



import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase	       =	logging.get_logger(__name__)


__UpperCAmelCase	       =	{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

__UpperCAmelCase	       =	{
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
}

__UpperCAmelCase	       =	{
    '''allenai/longformer-base-4096''': 4_096,
    '''allenai/longformer-large-4096''': 4_096,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4_096,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4_096,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4_096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 		UpperCamelCase     (       )     ->					Union[str, Any]:
   UpperCamelCase   :  Dict						     =     (
       list(range(ord('!'     )	,					ord('~'     ) + 1     )     ) + list(range(ord('¡'     )	,					ord('¬'     ) + 1     )     ) + list(range(ord('®'     )	,					ord('ÿ'     ) + 1     )     )
   )
   UpperCamelCase   :  Tuple						     =     bs[:]
   UpperCamelCase   :  List[Any]						     =     0
   for b in range(2**8     ):
      if b not in bs:
         bs.append(snake_case__     )
         cs.append(2**8 + n     )
         n += 1
   UpperCamelCase   :  Optional[Any]						     =     [chr(snake_case__     ) for n in cs]
   return dict(zip(snake_case__	,					snake_case__     )     )
def 		UpperCamelCase     (       snake_case__	:   Tuple     )     ->					Tuple:
   UpperCamelCase   :  int						     =     set()
   UpperCamelCase   :  Any						     =     word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)     )
      UpperCamelCase   :  str						     =     char
   return pairs



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : Optional[int]       					=  VOCAB_FILES_NAMES
       UpperCAmelCase__    : str       					=  PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__    : List[str]       					=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__    : List[str]       					=  ["input_ids", "attention_mask"]
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_="replace",      SCREAMING_SNAKE_CASE_="<s>",      SCREAMING_SNAKE_CASE_="</s>",      SCREAMING_SNAKE_CASE_="</s>",      SCREAMING_SNAKE_CASE_="<s>",      SCREAMING_SNAKE_CASE_="<unk>",      SCREAMING_SNAKE_CASE_="<pad>",      SCREAMING_SNAKE_CASE_="<mask>",      SCREAMING_SNAKE_CASE_=False,      **SCREAMING_SNAKE_CASE_,      )					->		Dict:
          UpperCamelCase   :  int						     =     AddedToken(SCREAMING_SNAKE_CASE_,      lstrip=SCREAMING_SNAKE_CASE_,      rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	) else bos_token
          UpperCamelCase   :  Dict						     =     AddedToken(SCREAMING_SNAKE_CASE_,      lstrip=SCREAMING_SNAKE_CASE_,      rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	) else eos_token
          UpperCamelCase   :  Optional[int]						     =     AddedToken(SCREAMING_SNAKE_CASE_,      lstrip=SCREAMING_SNAKE_CASE_,      rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	) else sep_token
          UpperCamelCase   :  str						     =     AddedToken(SCREAMING_SNAKE_CASE_,      lstrip=SCREAMING_SNAKE_CASE_,      rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	) else cls_token
          UpperCamelCase   :  int						     =     AddedToken(SCREAMING_SNAKE_CASE_,      lstrip=SCREAMING_SNAKE_CASE_,      rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	) else unk_token
          UpperCamelCase   :  Optional[int]						     =     AddedToken(SCREAMING_SNAKE_CASE_,      lstrip=SCREAMING_SNAKE_CASE_,      rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	) else pad_token

          # Mask token behave like a normal word, i.e. include the space before it
          UpperCamelCase   :  Any						     =     AddedToken(SCREAMING_SNAKE_CASE_,      lstrip=SCREAMING_SNAKE_CASE_,      rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	) else mask_token

          super().__init__(
              errors=SCREAMING_SNAKE_CASE_,      bos_token=SCREAMING_SNAKE_CASE_,      eos_token=SCREAMING_SNAKE_CASE_,      unk_token=SCREAMING_SNAKE_CASE_,      sep_token=SCREAMING_SNAKE_CASE_,      cls_token=SCREAMING_SNAKE_CASE_,      pad_token=SCREAMING_SNAKE_CASE_,      mask_token=SCREAMING_SNAKE_CASE_,      add_prefix_space=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_,      )

          with open(SCREAMING_SNAKE_CASE_,      encoding='utf-8'	) as vocab_handle:
             UpperCamelCase   :  Union[str, Any]						     =     json.load(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     {v: k for k, v in self.encoder.items()}
          UpperCamelCase   :  Tuple						     =     errors  # how to handle errors in decoding
          UpperCamelCase   :  Optional[int]						     =     bytes_to_unicode()
          UpperCamelCase   :  Any						     =     {v: k for k, v in self.byte_encoder.items()}
          with open(SCREAMING_SNAKE_CASE_,      encoding='utf-8'	) as merges_handle:
             UpperCamelCase   :  Optional[int]						     =     merges_handle.read().split('\n'	)[1:-1]
          UpperCamelCase   :  Any						     =     [tuple(merge.split()	) for merge in bpe_merges]
          UpperCamelCase   :  List[str]						     =     dict(zip(SCREAMING_SNAKE_CASE_,      range(len(SCREAMING_SNAKE_CASE_	)	)	)	)
          UpperCamelCase   :  int						     =     {}
          UpperCamelCase   :  str						     =     add_prefix_space

