


"""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_(		)    ->     int:
   '''simple docstring'''





   _lowerCamelCase   :				Any							=     "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
   _lowerCamelCase   :				int							=     Image.open(requests.get(_lowerCamelCase			,		stream=_lowerCamelCase       ).raw       ).convert("RGB"       )

   return image







def        lowerCamelCase_(		_lowerCamelCase       )    ->     List[str]:
   '''simple docstring'''





   _lowerCamelCase   :				str							=     []
   # 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_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     Union[str, Any]:
   '''simple docstring'''





   _lowerCamelCase   :				Tuple							=     dct.pop(_lowerCamelCase       )
   _lowerCamelCase   :				Tuple							=     val







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     Tuple:
   '''simple docstring'''





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

      # next, set bias in the state dict
      _lowerCamelCase   :				str							=     torch.cat((q_bias, torch.zeros_like(_lowerCamelCase			,		requires_grad=_lowerCamelCase       ), v_bias)       )
      _lowerCamelCase   :				int							=     qkv_bias







def        lowerCamelCase_(		_lowerCamelCase       )    ->     List[str]:
   '''simple docstring'''





   _lowerCamelCase   :				Optional[int]							=     364 if "coco" in model_name else 224
   _lowerCamelCase   :				Optional[int]							=     InstructBlipVisionConfig(image_size=_lowerCamelCase       ).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:
      _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   :				str							=     TaConfig.from_pretrained("google/flan-t5-xxl"			,		dense_act_fn="gelu"			,		bos_token_id=1       ).to_dict()
   elif "vicuna-7b" in model_name:
      _lowerCamelCase   :				List[str]							=     LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf"			,		vocab_size=32001       ).to_dict()
   elif "vicuna-13b" in model_name:
      _lowerCamelCase   :				str							=     LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf"			,		vocab_size=32001       ).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
   _lowerCamelCase   :				List[str]							=     InstructBlipQFormerConfig(vocab_size=30523       ).to_dict()
   _lowerCamelCase   :				int							=     InstructBlipConfig(vision_config=_lowerCamelCase			,		text_config=_lowerCamelCase			,		qformer_config=_lowerCamelCase       )

   return config, image_size







@torch.no_grad()
def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase=None			,		_lowerCamelCase=False       )    ->     Union[str, Any]:
   '''simple docstring'''





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

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

   _lowerCamelCase,					_lowerCamelCase   :				str							=     get_blipa_config(_lowerCamelCase       )
   _lowerCamelCase   :				List[Any]							=     InstructBlipForConditionalGeneration(_lowerCamelCase       ).eval()

   _lowerCamelCase   :				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"),
   }

   _lowerCamelCase,					_lowerCamelCase   :				Dict							=     model_name_to_original[model_name]

   # load original model
   print("Loading original model..."       )
   _lowerCamelCase   :				int							=     "cuda:1" if torch.cuda.is_available() else "cpu"
   _lowerCamelCase   :				Any							=     "cuda:2" if torch.cuda.is_available() else "cpu"
   _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase   :				Optional[int]							=     load_model_and_preprocess(
       name=_lowerCamelCase			,		model_type=_lowerCamelCase			,		is_eval=_lowerCamelCase			,		device=_lowerCamelCase       )
   original_model.eval()
   print("Done!"       )

   # update state dict keys
   _lowerCamelCase   :				Tuple							=     original_model.state_dict()
   _lowerCamelCase   :				Union[str, Any]							=     create_rename_keys(_lowerCamelCase       )
   for src, dest in rename_keys:
      rename_key(_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )

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

   # read in qv biases
   read_in_q_v_bias(_lowerCamelCase			,		_lowerCamelCase       )

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

   _lowerCamelCase   :				str							=     load_demo_image()
   _lowerCamelCase   :				Optional[Any]							=     "What is unusual about this image?"

