






'''simple docstring'''







import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


UpperCamelCase     :   int	      =		{
    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
    'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def     A__      (					__lowerCAmelCase		:      str    ):
						assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
						assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
						if args.mlm:
												assert os.path.isfile(args.token_counts    )
												assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
						else:
												assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

						assert args.teacher_type == args.student_type or (
						    args.student_type == "distilbert" and args.teacher_type == "bert"
						)
						assert os.path.isfile(args.student_config    )
						if args.student_pretrained_weights is not None:
												assert os.path.isfile(args.student_pretrained_weights    )

						if args.freeze_token_type_embds:
												assert args.student_type in ["roberta"]

						assert args.alpha_ce >= 0.0
						assert args.alpha_mlm >= 0.0
						assert args.alpha_clm >= 0.0
						assert args.alpha_mse >= 0.0
						assert args.alpha_cos >= 0.0
						assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def     A__      (					__lowerCAmelCase		:      Optional[Any] ,     __lowerCAmelCase		:      List[str]    ):
						if args.student_type == "roberta":
												lowerCamelCase__								=	False
						elif args.student_type == "gpt2":
												lowerCamelCase__								=	False
def     A__      (					__lowerCAmelCase		:      Dict ,     __lowerCAmelCase		:      Tuple    ):
						if args.student_type == "roberta":
												lowerCamelCase__								=	False
def     A__      (					):
						lowerCamelCase__								=	argparse.ArgumentParser(description="""Training"""    )
						parser.add_argument("""--force""" ,     action="""store_true""" ,     help="""Overwrite dump_path if it already exists."""    )

						parser.add_argument(
						    """--dump_path""" ,     type=__lowerCAmelCase ,     required=__lowerCAmelCase ,     help="""The output directory (log, checkpoints, parameters, etc.)"""    )
						parser.add_argument(
						    """--data_file""" ,     type=__lowerCAmelCase ,     required=__lowerCAmelCase ,     help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence.""" ,     )

						parser.add_argument(
						    """--student_type""" ,     type=__lowerCAmelCase ,     choices=["""distilbert""", """roberta""", """gpt2"""] ,     required=__lowerCAmelCase ,     help="""The student type (DistilBERT, RoBERTa).""" ,     )
						parser.add_argument("""--student_config""" ,     type=__lowerCAmelCase ,     required=__lowerCAmelCase ,     help="""Path to the student configuration."""    )
						parser.add_argument(
						    """--student_pretrained_weights""" ,     default=__lowerCAmelCase ,     type=__lowerCAmelCase ,     help="""Load student initialization checkpoint."""    )

						parser.add_argument(
						    """--teacher_type""" ,     choices=["""bert""", """roberta""", """gpt2"""] ,     required=__lowerCAmelCase ,     help="""Teacher type (BERT, RoBERTa)."""    )
						parser.add_argument("""--teacher_name""" ,     type=__lowerCAmelCase ,     required=__lowerCAmelCase ,     help="""The teacher model."""    )

						parser.add_argument("""--temperature""" ,     default=2.0 ,     type=__lowerCAmelCase ,     help="""Temperature for the softmax temperature."""    )
						parser.add_argument(
						    """--alpha_ce""" ,     default=0.5 ,     type=__lowerCAmelCase ,     help="""Linear weight for the distillation loss. Must be >=0."""    )
						parser.add_argument(
						    """--alpha_mlm""" ,     default=0.0 ,     type=__lowerCAmelCase ,     help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.""" ,     )
						parser.add_argument("""--alpha_clm""" ,     default=0.5 ,     type=__lowerCAmelCase ,     help="""Linear weight for the CLM loss. Must be >=0."""    )
						parser.add_argument("""--alpha_mse""" ,     default=0.0 ,     type=__lowerCAmelCase ,     help="""Linear weight of the MSE loss. Must be >=0."""    )
						parser.add_argument(
						    """--alpha_cos""" ,     default=0.0 ,     type=__lowerCAmelCase ,     help="""Linear weight of the cosine embedding loss. Must be >=0."""    )

						parser.add_argument(
						    """--mlm""" ,     action="""store_true""" ,     help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."""    )
						parser.add_argument(
						    """--mlm_mask_prop""" ,     default=0.15 ,     type=__lowerCAmelCase ,     help="""Proportion of tokens for which we need to make a prediction.""" ,     )
						parser.add_argument("""--word_mask""" ,     default=0.8 ,     type=__lowerCAmelCase ,     help="""Proportion of tokens to mask out."""    )
						parser.add_argument("""--word_keep""" ,     default=0.1 ,     type=__lowerCAmelCase ,     help="""Proportion of tokens to keep."""    )
						parser.add_argument("""--word_rand""" ,     default=0.1 ,     type=__lowerCAmelCase ,     help="""Proportion of tokens to randomly replace."""    )
						parser.add_argument(
						    """--mlm_smoothing""" ,     default=0.7 ,     type=__lowerCAmelCase ,     help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).""" ,     )
						parser.add_argument("""--token_counts""" ,     type=__lowerCAmelCase ,     help="""The token counts in the data_file for MLM."""    )

