



"""simple docstring"""




import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase							:   List[Any]				=				logging.get_logger(__name__)

_lowerCAmelCase							:   int				=				"▁"

_lowerCAmelCase							:   Optional[Any]				=				{"vocab_file": "vocab.txt", "sentencepiece_model_ckpt": "sentencepiece.bpe.model"}

_lowerCAmelCase							:   Any				=				{
    "sentencepiece_model_file": "sentencepiece.bpe.model",
    "vocab_file": "vocab.txt",
}

_lowerCAmelCase							:   List[str]				=				{
    "vocab_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
    },
    "sentencepiece_model_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
    },
}

_lowerCAmelCase							:   Union[str, Any]				=				{
    "ernie-m-base": 5_14,
    "ernie-m-large": 5_14,
}

_lowerCAmelCase							:   str				=				{
    "ernie-m-base": {"do_lower_case": False},
    "ernie-m-large": {"do_lower_case": False},
}






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  List[str]						=					["input_ids"]

      __SCREAMING_SNAKE_CASE		:  Tuple						=					VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					PRETRAINED_INIT_CONFIGURATION
      __SCREAMING_SNAKE_CASE		:  int						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE		:  List[Any]						=					RESOURCE_FILES_NAMES



      def __init__(	self	:      List[str]	,      A	:      Tuple	,      A	:      Union[str, Any]=None	,      A	:      Union[str, Any]=False	,      A	:      Optional[int]="utf8"	,      A	:      List[str]="[UNK]"	,      A	:      Union[str, Any]="[SEP]"	,      A	:      Union[str, Any]="[PAD]"	,      A	:      Dict="[CLS]"	,      A	:      int="[MASK]"	,      A	:      Optional[Dict[str, Any]] = None	,      **A	:      List[Any]	,      ):
           # Mask token behave like a normal word, i.e. include the space before it and
           # is included in the raw text, there should be a match in a non-normalized sentence.

           _UpperCAmelCase     :      Union[str, Any]														=       {} if sp_model_kwargs is None else sp_model_kwargs
           super().__init__(
               do_lower_case=A	,      unk_token=A	,      sep_token=A	,      pad_token=A	,      cls_token=A	,      mask_token=A	,      vocab_file=A	,      encoding=A	,      sp_model_kwargs=self.sp_model_kwargs	,      **A	,      )
           _UpperCAmelCase     :      Any														=       do_lower_case
           _UpperCAmelCase     :      Optional[int]														=       sentencepiece_model_ckpt
           _UpperCAmelCase     :      Any														=       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
           self.sp_model.Load(A		)

           # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
           if vocab_file is not None:
                _UpperCAmelCase     :      List[str]														=       self.load_vocab(filepath=A		)
           else:
                _UpperCAmelCase     :      Optional[Any]														=       {self.sp_model.id_to_piece(A		): id for id in range(self.sp_model.get_piece_size()		)}
           _UpperCAmelCase     :      Tuple														=       {v: k for k, v in self.vocab.items()}



      def 		snake_case_   (	self	:      Any	,      A	:      int		):
           if text is None:
                return None

           _UpperCAmelCase     :      Tuple														=       self.tokenize(A		)
           _UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       "", []

           for i, ch in enumerate(A		):
                if ch in self.SP_CHAR_MAPPING:
                     _UpperCAmelCase     :      Optional[Any]														=       self.SP_CHAR_MAPPING.get(A		)
                else:
                     _UpperCAmelCase     :      str														=       unicodedata.normalize("NFKC"	,      A		)
                if self.is_whitespace(A		):
                     continue
                normalized_text += ch
                char_mapping.extend([i] * len(A		)		)

           _UpperCAmelCase							,			_UpperCAmelCase							,			_UpperCAmelCase     :      int														=       normalized_text, [], 0

           if self.do_lower_case:
                _UpperCAmelCase     :      Optional[int]														=       text.lower()

           for token in split_tokens:
                if token[:1] == "▁":
                     _UpperCAmelCase     :      Any														=       token[1:]
                _UpperCAmelCase     :      List[str]														=       text[offset:].index(A		) + offset
                _UpperCAmelCase     :      Optional[Any]														=       start + len(A		)

                token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)		)
                _UpperCAmelCase     :      str														=       end
           return token_mapping



