





'''simple docstring'''




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 	_lowerCAmelCase      (  )					->					Dict:
     __A						:			int								=	'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
     __A						:			List[Any]								=	Image.open(requests.get(__snake_case			,							stream=__snake_case      ).raw      ).convert('RGB'      )

     return image






def 	_lowerCAmelCase      (  __snake_case  :			str      )					->					Dict:
     __A						:			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.embeddings.layernorm.weight')      )
     rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias')      )

     # fmt: on
     return rename_keys






def 	_lowerCAmelCase      (  __snake_case  :			Dict			,							__snake_case  :			Dict			,							__snake_case  :			Dict      )					->					str:
     __A						:			int								=	dct.pop(__snake_case      )
     __A						:			int								=	val






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

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






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

     return config, image_size






@torch.no_grad()
def 	_lowerCAmelCase      (  __snake_case  :			List[str]			,							__snake_case  :			int=None			,							__snake_case  :			Any=False      )					->					str:
     __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained('bert-base-uncased'			,							truncation_side='left'      )
     qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}      )

     if "t5" in model_name:
          __A						:			Optional[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						:			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[int]								=	get_blipa_config(__snake_case      )
     __A						:			Optional[Any]								=	InstructBlipForConditionalGeneration(__snake_case      ).eval()

     __A						:			Dict								=	{
         '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						:			Union[str, Any]								=	model_name_to_original[model_name]

     # load original model
     print('Loading original model...'      )
     __A						:			Optional[Any]								=	'cuda:1' if torch.cuda.is_available() else 'cpu'
     __A						:			Dict								=	'cuda:2' if torch.cuda.is_available() else 'cpu'
     __A			,__A			,__A						:			Optional[int]								=	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
     __A						:			Union[str, Any]								=	original_model.state_dict()
     __A						:			int								=	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():
          __A						:			Any								=	state_dict.pop(__snake_case      )
          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						:			List[str]								=	key.replace('llm_proj'			,							'language_projection'      )
          if "t5_proj" in key:
               __A						:			Optional[Any]								=	key.replace('t5_proj'			,							'language_projection'      )
          if key.startswith('llm_model'      ):
               __A						:			Tuple								=	key.replace('llm_model'			,							'language_model'      )
          if key.startswith('t5'      ):
               __A						:			Any								=	key.replace('t5'			,							'language'      )
          __A						:			Optional[int]								=	val

     # read in qv biases
     read_in_q_v_bias(__snake_case			,							__snake_case      )

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

     __A						:			List[Any]								=	load_demo_image()
     __A						:			Tuple								=	'What is unusual about this image?'

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

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

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

     print('Generating with original model...'      )
     __A						:			Tuple								=	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						:			Optional[Any]								=	hf_model.generate(
         **__snake_case			,							do_sample=__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?
          __A						:			Union[str, Any]								=	2
     print('Original generation:'			,							__snake_case      )
     __A						:			Optional[Any]								=	processor.batch_decode(__snake_case			,							skip_special_tokens=__snake_case      )
     __A						:			Dict								=	[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'Salesforce/{model_name}'      )
          hf_model.push_to_hub(f'Salesforce/{model_name}'      )


if __name__ == "__main__":
  lowercase__   :				List[Any]      =   argparse.ArgumentParser()
  lowercase__   :				Tuple      =   [
      '''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''',
  )

  lowercase__   :				int      =   parser.parse_args()

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






'''simple docstring'''




lowercase__   :				Any      =   {'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
lowercase__   :				List[Any]      =   ['''a''', '''b''', '''c''', '''d''', '''e''']






def 	_lowerCAmelCase      (  __snake_case  :			str			,							__snake_case  :			Tuple			,							__snake_case  :			int      )					->					Tuple:
     __A						:			List[str]								=	start
     # add current to visited
     visited.append(__snake_case      )
     __A						:			Optional[int]								=	edges[current]
     for neighbor in neighbors:
          # if neighbor not in visited, visit
          if neighbor not in visited:
               __A						:			int								=	topological_sort(__snake_case			,							__snake_case			,							__snake_case      )
    # if all neighbors visited add current to sort
     sort.append(__snake_case      )
     # if all vertices haven't been visited select a new one to visit
     if len(__snake_case      ) != len(__snake_case      ):
          for vertice in vertices:
               if vertice not in visited:
                    __A						:			Dict								=	topological_sort(__snake_case			,							__snake_case			,							__snake_case      )
    # return sort
     return sort


if __name__ == "__main__":
  lowercase__   :				Tuple      =   topological_sort('''a''', [], [])
  print(sort)