




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 						SCREAMING_SNAKE_CASE__ (   )  ->       int:
   snake_case				:    Any           =	"""https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"""
   snake_case				:    int           =	Image.open(requests.get(lowercase						,stream=lowercase					).raw					).convert("""RGB"""					)

   return image




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       int:
   snake_case				:    Any           =	[]
   # 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 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase						,lowercase					)  ->       Optional[int]:
   snake_case				:    List[str]           =	dct.pop(lowercase					)
   snake_case				:    Dict           =	val




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Union[str, Any]:
   for i in range(config.vision_config.num_hidden_layers					):
      # read in original q and v biases
      snake_case				:    Dict           =	state_dict.pop(f"""visual_encoder.blocks.{i}.attn.q_bias"""					)
      snake_case				:    str           =	state_dict.pop(f"""visual_encoder.blocks.{i}.attn.v_bias"""					)

      # next, set bias in the state dict
      snake_case				:    List[str]           =	torch.cat((q_bias, torch.zeros_like(lowercase						,requires_grad=lowercase					), v_bias)					)
      snake_case				:    List[Any]           =	qkv_bias




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Optional[Any]:
   snake_case				:    List[Any]           =	364 if """coco""" in model_name else 224
   snake_case				:    List[str]           =	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:
      snake_case				:    List[Any]           =	OPTConfig.from_pretrained("""facebook/opt-2.7b"""						,eos_token_id=lowercase					).to_dict()
   elif "opt-6.7b" in model_name:
      snake_case				:    List[str]           =	OPTConfig.from_pretrained("""facebook/opt-6.7b"""						,eos_token_id=lowercase					).to_dict()
   elif "t5-xl" in model_name:
      snake_case				:    Union[str, Any]           =	TaConfig.from_pretrained("""google/flan-t5-xl"""						,dense_act_fn="""gelu"""						,bos_token_id=1					).to_dict()
   elif "t5-xxl" in model_name:
      snake_case				:    Optional[int]           =	TaConfig.from_pretrained("""google/flan-t5-xxl"""						,dense_act_fn="""gelu"""						,bos_token_id=1					).to_dict()

   snake_case				:    Tuple           =	BlipaConfig(vision_config=lowercase						,text_config=lowercase					)

   return config, image_size




@torch.no_grad()
def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase=None						,lowercase=False					)  ->       List[str]:
   snake_case				:    Any           =	(
       AutoTokenizer.from_pretrained("""facebook/opt-2.7b"""					)
       if """opt""" in model_name
       else AutoTokenizer.from_pretrained("""google/flan-t5-xl"""					)
   )
   snake_case				:    Tuple           =	tokenizer("""\n"""						,add_special_tokens=lowercase					).input_ids[0]
   snake_case     , snake_case				:    Tuple           =	get_blipa_config(lowercase						,eos_token_id=lowercase					)

   snake_case				:    List[str]           =	BlipaForConditionalGeneration(lowercase					).eval()

   snake_case				:    Optional[Any]           =	{
       """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"""),
   }

   snake_case     , snake_case				:    Optional[Any]           =	model_name_to_original[model_name]

   # load original model
   print("""Loading original model..."""					)
   snake_case				:    int           =	"""cuda""" if torch.cuda.is_available() else """cpu"""
   snake_case     , snake_case     , snake_case				:    Union[str, Any]           =	load_model_and_preprocess(
       name=lowercase						,model_type=lowercase						,is_eval=lowercase						,device=lowercase					)
   original_model.eval()
   print("""Done!"""					)

   # update state dict keys
   snake_case				:    int           =	original_model.state_dict()
   snake_case				:    Optional[int]           =	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():
      snake_case				:    List[str]           =	state_dict.pop(lowercase					)
      if key.startswith("""Qformer.bert"""					):
         snake_case				:    Optional[Any]           =	key.replace("""Qformer.bert"""						,"""qformer"""					)
      if "attention.self" in key:
         snake_case				:    List[str]           =	key.replace("""self"""						,"""attention"""					)
      if "opt_proj" in key:
         snake_case				:    int           =	key.replace("""opt_proj"""						,"""language_projection"""					)
      if "t5_proj" in key:
         snake_case				:    Any           =	key.replace("""t5_proj"""						,"""language_projection"""					)
      if key.startswith("""opt"""					):
         snake_case				:    Optional[int]           =	key.replace("""opt"""						,"""language"""					)
      if key.startswith("""t5"""					):
         snake_case				:    Union[str, Any]           =	key.replace("""t5"""						,"""language"""					)
      snake_case				:    str           =	val

