






'''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 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""




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

   return image




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Union[str, 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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    str           =     dct.pop(_UpperCamelCase							)
   lowercase_	:    str           =     val




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




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

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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[str]           =     364 if "coco" in model_name else 224
   lowercase_	:    Any           =     InstructBlipVisionConfig(image_size=_UpperCamelCase							).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:
      lowercase_	:    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:
      lowercase_	:    Optional[Any]           =     TaConfig.from_pretrained("google/flan-t5-xxl"						,		dense_act_fn="gelu"						,		bos_token_id=1							).to_dict()
   elif "vicuna-7b" in model_name:
      lowercase_	:    Union[str, Any]           =     LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf"						,		vocab_size=3_2001							).to_dict()
   elif "vicuna-13b" in model_name:
      lowercase_	:    int           =     LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf"						,		vocab_size=3_2001							).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
   lowercase_	:    Optional[Any]           =     InstructBlipQFormerConfig(vocab_size=3_0523							).to_dict()
   lowercase_	:    str           =     InstructBlipConfig(vision_config=_UpperCamelCase						,		text_config=_UpperCamelCase						,		qformer_config=_UpperCamelCase							)

   return config, image_size




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=None						,		_UpperCamelCase=False							):


   """simple docstring"""




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

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

   lowercase_    ,		lowercase_	:    List[Any]           =     get_blipa_config(_UpperCamelCase							)
   lowercase_	:    List[Any]           =     InstructBlipForConditionalGeneration(_UpperCamelCase							).eval()

   lowercase_	:    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"),
   }

   lowercase_    ,		lowercase_	:    Tuple           =     model_name_to_original[model_name]

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

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

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

   # read in qv biases
   read_in_q_v_bias(_UpperCamelCase						,		_UpperCamelCase							)

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

   lowercase_	:    Dict           =     load_demo_image()
   lowercase_	:    Dict           =     "What is unusual about this image?"

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

   # make sure processor creates exact same pixel values
   lowercase_	:    List[str]           =     vis_processors["eval"](_UpperCamelCase							).unsqueeze(0							).to(_UpperCamelCase							)
   lowercase_	:    Tuple           =     inputs.pixel_values
   assert torch.allclose(original_pixel_values.to(pixel_values.device							)						,		_UpperCamelCase							)

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

   print("Generating with original model..."							)
   lowercase_	:    List[str]           =     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..."							)
   lowercase_	:    Tuple           =     hf_model.generate(
       **_UpperCamelCase						,		do_sample=_UpperCamelCase						,		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?
      lowercase_	:    Any           =     2
   print("Original generation:"						,		_UpperCamelCase							)
   lowercase_	:    List[Any]           =     processor.batch_decode(_UpperCamelCase						,		skip_special_tokens=_UpperCamelCase							)
   lowercase_	:    Tuple           =     [text.strip() for text in output_text]
   print("HF generation:"						,		_UpperCamelCase							)

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

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


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

 UpperCamelCase__ =			parser.parse_args()

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












'''simple docstring'''
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Any           =     False
   while is_sorted is False:  # Until all the indices are traversed keep looping
      lowercase_	:    List[str]           =     True
      for i in range(0						,		len(_UpperCamelCase							) - 1						,		2							):  # iterating over all even indices
         if input_list[i] > input_list[i + 1]:
            lowercase_    ,		lowercase_	:    Union[str, Any]           =     input_list[i + 1], input_list[i]
            # swapping if elements not in order
            lowercase_	:    Any           =     False

      for i in range(1						,		len(_UpperCamelCase							) - 1						,		2							):  # iterating over all odd indices
         if input_list[i] > input_list[i + 1]:
            lowercase_    ,		lowercase_	:    Tuple           =     input_list[i + 1], input_list[i]
            # swapping if elements not in order
            lowercase_	:    List[Any]           =     False
   return input_list


if __name__ == "__main__":
 print('Enter list to be sorted')
 UpperCamelCase__ =			[int(x) for x in input().split()]
 # inputing elements of the list in one line
 UpperCamelCase__ =			odd_even_sort(input_list)
 print('The sorted list is')
 print(sorted_list)




