


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  SCREAMING_SNAKE_CASE_		(    )			->	int:

       """simple docstring"""

       a_      :	Optional[Any]							=						'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
       a_      :	Union[str, Any]							=						Image.open(requests.get(__A						,					stream=__A       ).raw       ).convert('RGB'       )

       return image







def  SCREAMING_SNAKE_CASE_		(    __A				:    str       )			->	str:

       """simple docstring"""

       a_      :	int							=						[]
       # 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  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    Optional[Any]						,					__A				:    Union[str, Any]       )			->	int:

       """simple docstring"""

       a_      :	Optional[Any]							=						dct.pop(__A       )
       a_      :	Any							=						val







def  SCREAMING_SNAKE_CASE_		(    __A				:    Union[str, Any]						,					__A				:    int       )			->	List[str]:

       """simple docstring"""

       for i in range(config.vision_config.num_hidden_layers       ):
              # read in original q and v biases
              a_      :	List[Any]							=						state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""       )
              a_      :	Tuple							=						state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""       )

              # next, set bias in the state dict
              a_      :	int							=						torch.cat((q_bias, torch.zeros_like(__A						,					requires_grad=__A       ), v_bias)       )
              a_      :	Union[str, Any]							=						qkv_bias







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[int]       )			->	Optional[int]:

       """simple docstring"""

       a_      :	str							=						3_64 if 'coco' in model_name else 2_24
       a_      :	str							=						InstructBlipVisionConfig(image_size=__A       ).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:
              a_      :	Any							=						TaConfig.from_pretrained('google/flan-t5-xl'						,					dense_act_fn='gelu'						,					bos_token_id=1       ).to_dict()
       elif "t5-xxl" in model_name:
              a_      :	int							=						TaConfig.from_pretrained('google/flan-t5-xxl'						,					dense_act_fn='gelu'						,					bos_token_id=1       ).to_dict()
       elif "vicuna-7b" in model_name:
              a_      :	Tuple							=						LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'						,					vocab_size=3_20_01       ).to_dict()
       elif "vicuna-13b" in model_name:
              a_      :	List[str]							=						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
       a_      :	Union[str, Any]							=						InstructBlipQFormerConfig(vocab_size=3_05_23       ).to_dict()
       a_      :	Any							=						InstructBlipConfig(vision_config=__A						,					text_config=__A						,					qformer_config=__A       )

       return config, image_size







@torch.no_grad()
def  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]						,					__A				:    List[str]=None						,					__A				:    Optional[Any]=False       )			->	List[Any]:

       """simple docstring"""

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

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

       a_							,	a_      :	Optional[Any]							=						get_blipa_config(__A       )
       a_      :	int							=						InstructBlipForConditionalGeneration(__A       ).eval()

       a_      :	List[str]							=						{
           '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'),
       }

       a_							,	a_      :	List[str]							=						model_name_to_original[model_name]

       # load original model
       print('Loading original model...'       )
       a_      :	Union[str, Any]							=						'cuda:1' if torch.cuda.is_available() else 'cpu'
       a_      :	Optional[Any]							=						'cuda:2' if torch.cuda.is_available() else 'cpu'
       a_							,	a_							,	a_      :	Dict							=						load_model_and_preprocess(
           name=__A						,					model_type=__A						,					is_eval=__A						,					device=__A       )
       original_model.eval()
       print('Done!'       )

       # update state dict keys
       a_      :	List[str]							=						original_model.state_dict()
       a_      :	Dict							=						create_rename_keys(__A       )
       for src, dest in rename_keys:
              rename_key(__A						,					__A						,					__A       )

       # some keys can be renamed efficiently
       for key, val in state_dict.copy().items():
              a_      :	List[str]							=						state_dict.pop(__A       )
              if key.startswith('Qformer.bert'       ):
                     a_      :	Union[str, Any]							=						key.replace('Qformer.bert'						,					'qformer'       )
              if "attention.self" in key:
                     a_      :	Dict							=						key.replace('self'						,					'attention'       )
              if "llm_proj" in key:
                     a_      :	Dict							=						key.replace('llm_proj'						,					'language_projection'       )
              if "t5_proj" in key:
                     a_      :	int							=						key.replace('t5_proj'						,					'language_projection'       )
              if key.startswith('llm_model'       ):
                     a_      :	Dict							=						key.replace('llm_model'						,					'language_model'       )
              if key.startswith('t5'       ):
                     a_      :	Optional[int]							=						key.replace('t5'						,					'language'       )
              a_      :	Optional[Any]							=						val

       # read in qv biases
       read_in_q_v_bias(__A						,					__A       )

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

       a_      :	Optional[int]							=						load_demo_image()
       a_      :	Any							=						'What is unusual about this image?'

       # create processor
       a_      :	List[Any]							=						BlipImageProcessor(
           size={'height': image_size, 'width': image_size}						,					image_mean=__A						,					image_std=__A       )
       a_      :	str							=						InstructBlipProcessor(
           image_processor=__A						,					tokenizer=__A						,					qformer_tokenizer=__A						,					)
       a_      :	str							=						processor(images=__A						,					text=__A						,					return_tensors='pt'       ).to(__A       )

       # make sure processor creates exact same pixel values
       a_      :	str							=						vis_processors['eval'](__A       ).unsqueeze(0       ).to(__A       )
       a_      :	Optional[int]							=						inputs.pixel_values
       assert torch.allclose(original_pixel_values.to(pixel_values.device       )						,					__A       )

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

       print('Generating with original model...'       )
       a_      :	Optional[Any]							=						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...'       )
       a_      :	Union[str, Any]							=						hf_model.generate(
           **__A						,					do_sample=__A						,					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?
              a_      :	List[str]							=						2
       print('Original generation:'						,					__A       )
       a_      :	Any							=						processor.batch_decode(__A						,					skip_special_tokens=__A       )
       a_      :	Union[str, Any]							=						[text.strip() for text in output_text]
       print('HF generation:'						,					__A       )

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

       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_   :				Optional[int]						      = argparse.ArgumentParser()
   UpperCAmelCase_   :				int						      = [
       '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_   :				List[str]						      = parser.parse_args()

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






def  SCREAMING_SNAKE_CASE_		(    __A				:    int       )			->	bool:

       """simple docstring"""

       a_      :	List[Any]							=						n ** (1 / 3)
       return (val * val * val) == n


if __name__ == "__main__":
   print(perfect_cube(27))
   print(perfect_cube(4))


