"""simple docstring"""






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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_nllb import NllbTokenizer
else:
	_A   					=    None


_A   					=    logging.get_logger(__name__)


_A   					=    {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_A   					=    {
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
        ),
    },
}

_A   					=    {
    'facebook/nllb-large-en-ro': 1_0_2_4,
    'facebook/nllb-200-distilled-600M': 1_0_2_4,
}

# fmt: off
_A   					=    ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']

class lowerCamelCase		(a__      ):






					'''simple docstring'''


					a   	=		VOCAB_FILES_NAMES
					a   	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a   	=		PRETRAINED_VOCAB_FILES_MAP
					a   	=		["""input_ids""", """attention_mask"""]
					a   	=		NllbTokenizer

					a   	=		[]
					a   	=		[]



					def __init__(						self			:				List[Any]   , _snake_case			:				str=None   , _snake_case			:				Tuple=None   , _snake_case			:				List[str]="<s>"   , _snake_case			:				Optional[int]="</s>"   , _snake_case			:				Any="</s>"   , _snake_case			:				Optional[int]="<s>"   , _snake_case			:				str="<unk>"   , _snake_case			:				Any="<pad>"   , _snake_case			:				Optional[Any]="<mask>"   , _snake_case			:				List[Any]=None   , _snake_case			:				Tuple=None   , _snake_case			:				List[Any]=None   , _snake_case			:				Optional[int]=False   , **_snake_case			:				Optional[Any]   , )							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				AddedToken(lowercase__   , lstrip=lowercase__   , rstrip=lowercase__		) if isinstance(lowercase__   , lowercase__		) else mask_token
									SCREAMING_SNAKE_CASE__		  =				legacy_behaviour
									super().__init__(
									    vocab_file=lowercase__   , tokenizer_file=lowercase__   , bos_token=lowercase__   , eos_token=lowercase__   , sep_token=lowercase__   , cls_token=lowercase__   , unk_token=lowercase__   , pad_token=lowercase__   , mask_token=lowercase__   , src_lang=lowercase__   , tgt_lang=lowercase__   , additional_special_tokens=lowercase__   , legacy_behaviour=lowercase__   , **lowercase__   , )

									SCREAMING_SNAKE_CASE__		  =				vocab_file
									SCREAMING_SNAKE_CASE__		  =				False if not self.vocab_file else True

									SCREAMING_SNAKE_CASE__		  =				FAIRSEQ_LANGUAGE_CODES.copy()

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

									self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}		)
									SCREAMING_SNAKE_CASE__		  =				{
									    lang_code: self.convert_tokens_to_ids(lowercase__		) for lang_code in FAIRSEQ_LANGUAGE_CODES
									}

									SCREAMING_SNAKE_CASE__		  =				src_lang if src_lang is not None else '''eng_Latn'''
									SCREAMING_SNAKE_CASE__		  =				self.convert_tokens_to_ids(self._src_lang		)
									SCREAMING_SNAKE_CASE__		  =				tgt_lang
									self.set_src_lang_special_tokens(self._src_lang		)



					@property
					def 			lowerCAmelCase_					(						self			:				int		)							->       str:
									return self._src_lang



					@src_lang.setter
					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				Any		)							->       None:
									SCREAMING_SNAKE_CASE__		  =				new_src_lang
									self.set_src_lang_special_tokens(self._src_lang		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				Union[str, Any]   , _snake_case			:				Optional[int] = 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 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				List[Any]   , _snake_case			:				Dict = None		)							->       List[int]:
									SCREAMING_SNAKE_CASE__		  =				[self.sep_token_id]
									SCREAMING_SNAKE_CASE__		  =				[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 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				Tuple   , _snake_case			:				Any   , _snake_case			:				Optional[int]   , _snake_case			:				List[str]   , **_snake_case			:				List[str]		)							->       Any:





									if src_lang is None or tgt_lang is None:
													raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"		)
									SCREAMING_SNAKE_CASE__		  =				src_lang
									SCREAMING_SNAKE_CASE__		  =				self(lowercase__   , add_special_tokens=lowercase__   , return_tensors=lowercase__   , **lowercase__		)
									SCREAMING_SNAKE_CASE__		  =				self.convert_tokens_to_ids(lowercase__		)
									SCREAMING_SNAKE_CASE__		  =				tgt_lang_id
									return inputs