   # read in qv biases
   read_in_q_v_bias(lowercase						,lowercase					)

   snake_case     , snake_case				:    str           =	hf_model.load_state_dict(lowercase						,strict=lowercase					)
   assert len(lowercase					) == 0
   assert unexpected_keys == ["qformer.embeddings.position_ids"]

   snake_case				:    List[Any]           =	load_demo_image()
   snake_case				:    Dict           =	vis_processors["""eval"""](lowercase					).unsqueeze(0					).to(lowercase					)
   snake_case				:    Optional[Any]           =	tokenizer(["""\n"""]						,return_tensors="""pt"""					).input_ids.to(lowercase					)

   # create processor
   snake_case				:    Tuple           =	BlipImageProcessor(
       size={"""height""": image_size, """width""": image_size}						,image_mean=lowercase						,image_std=lowercase					)
   snake_case				:    Dict           =	BlipaProcessor(image_processor=lowercase						,tokenizer=lowercase					)
   snake_case				:    Optional[int]           =	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:
         snake_case				:    Tuple           =	original_model({"""image""": original_pixel_values, """text_input""": [""""""]}					).logits
         snake_case				:    str           =	hf_model(lowercase						,lowercase					).logits
      else:
         snake_case				:    Optional[int]           =	original_model(
             {"""image""": original_pixel_values, """text_input""": ["""\n"""], """text_output""": ["""\n"""]}					).logits
         snake_case				:    List[str]           =	input_ids.masked_fill(input_ids == tokenizer.pad_token_id						,-100					)
         snake_case				:    Union[str, Any]           =	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":
      snake_case				:    Optional[Any]           =	torch.tensor(
          [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]						,device=lowercase					)
      assert torch.allclose(logits[0, :3, :3]						,lowercase						,atol=1E-4					)
   elif model_name == "blip2-flan-t5-xl-coco":
      snake_case				:    Union[str, Any]           =	torch.tensor(
          [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]						,device=lowercase					)
   else:
      # cast to same type
      snake_case				:    List[Any]           =	logits.dtype
      assert torch.allclose(original_logits.to(lowercase					)						,lowercase						,atol=1E-2					)
   print("""Looks ok!"""					)

   print("""Generating a caption..."""					)
   snake_case				:    Union[str, Any]           =	""""""
   snake_case				:    Dict           =	tokenizer(lowercase						,return_tensors="""pt"""					).input_ids.to(lowercase					)

   snake_case				:    Optional[int]           =	original_model.generate({"""image""": original_pixel_values}					)
   snake_case				:    int           =	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					)
   snake_case				:    Dict           =	input_ids.shape[1]
   snake_case				:    List[Any]           =	processor.batch_decode(outputs[:, prompt_length:]						,skip_special_tokens=lowercase					)
   snake_case				:    List[str]           =	[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__":
       lowerCamelCase	:	Dict				=   argparse.ArgumentParser()
       lowerCamelCase	:	List[Any]				=   [
           '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',
       )

       lowerCamelCase	:	Tuple				=   parser.parse_args()

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











from collections import defaultdict




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       bool:
   snake_case				:    List[str]           =	first_str.lower().strip()
   snake_case				:    List[str]           =	second_str.lower().strip()

   # Remove whitespace
   snake_case				:    Any           =	first_str.replace(""" """						,""""""					)
   snake_case				:    List[str]           =	second_str.replace(""" """						,""""""					)

   # Strings of different lengths are not anagrams
   if len(lowercase					) != len(lowercase					):
      return False

   # Default values for count should be 0
   snake_case				:    defaultdict[str, int]           =	defaultdict(lowercase					)

   # For each character in input strings,
   # increment count in the corresponding
   for i in range(len(lowercase					)					):
      count[first_str[i]] += 1
      count[second_str[i]] -= 1

   return all(_count == 0 for _count in count.values()					)


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       lowerCamelCase	:	List[Any]				=   input('Enter the first string ').strip()
       lowerCamelCase	:	Optional[int]				=   input('Enter the second string ').strip()

       lowerCamelCase	:	Optional[Any]				=   check_anagrams(input_a, input_b)
       print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")





