


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

   return image



def     __lowerCAmelCase						(							snake_case	:    int )       ->							Tuple:
   __lowerCamelCase:				Dict     =	[]
   # 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     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    List[Any]						,      snake_case	:    Tuple )       ->							int:
   __lowerCamelCase:				Union[str, Any]     =	dct.pop(snake_case )
   __lowerCamelCase:				Any     =	val



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

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



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

   __lowerCamelCase:				List[Any]     =	BlipaConfig(vision_config=snake_case						,      text_config=snake_case )

   return config, image_size



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

   __lowerCamelCase:				Optional[Any]     =	BlipaForConditionalGeneration(snake_case ).eval()

   __lowerCamelCase:				Union[str, 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"""),
   }

   __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	model_name_to_original[model_name]

   # load original model
   print("""Loading original model...""" )
   __lowerCamelCase:				List[Any]     =	"""cuda""" if torch.cuda.is_available() else """cpu"""
   __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Dict     =	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:				Any     =	original_model.state_dict()
   __lowerCamelCase:				str     =	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:				int     =	state_dict.pop(snake_case )
      if key.startswith("""Qformer.bert""" ):
         __lowerCamelCase:				Union[str, Any]     =	key.replace("""Qformer.bert"""						,      """qformer""" )
      if "attention.self" in key:
         __lowerCamelCase:				List[Any]     =	key.replace("""self"""						,      """attention""" )
      if "opt_proj" in key:
         __lowerCamelCase:				int     =	key.replace("""opt_proj"""						,      """language_projection""" )
      if "t5_proj" in key:
         __lowerCamelCase:				List[Any]     =	key.replace("""t5_proj"""						,      """language_projection""" )
      if key.startswith("""opt""" ):
         __lowerCamelCase:				Dict     =	key.replace("""opt"""						,      """language""" )
      if key.startswith("""t5""" ):
         __lowerCamelCase:				Any     =	key.replace("""t5"""						,      """language""" )
      __lowerCamelCase:				str     =	val

   # read in qv biases
   read_in_q_v_bias(snake_case						,      snake_case )

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

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

   # create processor
   __lowerCamelCase:				List[str]     =	BlipImageProcessor(
       size={"""height""": image_size, """width""": image_size}						,      image_mean=snake_case						,      image_std=snake_case )
   __lowerCamelCase:				Optional[Any]     =	BlipaProcessor(image_processor=snake_case						,      tokenizer=snake_case )
   __lowerCamelCase:				str     =	processor(images=snake_case						,      return_tensors="""pt""" ).pixel_values.to(snake_case )

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

   original_model.to(snake_case )
   hf_model.to(snake_case )
   with torch.no_grad():
      if "opt" in model_name:
         __lowerCamelCase:				Optional[Any]     =	original_model({"""image""": original_pixel_values, """text_input""": [""""""]} ).logits
         __lowerCamelCase:				Optional[Any]     =	hf_model(snake_case						,      snake_case ).logits
      else:
         __lowerCamelCase:				List[Any]     =	original_model(
             {"""image""": original_pixel_values, """text_input""": ["""\n"""], """text_output""": ["""\n"""]} ).logits
         __lowerCamelCase:				List[str]     =	input_ids.masked_fill(input_ids == tokenizer.pad_token_id						,      -100 )
         __lowerCamelCase:				Tuple     =	hf_model(snake_case						,      snake_case						,      labels=snake_case ).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":
      __lowerCamelCase:				List[str]     =	torch.tensor(
          [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]]						,      device=snake_case )
      assert torch.allclose(logits[0, :3, :3]						,      snake_case						,      atol=1e-4 )
   elif model_name == "blip2-flan-t5-xl-coco":
      __lowerCamelCase:				List[Any]     =	torch.tensor(
          [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]]						,      device=snake_case )
   else:
      # cast to same type
      __lowerCamelCase:				List[str]     =	logits.dtype
      assert torch.allclose(original_logits.to(snake_case )						,      snake_case						,      atol=1e-2 )
   print("""Looks ok!""" )

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

   __lowerCamelCase:				Tuple     =	original_model.generate({"""image""": original_pixel_values} )
   __lowerCamelCase:				List[Any]     =	hf_model.generate(
       snake_case						,      snake_case						,      do_sample=snake_case						,      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:"""						,      snake_case )
   __lowerCamelCase:				Any     =	input_ids.shape[1]
   __lowerCamelCase:				Dict     =	processor.batch_decode(outputs[:, prompt_length:]						,      skip_special_tokens=snake_case )
   __lowerCamelCase:				int     =	[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'nielsr/{model_name}' )
      hf_model.push_to_hub(f'nielsr/{model_name}' )


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

   _A		:				Optional[int]									=       parser.parse_args()

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






from __future__ import annotations

_A		:				List[str]									=       {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}



class        a					:





     def __init__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				dict[str, list[str]]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				Dict     =	graph
        # mapping node to its parent in resulting breadth first tree
        __lowerCamelCase:				dict[str, str | None]     =	{}
        __lowerCamelCase:				str     =	source_vertex





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Optional[int]     =	{self.source_vertex}
        __lowerCamelCase:				Optional[int]     =	None
        __lowerCamelCase:				Optional[int]     =	[self.source_vertex]  # first in first out queue

        while queue:
           __lowerCamelCase:				int     =	queue.pop(0							)
           for adjacent_vertex in self.graph[vertex]:
              if adjacent_vertex not in visited:
                 visited.add(SCREAMING_SNAKE_CASE_							)
                 __lowerCamelCase:				Dict     =	vertex
                 queue.append(SCREAMING_SNAKE_CASE_							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        if target_vertex == self.source_vertex:
           return self.source_vertex

        __lowerCamelCase:				List[str]     =	self.parent.get(SCREAMING_SNAKE_CASE_							)
        if target_vertex_parent is None:
           __lowerCamelCase:				Optional[int]     =	(
               F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
           )
           raise ValueError(SCREAMING_SNAKE_CASE_							)

        return self.shortest_path(SCREAMING_SNAKE_CASE_							) + F'->{target_vertex}'


if __name__ == "__main__":
   _A		:				int									=       Graph(graph, '''G''')
   g.breath_first_search()
   print(g.shortest_path('''D'''))
   print(g.shortest_path('''G'''))
   print(g.shortest_path('''Foo'''))