      @property
      def 		snake_case_   (	self	:      int		):
           return len(self.vocab		)



      def 		snake_case_   (	self	:      int		):
           return dict(self.vocab	,      **self.added_tokens_encoder		)



      def __getstate__(	self	:      List[str]		):
           _UpperCAmelCase     :      Union[str, Any]														=       self.__dict__.copy()
           _UpperCAmelCase     :      List[str]														=       None
           return state



      def __setstate__(	self	:      str	,      A	:      Tuple		):
           _UpperCAmelCase     :      Optional[int]														=       d

           # for backward compatibility
           if not hasattr(self	,      "sp_model_kwargs"		):
                _UpperCAmelCase     :      str														=       {}

           _UpperCAmelCase     :      Any														=       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
           self.sp_model.Load(self.sentencepiece_model_ckpt		)



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      Tuple		):
           return "".join((self.SP_CHAR_MAPPING.get(A	,      A		) for c in text)		)



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      Dict	,      A	:      Union[str, Any]=False	,      A	:      Tuple=6_4	,      A	:      str=0.1		):

           if self.sp_model_kwargs.get("enable_sampling"		) is True:
                _UpperCAmelCase     :      List[str]														=       True
           if self.sp_model_kwargs.get("alpha"		) is not None:
                _UpperCAmelCase     :      Dict														=       self.sp_model_kwargs.get("alpha"		)
           if self.sp_model_kwargs.get("nbest_size"		) is not None:
                _UpperCAmelCase     :      Any														=       self.sp_model_kwargs.get("nbest_size"		)

           if not enable_sampling:
                _UpperCAmelCase     :      Optional[Any]														=       self.sp_model.EncodeAsPieces(A		)
           else:
                _UpperCAmelCase     :      List[str]														=       self.sp_model.SampleEncodeAsPieces(A	,      A	,      A		)
           _UpperCAmelCase     :      List[str]														=       []
           for pi, piece in enumerate(A		):
                if piece == SPIECE_UNDERLINE:
                     if not pieces[pi + 1].startswith(A		) and pi != 0:
                          new_pieces.append(A		)
                          continue
                     else:
                          continue
                _UpperCAmelCase     :      List[Any]														=       0
                for i, chunk in enumerate(A		):
                     if chunk == SPIECE_UNDERLINE:
                          continue
                     if self.is_ch_char(A		) or self.is_punct(A		):
                          if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                               new_pieces.append(piece[lst_i:i]		)
                          new_pieces.append(A		)
                          _UpperCAmelCase     :      Union[str, Any]														=       i + 1
                     elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                          if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                               new_pieces.append(piece[lst_i:i]		)
                          _UpperCAmelCase     :      List[str]														=       i
                     elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                          if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                               new_pieces.append(piece[lst_i:i]		)
                          _UpperCAmelCase     :      Optional[Any]														=       i
                if len(A		) > lst_i:
                     new_pieces.append(piece[lst_i:]		)
           return new_pieces



      def 		snake_case_   (	self	:      List[str]	,      A	:      List[str]		):
           _UpperCAmelCase     :      int														=       "".join(A		).replace(A	,      " "		).strip()
           return out_string



