



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 (       ):

    """simple docstring"""


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

    return image


def  __lowerCamelCase (       lowerCamelCase__						):

    """simple docstring"""


    lowercase__					:   Tuple													=					[]
    # 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 (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

    """simple docstring"""


    lowercase__					:   Union[str, Any]													=					dct.pop(snake_case_						)
    lowercase__					:   Optional[Any]													=					val


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

    """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__					:   List[Any]													=					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(snake_case_					,			requires_grad=snake_case_						), v_bias)						)
        lowercase__					:   Optional[Any]													=					qkv_bias


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

    """simple docstring"""


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

    lowercase__					:   Optional[int]													=					BlipaConfig(vision_config=snake_case_					,			text_config=snake_case_						)

    return config, image_size


@torch.no_grad()
def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=None					,			lowerCamelCase__=False						):

    """simple docstring"""


    lowercase__					:   int													=					(
        AutoTokenizer.from_pretrained("facebook/opt-2.7b"						)
        if "opt" in model_name
        else AutoTokenizer.from_pretrained("google/flan-t5-xl"						)
    )
    lowercase__					:   Optional[int]													=					tokenizer("\n"					,			add_special_tokens=snake_case_						).input_ids[0]
    lowercase__							,							lowercase__					:   Tuple													=					get_blipa_config(snake_case_					,			eos_token_id=snake_case_						)

    lowercase__					:   str													=					BlipaForConditionalGeneration(snake_case_						).eval()

    lowercase__					:   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"),
    }

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

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

    # read in qv biases
    read_in_q_v_bias(snake_case_					,			snake_case_						)

    lowercase__							,							lowercase__					:   Optional[int]													=					hf_model.load_state_dict(snake_case_					,			strict=snake_case_						)
    assert len(snake_case_						) == 0
    assert unexpected_keys == ["qformer.embeddings.position_ids"]

    lowercase__					:   Optional[Any]													=					load_demo_image()
    lowercase__					:   int													=					vis_processors["eval"](snake_case_						).unsqueeze(0						).to(snake_case_						)
    lowercase__					:   Optional[int]													=					tokenizer(["\n"]					,			return_tensors="pt"						).input_ids.to(snake_case_						)

    # create processor
    lowercase__					:   Union[str, Any]													=					BlipImageProcessor(
        size={"height": image_size, "width": image_size}					,			image_mean=snake_case_					,			image_std=snake_case_						)
    lowercase__					:   Optional[int]													=					BlipaProcessor(image_processor=snake_case_					,			tokenizer=snake_case_						)
    lowercase__					:   int													=					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:
            lowercase__					:   Dict													=					original_model({"image": original_pixel_values, "text_input": [""]}						).logits
            lowercase__					:   Any													=					hf_model(snake_case_					,			snake_case_						).logits
        else:
            lowercase__					:   Any													=					original_model(
                {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}						).logits
            lowercase__					:   Optional[Any]													=					input_ids.masked_fill(input_ids == tokenizer.pad_token_id					,			-100						)
            lowercase__					:   str													=					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":
        lowercase__					:   Dict													=					torch.tensor(
            [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]					,			device=snake_case_						)
        assert torch.allclose(logits[0, :3, :3]					,			snake_case_					,			atol=1e-4						)
    elif model_name == "blip2-flan-t5-xl-coco":
        lowercase__					:   Union[str, Any]													=					torch.tensor(
            [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]					,			device=snake_case_						)
    else:
        # cast to same type
        lowercase__					:   Tuple													=					logits.dtype
        assert torch.allclose(original_logits.to(snake_case_						)					,			snake_case_					,			atol=1e-2						)
    print("Looks ok!"						)

    print("Generating a caption..."						)
    lowercase__					:   int													=					""
    lowercase__					:   Any													=					tokenizer(snake_case_					,			return_tensors="pt"						).input_ids.to(snake_case_						)

    lowercase__					:   Optional[Any]													=					original_model.generate({"image": original_pixel_values}						)
    lowercase__					:   Optional[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_						)
    lowercase__					:   Optional[Any]													=					input_ids.shape[1]
    lowercase__					:   List[str]													=					processor.batch_decode(outputs[:, prompt_length:]					,			skip_special_tokens=snake_case_						)
    lowercase__					:   Optional[Any]													=					[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__":
    lowerCAmelCase__					  =  argparse.ArgumentParser()
    lowerCAmelCase__					  =  [
        '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''',
    )

    lowerCAmelCase__					  =  parser.parse_args()

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









def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Union[str, Any]													=					[]
				lowercase__					:   Tuple													=					[]
				lowercase__					:   Any													=					{
				    "^": 3,
				    "*": 2,
				    "/": 2,
				    "%": 2,
				    "+": 1,
				    "-": 1,
				}  # Priority of each operator
				lowercase__					:   Any													=					len(lowerCamelCase__						) if (len(lowerCamelCase__						) > 7) else 7

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

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

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

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

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


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Optional[int]													=					list(infix[::-1]						)  # reverse the infix equation

				for i in range(len(lowerCamelCase__						)						):
								if infix[i] == "(":
												lowercase__					:   Tuple													=					")"  # change "(" to ")"
								elif infix[i] == ")":
												lowercase__					:   Optional[Any]													=					"("  # change ")" to "("

				return (infix_2_postfix("".join(lowerCamelCase__						)						))[
				    ::-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)''')