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

   # make sure processor creates exact same pixel values
   _lowerCamelCase   :				List[Any]							=     vis_processors["eval"](_lowerCamelCase       ).unsqueeze(0       ).to(_lowerCamelCase       )
   _lowerCamelCase   :				Optional[int]							=     inputs.pixel_values
   assert torch.allclose(original_pixel_values.to(pixel_values.device       )			,		_lowerCamelCase       )

   original_model.to(_lowerCamelCase       )
   hf_model.to(_lowerCamelCase       )
   with torch.no_grad():
      if "vicuna" in model_name:
         _lowerCamelCase   :				Optional[int]							=     original_model({"image": original_pixel_values, "text_input": [prompt]}       ).logits
         _lowerCamelCase   :				Union[str, Any]							=     hf_model(**_lowerCamelCase       ).logits
      else:
         _lowerCamelCase   :				Union[str, Any]							=     original_model(
             {"image": original_pixel_values, "text_input": [prompt], "text_output": ["\n"]}       ).logits
         _lowerCamelCase   :				Union[str, Any]							=     tokenizer("\n"			,		return_tensors="pt"       ).input_ids.to(_lowerCamelCase       )
         _lowerCamelCase   :				Tuple							=     label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id			,		-100       )
         _lowerCamelCase   :				List[str]							=     hf_model(**_lowerCamelCase			,		labels=_lowerCamelCase       ).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
   _lowerCamelCase   :				str							=     1e-4 if "vicuna" in model_name else 1e-5
   assert torch.allclose(original_logits.to(logits.device       )			,		_lowerCamelCase			,		atol=_lowerCamelCase       )
   print("Looks ok!"       )

   print("Generating with original model..."       )
   _lowerCamelCase   :				List[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..."       )
   _lowerCamelCase   :				List[str]							=     hf_model.generate(
       **_lowerCamelCase			,		do_sample=_lowerCamelCase			,		num_beams=5			,		max_length=256			,		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?
      _lowerCamelCase   :				Optional[Any]							=     2
   print("Original generation:"			,		_lowerCamelCase       )
   _lowerCamelCase   :				int							=     processor.batch_decode(_lowerCamelCase			,		skip_special_tokens=_lowerCamelCase       )
   _lowerCamelCase   :				Optional[int]							=     [text.strip() for text in output_text]
   print("HF generation:"			,		_lowerCamelCase       )

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

   if push_to_hub:
      processor.push_to_hub(F"""Salesforce/{model_name}"""       )
      hf_model.push_to_hub(F"""Salesforce/{model_name}"""       )


if __name__ == "__main__":
  _lowerCAmelCase							:    Union[str, Any]					   =	argparse.ArgumentParser()
  _lowerCAmelCase							:    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''',
  )

  _lowerCAmelCase							:    Tuple					   =	parser.parse_args()

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


__lowercase				      =						"""Alexander Joslin"""

import operator as op

from .stack import Stack







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		{'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}

      A_							       =		Stack()
      A_							       =		Stack()

      for i in equation:
            if i.isdigit():
                  # RULE 1
                  operand_stack.push(int(SCREAMING_SNAKE_CASE   )   )
            elif i in operators:
                  # RULE 2
                  operator_stack.push(SCREAMING_SNAKE_CASE   )
            elif i == ")":
                  # RULE 4
                  A_							       =		operator_stack.peek()
                  operator_stack.pop()
                  A_							       =		operand_stack.peek()
                  operand_stack.pop()
                  A_							       =		operand_stack.peek()
                  operand_stack.pop()

                  A_							       =		operators[opr](SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
                  operand_stack.push(SCREAMING_SNAKE_CASE   )

    # RULE 5
      return operand_stack.peek()


if __name__ == "__main__":
      __lowercase				      =						"""(5 + ((4 * 2) * (2 + 3)))"""
      # answer = 45
      print(f'{equation} = {dijkstras_two_stack_algorithm(equation)}')





