



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


__UpperCAmelCase	       =	logging.get_logger(__name__)

__UpperCAmelCase	       =	'''▁'''

__UpperCAmelCase	       =	{'''vocab_file''': '''sentencepiece.bpe.model'''}

__UpperCAmelCase	       =	{
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

__UpperCAmelCase	       =	{
    '''facebook/mbart-large-en-ro''': 1_024,
    '''facebook/mbart-large-cc25''': 1_024,
}

# fmt: off
__UpperCAmelCase	       =	['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']



class     lowerCAmelCase_		(  a__							):
       UpperCAmelCase__    : Tuple       					=  VOCAB_FILES_NAMES
       UpperCAmelCase__    : List[Any]       					=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCAmelCase__    : List[str]       					=  PRETRAINED_VOCAB_FILES_MAP
       UpperCAmelCase__    : Optional[int]       					=  ["input_ids", "attention_mask"]

       UpperCAmelCase__    : List[int]       					=  []
       UpperCAmelCase__    : List[int]       					=  []
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      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_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_=None,      **SCREAMING_SNAKE_CASE_,      )					->		Optional[Any]:
          # Mask token behave like a normal word, i.e. include the space before it
          UpperCamelCase   :  List[Any]						     =     AddedToken(SCREAMING_SNAKE_CASE_,      lstrip=SCREAMING_SNAKE_CASE_,      rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	) else mask_token

          UpperCamelCase   :  List[Any]						     =     {} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              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_,      tokenizer_file=SCREAMING_SNAKE_CASE_,      src_lang=SCREAMING_SNAKE_CASE_,      tgt_lang=SCREAMING_SNAKE_CASE_,      additional_special_tokens=SCREAMING_SNAKE_CASE_,      sp_model_kwargs=self.sp_model_kwargs,      **SCREAMING_SNAKE_CASE_,      )

          UpperCamelCase   :  Tuple						     =     spm.SentencePieceProcessor(**self.sp_model_kwargs	)
          self.sp_model.Load(str(SCREAMING_SNAKE_CASE_	)	)
          UpperCamelCase   :  int						     =     vocab_file

          # Original fairseq vocab and spm vocab must be "aligned":
          # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
          # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
          # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
          # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

          # Mimic fairseq token-to-id alignment for the first 4 token
          UpperCamelCase   :  Any						     =     {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

          # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
          UpperCamelCase   :  List[Any]						     =     1

          UpperCamelCase   :  Optional[int]						     =     len(self.sp_model	)
          UpperCamelCase   :  int						     =     {
              code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(SCREAMING_SNAKE_CASE_	)
          }
          UpperCamelCase   :  List[Any]						     =     {v: k for k, v in self.lang_code_to_id.items()}
          UpperCamelCase   :  Tuple						     =     len(self.sp_model	) + len(self.lang_code_to_id	) + self.fairseq_offset

          self.fairseq_tokens_to_ids.update(self.lang_code_to_id	)
          UpperCamelCase   :  Union[str, Any]						     =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}
          UpperCamelCase   :  str						     =     list(self.lang_code_to_id.keys()	)

          if additional_special_tokens is not None:
             # Only add those special tokens if they are not already there.
             self._additional_special_tokens.extend(
                 [t for t in additional_special_tokens if t not in self._additional_special_tokens]	)

          UpperCamelCase   :  Union[str, Any]						     =     src_lang if src_lang is not None else 'en_XX'
          UpperCamelCase   :  Union[str, Any]						     =     self.lang_code_to_id[self._src_lang]
          UpperCamelCase   :  List[str]						     =     tgt_lang
          self.set_src_lang_special_tokens(self._src_lang	)
       def __getstate__(      self	)					->		List[str]:
          UpperCamelCase   :  Union[str, Any]						     =     self.__dict__.copy()
          UpperCamelCase   :  Tuple						     =     None
          UpperCamelCase   :  List[Any]						     =     self.sp_model.serialized_model_proto()
          return state
       def __setstate__(      self,      SCREAMING_SNAKE_CASE_	)					->		Optional[int]:
          UpperCamelCase   :  Any						     =     d

          # for backward compatibility
          if not hasattr(self,      'sp_model_kwargs'	):
             UpperCamelCase   :  Optional[Any]						     =     {}

