




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
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD







def 	_a      (						):
					__lowerCAmelCase				   =       "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
					__lowerCAmelCase				   =       Image.open(requests.get(SCREAMING_SNAKE_CASE_      ,      stream=SCREAMING_SNAKE_CASE_     ).raw     ).convert("RGB"     )

					return image







def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[Any]     ):
					__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.layernorm.weight")     )
					rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias")     )

					# fmt: on
					return rename_keys







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				Dict     ):
					__lowerCAmelCase				   =       dct.pop(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       val







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Union[str, Any]      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]     ):
					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(SCREAMING_SNAKE_CASE_      ,      requires_grad=SCREAMING_SNAKE_CASE_     ), v_bias)     )
										__lowerCAmelCase				   =       qkv_bias







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Any      ,      SCREAMING_SNAKE_CASE_							:				Any     ):
					__lowerCAmelCase				   =       3_64 if "coco" in model_name else 2_24
					__lowerCAmelCase				   =       BlipaVisionConfig(image_size=SCREAMING_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 "opt-2.7b" in model_name:
										__lowerCAmelCase				   =       OPTConfig.from_pretrained("facebook/opt-2.7b"      ,      eos_token_id=SCREAMING_SNAKE_CASE_     ).to_dict()
					elif "opt-6.7b" in model_name:
										__lowerCAmelCase				   =       OPTConfig.from_pretrained("facebook/opt-6.7b"      ,      eos_token_id=SCREAMING_SNAKE_CASE_     ).to_dict()
					elif "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()

					__lowerCAmelCase				   =       BlipaConfig(vision_config=SCREAMING_SNAKE_CASE_      ,      text_config=SCREAMING_SNAKE_CASE_     )

					return config, image_size







@torch.no_grad()
def 	_a      (						SCREAMING_SNAKE_CASE_							:				Any      ,      SCREAMING_SNAKE_CASE_							:				Dict=None      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]=False     ):
					__lowerCAmelCase				   =       (
					    AutoTokenizer.from_pretrained("facebook/opt-2.7b"     )
					    if "opt" in model_name
					    else AutoTokenizer.from_pretrained("google/flan-t5-xl"     )
					)
					__lowerCAmelCase				   =       tokenizer("\n"      ,      add_special_tokens=SCREAMING_SNAKE_CASE_     ).input_ids[0]
					__lowerCAmelCase   ,							__lowerCAmelCase				   =       get_blipa_config(SCREAMING_SNAKE_CASE_      ,      eos_token_id=SCREAMING_SNAKE_CASE_     )

					__lowerCAmelCase				   =       BlipaForConditionalGeneration(SCREAMING_SNAKE_CASE_     ).eval()

					__lowerCAmelCase				   =       {
					    "blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
					    "blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
					    "blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
					    "blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
					    "blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
					    "blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"),
					    "blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
					}

					__lowerCAmelCase   ,							__lowerCAmelCase				   =       model_name_to_original[model_name]

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

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

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

					# read in qv biases
					read_in_q_v_bias(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )

					__lowerCAmelCase   ,							__lowerCAmelCase				   =       hf_model.load_state_dict(SCREAMING_SNAKE_CASE_      ,      strict=SCREAMING_SNAKE_CASE_     )
					assert len(SCREAMING_SNAKE_CASE_     ) == 0
					assert unexpected_keys == ["qformer.embeddings.position_ids"]

					__lowerCAmelCase				   =       load_demo_image()
					__lowerCAmelCase				   =       vis_processors["eval"](SCREAMING_SNAKE_CASE_     ).unsqueeze(0     ).to(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       tokenizer(["\n"]      ,      return_tensors="pt"     ).input_ids.to(SCREAMING_SNAKE_CASE_     )

					# create processor
					__lowerCAmelCase				   =       BlipImageProcessor(
					    size={"height": image_size, "width": image_size}      ,      image_mean=SCREAMING_SNAKE_CASE_      ,      image_std=SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       BlipaProcessor(image_processor=SCREAMING_SNAKE_CASE_      ,      tokenizer=SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       processor(images=SCREAMING_SNAKE_CASE_      ,      return_tensors="pt"     ).pixel_values.to(SCREAMING_SNAKE_CASE_     )

