
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder





@dataclass
class       _lowerCAmelCase   (      __a			):
				_lowercase     						=42





class       _lowerCAmelCase   (      __a   , __a			):
				_lowercase     						=True




				@register_to_config
				def __init__(   self							,							_UpperCamelCase = 3							,							_UpperCamelCase = 3							,							_UpperCamelCase = ("DownEncoderBlock2D",)							,							_UpperCamelCase = ("UpDecoderBlock2D",)							,							_UpperCamelCase = (64,)							,							_UpperCamelCase = 1							,							_UpperCamelCase = "silu"							,							_UpperCamelCase = 4							,							_UpperCamelCase = 32							,							_UpperCamelCase = 32							,							_UpperCamelCase = 0.18215							,							)							-> Optional[int]:
							super().__init__()

							# pass init params to Encoder
							lowerCAmelCase_         =      Encoder(
							    in_channels=_UpperCamelCase							,							out_channels=_UpperCamelCase							,							down_block_types=_UpperCamelCase							,							block_out_channels=_UpperCamelCase							,							layers_per_block=_UpperCamelCase							,							act_fn=_UpperCamelCase							,							norm_num_groups=_UpperCamelCase							,							double_z=_UpperCamelCase							,							)

							# pass init params to Decoder
							lowerCAmelCase_         =      Decoder(
							    in_channels=_UpperCamelCase							,							out_channels=_UpperCamelCase							,							up_block_types=_UpperCamelCase							,							block_out_channels=_UpperCamelCase							,							layers_per_block=_UpperCamelCase							,							norm_num_groups=_UpperCamelCase							,							act_fn=_UpperCamelCase							,							)

							lowerCAmelCase_         =      nn.Convad(2 * latent_channels							,							2 * latent_channels							,							1	)
							lowerCAmelCase_         =      nn.Convad(_UpperCamelCase							,							_UpperCamelCase							,							1	)

							lowerCAmelCase_         =      False
							lowerCAmelCase_         =      False

							# only relevant if vae tiling is enabled
							lowerCAmelCase_         =      self.config.sample_size
							lowerCAmelCase_         =      (
							    self.config.sample_size[0]
							    if isinstance(self.config.sample_size							,							(list, tuple)	)
							    else self.config.sample_size
							)
							lowerCAmelCase_         =      int(sample_size / (2 ** (len(self.config.block_out_channels	) - 1))	)
							lowerCAmelCase_         =      0.25




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase=False	)							-> str:
							if isinstance(_UpperCamelCase							,							(Encoder, Decoder)	):
										lowerCAmelCase_         =      value




				def 	__a		(   self							,							_UpperCamelCase = True	)							-> List[Any]:
							lowerCAmelCase_         =      use_tiling




				def 	__a		(   self	)							-> Optional[Any]:
							self.enable_tiling(_UpperCamelCase	)




				def 	__a		(   self	)							-> str:
							lowerCAmelCase_         =      True




				def 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      False




				@property
				# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
				def 	__a		(   self	)							-> Dict[str, AttentionProcessor]:
							lowerCAmelCase_         =      {}

							def fn_recursive_add_processors(_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	):
										if hasattr(_UpperCamelCase							,							"set_processor"	):
													lowerCAmelCase_         =      module.processor

										for sub_name, child in module.named_children():
													fn_recursive_add_processors(f"""{name}.{sub_name}"""							,							_UpperCamelCase							,							_UpperCamelCase	)

										return processors

							for name, module in self.named_children():
										fn_recursive_add_processors(_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)

							return processors




				def 	__a		(   self							,							_UpperCamelCase	)							-> Optional[int]:
							lowerCAmelCase_         =      len(self.attn_processors.keys()	)

							if isinstance(_UpperCamelCase							,							_UpperCamelCase	) and len(_UpperCamelCase	) != count:
										raise ValueError(
										    f"""A dict of processors was passed, but the number of processors {len(_UpperCamelCase	)} does not match the"""
										    f""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""	)