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      List[Any]		):
           _UpperCAmelCase     :      Any														=       self.convert_ids_to_tokens(A		)
           _UpperCAmelCase     :      Union[str, Any]														=       "".join(A		).replace(A	,      " "		).strip()
           return out_string



      def 		snake_case_   (	self	:      List[Any]	,      A	:      List[Any]		):
           return self.vocab.get(A	,      self.vocab.get(self.unk_token		)		)



      def 		snake_case_   (	self	:      Dict	,      A	:      Any		):
           return self.reverse_vocab.get(A	,      self.unk_token		)



      def 		snake_case_   (	self	:      Optional[Any]	,      A	:      int	,      A	:      Union[str, Any]=None		):
           if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           _UpperCAmelCase     :      Dict														=       [self.cls_token_id]
           _UpperCAmelCase     :      Dict														=       [self.sep_token_id]
           return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      Optional[Any]	,      A	:      Any=None		):
           if offset_mapping_a is None:
                return [(0, 0)] + offset_mapping_a + [(0, 0)]

           return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]



      def 		snake_case_   (	self	:      List[Any]	,      A	:      Tuple	,      A	:      Dict=None	,      A	:      Tuple=False		):

           if already_has_special_tokens:
                if token_ids_a is not None:
                     raise ValueError(
                         "You should not supply a second sequence if the provided sequence of "
                         "ids is already formatted with special tokens for the model."		)
                return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

           if token_ids_a is not None:
                return [1] + ([0] * len(A		)) + [1, 1] + ([0] * len(A		)) + [1]
           return [1] + ([0] * len(A		)) + [1]



      def 		snake_case_   (	self	:      Any	,      A	:      List[int]	,      A	:      Optional[List[int]] = None		):
           # called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
           if token_ids_a is None:
                # [CLS] X [SEP]
                return (len(A		) + 2) * [0]

           # [CLS] A [SEP] [SEP] B [SEP]
           return [0] * (len(A		) + 1) + [1] * (len(A		) + 3)



      def 		snake_case_   (	self	:      List[str]	,      A	:      Union[str, Any]		):
           if "\u4e00" <= char <= "\u9fff":
                return True
           return False



      def 		snake_case_   (	self	:      Optional[Any]	,      A	:      Tuple		):
           if ("a" <= char <= "z") or ("A" <= char <= "Z"):
                return True
           return False



      def 		snake_case_   (	self	:      Optional[int]	,      A	:      Optional[Any]		):
           if char in ",;:.?!~，；：。？！《》【】":
                return True
           return False



      def 		snake_case_   (	self	:      str	,      A	:      Union[str, Any]		):
           if char == " " or char == "\t" or char == "\n" or char == "\r":
                return True
           if len(A		) == 1:
                _UpperCAmelCase     :      Tuple														=       unicodedata.category(A		)
                if cat == "Zs":
                     return True
           return False



      def 		snake_case_   (	self	:      Any	,      A	:      List[Any]		):
           _UpperCAmelCase     :      Tuple														=       {}
           with io.open(A	,      "r"	,      encoding="utf-8"		) as f:
                for index, line in enumerate(A		):
                     _UpperCAmelCase     :      Any														=       line.rstrip("\n"		)
                     _UpperCAmelCase     :      Optional[int]														=       int(A		)

           return token_to_idx



      def 		snake_case_   (	self	:      Optional[Any]	,      A	:      str	,      A	:      Optional[str] = None		):
           _UpperCAmelCase     :      Optional[int]														=       0
           if os.path.isdir(A		):
                _UpperCAmelCase     :      Any														=       os.path.join(
                    A	,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
           else:
                _UpperCAmelCase     :      int														=       (filename_prefix + "-" if filename_prefix else "") + save_directory
           with open(A	,      "w"	,      encoding="utf-8"		) as writer:
                for token, token_index in sorted(self.vocab.items()	,      key=lambda A		: kv[1]		):
                     if index != token_index:
                          logger.warning(
                              f'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
                              " Please check that the vocabulary is not corrupted!"		)
                          _UpperCAmelCase     :      Optional[int]														=       token_index
                     writer.write(token + "\n"		)
                     index += 1

           _UpperCAmelCase     :      Union[str, Any]														=       os.path.join(A	,      "sentencepiece.bpe.model"		)
           with open(A	,      "wb"		) as fi:
                _UpperCAmelCase     :      List[str]														=       self.sp_model.serialized_model_proto()
                fi.write(A		)

           return (vocab_file,)





