=re.MULTILINE						)
     # Unique-ify
     return list(set(_lowerCamelCase						)						)







def       _UpperCamelCase			(_lowerCamelCase				:  Dict						)-> Optional[Any]:




     '''simple docstring'''





     __snake_case            =	False
     __snake_case            =	[module_file]
     __snake_case            =	[]

     # Let's recurse through all relative imports
     while not no_change:
          __snake_case            =	[]
          for f in files_to_check:
               new_imports.extend(get_relative_imports(_lowerCamelCase						)						)

          __snake_case            =	Path(_lowerCamelCase						).parent
          __snake_case            =	[str(module_path / m						) for m in new_imports]
          __snake_case            =	[f for f in new_import_files if f not in all_relative_imports]
          __snake_case            =	[f'''{f}.py''' for f in new_import_files]

          __snake_case            =	len(_lowerCamelCase						) == 0
          all_relative_imports.extend(_lowerCamelCase						)

     return all_relative_imports







def       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any]						)-> int:




     '''simple docstring'''





     with open(_lowerCamelCase ,						'''r''' ,						encoding='''utf-8'''						) as f:
          __snake_case            =	f.read()

     # Imports of the form `import xxx`
     __snake_case            =	re.findall('''^\s*import\s+(\S+)\s*$''' ,						_lowerCamelCase ,						flags=re.MULTILINE						)
     # Imports of the form `from xxx import yyy`
     imports += re.findall('''^\s*from\s+(\S+)\s+import''' ,						_lowerCamelCase ,						flags=re.MULTILINE						)
     # Only keep the top-level module
     __snake_case            =	[imp.split('''.'''						)[0] for imp in imports if not imp.startswith('''.'''						)]

     # Unique-ify and test we got them all
     __snake_case            =	list(set(_lowerCamelCase						)						)
     __snake_case            =	[]
     for imp in imports:
          try:
               importlib.import_module(_lowerCamelCase						)
          except ImportError:
               missing_packages.append(_lowerCamelCase						)

     if len(_lowerCamelCase						) > 0:
          raise ImportError(
              '''This modeling file requires the following packages that were not found in your environment: '''
              f'''{", ".join(_lowerCamelCase						)}. Run `pip install {" ".join(_lowerCamelCase						)}`'''						)

     return get_relative_imports(_lowerCamelCase						)







def       _UpperCamelCase			(_lowerCamelCase				:  str ,						_lowerCamelCase				:  Dict						)-> List[Any]:




     '''simple docstring'''





     __snake_case            =	module_path.replace(os.path.sep ,						'''.'''						)
     __snake_case            =	importlib.import_module(_lowerCamelCase						)

     if class_name is None:
          return find_pipeline_class(_lowerCamelCase						)
     return getattr(_lowerCamelCase ,						_lowerCamelCase						)







def       _UpperCamelCase			(_lowerCamelCase				:  List[str]						)-> List[str]:




     '''simple docstring'''





     from ..pipelines import DiffusionPipeline

     __snake_case            =	dict(inspect.getmembers(_lowerCamelCase ,						inspect.isclass						)						)

     __snake_case            =	None
     for cls_name, cls in cls_members.items():
          if (
              cls_name != DiffusionPipeline.__name__
              and issubclass(cls ,						_lowerCamelCase						)
              and cls.__module__.split('''.'''						)[0] != "diffusers"
          ):
               if pipeline_class is not None:
                    raise ValueError(
                        f'''Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'''
                        f''' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'''
                        f''' {loaded_module}.'''						)
               __snake_case            =	cls

     return pipeline_class







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, os.PathLike] ,						_lowerCamelCase				:  str ,						_lowerCamelCase				:  Optional[Union[str, os.PathLike]] = None ,						_lowerCamelCase				:  bool = False ,						_lowerCamelCase				:  bool = False ,						_lowerCamelCase				:  Optional[Dict[str, str]] = None ,						_lowerCamelCase				:  Optional[Union[bool, str]] = None ,						_lowerCamelCase				:  Optional[str] = None ,						_lowerCamelCase				:  bool = False ,						)-> str:




     '''simple docstring'''





     __snake_case            =	str(_lowerCamelCase						)

     __snake_case            =	os.path.join(_lowerCamelCase ,						_lowerCamelCase						)

     if os.path.isfile(_lowerCamelCase						):
          __snake_case            =	module_file_or_url
          __snake_case            =	'''local'''
     elif pretrained_model_name_or_path.count('''/'''						) == 0:
          __snake_case            =	get_diffusers_versions()
          # cut ".dev0"
          __snake_case            =	'''v''' + '''.'''.join(__version__.split('''.'''						)[:3]						)

