


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 		_A		(		)					-> int:


     """simple docstring"""


     __UpperCamelCase =			'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
     __UpperCamelCase =			Image.open(requests.get(_lowercase					,  stream=_lowercase ).raw ).convert('RGB' )

     return image






def 		_A		(		_lowercase )					-> int:


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

     # fmt: on
     return rename_keys






def 		_A		(		_lowercase					,  _lowercase					,  _lowercase )					-> Optional[int]:


     """simple docstring"""


     __UpperCamelCase =			dct.pop(_lowercase )
     __UpperCamelCase =			val






def 		_A		(		_lowercase					,  _lowercase )					-> int:


     """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(_lowercase					,  requires_grad=_lowercase ), v_bias) )
          __UpperCamelCase =			qkv_bias






def 		_A		(		_lowercase					,  _lowercase )					-> Any:


     """simple docstring"""


     __UpperCamelCase =			3_64 if 'coco' in model_name else 2_24
     __UpperCamelCase =			BlipaVisionConfig(image_size=_lowercase ).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:
          __UpperCamelCase =			OPTConfig.from_pretrained('facebook/opt-2.7b'					,  eos_token_id=_lowercase ).to_dict()
     elif "opt-6.7b" in model_name:
          __UpperCamelCase =			OPTConfig.from_pretrained('facebook/opt-6.7b'					,  eos_token_id=_lowercase ).to_dict()
     elif "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()

     __UpperCamelCase =			BlipaConfig(vision_config=_lowercase					,  text_config=_lowercase )

     return config, image_size






@torch.no_grad()
def 		_A		(		_lowercase					,  _lowercase=None					,  _lowercase=False )					-> Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase =			(
         AutoTokenizer.from_pretrained('facebook/opt-2.7b' )
         if 'opt' in model_name
         else AutoTokenizer.from_pretrained('google/flan-t5-xl' )
     )
     __UpperCamelCase =			tokenizer('\n'					,  add_special_tokens=_lowercase ).input_ids[0]
     __UpperCamelCase,    __UpperCamelCase =			get_blipa_config(_lowercase					,  eos_token_id=_lowercase )

     __UpperCamelCase =			BlipaForConditionalGeneration(_lowercase ).eval()

     __UpperCamelCase =			{
         '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'),
     }

     __UpperCamelCase,    __UpperCamelCase =			model_name_to_original[model_name]

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

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

     # some keys can be renamed efficiently
     for key, val in state_dict.copy().items():
          __UpperCamelCase =			state_dict.pop(_lowercase )
          if key.startswith('Qformer.bert' ):
               __UpperCamelCase =			key.replace('Qformer.bert'					,  'qformer' )
          if "attention.self" in key:
               __UpperCamelCase =			key.replace('self'					,  'attention' )
          if "opt_proj" in key:
               __UpperCamelCase =			key.replace('opt_proj'					,  'language_projection' )
          if "t5_proj" in key:
               __UpperCamelCase =			key.replace('t5_proj'					,  'language_projection' )
          if key.startswith('opt' ):
               __UpperCamelCase =			key.replace('opt'					,  'language' )
          if key.startswith('t5' ):
               __UpperCamelCase =			key.replace('t5'					,  'language' )
          __UpperCamelCase =			val

     # read in qv biases
     read_in_q_v_bias(_lowercase					,  _lowercase )

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

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

     # create processor
     __UpperCamelCase =			BlipImageProcessor(
         size={'height': image_size, 'width': image_size}					,  image_mean=_lowercase					,  image_std=_lowercase )
     __UpperCamelCase =			BlipaProcessor(image_processor=_lowercase					,  tokenizer=_lowercase )
     __UpperCamelCase =			processor(images=_lowercase					,  return_tensors='pt' ).pixel_values.to(_lowercase )

     # make sure processor creates exact same pixel values
     assert torch.allclose(_lowercase					,  _lowercase )

     original_model.to(_lowercase )
     hf_model.to(_lowercase )
     with torch.no_grad():
          if "opt" in model_name:
               __UpperCamelCase =			original_model({'image': original_pixel_values, 'text_input': ['']} ).logits
               __UpperCamelCase =			hf_model(_lowercase					,  _lowercase ).logits
          else:
               __UpperCamelCase =			original_model(
                   {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']} ).logits
               __UpperCamelCase =			input_ids.masked_fill(input_ids == tokenizer.pad_token_id					,  -1_00 )
               __UpperCamelCase =			hf_model(_lowercase					,  _lowercase					,  labels=_lowercase ).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":
          __UpperCamelCase =			torch.tensor(
              [[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]]					,  device=_lowercase )
          assert torch.allclose(logits[0, :3, :3]					,  _lowercase					,  atol=1e-4 )
     elif model_name == "blip2-flan-t5-xl-coco":
          __UpperCamelCase =			torch.tensor(
              [[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]]					,  device=_lowercase )
     else:
          # cast to same type
          __UpperCamelCase =			logits.dtype
          assert torch.allclose(original_logits.to(_lowercase )					,  _lowercase					,  atol=1e-2 )
     print('Looks ok!' )

     print('Generating a caption...' )
     __UpperCamelCase =			''
     __UpperCamelCase =			tokenizer(_lowercase					,  return_tensors='pt' ).input_ids.to(_lowercase )

     __UpperCamelCase =			original_model.generate({'image': original_pixel_values} )
     __UpperCamelCase =			hf_model.generate(
         _lowercase					,  _lowercase					,  do_sample=_lowercase					,  num_beams=5					,  max_length=30					,  min_length=1					,  top_p=0.9					,  repetition_penalty=1.0					,  length_penalty=1.0					,  temperature=1					,  )
     print('Original generation:'					,  _lowercase )
     __UpperCamelCase =			input_ids.shape[1]
     __UpperCamelCase =			processor.batch_decode(outputs[:, prompt_length:]					,  skip_special_tokens=_lowercase )
     __UpperCamelCase =			[text.strip() for text in output_text]
     print('HF generation:'					,  _lowercase )

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

     if push_to_hub:
          processor.push_to_hub(f'''nielsr/{model_name}''' )
          hf_model.push_to_hub(f'''nielsr/{model_name}''' )


if __name__ == "__main__":
    __snake_case   							=						argparse.ArgumentParser()
    __snake_case   							=						[
        '''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''',
    )

    __snake_case   							=						parser.parse_args()

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







'''simple docstring'''

def 	__lowerCamelCase	( lowerCAmelCase_ = 1000000    )     -> int:
		_a	: Optional[int]								=				set(range(3		,     lowerCAmelCase_		,     2    )    )
		primes.add(2    )
		for p in range(3		,     lowerCAmelCase_		,     2    ):
				if p not in primes:
						continue
				primes.difference_update(set(range(p * p		,     lowerCAmelCase_		,     lowerCAmelCase_    )    )    )

		_a	: Tuple								=				[float(lowerCAmelCase_    ) for n in range(limit + 1    )]

		for p in primes:
				for n in range(lowerCAmelCase_		,     limit + 1		,     lowerCAmelCase_    ):
						phi[n] *= 1 - 1 / p

		return int(sum(phi[2:]    )    )


if __name__ == "__main__":
							print(f"""{solution() = }""")




