






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

     return image





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					[]
     # 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      (						__snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					dct.pop(__snake_case       )
     __lowerCAmelCase								=					val





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

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





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase								=					364 if "coco" in model_name else 224
     __lowerCAmelCase								=					InstructBlipVisionConfig(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 "t5-xl" in model_name:
          __lowerCAmelCase								=					TaConfig.from_pretrained("google/flan-t5-xl"					, dense_act_fn="gelu"					, bos_token_id=1       ).to_dict()
     elif "t5-xxl" in model_name:
          __lowerCAmelCase								=					TaConfig.from_pretrained("google/flan-t5-xxl"					, dense_act_fn="gelu"					, bos_token_id=1       ).to_dict()
     elif "vicuna-7b" in model_name:
          __lowerCAmelCase								=					LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf"					, vocab_size=3_2001       ).to_dict()
     elif "vicuna-13b" in model_name:
          __lowerCAmelCase								=					LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf"					, vocab_size=3_2001       ).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
     __lowerCAmelCase								=					InstructBlipQFormerConfig(vocab_size=3_0523       ).to_dict()
     __lowerCAmelCase								=					InstructBlipConfig(vision_config=__snake_case					, text_config=__snake_case					, qformer_config=__snake_case       )

     return config, image_size





@torch.no_grad()
def       __lowerCAmelCase      (						__snake_case					, __snake_case=None					, __snake_case=False       ):
     __lowerCAmelCase								=					AutoTokenizer.from_pretrained("bert-base-uncased"					, truncation_side="left"       )
     qformer_tokenizer.add_special_tokens({"bos_token": "[DEC]"}       )

     if "t5" in model_name:
          __lowerCAmelCase								=					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>"})
          __lowerCAmelCase								=					LlamaTokenizerFast.from_pretrained(
              "huggyllama/llama-7b"					, truncation_side="left"					, bos_token="</s>"					, unk_token="</s>"       )
          tokenizer.add_special_tokens({"pad_token": "[PAD]"}       )

     __lowerCAmelCase			, __lowerCAmelCase								=					get_blipa_config(__snake_case       )
     __lowerCAmelCase								=					InstructBlipForConditionalGeneration(__snake_case       ).eval()

     __lowerCAmelCase								=					{
         "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"),
     }

     __lowerCAmelCase			, __lowerCAmelCase								=					model_name_to_original[model_name]

     # load original model
     print("Loading original model..."       )
     __lowerCAmelCase								=					"cuda:1" if torch.cuda.is_available() else "cpu"
     __lowerCAmelCase								=					"cuda:2" if torch.cuda.is_available() else "cpu"
     __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase								=					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
     __lowerCAmelCase								=					original_model.state_dict()
     __lowerCAmelCase								=					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():
          __lowerCAmelCase								=					state_dict.pop(__snake_case       )
          if key.startswith("Qformer.bert"       ):
               __lowerCAmelCase								=					key.replace("Qformer.bert"					, "qformer"       )
          if "attention.self" in key:
               __lowerCAmelCase								=					key.replace("self"					, "attention"       )
          if "llm_proj" in key:
               __lowerCAmelCase								=					key.replace("llm_proj"					, "language_projection"       )
          if "t5_proj" in key:
               __lowerCAmelCase								=					key.replace("t5_proj"					, "language_projection"       )
          if key.startswith("llm_model"       ):
               __lowerCAmelCase								=					key.replace("llm_model"					, "language_model"       )
          if key.startswith("t5"       ):
               __lowerCAmelCase								=					key.replace("t5"					, "language"       )
          __lowerCAmelCase								=					val

     # read in qv biases
     read_in_q_v_bias(__snake_case					, __snake_case       )

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

     __lowerCAmelCase								=					load_demo_image()
     __lowerCAmelCase								=					"What is unusual about this image?"

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

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

     original_model.to(__snake_case       )
     hf_model.to(__snake_case       )
     with torch.no_grad():
          if "vicuna" in model_name:
               __lowerCAmelCase								=					original_model({"image": original_pixel_values, "text_input": [prompt]}       ).logits
               __lowerCAmelCase								=					hf_model(**__snake_case       ).logits
          else:
               __lowerCAmelCase								=					original_model(
                   {"image": original_pixel_values, "text_input": [prompt], "text_output": ["\n"]}       ).logits
               __lowerCAmelCase								=					tokenizer("\n"					, return_tensors="pt"       ).input_ids.to(__snake_case       )
               __lowerCAmelCase								=					label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id					, -100       )
               __lowerCAmelCase								=					hf_model(**__snake_case					, labels=__snake_case       ).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
     __lowerCAmelCase								=					1E-4 if "vicuna" in model_name else 1E-5
     assert torch.allclose(original_logits.to(logits.device       )					, __snake_case					, atol=__snake_case       )
     print("Looks ok!"       )

     print("Generating with original model..."       )
     __lowerCAmelCase								=					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..."       )
     __lowerCAmelCase								=					hf_model.generate(
         **__snake_case					, do_sample=__snake_case					, num_beams=5					, max_length=256					, 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?
          __lowerCAmelCase								=					2
     print("Original generation:"					, __snake_case       )
     __lowerCAmelCase								=					processor.batch_decode(__snake_case					, skip_special_tokens=__snake_case       )
     __lowerCAmelCase								=					[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"""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			:					Optional[int]         =	parser.parse_args()

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














from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase			:					List[str]         =	logging.get_logger(__name__)

lowerCamelCase			:					Dict         =	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}



class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""biogpt"""


      def __init__(	self					,						__UpperCamelCase=4_2_3_8_4					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=2_4					,						__UpperCamelCase=1_6					,						__UpperCamelCase=4_0_9_6					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.1					,						__UpperCamelCase=1_0_2_4					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=1e-12					,						__UpperCamelCase=True					,						__UpperCamelCase=True					,						__UpperCamelCase=0.0					,						__UpperCamelCase=0.0					,						__UpperCamelCase=1					,						__UpperCamelCase=0					,						__UpperCamelCase=2					,						**__UpperCamelCase					,						)->	Optional[Any]:
           __lowerCAmelCase								=					vocab_size
           __lowerCAmelCase								=					max_position_embeddings
           __lowerCAmelCase								=					hidden_size
           __lowerCAmelCase								=					num_hidden_layers
           __lowerCAmelCase								=					num_attention_heads
           __lowerCAmelCase								=					intermediate_size
           __lowerCAmelCase								=					hidden_act
           __lowerCAmelCase								=					hidden_dropout_prob
           __lowerCAmelCase								=					attention_probs_dropout_prob
           __lowerCAmelCase								=					initializer_range
           __lowerCAmelCase								=					layer_norm_eps
           __lowerCAmelCase								=					scale_embedding
           __lowerCAmelCase								=					use_cache
           __lowerCAmelCase								=					layerdrop
           __lowerCAmelCase								=					activation_dropout
           super().__init__(pad_token_id=__UpperCamelCase					,						bos_token_id=__UpperCamelCase					,						eos_token_id=__UpperCamelCase					,						**__UpperCamelCase	)