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				List[str]   , _snake_case			:				str = "eng_Latn"   , _snake_case			:				str = None   , _snake_case			:				Tuple = "fra_Latn"   , **_snake_case			:				int   , )							->       BatchEncoding:
									SCREAMING_SNAKE_CASE__		  =				src_lang
									SCREAMING_SNAKE_CASE__		  =				tgt_lang
									return super().prepare_seqaseq_batch(lowercase__   , lowercase__   , **lowercase__		)



					def 			lowerCAmelCase_					(						self			:				str		)							->       Optional[Any]:
									return self.set_src_lang_special_tokens(self.src_lang		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       int:
									return self.set_tgt_lang_special_tokens(self.tgt_lang		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Optional[Any]		)							->       None:
									SCREAMING_SNAKE_CASE__		  =				self.convert_tokens_to_ids(lowercase__		)

									if self.legacy_behaviour:
													SCREAMING_SNAKE_CASE__		  =				[]
													SCREAMING_SNAKE_CASE__		  =				[self.eos_token_id, self.cur_lang_code]
									else:
													SCREAMING_SNAKE_CASE__		  =				[self.cur_lang_code]
													SCREAMING_SNAKE_CASE__		  =				[self.eos_token_id]

									SCREAMING_SNAKE_CASE__		  =				self.convert_ids_to_tokens(self.prefix_tokens		)
									SCREAMING_SNAKE_CASE__		  =				self.convert_ids_to_tokens(self.suffix_tokens		)

									SCREAMING_SNAKE_CASE__		  =				processors.TemplateProcessing(
									    single=prefix_tokens_str + ["$A"] + suffix_tokens_str   , pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str   , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   , self.prefix_tokens + self.suffix_tokens		)		)   , )



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				List[str]		)							->       None:
									SCREAMING_SNAKE_CASE__		  =				self.convert_tokens_to_ids(lowercase__		)
									if self.legacy_behaviour:
													SCREAMING_SNAKE_CASE__		  =				[]
													SCREAMING_SNAKE_CASE__		  =				[self.eos_token_id, self.cur_lang_code]
									else:
													SCREAMING_SNAKE_CASE__		  =				[self.cur_lang_code]
													SCREAMING_SNAKE_CASE__		  =				[self.eos_token_id]

									SCREAMING_SNAKE_CASE__		  =				self.convert_ids_to_tokens(self.prefix_tokens		)
									SCREAMING_SNAKE_CASE__		  =				self.convert_ids_to_tokens(self.suffix_tokens		)

									SCREAMING_SNAKE_CASE__		  =				processors.TemplateProcessing(
									    single=prefix_tokens_str + ["$A"] + suffix_tokens_str   , pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str   , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   , self.prefix_tokens + self.suffix_tokens		)		)   , )



					def 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				int   , _snake_case			:				Optional[Any] = None		)							->       Tuple[str]:





									if not self.can_save_slow_tokenizer:
													raise ValueError(
													    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
													    "tokenizer."		)

									if not os.path.isdir(lowercase__		):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""		)
													return
									SCREAMING_SNAKE_CASE__		  =				os.path.join(
									    lowercase__   , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

									if os.path.abspath(self.vocab_file		) != os.path.abspath(lowercase__		):
													copyfile(self.vocab_file   , lowercase__		)

									return (out_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 						SCREAMING_SNAKE_CASE					(  )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				"https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
				SCREAMING_SNAKE_CASE__		  =				Image.open(requests.get(__UpperCAmelCase  ,					stream=__UpperCAmelCase       ).raw       ).convert("RGB"       )

				return image




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   int:
				SCREAMING_SNAKE_CASE__		  =				[]
				# 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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   int:
				SCREAMING_SNAKE_CASE__		  =				dct.pop(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				val




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Dict:
				for i in range(config.vision_config.num_hidden_layers       ):
								# read in original q and v biases
								SCREAMING_SNAKE_CASE__		  =				state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""       )
								SCREAMING_SNAKE_CASE__		  =				state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""       )

