
# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[Any]       ,				__lowerCAmelCase    :    int       ,				__lowerCAmelCase    :    Tuple=0							):




			"""simple docstring"""


			if name is None:
						lowerCAmelCase_         =      None
			else:
						lowerCAmelCase_         =      "." * max(0       ,				spaces - 2							) + "# {:" + str(50 - spaces							) + "s}"
						lowerCAmelCase_         =      fmt.format(__lowerCAmelCase							)

			# Print and recurse (if needed).
			if isinstance(__lowerCAmelCase       ,				__lowerCAmelCase							):
						if msg is not None:
									print(__lowerCAmelCase							)
						for k in val.keys():
									recursive_print(__lowerCAmelCase       ,				val[k]       ,				spaces + 2							)
			elif isinstance(__lowerCAmelCase       ,				torch.Tensor							):
						print(__lowerCAmelCase       ,				":"       ,				val.size()							)
			else:
						print(__lowerCAmelCase       ,				":"       ,				__lowerCAmelCase							)






def   lowerCamelCase__							(							__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    Any       ,				__lowerCAmelCase    :    Tuple       ,				__lowerCAmelCase    :    Optional[Any]       ,				__lowerCAmelCase    :    Tuple							):




			"""simple docstring"""


			lowerCAmelCase_         =      param.size()
			if checkpoint_version == 1.0:
						# version 1.0 stores [num_heads * hidden_size * num_splits, :]
						lowerCAmelCase_         =      (num_heads, hidden_size, num_splits) + input_shape[1:]
						lowerCAmelCase_         =      param.view(*__lowerCAmelCase							)
						lowerCAmelCase_         =      param.transpose(0       ,				2							)
						lowerCAmelCase_         =      param.transpose(1       ,				2							).contiguous()
			elif checkpoint_version >= 2.0:
						# other versions store [num_heads * num_splits * hidden_size, :]
						lowerCAmelCase_         =      (num_heads, num_splits, hidden_size) + input_shape[1:]
						lowerCAmelCase_         =      param.view(*__lowerCAmelCase							)
						lowerCAmelCase_         =      param.transpose(0       ,				1							).contiguous()
			lowerCAmelCase_         =      param.view(*__lowerCAmelCase							)
			return param






def   lowerCamelCase__							(							__lowerCAmelCase    :    Dict       ,				__lowerCAmelCase    :    Optional[Any]       ,				__lowerCAmelCase    :    List[Any]							):




			"""simple docstring"""


			lowerCAmelCase_         =      {}

			# old versions did not store training args
			lowerCAmelCase_         =      input_state_dict.get("args"       ,				__lowerCAmelCase							)
			if ds_args is not None:
						# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
						# from pprint import pprint
						# pprint(vars(ds_args))

						lowerCAmelCase_         =      ds_args.padded_vocab_size
						lowerCAmelCase_         =      ds_args.max_position_embeddings
						lowerCAmelCase_         =      ds_args.hidden_size
						lowerCAmelCase_         =      ds_args.num_layers
						lowerCAmelCase_         =      ds_args.num_attention_heads
						lowerCAmelCase_         =      ds_args.ffn_hidden_size
						# pprint(config)

			# The number of heads.
			lowerCAmelCase_         =      config.n_head
			# The hidden_size per head.
			lowerCAmelCase_         =      config.n_embd // config.n_head
			# Megatron-LM checkpoint version
			if "checkpoint_version" in input_state_dict.keys():
						lowerCAmelCase_         =      input_state_dict["checkpoint_version"]
			else:
						lowerCAmelCase_         =      0.0

			# The model.
			lowerCAmelCase_         =      input_state_dict["model"]
			# The language model.
			lowerCAmelCase_         =      model["language_model"]
			# The embeddings.
			lowerCAmelCase_         =      lm["embedding"]

			# The word embeddings.
			lowerCAmelCase_         =      embeddings["word_embeddings"]["weight"]
			# Truncate the embedding table to vocab_size rows.
			lowerCAmelCase_         =      word_embeddings[: config.vocab_size, :]
			lowerCAmelCase_         =      word_embeddings