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          UpperCamelCase   :  int						     =     re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'	)
       @property
       def  snake_case_	(      self	)					->		Optional[Any]:
          return len(self.encoder	)
       def  snake_case_	(      self	)					->		Any:
          return dict(self.encoder,      **self.added_tokens_encoder	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Tuple:
          if token in self.cache:
             return self.cache[token]
          UpperCamelCase   :  int						     =     tuple(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     get_pairs(SCREAMING_SNAKE_CASE_	)

          if not pairs:
             return token

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

                if word[i] == first and i < len(SCREAMING_SNAKE_CASE_	) - 1 and word[i + 1] == second:
                   new_word.append(first + second	)
                   i += 2
                else:
                   new_word.append(word[i]	)
                   i += 1
             UpperCamelCase   :  Optional[Any]						     =     tuple(SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  List[str]						     =     new_word
             if len(SCREAMING_SNAKE_CASE_	) == 1:
                break
             else:
                UpperCamelCase   :  Optional[Any]						     =     get_pairs(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  int						     =     ' '.join(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[int]						     =     word
          return word
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		List[str]:
          UpperCamelCase   :  Dict						     =     []
          for token in re.findall(self.pat,      SCREAMING_SNAKE_CASE_	):
             UpperCamelCase   :  Union[str, Any]						     =     ''.join(
                 self.byte_encoder[b] for b in token.encode('utf-8'	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
             bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_	).split(' '	)	)
          return bpe_tokens
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Tuple:
          return self.encoder.get(SCREAMING_SNAKE_CASE_,      self.encoder.get(self.unk_token	)	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Optional[int]:
          return self.decoder.get(SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          UpperCamelCase   :  int						     =     ''.join(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8',      errors=self.errors	)
          return text
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None	)					->		Tuple[str]:
          if not os.path.isdir(SCREAMING_SNAKE_CASE_	):
             logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
             return
          UpperCamelCase   :  str						     =     os.path.join(
              SCREAMING_SNAKE_CASE_,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
          UpperCamelCase   :  Any						     =     os.path.join(
              SCREAMING_SNAKE_CASE_,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']	)

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

          UpperCamelCase   :  Dict						     =     0
          with open(SCREAMING_SNAKE_CASE_,      '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 SCREAMING_SNAKE_CASE_	: 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   :  Tuple						     =     token_index
                writer.write(' '.join(SCREAMING_SNAKE_CASE_	) + '\n'	)
                index += 1

          return vocab_file, merge_file
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None	)					->		List[int]:
          if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          UpperCamelCase   :  Any						     =     [self.cls_token_id]
          UpperCamelCase   :  int						     =     [self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = False	)					->		List[int]:
          if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=SCREAMING_SNAKE_CASE_,      token_ids_a=SCREAMING_SNAKE_CASE_,      already_has_special_tokens=SCREAMING_SNAKE_CASE_	)

          if token_ids_a is None:
             return [1] + ([0] * len(SCREAMING_SNAKE_CASE_	)) + [1]
          return [1] + ([0] * len(SCREAMING_SNAKE_CASE_	)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_	)) + [1]
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None	)					->		List[int]:
          UpperCamelCase   :  List[Any]						     =     [self.sep_token_id]
          UpperCamelCase   :  Dict						     =     [self.cls_token_id]

          if token_ids_a is None:
             return len(cls + token_ids_a + sep	) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=False,      **SCREAMING_SNAKE_CASE_	)					->		Optional[int]:
          UpperCamelCase   :  Tuple						     =     kwargs.pop('add_prefix_space',      self.add_prefix_space	)
          if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE_	) > 0 and not text[0].isspace()):
             UpperCamelCase   :  Dict						     =     ' ' + text
          return (text, kwargs)








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

   return image
def 		UpperCamelCase     (       snake_case__	:   int     )     ->					List[Any]:
   UpperCamelCase   :  Optional[int]						     =     []
   # 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     (       snake_case__	:   Optional[Any]	,					snake_case__	:   Dict	,					snake_case__	:   Optional[int]     )     ->					Optional[int]:
   UpperCamelCase   :  Dict						     =     dct.pop(snake_case__     )
   UpperCamelCase   :  str						     =     val
def 		UpperCamelCase     (       snake_case__	:   str	,					snake_case__	:   Union[str, Any]     )     ->					Optional[int]:
   for i in range(config.vision_config.num_hidden_layers     ):
      # read in original q and v biases
      UpperCamelCase   :  Optional[Any]						     =     state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""     )
      UpperCamelCase   :  int						     =     state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""     )

