

'''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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD


def 	_lowerCAmelCase (						)				->      Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   ='https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
     _SCREAMING_SNAKE_CASE							   =Image.open(requests.get(_UpperCamelCase							,			stream=_UpperCamelCase    ).raw    ).convert('RGB'    )

     return image


def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[Any]    )				->      Optional[Any]:
     """simple docstring"""




     _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.embeddings.layernorm.weight')    )
     rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias')    )

     # fmt: on
     return rename_keys


def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					Union[str, Any]							,			_UpperCamelCase						:					int    )				->      Tuple:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =dct.pop(_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =val


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[str]							,			_UpperCamelCase						:					str    )				->      int:
     """simple docstring"""




     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 	_lowerCAmelCase (						_UpperCamelCase						:					Dict    )				->      Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =3_64 if 'coco' in model_name else 2_24
     _SCREAMING_SNAKE_CASE							   =InstructBlipVisionConfig(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 "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()
     elif "vicuna-7b" in model_name:
          _SCREAMING_SNAKE_CASE							   =LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'							,			vocab_size=3_20_01    ).to_dict()
     elif "vicuna-13b" in model_name:
          _SCREAMING_SNAKE_CASE							   =LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf'							,			vocab_size=3_20_01    ).to_dict()
     else:
          raise ValueError('Model name not supported'    )

     # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
     _SCREAMING_SNAKE_CASE							   =InstructBlipQFormerConfig(vocab_size=3_05_23    ).to_dict()
     _SCREAMING_SNAKE_CASE							   =InstructBlipConfig(vision_config=_UpperCamelCase							,			text_config=_UpperCamelCase							,			qformer_config=_UpperCamelCase    )

     return config, image_size


@torch.no_grad()
def 	_lowerCAmelCase (						_UpperCamelCase						:					Dict							,			_UpperCamelCase						:					Union[str, Any]=None							,			_UpperCamelCase						:					Optional[int]=False    )				->      int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =AutoTokenizer.from_pretrained('bert-base-uncased'							,			truncation_side='left'    )
     qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}    )

     if "t5" in model_name:
          _SCREAMING_SNAKE_CASE							   =TaTokenizerFast.from_pretrained('google/flan-t5-xl'							,			truncation_side='left'    )
     elif "vicuna" in model_name:
          # the following was used in the original implementation:
          # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
          # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
          # tokenizer.add_special_tokens({"bos_token": "</s>"})
          # tokenizer.add_special_tokens({"eos_token": "</s>"})
          # tokenizer.add_special_tokens({"unk_token": "</s>"})
          _SCREAMING_SNAKE_CASE							   =LlamaTokenizerFast.from_pretrained(
              'huggyllama/llama-7b'							,			truncation_side='left'							,			bos_token='</s>'							,			unk_token='</s>'    )
          tokenizer.add_special_tokens({'pad_token': '[PAD]'}    )

     _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =get_blipa_config(_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =InstructBlipForConditionalGeneration(_UpperCamelCase    ).eval()

     _SCREAMING_SNAKE_CASE							   ={
         'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
         'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
         'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
         'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
     }

     _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =model_name_to_original[model_name]

     # load original model
     print('Loading original model...'    )
     _SCREAMING_SNAKE_CASE							   ='cuda:1' if torch.cuda.is_available() else 'cpu'
     _SCREAMING_SNAKE_CASE							   ='cuda:2' 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 "llm_proj" in key:
               _SCREAMING_SNAKE_CASE							   =key.replace('llm_proj'							,			'language_projection'    )
          if "t5_proj" in key:
               _SCREAMING_SNAKE_CASE							   =key.replace('t5_proj'							,			'language_projection'    )
          if key.startswith('llm_model'    ):
               _SCREAMING_SNAKE_CASE							   =key.replace('llm_model'							,			'language_model'    )
          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    )

     # note: weights get loaded in torch.float32 by default
     hf_model.load_state_dict(_UpperCamelCase							,			strict=_UpperCamelCase    )

     _SCREAMING_SNAKE_CASE							   =load_demo_image()
     _SCREAMING_SNAKE_CASE							   ='What is unusual about this image?'

