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

	return image






def    _snake_case					(		snake_case__ :  Optional[int]  ):
	A     = []
	# 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    _snake_case					(		snake_case__ :  Dict						,   snake_case__ :  Tuple						,   snake_case__ :  Optional[Any]  ):
	A     = dct.pop(snake_case__  )
	A     = val






def    _snake_case					(		snake_case__ :  List[str]						,   snake_case__ :  Optional[Any]  ):
	for i in range(config.vision_config.num_hidden_layers  ):
		# read in original q and v biases
		A     = state_dict.pop(F'visual_encoder.blocks.{i}.attn.q_bias'  )
		A     = state_dict.pop(F'visual_encoder.blocks.{i}.attn.v_bias'  )

		# next, set bias in the state dict
		A     = torch.cat((q_bias, torch.zeros_like(snake_case__						,   requires_grad=snake_case__  ), v_bias)  )
		A     = qkv_bias






def    _snake_case					(		snake_case__ :  Tuple  ):
	A     = 364 if 'coco' in model_name else 224
	A     = InstructBlipVisionConfig(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 "t5-xl" in model_name:
		A     = TaConfig.from_pretrained('google/flan-t5-xl'						,   dense_act_fn='gelu'						,   bos_token_id=1  ).to_dict()
	elif "t5-xxl" in model_name:
		A     = TaConfig.from_pretrained('google/flan-t5-xxl'						,   dense_act_fn='gelu'						,   bos_token_id=1  ).to_dict()
	elif "vicuna-7b" in model_name:
		A     = LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'						,   vocab_size=3_2001  ).to_dict()
	elif "vicuna-13b" in model_name:
		A     = 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
	A     = InstructBlipQFormerConfig(vocab_size=3_0523  ).to_dict()
	A     = InstructBlipConfig(vision_config=snake_case__						,   text_config=snake_case__						,   qformer_config=snake_case__  )

	return config, image_size






@torch.no_grad()
def    _snake_case					(		snake_case__ :  Tuple						,   snake_case__ :  List[Any]=None						,   snake_case__ :  List[Any]=False  ):
	A     = AutoTokenizer.from_pretrained('bert-base-uncased'						,   truncation_side='left'  )
	qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}  )

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

	A ,   A     = get_blipa_config(snake_case__  )
	A     = InstructBlipForConditionalGeneration(snake_case__  ).eval()

	A     = {
	    '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 ,   A     = model_name_to_original[model_name]

	# load original model
	print('Loading original model...'  )
	A     = 'cuda:1' if torch.cuda.is_available() else 'cpu'
	A     = 'cuda:2' if torch.cuda.is_available() else 'cpu'
	A ,   A ,   A     = 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
	A     = original_model.state_dict()
	A     = 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():
		A     = state_dict.pop(snake_case__  )
		if key.startswith('Qformer.bert'  ):
			A     = key.replace('Qformer.bert'						,   'qformer'  )
		if "attention.self" in key:
			A     = key.replace('self'						,   'attention'  )
		if "llm_proj" in key:
			A     = key.replace('llm_proj'						,   'language_projection'  )
		if "t5_proj" in key:
			A     = key.replace('t5_proj'						,   'language_projection'  )
		if key.startswith('llm_model'  ):
			A     = key.replace('llm_model'						,   'language_model'  )
		if key.startswith('t5'  ):
			A     = key.replace('t5'						,   'language'  )
		A     = val

	# read in qv biases
	read_in_q_v_bias(snake_case__						,   snake_case__  )

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

	A     = load_demo_image()
	A     = 'What is unusual about this image?'

	# create processor
	A     = BlipImageProcessor(
	    size={'height': image_size, 'width': image_size}						,   image_mean=snake_case__						,   image_std=snake_case__  )
	A     = InstructBlipProcessor(
	    image_processor=snake_case__						,   tokenizer=snake_case__						,   qformer_tokenizer=snake_case__						,   )
	A     = processor(images=snake_case__						,   text=snake_case__						,   return_tensors='pt'  ).to(snake_case__  )

	# make sure processor creates exact same pixel values
	A     = vis_processors['eval'](snake_case__  ).unsqueeze(0  ).to(snake_case__  )
	A     = inputs.pixel_values
	assert torch.allclose(original_pixel_values.to(pixel_values.device  )						,   snake_case__  )

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

	print('Generating with original model...'  )
	A     = 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     = hf_model.generate(
	    **snake_case__						,   do_sample=snake_case__						,   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?
		A     = 2
	print('Original generation:'						,   snake_case__  )
	A     = processor.batch_decode(snake_case__						,   skip_special_tokens=snake_case__  )
	A     = [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'Salesforce/{model_name}'  )
		hf_model.push_to_hub(F'Salesforce/{model_name}'  )


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

		_lowercase				 =					parser.parse_args()

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




a_    :  Dict						  =		8.314462  # Unit - J mol-1 K-1

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if moles < 0 or kelvin < 0 or volume < 0:
            raise ValueError("Invalid inputs. Enter positive value."			)
      return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if moles < 0 or kelvin < 0 or pressure < 0:
            raise ValueError("Invalid inputs. Enter positive value."			)
      return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
     from doctest import testmod

     testmod()