							def fn_recursive_attn_processor(_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	):
										if hasattr(_UpperCamelCase							,							"set_processor"	):
													if not isinstance(_UpperCamelCase							,							_UpperCamelCase	):
																module.set_processor(_UpperCamelCase	)
													else:
																module.set_processor(processor.pop(f"""{name}.processor"""	)	)

										for sub_name, child in module.named_children():
													fn_recursive_attn_processor(f"""{name}.{sub_name}"""							,							_UpperCamelCase							,							_UpperCamelCase	)

							for name, module in self.named_children():
										fn_recursive_attn_processor(_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)




				def 	__a		(   self	)							-> Optional[int]:
							self.set_attn_processor(AttnProcessor()	)




				@apply_forward_hook
				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase = True	)							-> AutoencoderKLOutput:
							if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
										return self.tiled_encode(_UpperCamelCase							,							return_dict=_UpperCamelCase	)

							if self.use_slicing and x.shape[0] > 1:
										lowerCAmelCase_         =      [self.encoder(_UpperCamelCase	) for x_slice in x.split(1	)]
										lowerCAmelCase_         =      torch.cat(_UpperCamelCase	)
							else:
										lowerCAmelCase_         =      self.encoder(_UpperCamelCase	)

							lowerCAmelCase_         =      self.quant_conv(_UpperCamelCase	)
							lowerCAmelCase_         =      DiagonalGaussianDistribution(_UpperCamelCase	)

							if not return_dict:
										return (posterior,)

							return AutoencoderKLOutput(latent_dist=_UpperCamelCase	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase = True	)							-> Union[DecoderOutput, torch.FloatTensor]:
							if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
										return self.tiled_decode(_UpperCamelCase							,							return_dict=_UpperCamelCase	)

							lowerCAmelCase_         =      self.post_quant_conv(_UpperCamelCase	)
							lowerCAmelCase_         =      self.decoder(_UpperCamelCase	)

							if not return_dict:
										return (dec,)

							return DecoderOutput(sample=_UpperCamelCase	)




				@apply_forward_hook
				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase = True	)							-> Union[DecoderOutput, torch.FloatTensor]:
							if self.use_slicing and z.shape[0] > 1:
										lowerCAmelCase_         =      [self._decode(_UpperCamelCase	).sample for z_slice in z.split(1	)]
										lowerCAmelCase_         =      torch.cat(_UpperCamelCase	)
							else:
										lowerCAmelCase_         =      self._decode(_UpperCamelCase	).sample

							if not return_dict:
										return (decoded,)

							return DecoderOutput(sample=_UpperCamelCase	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> str:
							lowerCAmelCase_         =      min(a.shape[2]							,							b.shape[2]							,							_UpperCamelCase	)
							for y in range(_UpperCamelCase	):
										lowerCAmelCase_         =      a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
							return b




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> int:
							lowerCAmelCase_         =      min(a.shape[3]							,							b.shape[3]							,							_UpperCamelCase	)
							for x in range(_UpperCamelCase	):
										lowerCAmelCase_         =      a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
							return b




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase = True	)							-> AutoencoderKLOutput:
							lowerCAmelCase_         =      int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)	)
							lowerCAmelCase_         =      int(self.tile_latent_min_size * self.tile_overlap_factor	)
							lowerCAmelCase_         =      self.tile_latent_min_size - blend_extent

