



from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


_lowercase    	=		logging.get_logger(__name__)


_lowercase    	=		OrderedDict(
    [
        # Base model mapping
        ("albert", "FlaxAlbertModel"),
        ("bart", "FlaxBartModel"),
        ("beit", "FlaxBeitModel"),
        ("bert", "FlaxBertModel"),
        ("big_bird", "FlaxBigBirdModel"),
        ("blenderbot", "FlaxBlenderbotModel"),
        ("blenderbot-small", "FlaxBlenderbotSmallModel"),
        ("clip", "FlaxCLIPModel"),
        ("distilbert", "FlaxDistilBertModel"),
        ("electra", "FlaxElectraModel"),
        ("gpt-sw3", "FlaxGPT2Model"),
        ("gpt2", "FlaxGPT2Model"),
        ("gpt_neo", "FlaxGPTNeoModel"),
        ("gptj", "FlaxGPTJModel"),
        ("longt5", "FlaxLongT5Model"),
        ("marian", "FlaxMarianModel"),
        ("mbart", "FlaxMBartModel"),
        ("mt5", "FlaxMT5Model"),
        ("opt", "FlaxOPTModel"),
        ("pegasus", "FlaxPegasusModel"),
        ("regnet", "FlaxRegNetModel"),
        ("resnet", "FlaxResNetModel"),
        ("roberta", "FlaxRobertaModel"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
        ("roformer", "FlaxRoFormerModel"),
        ("t5", "FlaxT5Model"),
        ("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
        ("vit", "FlaxViTModel"),
        ("wav2vec2", "FlaxWav2Vec2Model"),
        ("whisper", "FlaxWhisperModel"),
        ("xglm", "FlaxXGLMModel"),
        ("xlm-roberta", "FlaxXLMRobertaModel"),
    ]
)

_lowercase    	=		OrderedDict(
    [
        # Model for pre-training mapping
        ("albert", "FlaxAlbertForPreTraining"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForPreTraining"),
        ("big_bird", "FlaxBigBirdForPreTraining"),
        ("electra", "FlaxElectraForPreTraining"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("t5", "FlaxT5ForConditionalGeneration"),
        ("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

_lowercase    	=		OrderedDict(
    [
        # Model for Masked LM mapping
        ("albert", "FlaxAlbertForMaskedLM"),
        ("bart", "FlaxBartForConditionalGeneration"),
        ("bert", "FlaxBertForMaskedLM"),
        ("big_bird", "FlaxBigBirdForMaskedLM"),
        ("distilbert", "FlaxDistilBertForMaskedLM"),
        ("electra", "FlaxElectraForMaskedLM"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("roberta", "FlaxRobertaForMaskedLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
        ("roformer", "FlaxRoFormerForMaskedLM"),
        ("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
    ]
)

_lowercase    	=		OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ("bart", "FlaxBartForConditionalGeneration"),
        ("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
        ("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
        ("encoder-decoder", "FlaxEncoderDecoderModel"),
        ("longt5", "FlaxLongT5ForConditionalGeneration"),
        ("marian", "FlaxMarianMTModel"),
        ("mbart", "FlaxMBartForConditionalGeneration"),
        ("mt5", "FlaxMT5ForConditionalGeneration"),
        ("pegasus", "FlaxPegasusForConditionalGeneration"),
        ("t5", "FlaxT5ForConditionalGeneration"),
    ]
)

_lowercase    	=		OrderedDict(
    [
        # Model for Image-classsification
        ("beit", "FlaxBeitForImageClassification"),
        ("regnet", "FlaxRegNetForImageClassification"),
        ("resnet", "FlaxResNetForImageClassification"),
        ("vit", "FlaxViTForImageClassification"),
    ]
)

_lowercase    	=		OrderedDict(
    [
        ("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
    ]
)