          # retrieve github version that matches
          if revision is None:
               __snake_case            =	latest_version if latest_version[1:] in available_versions else '''main'''
               logger.info(f'''Defaulting to latest_version: {revision}.'''						)
          elif revision in available_versions:
               __snake_case            =	f'''v{revision}'''
          elif revision == "main":
               __snake_case            =	revision
          else:
               raise ValueError(
                   f'''`custom_revision`: {revision} does not exist. Please make sure to choose one of'''
                   f''' {", ".join(available_versions + ["main"]						)}.'''						)

          # community pipeline on GitHub
          __snake_case            =	COMMUNITY_PIPELINES_URL.format(revision=_lowerCamelCase ,						pipeline=_lowerCamelCase						)
          try:
               __snake_case            =	cached_download(
                   _lowerCamelCase ,						cache_dir=_lowerCamelCase ,						force_download=_lowerCamelCase ,						proxies=_lowerCamelCase ,						resume_download=_lowerCamelCase ,						local_files_only=_lowerCamelCase ,						use_auth_token=_lowerCamelCase ,						)
               __snake_case            =	'''git'''
               __snake_case            =	pretrained_model_name_or_path + '''.py'''
          except EnvironmentError:
               logger.error(f'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.'''						)
               raise
     else:
          try:
               # Load from URL or cache if already cached
               __snake_case            =	hf_hub_download(
                   _lowerCamelCase ,						_lowerCamelCase ,						cache_dir=_lowerCamelCase ,						force_download=_lowerCamelCase ,						proxies=_lowerCamelCase ,						resume_download=_lowerCamelCase ,						local_files_only=_lowerCamelCase ,						use_auth_token=_lowerCamelCase ,						)
               __snake_case            =	os.path.join('''local''' ,						'''--'''.join(pretrained_model_name_or_path.split('''/'''						)						)						)
          except EnvironmentError:
               logger.error(f'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.'''						)
               raise

    # Check we have all the requirements in our environment
     __snake_case            =	check_imports(_lowerCamelCase						)

     # Now we move the module inside our cached dynamic modules.
     __snake_case            =	DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
     create_dynamic_module(_lowerCamelCase						)
     __snake_case            =	Path(_lowerCamelCase						) / full_submodule
     if submodule == "local" or submodule == "git":
          # We always copy local files (we could hash the file to see if there was a change, and give them the name of
          # that hash, to only copy when there is a modification but it seems overkill for now).
          # The only reason we do the copy is to avoid putting too many folders in sys.path.
          shutil.copy(_lowerCamelCase ,						submodule_path / module_file						)
          for module_needed in modules_needed:
               __snake_case            =	f'''{module_needed}.py'''
               shutil.copy(os.path.join(_lowerCamelCase ,						_lowerCamelCase						) ,						submodule_path / module_needed						)
     else:
          # Get the commit hash
          # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
          if isinstance(_lowerCamelCase ,						_lowerCamelCase						):
               __snake_case            =	use_auth_token
          elif use_auth_token is True:
               __snake_case            =	HfFolder.get_token()
          else:
               __snake_case            =	None

          __snake_case            =	model_info(_lowerCamelCase ,						revision=_lowerCamelCase ,						token=_lowerCamelCase						).sha

          # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
          # benefit of versioning.
          __snake_case            =	submodule_path / commit_hash
          __snake_case            =	full_submodule + os.path.sep + commit_hash
          create_dynamic_module(_lowerCamelCase						)

          if not (submodule_path / module_file).exists():
               shutil.copy(_lowerCamelCase ,						submodule_path / module_file						)
          # Make sure we also have every file with relative
          for module_needed in modules_needed:
               if not (submodule_path / module_needed).exists():
                    get_cached_module_file(
                        _lowerCamelCase ,						f'''{module_needed}.py''' ,						cache_dir=_lowerCamelCase ,						force_download=_lowerCamelCase ,						resume_download=_lowerCamelCase ,						proxies=_lowerCamelCase ,						use_auth_token=_lowerCamelCase ,						revision=_lowerCamelCase ,						local_files_only=_lowerCamelCase ,						)
     return os.path.join(_lowerCamelCase ,						_lowerCamelCase						)







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, os.PathLike] ,						_lowerCamelCase				:  str ,						_lowerCamelCase				:  Optional[str] = None ,						_lowerCamelCase				:  Optional[Union[str, os.PathLike]] = None ,						_lowerCamelCase				:  bool = False ,						_lowerCamelCase				:  bool = False ,						_lowerCamelCase				:  Optional[Dict[str, str]] = None ,						_lowerCamelCase				:  Optional[Union[bool, str]] = None ,						_lowerCamelCase				:  Optional[str] = None ,						_lowerCamelCase				:  bool = False ,						**_lowerCamelCase				:  Optional[Any] ,						)-> Union[str, Any]:




     '''simple docstring'''





