





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      A__       (    )  ->  str:






   """simple docstring"""







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

   return image
def      A__       (    SCREAMING_SNAKE_CASE_   :      List[str]						)  ->  Optional[Any]:






   """simple docstring"""







   _UpperCAmelCase           =   []
   # 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      A__       (    SCREAMING_SNAKE_CASE_   :      Any				,	SCREAMING_SNAKE_CASE_   :      Optional[Any]				,	SCREAMING_SNAKE_CASE_   :      Optional[Any]						)  ->  List[Any]:






   """simple docstring"""







   _UpperCAmelCase           =   dct.pop(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   val
def      A__       (    SCREAMING_SNAKE_CASE_   :      Tuple				,	SCREAMING_SNAKE_CASE_   :      int						)  ->  Union[str, Any]:






   """simple docstring"""







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

      # next, set bias in the state dict
      _UpperCAmelCase           =   torch.cat((q_bias, torch.zeros_like(SCREAMING_SNAKE_CASE_				,	requires_grad=SCREAMING_SNAKE_CASE_						), v_bias)						)
      _UpperCAmelCase           =   qkv_bias
def      A__       (    SCREAMING_SNAKE_CASE_   :      Dict						)  ->  int:






   """simple docstring"""







   _UpperCAmelCase           =   3_64 if '''coco''' in model_name else 2_24
   _UpperCAmelCase           =   InstructBlipVisionConfig(image_size=SCREAMING_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:
      _UpperCAmelCase           =   TaConfig.from_pretrained('''google/flan-t5-xl'''				,	dense_act_fn='''gelu'''				,	bos_token_id=1						).to_dict()
   elif "t5-xxl" in model_name:
      _UpperCAmelCase           =   TaConfig.from_pretrained('''google/flan-t5-xxl'''				,	dense_act_fn='''gelu'''				,	bos_token_id=1						).to_dict()
   elif "vicuna-7b" in model_name:
      _UpperCAmelCase           =   LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf'''				,	vocab_size=3_20_01						).to_dict()
   elif "vicuna-13b" in model_name:
      _UpperCAmelCase           =   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
   _UpperCAmelCase           =   InstructBlipQFormerConfig(vocab_size=3_05_23						).to_dict()
   _UpperCAmelCase           =   InstructBlipConfig(vision_config=SCREAMING_SNAKE_CASE_				,	text_config=SCREAMING_SNAKE_CASE_				,	qformer_config=SCREAMING_SNAKE_CASE_						)

   return config, image_size
@torch.no_grad()
def      A__       (    SCREAMING_SNAKE_CASE_   :      Dict				,	SCREAMING_SNAKE_CASE_   :      List[Any]=None				,	SCREAMING_SNAKE_CASE_   :      Dict=False						)  ->  int:






   """simple docstring"""







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

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

   _UpperCAmelCase       ,      _UpperCAmelCase           =   get_blipa_config(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   InstructBlipForConditionalGeneration(SCREAMING_SNAKE_CASE_						).eval()

   _UpperCAmelCase           =   {
       '''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'''),
   }

   _UpperCAmelCase       ,      _UpperCAmelCase           =   model_name_to_original[model_name]

   # load original model
   print('''Loading original model...'''						)
   _UpperCAmelCase           =   '''cuda:1''' if torch.cuda.is_available() else '''cpu'''
   _UpperCAmelCase           =   '''cuda:2''' if torch.cuda.is_available() else '''cpu'''
   _UpperCAmelCase       ,      _UpperCAmelCase       ,      _UpperCAmelCase           =   load_model_and_preprocess(
       name=SCREAMING_SNAKE_CASE_				,	model_type=SCREAMING_SNAKE_CASE_				,	is_eval=SCREAMING_SNAKE_CASE_				,	device=SCREAMING_SNAKE_CASE_						)
   original_model.eval()
   print('''Done!'''						)

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

   # some keys can be renamed efficiently
   for key, val in state_dict.copy().items():
      _UpperCAmelCase           =   state_dict.pop(SCREAMING_SNAKE_CASE_						)
      if key.startswith('''Qformer.bert'''						):
         _UpperCAmelCase           =   key.replace('''Qformer.bert'''				,	'''qformer'''						)
      if "attention.self" in key:
         _UpperCAmelCase           =   key.replace('''self'''				,	'''attention'''						)
      if "llm_proj" in key:
         _UpperCAmelCase           =   key.replace('''llm_proj'''				,	'''language_projection'''						)
      if "t5_proj" in key:
         _UpperCAmelCase           =   key.replace('''t5_proj'''				,	'''language_projection'''						)
      if key.startswith('''llm_model'''						):
         _UpperCAmelCase           =   key.replace('''llm_model'''				,	'''language_model'''						)
      if key.startswith('''t5'''						):
         _UpperCAmelCase           =   key.replace('''t5'''				,	'''language'''						)
      _UpperCAmelCase           =   val