_lowercase    	=		OrderedDict(
    [
        # Model for Causal LM mapping
        ("bart", "FlaxBartForCausalLM"),
        ("bert", "FlaxBertForCausalLM"),
        ("big_bird", "FlaxBigBirdForCausalLM"),
        ("electra", "FlaxElectraForCausalLM"),
        ("gpt-sw3", "FlaxGPT2LMHeadModel"),
        ("gpt2", "FlaxGPT2LMHeadModel"),
        ("gpt_neo", "FlaxGPTNeoForCausalLM"),
        ("gptj", "FlaxGPTJForCausalLM"),
        ("opt", "FlaxOPTForCausalLM"),
        ("roberta", "FlaxRobertaForCausalLM"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
        ("xglm", "FlaxXGLMForCausalLM"),
        ("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
    ]
)

_lowercase    	=		OrderedDict(
    [
        # Model for Sequence Classification mapping
        ("albert", "FlaxAlbertForSequenceClassification"),
        ("bart", "FlaxBartForSequenceClassification"),
        ("bert", "FlaxBertForSequenceClassification"),
        ("big_bird", "FlaxBigBirdForSequenceClassification"),
        ("distilbert", "FlaxDistilBertForSequenceClassification"),
        ("electra", "FlaxElectraForSequenceClassification"),
        ("mbart", "FlaxMBartForSequenceClassification"),
        ("roberta", "FlaxRobertaForSequenceClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
        ("roformer", "FlaxRoFormerForSequenceClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
    ]
)

_lowercase    	=		OrderedDict(
    [
        # Model for Question Answering mapping
        ("albert", "FlaxAlbertForQuestionAnswering"),
        ("bart", "FlaxBartForQuestionAnswering"),
        ("bert", "FlaxBertForQuestionAnswering"),
        ("big_bird", "FlaxBigBirdForQuestionAnswering"),
        ("distilbert", "FlaxDistilBertForQuestionAnswering"),
        ("electra", "FlaxElectraForQuestionAnswering"),
        ("mbart", "FlaxMBartForQuestionAnswering"),
        ("roberta", "FlaxRobertaForQuestionAnswering"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
        ("roformer", "FlaxRoFormerForQuestionAnswering"),
        ("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
    ]
)

_lowercase    	=		OrderedDict(
    [
        # Model for Token Classification mapping
        ("albert", "FlaxAlbertForTokenClassification"),
        ("bert", "FlaxBertForTokenClassification"),
        ("big_bird", "FlaxBigBirdForTokenClassification"),
        ("distilbert", "FlaxDistilBertForTokenClassification"),
        ("electra", "FlaxElectraForTokenClassification"),
        ("roberta", "FlaxRobertaForTokenClassification"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
        ("roformer", "FlaxRoFormerForTokenClassification"),
        ("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
    ]
)

_lowercase    	=		OrderedDict(
    [
        # Model for Multiple Choice mapping
        ("albert", "FlaxAlbertForMultipleChoice"),
        ("bert", "FlaxBertForMultipleChoice"),
        ("big_bird", "FlaxBigBirdForMultipleChoice"),
        ("distilbert", "FlaxDistilBertForMultipleChoice"),
        ("electra", "FlaxElectraForMultipleChoice"),
        ("roberta", "FlaxRobertaForMultipleChoice"),
        ("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
        ("roformer", "FlaxRoFormerForMultipleChoice"),
        ("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
    ]
)

_lowercase    	=		OrderedDict(
    [
        ("bert", "FlaxBertForNextSentencePrediction"),
    ]
)

_lowercase    	=		OrderedDict(
    [
        ("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
        ("whisper", "FlaxWhisperForConditionalGeneration"),
    ]
)

_lowercase    	=		OrderedDict(
    [
        ("whisper", "FlaxWhisperForAudioClassification"),
    ]
)

_lowercase    	=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
_lowercase    	=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
_lowercase    	=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
_lowercase    	=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
_lowercase    	=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
_lowercase    	=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
_lowercase    	=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
_lowercase    	=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
_lowercase    	=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
_lowercase    	=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
_lowercase    	=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
_lowercase    	=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
_lowercase    	=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
_lowercase    	=		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)



class      _UpperCAmelCase      (							_BaseAutoModelClass  ):
     UpperCamelCase__		     =					FLAX_MODEL_MAPPING







_lowercase    	=		auto_class_update(FlaxAutoModel)



class      _UpperCAmelCase      (							_BaseAutoModelClass  ):
     UpperCamelCase__		     =					FLAX_MODEL_FOR_PRETRAINING_MAPPING