     __snake_case            =	get_cached_module_file(
         _lowerCamelCase ,						_lowerCamelCase ,						cache_dir=_lowerCamelCase ,						force_download=_lowerCamelCase ,						resume_download=_lowerCamelCase ,						proxies=_lowerCamelCase ,						use_auth_token=_lowerCamelCase ,						revision=_lowerCamelCase ,						local_files_only=_lowerCamelCase ,						)
     return get_class_in_module(_lowerCamelCase ,						final_module.replace('''.py''' ,						''''''						)						)












'''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       _UpperCamelCase			()-> int:




     '''simple docstring'''





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

     return image







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, Any]						)-> List[Any]:




     '''simple docstring'''





     __snake_case            =	[]
     # 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       _UpperCamelCase			(_lowerCamelCase				:  Dict ,						_lowerCamelCase				:  List[str] ,						_lowerCamelCase				:  Optional[int]						)-> Tuple:




     '''simple docstring'''





     __snake_case            =	dct.pop(_lowerCamelCase						)
     __snake_case            =	val







def       _UpperCamelCase			(_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  Tuple						)-> str:




     '''simple docstring'''





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

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







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  Tuple						)-> Dict:




     '''simple docstring'''





     __snake_case            =	3_64 if '''coco''' in model_name else 2_24
     __snake_case            =	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:
          __snake_case            =	OPTConfig.from_pretrained('''facebook/opt-2.7b''' ,						eos_token_id=_lowerCamelCase						).to_dict()
     elif "opt-6.7b" in model_name:
          __snake_case            =	OPTConfig.from_pretrained('''facebook/opt-6.7b''' ,						eos_token_id=_lowerCamelCase						).to_dict()
     elif "t5-xl" in model_name:
          __snake_case            =	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            =	TaConfig.from_pretrained('''google/flan-t5-xxl''' ,						dense_act_fn='''gelu''' ,						bos_token_id=1						).to_dict()

     __snake_case            =	BlipaConfig(vision_config=_lowerCamelCase ,						text_config=_lowerCamelCase						)

     return config, image_size







@torch.no_grad()
def       _UpperCamelCase			(_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  Union[str, Any]=None ,						_lowerCamelCase				:  Any=False						)-> Dict:




     '''simple docstring'''





     __snake_case            =	(
         AutoTokenizer.from_pretrained('''facebook/opt-2.7b'''						)
         if '''opt''' in model_name
         else AutoTokenizer.from_pretrained('''google/flan-t5-xl'''						)
     )
     __snake_case            =	tokenizer('''\n''' ,						add_special_tokens=_lowerCamelCase						).input_ids[0]
     __snake_case       ,     __snake_case            =	get_blipa_config(_lowerCamelCase ,						eos_token_id=_lowerCamelCase						)

     __snake_case            =	BlipaForConditionalGeneration(_lowerCamelCase						).eval()

     __snake_case            =	{
         '''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            =	model_name_to_original[model_name]

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

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

     # read in qv biases
     read_in_q_v_bias(_lowerCamelCase ,						_lowerCamelCase						)

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

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

     # create processor
     __snake_case            =	BlipImageProcessor(
         size={'''height''': image_size, '''width''': image_size} ,						image_mean=_lowerCamelCase ,						image_std=_lowerCamelCase						)
     __snake_case            =	BlipaProcessor(image_processor=_lowerCamelCase ,						tokenizer=_lowerCamelCase						)
     __snake_case            =	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:
               __snake_case            =	original_model({'''image''': original_pixel_values, '''text_input''': ['''''']}						).logits
               __snake_case            =	hf_model(_lowerCamelCase ,						_lowerCamelCase						).logits
          else:
               __snake_case            =	original_model(
                   {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']}						).logits
               __snake_case            =	input_ids.masked_fill(input_ids == tokenizer.pad_token_id ,						-1_00						)
               __snake_case            =	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":
          __snake_case            =	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":
          __snake_case            =	torch.tensor(
              [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]] ,						device=_lowerCamelCase						)
     else:
          # cast to same type
          __snake_case            =	logits.dtype
          assert torch.allclose(original_logits.to(_lowerCamelCase						) ,						_lowerCamelCase ,						atol=1E-2						)
     print('''Looks ok!'''						)

     print('''Generating a caption...'''						)
     __snake_case            =	''''''
     __snake_case            =	tokenizer(_lowerCamelCase ,						return_tensors='''pt'''						).input_ids.to(_lowerCamelCase						)

     __snake_case            =	original_model.generate({'''image''': original_pixel_values}						)
     __snake_case            =	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						)
     __snake_case            =	input_ids.shape[1]
     __snake_case            =	processor.batch_decode(outputs[:, prompt_length:] ,						skip_special_tokens=_lowerCamelCase						)
     __snake_case            =	[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__":
     UpperCAmelCase_							:	Any	     =		argparse.ArgumentParser()
     UpperCAmelCase_							:	Tuple	     =		[
         '''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''',
     )

     UpperCAmelCase_							:	List[str]	     =		parser.parse_args()

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






