


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

 return image





def __snake_case					(      UpperCAmelCase_						:						int		):
 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.layernorm.weight")		)
 rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias")		)

 # fmt: on
 return rename_keys





def __snake_case					(      UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						Dict		):
 lowerCamelCase_      =  dct.pop(UpperCAmelCase_		)
 lowerCamelCase_      =  val





def __snake_case					(      UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						Union[str, Any]		):
 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 __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  364 if "coco" in model_name else 224
 lowerCamelCase_      =  BlipaVisionConfig(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 "opt-2.7b" in model_name:
  lowerCamelCase_      =  OPTConfig.from_pretrained("facebook/opt-2.7b"					,				eos_token_id=UpperCAmelCase_		).to_dict()
 elif "opt-6.7b" in model_name:
  lowerCamelCase_      =  OPTConfig.from_pretrained("facebook/opt-6.7b"					,				eos_token_id=UpperCAmelCase_		).to_dict()
 elif "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()

 lowerCamelCase_      =  BlipaConfig(vision_config=UpperCAmelCase_					,				text_config=UpperCAmelCase_		)

 return config, image_size





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

 lowerCamelCase_      =  BlipaForConditionalGeneration(UpperCAmelCase_		).eval()

 lowerCamelCase_      =  {
     "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_      =  model_name_to_original[model_name]

 # load original model
 print("Loading original model..."		)
 lowerCamelCase_      =  "cuda" 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 "opt_proj" in key:
   lowerCamelCase_      =  key.replace("opt_proj"					,				"language_projection"		)
  if "t5_proj" in key:
   lowerCamelCase_      =  key.replace("t5_proj"					,				"language_projection"		)
  if key.startswith("opt"		):
   lowerCamelCase_      =  key.replace("opt"					,				"language"		)
  if key.startswith("t5"		):
   lowerCamelCase_      =  key.replace("t5"					,				"language"		)
  lowerCamelCase_      =  val

 # read in qv biases
 read_in_q_v_bias(UpperCAmelCase_					,				UpperCAmelCase_		)

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

 lowerCamelCase_      =  load_demo_image()
 lowerCamelCase_      =  vis_processors["eval"](UpperCAmelCase_		).unsqueeze(0		).to(UpperCAmelCase_		)
 lowerCamelCase_      =  tokenizer(["\n"]					,				return_tensors="pt"		).input_ids.to(UpperCAmelCase_		)

 # create processor
 lowerCamelCase_      =  BlipImageProcessor(
     size={"height": image_size, "width": image_size}					,				image_mean=UpperCAmelCase_					,				image_std=UpperCAmelCase_		)
 lowerCamelCase_      =  BlipaProcessor(image_processor=UpperCAmelCase_					,				tokenizer=UpperCAmelCase_		)
 lowerCamelCase_      =  processor(images=UpperCAmelCase_					,				return_tensors="pt"		).pixel_values.to(UpperCAmelCase_		)

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

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

 print("Generating a caption..."		)
 lowerCamelCase_      =  ""
 lowerCamelCase_      =  tokenizer(UpperCAmelCase_					,				return_tensors="pt"		).input_ids.to(UpperCAmelCase_		)

 lowerCamelCase_      =  original_model.generate({"image": original_pixel_values}		)
 lowerCamelCase_      =  hf_model.generate(
     UpperCAmelCase_					,				UpperCAmelCase_					,				do_sample=UpperCAmelCase_					,				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:"					,				UpperCAmelCase_		)
 lowerCamelCase_      =  input_ids.shape[1]
 lowerCamelCase_      =  processor.batch_decode(outputs[:, prompt_length:]					,				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'''nielsr/{model_name}'''		)
  hf_model.push_to_hub(F'''nielsr/{model_name}'''		)


if __name__ == "__main__":
     a_  :				Union[str, Any]						= argparse.ArgumentParser()
     a_  :				Optional[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_  :				Any						= parser.parse_args()

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







"""simple docstring"""


import argparse
import os
import re

import packaging.version


A     :   Any			    =			"examples/"
A     :   Optional[Any]			    =			{
    "examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
    "init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
    "setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
    "doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
A     :   Optional[int]			    =			{
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
A     :   List[Any]			    =			"README.md"