							# Split the image into 512x512 tiles and encode them separately.
							lowerCAmelCase_         =      []
							for i in range(0							,							x.shape[2]							,							_UpperCamelCase	):
										lowerCAmelCase_         =      []
										for j in range(0							,							x.shape[3]							,							_UpperCamelCase	):
													lowerCAmelCase_         =      x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
													lowerCAmelCase_         =      self.encoder(_UpperCamelCase	)
													lowerCAmelCase_         =      self.quant_conv(_UpperCamelCase	)
													row.append(_UpperCamelCase	)
										rows.append(_UpperCamelCase	)
							lowerCAmelCase_         =      []
							for i, row in enumerate(_UpperCamelCase	):
										lowerCAmelCase_         =      []
										for j, tile in enumerate(_UpperCamelCase	):
													# blend the above tile and the left tile
													# to the current tile and add the current tile to the result row
													if i > 0:
																lowerCAmelCase_         =      self.blend_v(rows[i - 1][j]							,							_UpperCamelCase							,							_UpperCamelCase	)
													if j > 0:
																lowerCAmelCase_         =      self.blend_h(row[j - 1]							,							_UpperCamelCase							,							_UpperCamelCase	)
													result_row.append(tile[:, :, :row_limit, :row_limit]	)
										result_rows.append(torch.cat(_UpperCamelCase							,							dim=3	)	)

							lowerCAmelCase_         =      torch.cat(_UpperCamelCase							,							dim=2	)
							lowerCAmelCase_         =      DiagonalGaussianDistribution(_UpperCamelCase	)

							if not return_dict:
										return (posterior,)

							return AutoencoderKLOutput(latent_dist=_UpperCamelCase	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase = True	)							-> Union[DecoderOutput, torch.FloatTensor]:
							lowerCAmelCase_         =      int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)	)
							lowerCAmelCase_         =      int(self.tile_sample_min_size * self.tile_overlap_factor	)
							lowerCAmelCase_         =      self.tile_sample_min_size - blend_extent

							# Split z into overlapping 64x64 tiles and decode them separately.
							# The tiles have an overlap to avoid seams between tiles.
							lowerCAmelCase_         =      []
							for i in range(0							,							z.shape[2]							,							_UpperCamelCase	):
										lowerCAmelCase_         =      []
										for j in range(0							,							z.shape[3]							,							_UpperCamelCase	):
													lowerCAmelCase_         =      z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
													lowerCAmelCase_         =      self.post_quant_conv(_UpperCamelCase	)
													lowerCAmelCase_         =      self.decoder(_UpperCamelCase	)
													row.append(_UpperCamelCase	)
										rows.append(_UpperCamelCase	)
							lowerCAmelCase_         =      []
							for i, row in enumerate(_UpperCamelCase	):
										lowerCAmelCase_         =      []
										for j, tile in enumerate(_UpperCamelCase	):
													# blend the above tile and the left tile
													# to the current tile and add the current tile to the result row
													if i > 0:
																lowerCAmelCase_         =      self.blend_v(rows[i - 1][j]							,							_UpperCamelCase							,							_UpperCamelCase	)
													if j > 0:
																lowerCAmelCase_         =      self.blend_h(row[j - 1]							,							_UpperCamelCase							,							_UpperCamelCase	)
													result_row.append(tile[:, :, :row_limit, :row_limit]	)
										result_rows.append(torch.cat(_UpperCamelCase							,							dim=3	)	)

							lowerCAmelCase_         =      torch.cat(_UpperCamelCase							,							dim=2	)
							if not return_dict:
										return (dec,)

							return DecoderOutput(sample=_UpperCamelCase	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase = False							,							_UpperCamelCase = True							,							_UpperCamelCase = None							,							)							-> Union[DecoderOutput, torch.FloatTensor]:
							lowerCAmelCase_         =      sample
							lowerCAmelCase_         =      self.encode(_UpperCamelCase	).latent_dist
							if sample_posterior:
										lowerCAmelCase_         =      posterior.sample(generator=_UpperCamelCase	)
							else:
										lowerCAmelCase_         =      posterior.mode()
							lowerCAmelCase_         =      self.decode(_UpperCamelCase	).sample

							if not return_dict:
										return (dec,)

							return DecoderOutput(sample=_UpperCamelCase	)





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)