								# next, set bias in the state dict
								SCREAMING_SNAKE_CASE__		  =				torch.cat((q_bias, torch.zeros_like(__UpperCAmelCase  ,					requires_grad=__UpperCAmelCase       ), v_bias)       )
								SCREAMING_SNAKE_CASE__		  =				qkv_bias




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Tuple:
				SCREAMING_SNAKE_CASE__		  =				364 if "coco" in model_name else 224
				SCREAMING_SNAKE_CASE__		  =				BlipaVisionConfig(image_size=__UpperCAmelCase       ).to_dict()

				# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
				# seems like flan-T5 models don't have bos_token_id properly set?
				if "opt-2.7b" in model_name:
								SCREAMING_SNAKE_CASE__		  =				OPTConfig.from_pretrained("facebook/opt-2.7b"  ,					eos_token_id=__UpperCAmelCase       ).to_dict()
				elif "opt-6.7b" in model_name:
								SCREAMING_SNAKE_CASE__		  =				OPTConfig.from_pretrained("facebook/opt-6.7b"  ,					eos_token_id=__UpperCAmelCase       ).to_dict()
				elif "t5-xl" in model_name:
								SCREAMING_SNAKE_CASE__		  =				TaConfig.from_pretrained("google/flan-t5-xl"  ,					dense_act_fn="gelu"  ,					bos_token_id=1       ).to_dict()
				elif "t5-xxl" in model_name:
								SCREAMING_SNAKE_CASE__		  =				TaConfig.from_pretrained("google/flan-t5-xxl"  ,					dense_act_fn="gelu"  ,					bos_token_id=1       ).to_dict()

				SCREAMING_SNAKE_CASE__		  =				BlipaConfig(vision_config=__UpperCAmelCase  ,					text_config=__UpperCAmelCase       )

				return config, image_size




@torch.no_grad()
def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=False       )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				(
				    AutoTokenizer.from_pretrained("facebook/opt-2.7b"       )
				    if "opt" in model_name
				    else AutoTokenizer.from_pretrained("google/flan-t5-xl"       )
				)
				SCREAMING_SNAKE_CASE__		  =				tokenizer("\n"  ,					add_special_tokens=__UpperCAmelCase       ).input_ids[0]
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				get_blipa_config(__UpperCAmelCase  ,					eos_token_id=__UpperCAmelCase       )

				SCREAMING_SNAKE_CASE__		  =				BlipaForConditionalGeneration(__UpperCAmelCase       ).eval()

				SCREAMING_SNAKE_CASE__		  =				{
				    "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"),
				}

				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				model_name_to_original[model_name]

				# load original model
				print("Loading original model..."       )
				SCREAMING_SNAKE_CASE__		  =				"cuda" if torch.cuda.is_available() else "cpu"
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				load_model_and_preprocess(
				    name=__UpperCAmelCase  ,					model_type=__UpperCAmelCase  ,					is_eval=__UpperCAmelCase  ,					device=__UpperCAmelCase       )
				original_model.eval()
				print("Done!"       )

				# update state dict keys
				SCREAMING_SNAKE_CASE__		  =				original_model.state_dict()
				SCREAMING_SNAKE_CASE__		  =				create_rename_keys(__UpperCAmelCase       )
				for src, dest in rename_keys:
								rename_key(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )

				# some keys can be renamed efficiently
				for key, val in state_dict.copy().items():
								SCREAMING_SNAKE_CASE__		  =				state_dict.pop(__UpperCAmelCase       )
								if key.startswith("Qformer.bert"       ):
												SCREAMING_SNAKE_CASE__		  =				key.replace("Qformer.bert"  ,					"qformer"       )
								if "attention.self" in key:
												SCREAMING_SNAKE_CASE__		  =				key.replace("self"  ,					"attention"       )
								if "opt_proj" in key:
												SCREAMING_SNAKE_CASE__		  =				key.replace("opt_proj"  ,					"language_projection"       )
								if "t5_proj" in key:
												SCREAMING_SNAKE_CASE__		  =				key.replace("t5_proj"  ,					"language_projection"       )
								if key.startswith("opt"       ):
												SCREAMING_SNAKE_CASE__		  =				key.replace("opt"  ,					"language"       )
								if key.startswith("t5"       ):
												SCREAMING_SNAKE_CASE__		  =				key.replace("t5"  ,					"language"       )
								SCREAMING_SNAKE_CASE__		  =				val