          UpperCamelCase   :  Optional[int]						     =     spm.SentencePieceProcessor(**self.sp_model_kwargs	)
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)
       @property
       def  snake_case_	(      self	)					->		Optional[int]:
          return len(self.sp_model	) + len(self.lang_code_to_id	) + self.fairseq_offset + 1  # Plus 1 for the mask token
       @property
       def  snake_case_	(      self	)					->		str:
          return self._src_lang
       @src_lang.setter
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		None:
          UpperCamelCase   :  Union[str, Any]						     =     new_src_lang
          self.set_src_lang_special_tokens(self._src_lang	)
       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_	)

          UpperCamelCase   :  Any						     =     [1] * len(self.prefix_tokens	)
          UpperCamelCase   :  List[Any]						     =     [1] * len(self.suffix_tokens	)
          if token_ids_a is None:
             return prefix_ones + ([0] * len(SCREAMING_SNAKE_CASE_	)) + suffix_ones
          return prefix_ones + ([0] * len(SCREAMING_SNAKE_CASE_	)) + ([0] * len(SCREAMING_SNAKE_CASE_	)) + suffix_ones
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None	)					->		List[int]:
          if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = None	)					->		List[int]:
          UpperCamelCase   :  List[str]						     =     [self.sep_token_id]
          UpperCamelCase   :  Tuple						     =     [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_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		Dict:
          if src_lang is None or tgt_lang is None:
             raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'	)
          UpperCamelCase   :  Optional[Any]						     =     src_lang
          UpperCamelCase   :  Any						     =     self(SCREAMING_SNAKE_CASE_,      add_special_tokens=SCREAMING_SNAKE_CASE_,      return_tensors=SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     tgt_lang_id
          return inputs
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  Union[str, Any]						     =     {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	): i for i in range(self.vocab_size	)}
          vocab.update(self.added_tokens_encoder	)
          return vocab
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		List[str]:
          return self.sp_model.encode(SCREAMING_SNAKE_CASE_,      out_type=SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		List[Any]:
          if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
          UpperCamelCase   :  List[Any]						     =     self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_	)

          # Need to return unknown token if the SP model returned 0
          return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(index - self.fairseq_offset	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		Dict:
          UpperCamelCase   :  Optional[int]						     =     ''.join(SCREAMING_SNAKE_CASE_	).replace(SCREAMING_SNAKE_CASE_,      ' '	).strip()
          return out_string
       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   :  Union[str, Any]						     =     os.path.join(
              SCREAMING_SNAKE_CASE_,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

          if os.path.abspath(self.vocab_file	) != os.path.abspath(SCREAMING_SNAKE_CASE_	) and os.path.isfile(self.vocab_file	):
             copyfile(self.vocab_file,      SCREAMING_SNAKE_CASE_	)
          elif not os.path.isfile(self.vocab_file	):
             with open(SCREAMING_SNAKE_CASE_,      'wb'	) as fi:
                UpperCamelCase   :  List[Any]						     =     self.sp_model.serialized_model_proto()
                fi.write(SCREAMING_SNAKE_CASE_	)

          return (out_vocab_file,)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ = "en_XX",      SCREAMING_SNAKE_CASE_ = None,      SCREAMING_SNAKE_CASE_ = "ro_RO",      **SCREAMING_SNAKE_CASE_,      )					->		BatchEncoding:
          UpperCamelCase   :  Optional[Any]						     =     src_lang
          UpperCamelCase   :  Any						     =     tgt_lang
          return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self	)					->		Tuple:
          return self.set_src_lang_special_tokens(self.src_lang	)
       def  snake_case_	(      self	)					->		str:
          return self.set_tgt_lang_special_tokens(self.tgt_lang	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		None:
          UpperCamelCase   :  Union[str, Any]						     =     self.lang_code_to_id[src_lang]
          UpperCamelCase   :  Optional[Any]						     =     []
          UpperCamelCase   :  List[Any]						     =     [self.eos_token_id, self.cur_lang_code]






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		None:
          UpperCamelCase   :  Dict						     =     self.lang_code_to_id[lang]
          UpperCamelCase   :  Optional[Any]						     =     []
          UpperCamelCase   :  Any						     =     [self.eos_token_id, self.cur_lang_code]







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)