						parser.add_argument(
						    """--restrict_ce_to_mask""" ,     action="""store_true""" ,     help="""If true, compute the distillation loss only the [MLM] prediction distribution.""" ,     )
						parser.add_argument(
						    """--freeze_pos_embs""" ,     action="""store_true""" ,     help="""Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.""" ,     )
						parser.add_argument(
						    """--freeze_token_type_embds""" ,     action="""store_true""" ,     help="""Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.""" ,     )

						parser.add_argument("""--n_epoch""" ,     type=__lowerCAmelCase ,     default=3 ,     help="""Number of pass on the whole dataset."""    )
						parser.add_argument("""--batch_size""" ,     type=__lowerCAmelCase ,     default=5 ,     help="""Batch size (for each process)."""    )
						parser.add_argument(
						    """--group_by_size""" ,     action="""store_false""" ,     help="""If true, group sequences that have similar length into the same batch. Default is true.""" ,     )

						parser.add_argument(
						    """--gradient_accumulation_steps""" ,     type=__lowerCAmelCase ,     default=50 ,     help="""Gradient accumulation for larger training batches.""" ,     )
						parser.add_argument("""--warmup_prop""" ,     default=0.05 ,     type=__lowerCAmelCase ,     help="""Linear warmup proportion."""    )
						parser.add_argument("""--weight_decay""" ,     default=0.0 ,     type=__lowerCAmelCase ,     help="""Weight decay if we apply some."""    )
						parser.add_argument("""--learning_rate""" ,     default=5e-4 ,     type=__lowerCAmelCase ,     help="""The initial learning rate for Adam."""    )
						parser.add_argument("""--adam_epsilon""" ,     default=1e-6 ,     type=__lowerCAmelCase ,     help="""Epsilon for Adam optimizer."""    )
						parser.add_argument("""--max_grad_norm""" ,     default=5.0 ,     type=__lowerCAmelCase ,     help="""Max gradient norm."""    )
						parser.add_argument("""--initializer_range""" ,     default=0.02 ,     type=__lowerCAmelCase ,     help="""Random initialization range."""    )

						parser.add_argument(
						    """--fp16""" ,     action="""store_true""" ,     help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit""" ,     )
						parser.add_argument(
						    """--fp16_opt_level""" ,     type=__lowerCAmelCase ,     default="""O1""" ,     help=(
						        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
						        """See details at https://nvidia.github.io/apex/amp.html"""
						    ) ,     )
						parser.add_argument("""--n_gpu""" ,     type=__lowerCAmelCase ,     default=1 ,     help="""Number of GPUs in the node."""    )
						parser.add_argument("""--local_rank""" ,     type=__lowerCAmelCase ,     default=-1 ,     help="""Distributed training - Local rank"""    )
						parser.add_argument("""--seed""" ,     type=__lowerCAmelCase ,     default=56 ,     help="""Random seed"""    )

						parser.add_argument("""--log_interval""" ,     type=__lowerCAmelCase ,     default=500 ,     help="""Tensorboard logging interval."""    )
						parser.add_argument("""--checkpoint_interval""" ,     type=__lowerCAmelCase ,     default=4000 ,     help="""Checkpoint interval."""    )
						lowerCamelCase__								=	parser.parse_args()
						sanity_checks(__lowerCAmelCase    )

						# ARGS #
						init_gpu_params(__lowerCAmelCase    )
						set_seed(__lowerCAmelCase    )
						if args.is_master:
												if os.path.exists(args.dump_path    ):
																		if not args.force:
																								raise ValueError(
																								    F'''Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'''
																								    """ itUse `--force` if you want to overwrite it"""    )
																		else:
																								shutil.rmtree(args.dump_path    )

												if not os.path.exists(args.dump_path    ):
																		os.makedirs(args.dump_path    )
												logger.info(F'''Experiment will be dumped and logged in {args.dump_path}'''    )