				# read in qv biases
				read_in_q_v_bias(__UpperCAmelCase  ,					__UpperCAmelCase       )

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

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

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

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

				original_model.to(__UpperCAmelCase       )
				hf_model.to(__UpperCAmelCase       )
				with torch.no_grad():
								if "opt" in model_name:
												SCREAMING_SNAKE_CASE__		  =				original_model({"image": original_pixel_values, "text_input": [""]}       ).logits
												SCREAMING_SNAKE_CASE__		  =				hf_model(__UpperCAmelCase  ,					__UpperCAmelCase       ).logits
								else:
												SCREAMING_SNAKE_CASE__		  =				original_model(
												    {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}       ).logits
												SCREAMING_SNAKE_CASE__		  =				input_ids.masked_fill(input_ids == tokenizer.pad_token_id  ,					-100       )
												SCREAMING_SNAKE_CASE__		  =				hf_model(__UpperCAmelCase  ,					__UpperCAmelCase  ,					labels=__UpperCAmelCase       ).logits

				assert original_logits.shape == logits.shape
				print("First values of original logits:"  ,					original_logits[0, :3, :3]       )
				print("First values of HF logits:"  ,					logits[0, :3, :3]       )

				# assert values
				if model_name == "blip2-flan-t5-xl":
								SCREAMING_SNAKE_CASE__		  =				torch.tensor(
								    [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]]  ,					device=__UpperCAmelCase       )
								assert torch.allclose(logits[0, :3, :3]  ,					__UpperCAmelCase  ,					atol=1e-4       )
				elif model_name == "blip2-flan-t5-xl-coco":
								SCREAMING_SNAKE_CASE__		  =				torch.tensor(
								    [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]]  ,					device=__UpperCAmelCase       )
				else:
								# cast to same type
								SCREAMING_SNAKE_CASE__		  =				logits.dtype
								assert torch.allclose(original_logits.to(__UpperCAmelCase       )  ,					__UpperCAmelCase  ,					atol=1e-2       )
				print("Looks ok!"       )

				print("Generating a caption..."       )
				SCREAMING_SNAKE_CASE__		  =				""
				SCREAMING_SNAKE_CASE__		  =				tokenizer(__UpperCAmelCase  ,					return_tensors="pt"       ).input_ids.to(__UpperCAmelCase       )

				SCREAMING_SNAKE_CASE__		  =				original_model.generate({"image": original_pixel_values}       )
				SCREAMING_SNAKE_CASE__		  =				hf_model.generate(
				    __UpperCAmelCase  ,					__UpperCAmelCase  ,					do_sample=__UpperCAmelCase  ,					num_beams=5  ,					max_length=30  ,					min_length=1  ,					top_p=0.9  ,					repetition_penalty=1.0  ,					length_penalty=1.0  ,					temperature=1  ,					)
				print("Original generation:"  ,					__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				input_ids.shape[1]
				SCREAMING_SNAKE_CASE__		  =				processor.batch_decode(outputs[:, prompt_length:]  ,					skip_special_tokens=__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				[text.strip() for text in output_text]
				print("HF generation:"  ,					__UpperCAmelCase       )

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

				if push_to_hub:
								processor.push_to_hub(F"""nielsr/{model_name}"""       )
								hf_model.push_to_hub(F"""nielsr/{model_name}"""       )


if __name__ == "__main__":
						_A   					=    argparse.ArgumentParser()
						_A   					=    [
						    'blip2-opt-2.7b',
						    'blip2-opt-6.7b',
						    'blip2-opt-2.7b-coco',
						    'blip2-opt-6.7b-coco',
						    'blip2-flan-t5-xl',
						    'blip2-flan-t5-xl-coco',
						    'blip2-flan-t5-xxl',
						]
						parser.add_argument(
						    '--model_name',
						    default='blip2-opt-2.7b',
						    choices=choices,
						    type=str,
						    help='Path to hf config.json of model to convert',
						)
						parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument(
						    '--push_to_hub',
						    action='store_true',
						    help='Whether to push the model and processor to the hub after converting',
						)

						_A   					=    parser.parse_args()

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