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			with open(_UpperCamelCase    , "r"    , encoding="utf-8"    , newline="\n"      ) as f:
						__lowerCAmelCase				    =			f.read()
			__lowerCAmelCase    ,	__lowerCAmelCase				    =			REPLACE_PATTERNS[pattern]
			__lowerCAmelCase				    =			replace.replace("VERSION"    , _UpperCamelCase      )
			__lowerCAmelCase				    =			re_pattern.sub(_UpperCamelCase    , _UpperCamelCase      )
			with open(_UpperCamelCase    , "w"    , encoding="utf-8"    , newline="\n"      ) as f:
						f.write(_UpperCamelCase      )


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			for folder, directories, fnames in os.walk(_UpperCamelCase      ):
						# Removing some of the folders with non-actively maintained examples from the walk
						if "research_projects" in directories:
									directories.remove("research_projects"      )
						if "legacy" in directories:
									directories.remove("legacy"      )
						for fname in fnames:
									if fname.endswith(".py"      ):
												update_version_in_file(os.path.join(_UpperCamelCase    , _UpperCamelCase      )    , _UpperCamelCase    , pattern="examples"      )


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=False      ):

			'''simple docstring'''




			for pattern, fname in REPLACE_FILES.items():
						update_version_in_file(_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )
			if not patch:
						update_version_in_examples(_UpperCamelCase      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			"🤗 Transformers currently provides the following architectures"
			__lowerCAmelCase				    =			"1. Want to contribute a new model?"
			with open(_UpperCamelCase    , "r"    , encoding="utf-8"    , newline="\n"      ) as f:
						__lowerCAmelCase				    =			f.readlines()

			# Find the start of the list.
			__lowerCAmelCase				    =			0
			while not lines[start_index].startswith(_start_prompt      ):
						start_index += 1
			start_index += 1

			__lowerCAmelCase				    =			start_index
			# Update the lines in the model list.
			while not lines[index].startswith(_end_prompt      ):
						if lines[index].startswith("1."      ):
									__lowerCAmelCase				    =			lines[index].replace(
									    "https://huggingface.co/docs/transformers/main/model_doc"    , "https://huggingface.co/docs/transformers/model_doc"    , )
						index += 1

			with open(_UpperCamelCase    , "w"    , encoding="utf-8"    , newline="\n"      ) as f:
						f.writelines(_UpperCamelCase      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			with open(REPLACE_FILES["init"]    , "r"      ) as f:
						__lowerCAmelCase				    =			f.read()
			__lowerCAmelCase				    =			REPLACE_PATTERNS["init"][0].search(_UpperCamelCase      ).groups()[0]
			return packaging.version.parse(_UpperCamelCase      )


def _lowerCamelCase	(		_UpperCamelCase=False      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			get_version()
			if patch and default_version.is_devrelease:
						raise ValueError("Can't create a patch version from the dev branch, checkout a released version!"      )
			if default_version.is_devrelease:
						__lowerCAmelCase				    =			default_version.base_version
			elif patch:
						__lowerCAmelCase				    =			f"{default_version.major}.{default_version.minor}.{default_version.micro + 1}"
			else:
						__lowerCAmelCase				    =			f"{default_version.major}.{default_version.minor + 1}.0"

			# Now let's ask nicely if that's the right one.
			__lowerCAmelCase				    =			input(f"Which version are you releasing? [{default_version}]"      )
			if len(_UpperCamelCase      ) == 0:
						__lowerCAmelCase				    =			default_version

			print(f"Updating version to {version}."      )
			global_version_update(_UpperCamelCase    , patch=_UpperCamelCase      )
			if not patch:
						print("Cleaning main README, don't forget to run `make fix-copies`."      )
						clean_main_ref_in_model_list()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			get_version()
			__lowerCAmelCase				    =			f"{current_version.major}.{current_version.minor + 1}.0.dev0"
			__lowerCAmelCase				    =			current_version.base_version

			# Check with the user we got that right.
			__lowerCAmelCase				    =			input(f"Which version are we developing now? [{dev_version}]"      )
			if len(_UpperCamelCase      ) == 0:
						__lowerCAmelCase				    =			dev_version

			print(f"Updating version to {version}."      )
			global_version_update(_UpperCamelCase      )
			print("Cleaning main README, don't forget to run `make fix-copies`."      )
			clean_main_ref_in_model_list()


if __name__ == "__main__":
	A     :   Union[str, Any]			    =			argparse.ArgumentParser()
	parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
	parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
	A     :   Dict			    =			parser.parse_args()
	if not args.post_release:
		pre_release_work(patch=args.patch)
	elif args.patch:
		print("Nothing to do after a patch :-)")
	else:
		post_release_work()