												# SAVE PARAMS #
												logger.info(F'''Param: {args}'''    )
												with open(os.path.join(args.dump_path ,     """parameters.json"""    ) ,     """w"""    ) as f:
																		json.dump(vars(__lowerCAmelCase    ) ,     __lowerCAmelCase ,     indent=4    )
												git_log(args.dump_path    )

						lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__								=	MODEL_CLASSES[args.student_type]
						lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__								=	MODEL_CLASSES[args.teacher_type]

						# TOKENIZER #
						lowerCamelCase__								=	teacher_tokenizer_class.from_pretrained(args.teacher_name    )
						lowerCamelCase__								=	{}
						for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
												lowerCamelCase__								=	tokenizer.all_special_tokens.index(__lowerCAmelCase    )
												lowerCamelCase__								=	tokenizer.all_special_ids[idx]
						logger.info(F'''Special tokens {special_tok_ids}'''    )
						lowerCamelCase__								=	special_tok_ids
						lowerCamelCase__								=	tokenizer.max_model_input_sizes[args.teacher_name]

						# DATA LOADER #
						logger.info(F'''Loading data from {args.data_file}'''    )
						with open(args.data_file ,     """rb"""    ) as fp:
												lowerCamelCase__								=	pickle.load(__lowerCAmelCase    )

						if args.mlm:
												logger.info(F'''Loading token counts from {args.token_counts} (already pre-computed)'''    )
												with open(args.token_counts ,     """rb"""    ) as fp:
																		lowerCamelCase__								=	pickle.load(__lowerCAmelCase    )

												lowerCamelCase__								=	np.maximum(__lowerCAmelCase ,     1    ) ** -args.mlm_smoothing
												for idx in special_tok_ids.values():
																		lowerCamelCase__								=	0.0  # do not predict special tokens
												lowerCamelCase__								=	torch.from_numpy(__lowerCAmelCase    )
						else:
												lowerCamelCase__								=	None

						lowerCamelCase__								=	LmSeqsDataset(params=__lowerCAmelCase ,     data=__lowerCAmelCase    )
						logger.info("""Data loader created."""    )

						# STUDENT #
						logger.info(F'''Loading student config from {args.student_config}'''    )
						lowerCamelCase__								=	student_config_class.from_pretrained(args.student_config    )
						lowerCamelCase__								=	True

						if args.student_pretrained_weights is not None:
												logger.info(F'''Loading pretrained weights from {args.student_pretrained_weights}'''    )
												lowerCamelCase__								=	student_model_class.from_pretrained(args.student_pretrained_weights ,     config=__lowerCAmelCase    )
						else:
												lowerCamelCase__								=	student_model_class(__lowerCAmelCase    )

						if args.n_gpu > 0:
												student.to(F'''cuda:{args.local_rank}'''    )
						logger.info("""Student loaded."""    )

						# TEACHER #
						lowerCamelCase__								=	teacher_model_class.from_pretrained(args.teacher_name ,     output_hidden_states=__lowerCAmelCase    )
						if args.n_gpu > 0:
												teacher.to(F'''cuda:{args.local_rank}'''    )
						logger.info(F'''Teacher loaded from {args.teacher_name}.'''    )

						# FREEZING #
						if args.freeze_pos_embs:
												freeze_pos_embeddings(__lowerCAmelCase ,     __lowerCAmelCase    )
						if args.freeze_token_type_embds:
												freeze_token_type_embeddings(__lowerCAmelCase ,     __lowerCAmelCase    )

						# SANITY CHECKS #
						assert student.config.vocab_size == teacher.config.vocab_size
						assert student.config.hidden_size == teacher.config.hidden_size
						assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
						if args.mlm:
												assert token_probs.size(0    ) == stu_architecture_config.vocab_size

						# DISTILLER #
						torch.cuda.empty_cache()
						lowerCamelCase__								=	Distiller(
						    params=__lowerCAmelCase ,     dataset=__lowerCAmelCase ,     token_probs=__lowerCAmelCase ,     student=__lowerCAmelCase ,     teacher=__lowerCAmelCase    )
						distiller.train()
						logger.info("""Let's go get some drinks."""    )


if __name__ == "__main__":
							main()



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   lowerCamelCase__							(							):




			"""simple docstring"""


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

			return image






def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[int]							):




			"""simple docstring"""


			lowerCAmelCase_         =      []
			# 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   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[Any]       ,				__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    Union[str, Any]							):




			"""simple docstring"""


			lowerCAmelCase_         =      dct.pop(__lowerCAmelCase							)
			lowerCAmelCase_         =      val






def   lowerCamelCase__							(							__lowerCAmelCase    :    Any       ,				__lowerCAmelCase    :    Union[str, Any]							):




			"""simple docstring"""