_lowercase    	=		auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")



class      _UpperCAmelCase      (							_BaseAutoModelClass  ):
     UpperCamelCase__		     =					FLAX_MODEL_FOR_CAUSAL_LM_MAPPING







_lowercase    	=		auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")



class      _UpperCAmelCase      (							_BaseAutoModelClass  ):
     UpperCamelCase__		     =					FLAX_MODEL_FOR_MASKED_LM_MAPPING







_lowercase    	=		auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")



class      _UpperCAmelCase      (							_BaseAutoModelClass  ):
     UpperCamelCase__		     =					FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







_lowercase    	=		auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)



class      _UpperCAmelCase      (							_BaseAutoModelClass  ):
     UpperCamelCase__		     =					FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING







_lowercase    	=		auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)



class      _UpperCAmelCase      (							_BaseAutoModelClass  ):
     UpperCamelCase__		     =					FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING







_lowercase    	=		auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")



class      _UpperCAmelCase      (							_BaseAutoModelClass  ):
     UpperCamelCase__		     =					FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING







_lowercase    	=		auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc="token classification"
)



class      _UpperCAmelCase      (							_BaseAutoModelClass  ):
     UpperCamelCase__		     =					FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING







_lowercase    	=		auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")



class      _UpperCAmelCase      (							_BaseAutoModelClass  ):
     UpperCamelCase__		     =					FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING







_lowercase    	=		auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)



class      _UpperCAmelCase      (							_BaseAutoModelClass  ):
     UpperCamelCase__		     =					FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING







_lowercase    	=		auto_class_update(
    FlaxAutoModelForImageClassification, head_doc="image classification"
)



class      _UpperCAmelCase      (							_BaseAutoModelClass  ):
     UpperCamelCase__		     =					FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING







_lowercase    	=		auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")



class      _UpperCAmelCase      (							_BaseAutoModelClass  ):
     UpperCamelCase__		     =					FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING







_lowercase    	=		auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)





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

    return image



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Dict						)->	Any:
    A__					=       []
    # 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 		lowerCAmelCase__     (       UpperCamelCase_					:							int			,							UpperCamelCase_					:							Optional[int]			,							UpperCamelCase_					:							int						)->	List[Any]:
    A__					=       dct.pop(UpperCamelCase_						)
    A__					=       val



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]			,							UpperCamelCase_					:							Optional[int]						)->	Optional[int]:
    for i in range(config.vision_config.num_hidden_layers						):
        # read in original q and v biases
        A__					=       state_dict.pop(f"visual_encoder.blocks.{i}.attn.q_bias"						)
        A__					=       state_dict.pop(f"visual_encoder.blocks.{i}.attn.v_bias"						)

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



def 		lowerCAmelCase__     (       UpperCamelCase_					:							List[Any]			,							UpperCamelCase_					:							List[Any]						)->	int:
    A__					=       3_6_4 if '''coco''' in model_name else 2_2_4
    A__					=       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:
        A__					=       OPTConfig.from_pretrained('''facebook/opt-2.7b'''			,							eos_token_id=UpperCamelCase_						).to_dict()
    elif "opt-6.7b" in model_name:
        A__					=       OPTConfig.from_pretrained('''facebook/opt-6.7b'''			,							eos_token_id=UpperCamelCase_						).to_dict()
    elif "t5-xl" in model_name:
        A__					=       TaConfig.from_pretrained('''google/flan-t5-xl'''			,							dense_act_fn='''gelu'''			,							bos_token_id=1						).to_dict()
    elif "t5-xxl" in model_name:
        A__					=       TaConfig.from_pretrained('''google/flan-t5-xxl'''			,							dense_act_fn='''gelu'''			,							bos_token_id=1						).to_dict()

    A__					=       BlipaConfig(vision_config=UpperCamelCase_			,							text_config=UpperCamelCase_						)

    return config, image_size



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							Tuple			,							UpperCamelCase_					:							Optional[Any]=None			,							UpperCamelCase_					:							Any=False						)->	Optional[Any]:
    A__					=       (
        AutoTokenizer.from_pretrained('''facebook/opt-2.7b'''						)
        if '''opt''' in model_name
        else AutoTokenizer.from_pretrained('''google/flan-t5-xl'''						)
    )
    A__					=       tokenizer('''\n'''			,							add_special_tokens=UpperCamelCase_						).input_ids[0]
    A__			,						A__					=       get_blipa_config(UpperCamelCase_			,							eos_token_id=UpperCamelCase_						)

