





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

				return image

def       __A (     a_  :List[str])						->						Union[str, Any]:
				__a	:  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.embeddings.layernorm.weight'''))
				rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.embeddings.layernorm.bias'''))

				# fmt: on
				return rename_keys

def       __A (     a_  :Dict				,    a_  :List[str]				,    a_  :Optional[int])						->						Any:
				__a	:  Optional[Any]         =					dct.pop(a_)
				__a	:  Any         =					val

def       __A (     a_  :Optional[int]				,    a_  :str)						->						Any:
				for i in range(config.vision_config.num_hidden_layers):
								# read in original q and v biases
								__a	:  Optional[Any]         =					state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias""")
								__a	:  Optional[int]         =					state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias""")

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

def       __A (     a_  :int)						->						Optional[Any]:
				__a	:  Optional[int]         =					3_64 if '''coco''' in model_name else 2_24
				__a	:  str         =					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:
								__a	:  Optional[Any]         =					TaConfig.from_pretrained('''google/flan-t5-xl'''				,    dense_act_fn='''gelu'''				,    bos_token_id=1).to_dict()
				elif "t5-xxl" in model_name:
								__a	:  Dict         =					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	:  List[Any]         =					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	:  int         =					InstructBlipQFormerConfig(vocab_size=3_05_23).to_dict()
				__a	:  Optional[int]         =					InstructBlipConfig(vision_config=a_				,    text_config=a_				,    qformer_config=a_)

				return config, image_size

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

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

				__a	:  Dict         =					get_blipa_config(a_)
				__a	:  Optional[int]         =					InstructBlipForConditionalGeneration(a_).eval()

				__a	:  Optional[Any]         =					{
				    '''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	:  Any         =					model_name_to_original[model_name]

				# load original model
				print('''Loading original model...''')
				__a	:  Dict         =					'''cuda:1''' if torch.cuda.is_available() else '''cpu'''
				__a	:  List[Any]         =					'''cuda:2''' if torch.cuda.is_available() else '''cpu'''
				__a	:  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
				__a	:  Union[str, Any]         =					original_model.state_dict()
				__a	:  int         =					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():
								__a	:  Optional[int]         =					state_dict.pop(a_)
								if key.startswith('''Qformer.bert'''):
												__a	:  Tuple         =					key.replace('''Qformer.bert'''				,    '''qformer''')
								if "attention.self" in key:
												__a	:  Dict         =					key.replace('''self'''				,    '''attention''')
								if "llm_proj" in key:
												__a	:  List[Any]         =					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	:  str         =					key.replace('''llm_model'''				,    '''language_model''')
								if key.startswith('''t5'''):
												__a	:  List[Any]         =					key.replace('''t5'''				,    '''language''')
								__a	:  Optional[Any]         =					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_)

				__a	:  Dict         =					load_demo_image()
				__a	:  Dict         =					'''What is unusual about this image?'''

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

				# make sure processor creates exact same pixel values
				__a	:  List[Any]         =					vis_processors['''eval'''](a_).unsqueeze(0).to(a_)
				__a	:  Any         =					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:
												__a	:  int         =					original_model({'''image''': original_pixel_values, '''text_input''': [prompt]}).logits
												__a	:  int         =					hf_model(**a_).logits
								else:
												__a	:  Optional[int]         =					original_model(
												    {'''image''': original_pixel_values, '''text_input''': [prompt], '''text_output''': ['''\n''']}).logits
												__a	:  Optional[Any]         =					tokenizer('''\n'''				,    return_tensors='''pt''').input_ids.to(a_)
												__a	:  Dict         =					label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id				,    -1_00)
												__a	:  Union[str, Any]         =					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
				__a	:  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...''')
				__a	:  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...''')
				__a	:  Any         =					hf_model.generate(
				    **a_				,    do_sample=a_				,    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	:  int         =					2
				print('''Original generation:'''				,    a_)
				__a	:  int         =					processor.batch_decode(a_				,    skip_special_tokens=a_)
				__a	:  Union[str, Any]         =					[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__":
							A	     =					argparse.ArgumentParser()
							A	     =					[
							    '''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''',
							)

							A	     =					parser.parse_args()

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






"""simple docstring"""






def       __A (     a_  :int)						->						Union[str, Any]:
				__a	:  int         =					[]
				__a	:  Dict         =					[]
				__a	:  str         =					{
				    '''^''': 3,
				    '''*''': 2,
				    '''/''': 2,
				    '''%''': 2,
				    '''+''': 1,
				    '''-''': 1,
				}  # Priority of each operator
				__a	:  Tuple         =					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       __A (     a_  :int)						->						List[Any]:
				__a	:  Dict         =					list(infix[::-1])  # reverse the infix equation

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

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


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