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

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






def   lowerCamelCase__							(							__lowerCAmelCase    :    Optional[int]							):




			"""simple docstring"""


			lowerCAmelCase_         =      364 if "coco" in model_name else 224
			lowerCAmelCase_         =      InstructBlipVisionConfig(image_size=__lowerCAmelCase							).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:
						lowerCAmelCase_         =      TaConfig.from_pretrained("google/flan-t5-xl"       ,				dense_act_fn="gelu"       ,				bos_token_id=1							).to_dict()
			elif "t5-xxl" in model_name:
						lowerCAmelCase_         =      TaConfig.from_pretrained("google/flan-t5-xxl"       ,				dense_act_fn="gelu"       ,				bos_token_id=1							).to_dict()
			elif "vicuna-7b" in model_name:
						lowerCAmelCase_         =      LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf"       ,				vocab_size=32001							).to_dict()
			elif "vicuna-13b" in model_name:
						lowerCAmelCase_         =      LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf"       ,				vocab_size=32001							).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
			lowerCAmelCase_         =      InstructBlipQFormerConfig(vocab_size=30523							).to_dict()
			lowerCAmelCase_         =      InstructBlipConfig(vision_config=__lowerCAmelCase       ,				text_config=__lowerCAmelCase       ,				qformer_config=__lowerCAmelCase							)

			return config, image_size






@torch.no_grad()
def   lowerCamelCase__							(							__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    int=None       ,				__lowerCAmelCase    :    Dict=False							):




			"""simple docstring"""


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

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

			lowerCAmelCase_      ,    lowerCAmelCase_         =      get_blipa_config(__lowerCAmelCase							)
			lowerCAmelCase_         =      InstructBlipForConditionalGeneration(__lowerCAmelCase							).eval()

			lowerCAmelCase_         =      {
			    "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"),
			}

			lowerCAmelCase_      ,    lowerCAmelCase_         =      model_name_to_original[model_name]

			# load original model
			print("Loading original model..."							)
			lowerCAmelCase_         =      "cuda:1" if torch.cuda.is_available() else "cpu"
			lowerCAmelCase_         =      "cuda:2" if torch.cuda.is_available() else "cpu"
			lowerCAmelCase_      ,    lowerCAmelCase_      ,    lowerCAmelCase_         =      load_model_and_preprocess(
			    name=__lowerCAmelCase       ,				model_type=__lowerCAmelCase       ,				is_eval=__lowerCAmelCase       ,				device=__lowerCAmelCase							)
			original_model.eval()
			print("Done!"							)

			# update state dict keys
			lowerCAmelCase_         =      original_model.state_dict()
			lowerCAmelCase_         =      create_rename_keys(__lowerCAmelCase							)
			for src, dest in rename_keys:
						rename_key(__lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase							)

			# some keys can be renamed efficiently
			for key, val in state_dict.copy().items():
						lowerCAmelCase_         =      state_dict.pop(__lowerCAmelCase							)
						if key.startswith("Qformer.bert"							):
									lowerCAmelCase_         =      key.replace("Qformer.bert"       ,				"qformer"							)
						if "attention.self" in key:
									lowerCAmelCase_         =      key.replace("self"       ,				"attention"							)
						if "llm_proj" in key:
									lowerCAmelCase_         =      key.replace("llm_proj"       ,				"language_projection"							)
						if "t5_proj" in key:
									lowerCAmelCase_         =      key.replace("t5_proj"       ,				"language_projection"							)
						if key.startswith("llm_model"							):
									lowerCAmelCase_         =      key.replace("llm_model"       ,				"language_model"							)
						if key.startswith("t5"							):
									lowerCAmelCase_         =      key.replace("t5"       ,				"language"							)
						lowerCAmelCase_         =      val

			# read in qv biases
			read_in_q_v_bias(__lowerCAmelCase       ,				__lowerCAmelCase							)

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

			lowerCAmelCase_         =      load_demo_image()
			lowerCAmelCase_         =      "What is unusual about this image?"

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

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

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

			print("Generating with original model..."							)
			lowerCAmelCase_         =      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..."							)
			lowerCAmelCase_         =      hf_model.generate(
			    **__lowerCAmelCase       ,				do_sample=__lowerCAmelCase       ,				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?
						lowerCAmelCase_         =      2
			print("Original generation:"       ,				__lowerCAmelCase							)
			lowerCAmelCase_         =      processor.batch_decode(__lowerCAmelCase       ,				skip_special_tokens=__lowerCAmelCase							)
			lowerCAmelCase_         =      [text.strip() for text in output_text]
			print("HF generation:"       ,				__lowerCAmelCase							)

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

			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)