    A__					=       BlipaForConditionalGeneration(UpperCamelCase_						).eval()

    A__					=       {
        '''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'''),
    }

    A__			,						A__					=       model_name_to_original[model_name]

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

    # update state dict keys
    A__					=       original_model.state_dict()
    A__					=       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():
        A__					=       state_dict.pop(UpperCamelCase_						)
        if key.startswith('''Qformer.bert'''						):
            A__					=       key.replace('''Qformer.bert'''			,							'''qformer'''						)
        if "attention.self" in key:
            A__					=       key.replace('''self'''			,							'''attention'''						)
        if "opt_proj" in key:
            A__					=       key.replace('''opt_proj'''			,							'''language_projection'''						)
        if "t5_proj" in key:
            A__					=       key.replace('''t5_proj'''			,							'''language_projection'''						)
        if key.startswith('''opt'''						):
            A__					=       key.replace('''opt'''			,							'''language'''						)
        if key.startswith('''t5'''						):
            A__					=       key.replace('''t5'''			,							'''language'''						)
        A__					=       val

    # read in qv biases
    read_in_q_v_bias(UpperCamelCase_			,							UpperCamelCase_						)

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

    A__					=       load_demo_image()
    A__					=       vis_processors['''eval'''](UpperCamelCase_						).unsqueeze(0						).to(UpperCamelCase_						)
    A__					=       tokenizer(['''\n''']			,							return_tensors='''pt'''						).input_ids.to(UpperCamelCase_						)

    # create processor
    A__					=       BlipImageProcessor(
        size={'''height''': image_size, '''width''': image_size}			,							image_mean=UpperCamelCase_			,							image_std=UpperCamelCase_						)
    A__					=       BlipaProcessor(image_processor=UpperCamelCase_			,							tokenizer=UpperCamelCase_						)
    A__					=       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:
            A__					=       original_model({'''image''': original_pixel_values, '''text_input''': ['''''']}						).logits
            A__					=       hf_model(UpperCamelCase_			,							UpperCamelCase_						).logits
        else:
            A__					=       original_model(
                {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']}						).logits
            A__					=       input_ids.masked_fill(input_ids == tokenizer.pad_token_id			,							-1_0_0						)
            A__					=       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":
        A__					=       torch.tensor(
            [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]			,							device=UpperCamelCase_						)
        assert torch.allclose(logits[0, :3, :3]			,							UpperCamelCase_			,							atol=1E-4						)
    elif model_name == "blip2-flan-t5-xl-coco":
        A__					=       torch.tensor(
            [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]			,							device=UpperCamelCase_						)
    else:
        # cast to same type
        A__					=       logits.dtype
        assert torch.allclose(original_logits.to(UpperCamelCase_						)			,							UpperCamelCase_			,							atol=1E-2						)
    print('''Looks ok!'''						)

    print('''Generating a caption...'''						)
    A__					=       ''''''
    A__					=       tokenizer(UpperCamelCase_			,							return_tensors='''pt'''						).input_ids.to(UpperCamelCase_						)

    A__					=       original_model.generate({'''image''': original_pixel_values}						)
    A__					=       hf_model.generate(
        UpperCamelCase_			,							UpperCamelCase_			,							do_sample=UpperCamelCase_			,							num_beams=5			,							max_length=3_0			,							min_length=1			,							top_p=0.9			,							repetition_penalty=1.0			,							length_penalty=1.0			,							temperature=1			,							)
    print('''Original generation:'''			,							UpperCamelCase_						)
    A__					=       input_ids.shape[1]
    A__					=       processor.batch_decode(outputs[:, prompt_length:]			,							skip_special_tokens=UpperCamelCase_						)
    A__					=       [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__":
   _lowercase    	=		argparse.ArgumentParser()
   _lowercase    	=		[
       "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",
   )

   _lowercase    	=		parser.parse_args()

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