			# The position embeddings.
			lowerCAmelCase_         =      embeddings["position_embeddings"]["weight"]
			# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
			lowerCAmelCase_         =      pos_embeddings.size(0							)
			if n_positions != config.n_positions:
						raise ValueError(
						    F"""pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match"""							)
			# Store the position embeddings.
			lowerCAmelCase_         =      pos_embeddings

			# The transformer.
			lowerCAmelCase_         =      lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

			# The regex to extract layer names.
			lowerCAmelCase_         =      re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"							)

			# The simple map of names for "automated" rules.
			lowerCAmelCase_         =      {
			    "attention.dense": ".attn.c_proj.",
			    "self_attention.dense": ".attn.c_proj.",
			    "mlp.dense_h_to_4h": ".mlp.c_fc.",
			    "mlp.dense_4h_to_h": ".mlp.c_proj.",
			}

			# Extract the layers.
			for key, val in transformer.items():
						# Match the name.
						lowerCAmelCase_         =      layer_re.match(__lowerCAmelCase							)

						# Stop if that's not a layer
						if m is None:
									break

						# The index of the layer.
						lowerCAmelCase_         =      int(m.group(1							)							)
						# The name of the operation.
						lowerCAmelCase_         =      m.group(2							)
						# Is it a weight or a bias?
						lowerCAmelCase_         =      m.group(3							)

						# The name of the layer.
						lowerCAmelCase_         =      F"""transformer.h.{layer_idx}"""

						# For layernorm(s), simply store the layer norm.
						if op_name.endswith("layernorm"							):
									lowerCAmelCase_         =      "ln_1" if op_name.startswith("input"							) else "ln_2"
									lowerCAmelCase_         =      val

						# Transpose the QKV matrix.
						elif (
						    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
						) and weight_or_bias == "weight":
									# Insert a tensor of 1x1xDxD bias.
									lowerCAmelCase_         =      torch.tril(torch.ones((n_positions, n_positions)       ,				dtype=torch.floataa							)							).view(
									    1       ,				1       ,				__lowerCAmelCase       ,				__lowerCAmelCase							)
									lowerCAmelCase_         =      causal_mask

									# Insert a "dummy" tensor for masked_bias.
									lowerCAmelCase_         =      torch.tensor(-1e4       ,				dtype=torch.floataa							)
									lowerCAmelCase_         =      masked_bias

									lowerCAmelCase_         =      fix_query_key_value_ordering(__lowerCAmelCase       ,				__lowerCAmelCase       ,				3       ,				__lowerCAmelCase       ,				__lowerCAmelCase							)
									# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
									lowerCAmelCase_         =      out_val.transpose(0       ,				1							).contiguous()
									# Store.
									lowerCAmelCase_         =      out_val

						# Transpose the bias.
						elif (
						    op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
						) and weight_or_bias == "bias":
									lowerCAmelCase_         =      fix_query_key_value_ordering(__lowerCAmelCase       ,				__lowerCAmelCase       ,				3       ,				__lowerCAmelCase       ,				__lowerCAmelCase							)
									# Store. No change of shape.
									lowerCAmelCase_         =      out_val

						# Transpose the weights.
						elif weight_or_bias == "weight":
									lowerCAmelCase_         =      megatron_to_transformers[op_name]
									lowerCAmelCase_         =      val.transpose(0       ,				1							)

						# Copy the bias.
						elif weight_or_bias == "bias":
									lowerCAmelCase_         =      megatron_to_transformers[op_name]
									lowerCAmelCase_         =      val

    # DEBUG.
			assert config.n_layer == layer_idx + 1

			# The final layernorm.
			lowerCAmelCase_         =      transformer["final_layernorm.weight"]
			lowerCAmelCase_         =      transformer["final_layernorm.bias"]

			# For LM head, transformers' wants the matrix to weight embeddings.
			lowerCAmelCase_         =      word_embeddings

			# It should be done!
			return output_state_dict






def   lowerCamelCase__							(							):




			"""simple docstring"""


			lowerCAmelCase_         =      argparse.ArgumentParser()
			parser.add_argument("--print-checkpoint-structure"       ,				action="store_true"							)
			parser.add_argument(
			    "path_to_checkpoint"       ,				type=__lowerCAmelCase       ,				help="Path to the checkpoint file (.zip archive or direct .pt file)"       ,				)
			parser.add_argument(
			    "--config_file"       ,				default=""       ,				type=__lowerCAmelCase       ,				help="An optional config json file describing the pre-trained model."       ,				)
			lowerCAmelCase_         =      parser.parse_args()