   # read in qv biases
   read_in_q_v_bias(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)

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

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

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

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

   original_model.to(SCREAMING_SNAKE_CASE_						)
   hf_model.to(SCREAMING_SNAKE_CASE_						)
   with torch.no_grad():
      if "vicuna" in model_name:
         _UpperCAmelCase           =   original_model({'''image''': original_pixel_values, '''text_input''': [prompt]}						).logits
         _UpperCAmelCase           =   hf_model(**SCREAMING_SNAKE_CASE_						).logits
      else:
         _UpperCAmelCase           =   original_model(
             {'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']}						).logits
         _UpperCAmelCase           =   tokenizer('''\n'''				,	return_tensors='''pt'''						).input_ids.to(SCREAMING_SNAKE_CASE_						)
         _UpperCAmelCase           =   label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id				,	-1_00						)
         _UpperCAmelCase           =   hf_model(**SCREAMING_SNAKE_CASE_				,	labels=SCREAMING_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
   _UpperCAmelCase           =   1E-4 if '''vicuna''' in model_name else 1E-5
   assert torch.allclose(original_logits.to(logits.device						)				,	SCREAMING_SNAKE_CASE_				,	atol=SCREAMING_SNAKE_CASE_						)
   print('''Looks ok!'''						)

   print('''Generating with original model...'''						)
   _UpperCAmelCase           =   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...'''						)
   _UpperCAmelCase           =   hf_model.generate(
       **SCREAMING_SNAKE_CASE_				,	do_sample=SCREAMING_SNAKE_CASE_				,	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?
      _UpperCAmelCase           =   2
   print('''Original generation:'''				,	SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   processor.batch_decode(SCREAMING_SNAKE_CASE_				,	skip_special_tokens=SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   [text.strip() for text in output_text]
   print('''HF generation:'''				,	SCREAMING_SNAKE_CASE_						)

   if pytorch_dump_folder_path is not None:
      processor.save_pretrained(SCREAMING_SNAKE_CASE_						)
      hf_model.save_pretrained(SCREAMING_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__":
   UpperCAmelCase_           =	argparse.ArgumentParser()
   UpperCAmelCase_           =	[
       "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",
   )

   UpperCAmelCase_           =	parser.parse_args()

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


import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


_UpperCAmelCase						:  List[Any]									=			{
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """n_guide_steps""": 2,  # can set to 0 for faster sampling, does not use value network
    """scale_grad_by_std""": True,
    """scale""": 0.1,
    """eta""": 0.0,
    """t_grad_cutoff""": 2,
    """device""": """cpu""",
}


if __name__ == "__main__":
					_UpperCAmelCase						:  List[str]									=			"""hopper-medium-v2"""
					_UpperCAmelCase						:  Any									=			gym.make(env_name)

					_UpperCAmelCase						:  Tuple									=			ValueGuidedRLPipeline.from_pretrained(
					    """bglick13/hopper-medium-v2-value-function-hor32""",
					    env=env,
					)

					env.seed(0)
					_UpperCAmelCase						:  Tuple									=			env.reset()
					_UpperCAmelCase						:  str									=			0
					_UpperCAmelCase						:  Union[str, Any]									=			0
					_UpperCAmelCase						:  List[str]									=			1000
					_UpperCAmelCase						:  Optional[int]									=			[obs.copy()]
					try:
										for t in tqdm.tqdm(range(T)):
															# call the policy
															_UpperCAmelCase						:  int									=			pipeline(obs, planning_horizon=32)

															# execute action in environment
															_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase							,		_UpperCAmelCase						:  str									=			env.step(denorm_actions)
															_UpperCAmelCase						:  Tuple									=			env.get_normalized_score(total_reward)

															# update return
															total_reward += reward
															total_score += score
															print(
															    F'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
															    F''' {total_score}'''
															)

															# save observations for rendering
															rollout.append(next_observation.copy())

															_UpperCAmelCase						:  Dict									=			next_observation
					except KeyboardInterrupt:
										pass

					print(F'''Total reward: {total_reward}''')