      # next, set bias in the state dict
      UpperCamelCase   :  int						     =     torch.cat((q_bias, torch.zeros_like(snake_case__	,					requires_grad=snake_case__     ), v_bias)     )
      UpperCamelCase   :  Tuple						     =     qkv_bias
def 		UpperCamelCase     (       snake_case__	:   Tuple	,					snake_case__	:   Optional[Any]     )     ->					Dict:
   UpperCamelCase   :  str						     =     364 if 'coco' in model_name else 224
   UpperCamelCase   :  Union[str, Any]						     =     BlipaVisionConfig(image_size=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   :  List[Any]						     =     OPTConfig.from_pretrained('facebook/opt-2.7b'	,					eos_token_id=snake_case__     ).to_dict()
   elif "opt-6.7b" in model_name:
      UpperCamelCase   :  int						     =     OPTConfig.from_pretrained('facebook/opt-6.7b'	,					eos_token_id=snake_case__     ).to_dict()
   elif "t5-xl" in model_name:
      UpperCamelCase   :  List[str]						     =     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   :  Any						     =     BlipaConfig(vision_config=snake_case__	,					text_config=snake_case__     )

   return config, image_size
@torch.no_grad()
def 		UpperCamelCase     (       snake_case__	:   int	,					snake_case__	:   Dict=None	,					snake_case__	:   int=False     )     ->					List[Any]:
   UpperCamelCase   :  str						     =     (
       AutoTokenizer.from_pretrained('facebook/opt-2.7b'     )
       if 'opt' in model_name
       else AutoTokenizer.from_pretrained('google/flan-t5-xl'     )
   )
   UpperCamelCase   :  int						     =     tokenizer('\n'	,					add_special_tokens=snake_case__     ).input_ids[0]
   UpperCamelCase     ,					UpperCamelCase   :  Union[str, Any]						     =     get_blipa_config(snake_case__	,					eos_token_id=snake_case__     )

   UpperCamelCase   :  Dict						     =     BlipaForConditionalGeneration(snake_case__     ).eval()

   UpperCamelCase   :  Optional[Any]						     =     {
       '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   :  Optional[Any]						     =     model_name_to_original[model_name]

   # load original model
   print('Loading original model...'     )
   UpperCamelCase   :  List[str]						     =     'cuda' if torch.cuda.is_available() else 'cpu'
   UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  Tuple						     =     load_model_and_preprocess(
       name=snake_case__	,					model_type=snake_case__	,					is_eval=snake_case__	,					device=snake_case__     )
   original_model.eval()
   print('Done!'     )

   # update state dict keys
   UpperCamelCase   :  List[Any]						     =     original_model.state_dict()
   UpperCamelCase   :  Tuple						     =     create_rename_keys(snake_case__     )
   for src, dest in rename_keys:
      rename_key(snake_case__	,					snake_case__	,					snake_case__     )

   # some keys can be renamed efficiently
   for key, val in state_dict.copy().items():
      UpperCamelCase   :  Optional[Any]						     =     state_dict.pop(snake_case__     )
      if key.startswith('Qformer.bert'     ):
         UpperCamelCase   :  List[str]						     =     key.replace('Qformer.bert'	,					'qformer'     )
      if "attention.self" in key:
         UpperCamelCase   :  Tuple						     =     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   :  Dict						     =     key.replace('t5'	,					'language'     )
      UpperCamelCase   :  Optional[int]						     =     val

   # read in qv biases
   read_in_q_v_bias(snake_case__	,					snake_case__     )

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

   UpperCamelCase   :  List[str]						     =     load_demo_image()
   UpperCamelCase   :  str						     =     vis_processors['eval'](snake_case__     ).unsqueeze(0     ).to(snake_case__     )
   UpperCamelCase   :  Any						     =     tokenizer(['\n']	,					return_tensors='pt'     ).input_ids.to(snake_case__     )

   # create processor
   UpperCamelCase   :  Optional[Any]						     =     BlipImageProcessor(
       size={'height': image_size, 'width': image_size}	,					image_mean=snake_case__	,					image_std=snake_case__     )
   UpperCamelCase   :  Any						     =     BlipaProcessor(image_processor=snake_case__	,					tokenizer=snake_case__     )
   UpperCamelCase   :  Optional[int]						     =     processor(images=snake_case__	,					return_tensors='pt'     ).pixel_values.to(snake_case__     )

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

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

   print('Generating a caption...'     )
   UpperCamelCase   :  Optional[int]						     =     ''
   UpperCamelCase   :  Union[str, Any]						     =     tokenizer(snake_case__	,					return_tensors='pt'     ).input_ids.to(snake_case__     )

   UpperCamelCase   :  str						     =     original_model.generate({'image': original_pixel_values}     )
   UpperCamelCase   :  str						     =     hf_model.generate(
       snake_case__	,					snake_case__	,					do_sample=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:'	,					snake_case__     )
   UpperCamelCase   :  Optional[int]						     =     input_ids.shape[1]
   UpperCamelCase   :  Union[str, Any]						     =     processor.batch_decode(outputs[:, prompt_length:]	,					skip_special_tokens=snake_case__     )
   UpperCamelCase   :  Dict						     =     [text.strip() for text in output_text]
   print('HF generation:'	,					snake_case__     )

   if pytorch_dump_folder_path is not None:
      processor.save_pretrained(snake_case__     )
      hf_model.save_pretrained(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__":
    __UpperCAmelCase	       =	argparse.ArgumentParser()
    __UpperCAmelCase	       =	[
        '''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	       =	parser.parse_args()

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