			# Extract the basename.
			lowerCAmelCase_         =      os.path.dirname(args.path_to_checkpoint							)

			# Load the model.
			# the .zip is very optional, let's keep it for backward compatibility
			print(F"""Extracting PyTorch state dictionary from {args.path_to_checkpoint}"""							)
			if args.path_to_checkpoint.endswith(".zip"							):
						with zipfile.ZipFile(args.path_to_checkpoint       ,				"r"							) as checkpoint:
									with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"							) as pytorch_dict:
												lowerCAmelCase_         =      torch.load(__lowerCAmelCase       ,				map_location="cpu"							)
			else:
						lowerCAmelCase_         =      torch.load(args.path_to_checkpoint       ,				map_location="cpu"							)

			lowerCAmelCase_         =      input_state_dict.get("args"       ,				__lowerCAmelCase							)

			# Read the config, or default to the model released by NVIDIA.
			if args.config_file == "":
						if ds_args is not None:
									if ds_args.bias_gelu_fusion:
												lowerCAmelCase_         =      "gelu_fast"
									elif ds_args.openai_gelu:
												lowerCAmelCase_         =      "gelu_new"
									else:
												lowerCAmelCase_         =      "gelu"
						else:
									# in the very early days this used to be "gelu_new"
									lowerCAmelCase_         =      "gelu_new"

						# Spell out all parameters in case the defaults change.
						lowerCAmelCase_         =      GPTaConfig(
						    vocab_size=50257       ,				n_positions=1024       ,				n_embd=1024       ,				n_layer=24       ,				n_head=16       ,				n_inner=4096       ,				activation_function=__lowerCAmelCase       ,				resid_pdrop=0.1       ,				embd_pdrop=0.1       ,				attn_pdrop=0.1       ,				layer_norm_epsilon=1e-5       ,				initializer_range=0.02       ,				summary_type="cls_index"       ,				summary_use_proj=__lowerCAmelCase       ,				summary_activation=__lowerCAmelCase       ,				summary_proj_to_labels=__lowerCAmelCase       ,				summary_first_dropout=0.1       ,				scale_attn_weights=__lowerCAmelCase       ,				use_cache=__lowerCAmelCase       ,				bos_token_id=50256       ,				eos_token_id=50256       ,				)
			else:
						lowerCAmelCase_         =      GPTaConfig.from_json_file(args.config_file							)

			lowerCAmelCase_         =      ["GPT2LMHeadModel"]

			# Convert.
			print("Converting"							)
			lowerCAmelCase_         =      convert_megatron_checkpoint(__lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase							)

			# Print the structure of converted state dict.
			if args.print_checkpoint_structure:
						recursive_print(__lowerCAmelCase       ,				__lowerCAmelCase							)

			# Add tokenizer class info to config
			# see https://github.com/huggingface/transformers/issues/13906)
			if ds_args is not None:
						lowerCAmelCase_         =      ds_args.tokenizer_type
						if tokenizer_type == "GPT2BPETokenizer":
									lowerCAmelCase_         =      "gpt2"
						elif tokenizer_type == "PretrainedFromHF":
									lowerCAmelCase_         =      ds_args.tokenizer_name_or_path
						else:
									raise ValueError(F"""Unrecognized tokenizer_type {tokenizer_type}"""							)
			else:
						lowerCAmelCase_         =      "gpt2"

			lowerCAmelCase_         =      AutoTokenizer.from_pretrained(__lowerCAmelCase							)
			lowerCAmelCase_         =      type(__lowerCAmelCase							).__name__
			lowerCAmelCase_         =      tokenizer_class

			# Store the config to file.
			print("Saving config"							)
			config.save_pretrained(__lowerCAmelCase							)

			# Save tokenizer based on args
			print(F"""Adding {tokenizer_class} tokenizer files"""							)
			tokenizer.save_pretrained(__lowerCAmelCase							)

			# Store the state_dict to file.
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				"pytorch_model.bin"							)
			print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""							)
			torch.save(__lowerCAmelCase       ,				__lowerCAmelCase							)


####################################################################################################

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)