     # create processor
     _SCREAMING_SNAKE_CASE							   =BlipImageProcessor(
         size={'height': image_size, 'width': image_size}							,			image_mean=_UpperCamelCase							,			image_std=_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =InstructBlipProcessor(
         image_processor=_UpperCamelCase							,			tokenizer=_UpperCamelCase							,			qformer_tokenizer=_UpperCamelCase							,			)
     _SCREAMING_SNAKE_CASE							   =processor(images=_UpperCamelCase							,			text=_UpperCamelCase							,			return_tensors='pt'    ).to(_UpperCamelCase    )

     # make sure processor creates exact same pixel values
     _SCREAMING_SNAKE_CASE							   =vis_processors['eval'](_UpperCamelCase    ).unsqueeze(0    ).to(_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =inputs.pixel_values
     assert torch.allclose(original_pixel_values.to(pixel_values.device    )							,			_UpperCamelCase    )

     original_model.to(_UpperCamelCase    )
     hf_model.to(_UpperCamelCase    )
     with torch.no_grad():
          if "vicuna" in model_name:
               _SCREAMING_SNAKE_CASE							   =original_model({'image': original_pixel_values, 'text_input': [prompt]}    ).logits
               _SCREAMING_SNAKE_CASE							   =hf_model(**_UpperCamelCase    ).logits
          else:
               _SCREAMING_SNAKE_CASE							   =original_model(
                   {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}    ).logits
               _SCREAMING_SNAKE_CASE							   =tokenizer('\n'							,			return_tensors='pt'    ).input_ids.to(_UpperCamelCase    )
               _SCREAMING_SNAKE_CASE							   =label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id							,			-1_00    )
               _SCREAMING_SNAKE_CASE							   =hf_model(**_UpperCamelCase							,			labels=_UpperCamelCase    ).logits

     print('First values of original logits:'							,			original_logits[0, :3, :3]    )
     print('First values of HF logits:'							,			logits[0, :3, :3]    )

     # assert values
     assert original_logits.shape == logits.shape
     _SCREAMING_SNAKE_CASE							   =1E-4 if 'vicuna' in model_name else 1E-5
     assert torch.allclose(original_logits.to(logits.device    )							,			_UpperCamelCase							,			atol=_UpperCamelCase    )
     print('Looks ok!'    )

     print('Generating with original model...'    )
     _SCREAMING_SNAKE_CASE							   =original_model.generate({'image': original_pixel_values, 'prompt': prompt}							,			num_beams=5    )

     # important: we need to cast the weights of the HF model to the appropriate type
     print('Generating with HF model...'    )
     _SCREAMING_SNAKE_CASE							   =hf_model.generate(
         **_UpperCamelCase							,			do_sample=_UpperCamelCase							,			num_beams=5							,			max_length=2_56							,			min_length=1							,			top_p=0.9							,			repetition_penalty=1.5							,			length_penalty=1.0							,			temperature=1							,			)
     if "vicuna" in model_name:
          # convert output id 0 to 2 (eos_token_id)
          # TODO add this in the generate method?
          _SCREAMING_SNAKE_CASE							   =2
     print('Original generation:'							,			_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =processor.batch_decode(_UpperCamelCase							,			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"Salesforce/{model_name}"    )
          hf_model.push_to_hub(f"Salesforce/{model_name}"    )


if __name__ == "__main__":
     lowerCamelCase			:				Any 			=					argparse.ArgumentParser()
     lowerCamelCase			:				Optional[Any] 			=					[
         "instructblip-vicuna-7b",
         "instructblip-vicuna-13b",
         "instructblip-flan-t5-xl",
         "instructblip-flan-t5-xxl",
     ]
     parser.add_argument(
         "--model_name",
         default="instructblip-flan-t5-xl",
         choices=choices,
         type=str,
         help="Path to hf config.json of model to convert",
     )
     parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
     parser.add_argument(
         "--push_to_hub",
         action="store_true",
         help="Whether to push the model and processor to the hub after converting",
     )

     lowerCamelCase			:				Tuple 			=					parser.parse_args()

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



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase			:				List[str] 			=					{"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase			:				Union[str, Any] 			=					[
         "IBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "IBertForMaskedLM",
         "IBertForMultipleChoice",
         "IBertForQuestionAnswering",
         "IBertForSequenceClassification",
         "IBertForTokenClassification",
         "IBertModel",
         "IBertPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_ibert import (
              IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              IBertForMaskedLM,
              IBertForMultipleChoice,
              IBertForQuestionAnswering,
              IBertForSequenceClassification,
              IBertForTokenClassification,
              IBertModel,
              IBertPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase			:				str 			=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
