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

    return image


def      UpperCAmelCase_	(    __lowerCAmelCase						)			->    List[Any]:
    __lowercase		:      List[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      UpperCAmelCase_	(    __lowerCAmelCase						,       __lowerCAmelCase						,       __lowerCAmelCase						)			->    Any:
    __lowercase		:      Optional[Any]						     =							dct.pop(__A						)
    __lowercase		:      Dict						     =							val


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

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


def      UpperCAmelCase_	(    __lowerCAmelCase						)			->    Any:
    __lowercase		:      Any						     =							364 if '''coco''' in model_name else 224
    __lowercase		:      Tuple						     =							InstructBlipVisionConfig(image_size=__A						).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		:      Dict						     =							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		:      List[Any]						     =							LlamaConfig.from_pretrained('''decapoda-research/llama-7b-hf'''						,       vocab_size=32_001						).to_dict()
    elif "vicuna-13b" in model_name:
        __lowercase		:      int						     =							LlamaConfig.from_pretrained('''decapoda-research/llama-13b-hf'''						,       vocab_size=32_001						).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[int]						     =							InstructBlipQFormerConfig(vocab_size=30_523						).to_dict()
    __lowercase		:      Optional[int]						     =							InstructBlipConfig(vision_config=__A						,       text_config=__A						,       qformer_config=__A						)

    return config, image_size


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

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

    __lowercase		:      Dict						     =							get_blipa_config(__A						)
    __lowercase		:      Dict						     =							InstructBlipForConditionalGeneration(__A						).eval()

    __lowercase		:      str						     =							{
        '''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		:      Optional[int]						     =							model_name_to_original[model_name]

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

    # update state dict keys
    __lowercase		:      Any						     =							original_model.state_dict()
    __lowercase		:      Tuple						     =							create_rename_keys(__A						)
    for src, dest in rename_keys:
        rename_key(__A						,       __A						,       __A						)

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

    # read in qv biases
    read_in_q_v_bias(__A						,       __A						)

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

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

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

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

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

    print('''Generating with original model...'''						)
    __lowercase		:      Optional[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...'''						)
    __lowercase		:      Any						     =							hf_model.generate(
        **__A						,       do_sample=__A						,       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		:      Union[str, Any]						     =							2
    print('''Original generation:'''						,       __A						)
    __lowercase		:      str						     =							processor.batch_decode(__A						,       skip_special_tokens=__A						)
    __lowercase		:      Optional[int]						     =							[text.strip() for text in output_text]
    print('''HF generation:'''						,       __A						)

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

    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       :      str         =		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       :      List[str]         =		parser.parse_args()

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










'''simple docstring'''







def 		_UpperCAmelCase (				__A				: int   ):
				a_		:	Optional[Any]									=   []
				a_		:	Optional[Any]									=   []
				a_		:	List[str]									=   {
				    '''^''': 3,
				    '''*''': 2,
				    '''/''': 2,
				    '''%''': 2,
				    '''+''': 1,
				    '''-''': 1,
				}  # Priority of each operator
				a_		:	int									=   len(__A   ) if (len(__A   ) > 7) else 7

				# Print table header for output
				print(
				    '''Symbol'''.center(8   )    ,		'''Stack'''.center(__A   )    ,		'''Postfix'''.center(__A   )    ,		sep=''' | '''    ,		)
				print('''-''' * (print_width * 3 + 7)   )

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(__A   )  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(__A   )  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop()   )  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(__A   ) == 0:
																stack.append(__A   )  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(__A   ) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop()   )  # pop stack & add to Postfix
																stack.append(__A   )  # push x to stack

								print(
								    x.center(8   )    ,		(''''''.join(__A   )).ljust(__A   )    ,		(''''''.join(__A   )).ljust(__A   )    ,		sep=''' | '''    ,		)  # Output in tabular format

				while len(__A   ) > 0:  # while stack is not empty
								post_fix.append(stack.pop()   )  # pop stack & add to Postfix
								print(
								    ''' '''.center(8   )    ,		(''''''.join(__A   )).ljust(__A   )    ,		(''''''.join(__A   )).ljust(__A   )    ,		sep=''' | '''    ,		)  # Output in tabular format

				return "".join(__A   )  # return Postfix as str





def 		_UpperCAmelCase (				__A				: Tuple   ):
				a_		:	Union[str, Any]									=   list(infix[::-1]   )  # reverse the infix equation

				for i in range(len(__A   )   ):
								if infix[i] == "(":
												a_		:	List[str]									=   ''')'''  # change "(" to ")"
								elif infix[i] == ")":
												a_		:	str									=   '''('''  # change ")" to "("

				return (infix_2_postfix(''''''.join(__A   )   ))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
		__lowerCAmelCase									=   input('\nEnter an Infix Equation = ')  # Input an Infix equation
		__lowerCAmelCase									=   ''.join(Infix.split())  # Remove spaces from the input
		print('\n\t', Infix, '(Infix) -> ', infix_2_prefix(Infix), '(Prefix)')




