





import json
import os
from typing import Optional, Tuple

import regex as re

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


UpperCAmelCase_					=					logging.get_logger(__name__)

UpperCAmelCase_					=					{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

UpperCAmelCase_					=					{
    "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
    "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}

UpperCAmelCase_					=					{
    "ctrl": 256,
}

UpperCAmelCase_					=					{
    "Pregnancy": 16_8629,
    "Christianity": 7675,
    "Explain": 10_6423,
    "Fitness": 6_3440,
    "Saving": 6_3163,
    "Ask": 2_7171,
    "Ass": 9_5985,
    "Joke": 16_3509,
    "Questions": 4_5622,
    "Thoughts": 4_9605,
    "Retail": 5_2342,
    "Feminism": 16_4338,
    "Writing": 1_1992,
    "Atheism": 19_2263,
    "Netflix": 4_8616,
    "Computing": 3_9639,
    "Opinion": 4_3213,
    "Alone": 4_4967,
    "Funny": 5_8917,
    "Gaming": 4_0358,
    "Human": 4088,
    "India": 1331,
    "Joker": 7_7138,
    "Diet": 3_6206,
    "Legal": 1_1859,
    "Norman": 4939,
    "Tip": 7_2689,
    "Weight": 5_2343,
    "Movies": 4_6273,
    "Running": 2_3425,
    "Science": 2090,
    "Horror": 3_7793,
    "Confession": 6_0572,
    "Finance": 1_2250,
    "Politics": 1_6360,
    "Scary": 19_1985,
    "Support": 1_2654,
    "Technologies": 3_2516,
    "Teenage": 6_6160,
    "Event": 3_2769,
    "Learned": 6_7460,
    "Notion": 18_2770,
    "Wikipedia": 3_7583,
    "Books": 6665,
    "Extract": 7_6050,
    "Confessions": 10_2701,
    "Conspiracy": 7_5932,
    "Links": 6_3674,
    "Narcissus": 15_0425,
    "Relationship": 5_4766,
    "Relationships": 13_4796,
    "Reviews": 4_1671,
    "News": 4256,
    "Translation": 2_6820,
    "multilingual": 12_8406,
}




def 						lowerCAmelCase_       (    __UpperCAmelCase:     str ) ->      Any:
    UpperCamelCase__				:							Optional[int]			  = set()
    UpperCamelCase__				:							List[str]			  = word[0]
    for char in word[1:]:
        pairs.add((prev_char, char) )
        UpperCamelCase__				:							List[Any]			  = char

    UpperCamelCase__				:							List[Any]			  = set(SCREAMING_SNAKE_CASE__ )
    return pairs






class        lowercase__			(      _UpperCamelCase							):

   '''simple docstring'''


   a		:				Dict  						=						VOCAB_FILES_NAMES
   a		:				Tuple  						=						PRETRAINED_VOCAB_FILES_MAP
   a		:				Union[str, Any]  						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a		:				Any  						=						CONTROL_CODES




   def __init__(       self,						__magic_name__,						__magic_name__,						__magic_name__="<unk>",						**__magic_name__				)					-> List[Any]:






       """simple docstring"""
       super().__init__(unk_token=__magic_name__,						**__magic_name__				)

       with open(__magic_name__,						encoding='''utf-8'''				) as vocab_handle:
           UpperCamelCase__				:							List[Any]			  = json.load(__magic_name__				)
       UpperCamelCase__				:							Dict			  = {v: k for k, v in self.encoder.items()}
       with open(__magic_name__,						encoding='''utf-8'''				) as merges_handle:
           UpperCamelCase__				:							Dict			  = merges_handle.read().split('''\n'''				)[1:-1]
       UpperCamelCase__				:							List[Any]			  = [tuple(merge.split()				) for merge in merges]
       UpperCamelCase__				:							Optional[int]			  = dict(zip(__magic_name__,						range(len(__magic_name__				)				)				)				)
       UpperCamelCase__				:							Union[str, Any]			  = {}




   @property
   def  UpperCamelCase__    (       self				)					-> str:






       """simple docstring"""
       return len(self.encoder				)




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






       """simple docstring"""
       return dict(self.encoder,						**self.added_tokens_encoder				)




   def  UpperCamelCase__    (       self,						__magic_name__				)					-> List[str]:






       """simple docstring"""
       if token in self.cache:
           return self.cache[token]
       UpperCamelCase__				:							Tuple			  = tuple(__magic_name__				)
       UpperCamelCase__				:							Optional[int]			  = tuple(list(word[:-1]				) + [word[-1] + '''</w>''']				)
       UpperCamelCase__				:							Any			  = get_pairs(__magic_name__				)

       if not pairs:
           return token

       while True:
           UpperCamelCase__				:							str			  = min(__magic_name__,						key=lambda __magic_name__				: self.bpe_ranks.get(__magic_name__,						float('''inf'''				)				)				)
           if bigram not in self.bpe_ranks:
               break
           UpperCamelCase__				:							str			  = bigram
           UpperCamelCase__				:							Dict			  = []
           UpperCamelCase__				:							int			  = 0
           while i < len(__magic_name__				):
               try:
                   UpperCamelCase__				:							Tuple			  = word.index(__magic_name__,						__magic_name__				)
               except ValueError:
                   new_word.extend(word[i:]				)
                   break
               else:
                   new_word.extend(word[i:j]				)
                   UpperCamelCase__				:							Tuple			  = j

               if word[i] == first and i < len(__magic_name__				) - 1 and word[i + 1] == second:
                   new_word.append(first + second				)
                   i += 2
               else:
                   new_word.append(word[i]				)
                   i += 1
           UpperCamelCase__				:							Union[str, Any]			  = tuple(__magic_name__				)
           UpperCamelCase__				:							Union[str, Any]			  = new_word
           if len(__magic_name__				) == 1:
               break
           else:
               UpperCamelCase__				:							List[Any]			  = get_pairs(__magic_name__				)
       UpperCamelCase__				:							Any			  = "@@ ".join(__magic_name__				)
       UpperCamelCase__				:							Union[str, Any]			  = word[:-4]
       UpperCamelCase__				:							Optional[int]			  = word
       return word




   def  UpperCamelCase__    (       self,						__magic_name__				)					-> Dict:






       """simple docstring"""
       UpperCamelCase__				:							Dict			  = []

       UpperCamelCase__				:							Optional[int]			  = re.findall(R'''\S+\n?''',						__magic_name__				)

       for token in words:
           split_tokens.extend(list(self.bpe(__magic_name__				).split(''' '''				)				)				)
       return split_tokens




   def  UpperCamelCase__    (       self,						__magic_name__				)					-> Union[str, Any]:






       """simple docstring"""
       return self.encoder.get(__magic_name__,						self.encoder.get(self.unk_token				)				)




   def  UpperCamelCase__    (       self,						__magic_name__				)					-> Tuple:






       """simple docstring"""
       return self.decoder.get(__magic_name__,						self.unk_token				)




   def  UpperCamelCase__    (       self,						__magic_name__				)					-> int:






       """simple docstring"""
       UpperCamelCase__				:							Dict			  = " ".join(__magic_name__				).replace('''@@ ''',						''''''				).strip()
       return out_string




   def  UpperCamelCase__    (       self,						__magic_name__,						__magic_name__ = None				)					-> Optional[int]:






       """simple docstring"""
       if not os.path.isdir(__magic_name__				):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
           return
       UpperCamelCase__				:							Optional[Any]			  = os.path.join(
           __magic_name__,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
       UpperCamelCase__				:							Tuple			  = os.path.join(
           __magic_name__,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']				)

       with open(__magic_name__,						'''w''',						encoding='''utf-8'''				) as f:
           f.write(json.dumps(self.encoder,						indent=2,						sort_keys=__magic_name__,						ensure_ascii=__magic_name__				) + '''\n'''				)

       UpperCamelCase__				:							int			  = 0
       with open(__magic_name__,						'''w''',						encoding='''utf-8'''				) as writer:
           writer.write('''#version: 0.2\n'''				)
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),						key=lambda __magic_name__				: kv[1]				):
               if index != token_index:
                   logger.warning(
                       f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                       ''' Please check that the tokenizer is not corrupted!'''				)
                   UpperCamelCase__				:							Optional[int]			  = token_index
               writer.write(''' '''.join(__magic_name__				) + '''\n'''				)
               index += 1

       return vocab_file, merge_file

   # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
   #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
   #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
   #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
   #     return ''.join(tokens_generated_so_far)






"""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)