					# make sure processor creates exact same pixel values
					assert torch.allclose(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )

					original_model.to(SCREAMING_SNAKE_CASE_     )
					hf_model.to(SCREAMING_SNAKE_CASE_     )
					with torch.no_grad():
										if "opt" in model_name:
															__lowerCAmelCase				   =       original_model({"image": original_pixel_values, "text_input": [""]}     ).logits
															__lowerCAmelCase				   =       hf_model(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     ).logits
										else:
															__lowerCAmelCase				   =       original_model(
															    {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}     ).logits
															__lowerCAmelCase				   =       input_ids.masked_fill(input_ids == tokenizer.pad_token_id      ,      -1_00     )
															__lowerCAmelCase				   =       hf_model(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      labels=SCREAMING_SNAKE_CASE_     ).logits

					assert original_logits.shape == logits.shape
					print("First values of original logits:"      ,      original_logits[0, :3, :3]     )
					print("First values of HF logits:"      ,      logits[0, :3, :3]     )

					# assert values
					if model_name == "blip2-flan-t5-xl":
										__lowerCAmelCase				   =       torch.tensor(
										    [[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]]      ,      device=SCREAMING_SNAKE_CASE_     )
										assert torch.allclose(logits[0, :3, :3]      ,      SCREAMING_SNAKE_CASE_      ,      atol=1E-4     )
					elif model_name == "blip2-flan-t5-xl-coco":
										__lowerCAmelCase				   =       torch.tensor(
										    [[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]]      ,      device=SCREAMING_SNAKE_CASE_     )
					else:
										# cast to same type
										__lowerCAmelCase				   =       logits.dtype
										assert torch.allclose(original_logits.to(SCREAMING_SNAKE_CASE_     )      ,      SCREAMING_SNAKE_CASE_      ,      atol=1E-2     )
					print("Looks ok!"     )

					print("Generating a caption..."     )
					__lowerCAmelCase				   =       ""
					__lowerCAmelCase				   =       tokenizer(SCREAMING_SNAKE_CASE_      ,      return_tensors="pt"     ).input_ids.to(SCREAMING_SNAKE_CASE_     )

					__lowerCAmelCase				   =       original_model.generate({"image": original_pixel_values}     )
					__lowerCAmelCase				   =       hf_model.generate(
					    SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      do_sample=SCREAMING_SNAKE_CASE_      ,      num_beams=5      ,      max_length=30      ,      min_length=1      ,      top_p=0.9      ,      repetition_penalty=1.0      ,      length_penalty=1.0      ,      temperature=1      ,      )
					print("Original generation:"      ,      SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       input_ids.shape[1]
					__lowerCAmelCase				   =       processor.batch_decode(outputs[:, prompt_length:]      ,      skip_special_tokens=SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       [text.strip() for text in output_text]
					print("HF generation:"      ,      SCREAMING_SNAKE_CASE_     )

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

					if push_to_hub:
										processor.push_to_hub(F"""nielsr/{model_name}"""     )
										hf_model.push_to_hub(F"""nielsr/{model_name}"""     )


if __name__ == "__main__":
					UpperCamelCase__    			=			argparse.ArgumentParser()
					UpperCamelCase__    			=			[
					    """blip2-opt-2.7b""",
					    """blip2-opt-6.7b""",
					    """blip2-opt-2.7b-coco""",
					    """blip2-opt-6.7b-coco""",
					    """blip2-flan-t5-xl""",
					    """blip2-flan-t5-xl-coco""",
					    """blip2-flan-t5-xxl""",
					]
					parser.add_argument(
					    """--model_name""",
					    default="""blip2-opt-2.7b""",
					    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""",
					)

					UpperCamelCase__    			=			parser.parse_args()

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










from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase__    			=			{"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}


if TYPE_CHECKING:
					from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
					import sys

					UpperCamelCase__    			=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