"""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		( )    ->  Tuple:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[int]														=       "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
     _UpperCAmelCase     :      int														=       Image.open(requests.get(SCREAMING_SNAKE_CASE__		,    stream=SCREAMING_SNAKE_CASE__							).raw							).convert("RGB"							)

     return image

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      List[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 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[str]		,    SCREAMING_SNAKE_CASE__      :		Dict		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[int]														=       dct.pop(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Tuple														=       val

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[Any]:





     '''simple docstring'''

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

          # next, set bias in the state dict
          _UpperCAmelCase     :      List[Any]														=       torch.cat((q_bias, torch.zeros_like(SCREAMING_SNAKE_CASE__		,    requires_grad=SCREAMING_SNAKE_CASE__							), v_bias)							)
          _UpperCAmelCase     :      Union[str, Any]														=       qkv_bias

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str		,    SCREAMING_SNAKE_CASE__      :		Any							)    ->  str:





     '''simple docstring'''

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

     _UpperCAmelCase     :      Optional[Any]														=       BlipaConfig(vision_config=SCREAMING_SNAKE_CASE__		,    text_config=SCREAMING_SNAKE_CASE__							)

     return config, image_size

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





     '''simple docstring'''

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

     _UpperCAmelCase     :      int														=       BlipaForConditionalGeneration(SCREAMING_SNAKE_CASE__							).eval()

     _UpperCAmelCase     :      Optional[int]														=       {
         "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"),
     }

     _UpperCAmelCase							,			_UpperCAmelCase     :      str														=       model_name_to_original[model_name]

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

     # update state dict keys
     _UpperCAmelCase     :      Optional[Any]														=       original_model.state_dict()
     _UpperCAmelCase     :      Union[str, Any]														=       create_rename_keys(SCREAMING_SNAKE_CASE__							)
     for src, dest in rename_keys:
          rename_key(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

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

     # read in qv biases
     read_in_q_v_bias(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

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

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

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

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

     original_model.to(SCREAMING_SNAKE_CASE__							)
     hf_model.to(SCREAMING_SNAKE_CASE__							)
     with torch.no_grad():
          if "opt" in model_name:
               _UpperCAmelCase     :      str														=       original_model({"image": original_pixel_values, "text_input": [""]}							).logits
               _UpperCAmelCase     :      Optional[int]														=       hf_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							).logits
          else:
               _UpperCAmelCase     :      List[Any]														=       original_model(
                   {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}							).logits
               _UpperCAmelCase     :      List[str]														=       input_ids.masked_fill(input_ids == tokenizer.pad_token_id		,    -100							)
               _UpperCAmelCase     :      Union[str, Any]														=       hf_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    labels=SCREAMING_SNAKE_CASE__							).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":
          _UpperCAmelCase     :      List[str]														=       torch.tensor(
              [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]]		,    device=SCREAMING_SNAKE_CASE__							)
          assert torch.allclose(logits[0, :3, :3]		,    SCREAMING_SNAKE_CASE__		,    atol=1E-4							)
     elif model_name == "blip2-flan-t5-xl-coco":
          _UpperCAmelCase     :      Any														=       torch.tensor(
              [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]]		,    device=SCREAMING_SNAKE_CASE__							)
     else:
          # cast to same type
          _UpperCAmelCase     :      List[str]														=       logits.dtype
          assert torch.allclose(original_logits.to(SCREAMING_SNAKE_CASE__							)		,    SCREAMING_SNAKE_CASE__		,    atol=1E-2							)
     print("Looks ok!"							)

     print("Generating a caption..."							)
     _UpperCAmelCase     :      int														=       ""
     _UpperCAmelCase     :      Any														=       tokenizer(SCREAMING_SNAKE_CASE__		,    return_tensors="pt"							).input_ids.to(SCREAMING_SNAKE_CASE__							)

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

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

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


if __name__ == "__main__":
 _lowerCAmelCase							:   Union[str, Any]				=				argparse.ArgumentParser()
 _lowerCAmelCase							:   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",
 )

 _lowerCAmelCase							:   str				=				parser.parse_args()

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