





'''simple docstring'''


import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
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    _lowerCamelCase  ()   ->    Any:
			a__     = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
			a__     = Image.open(requests.get(__lowerCamelCase					,  stream=__lowerCamelCase     ).raw     ).convert("RGB"     )

			return image







def    _lowerCamelCase  (__lowerCamelCase  :     Dict     )   ->    int:
			a__     = []
			# fmt: off

			# vision encoder
			rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding")     )
			rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding")     )
			rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight")     )
			rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias")     )
			rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight")     )
			rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias")     )

			for i in range(config.vision_config.num_hidden_layers     ):
						rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.weight''', f'''vision_model.encoder.layers.{i}.layer_norm1.weight''')     )
						rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.bias''', f'''vision_model.encoder.layers.{i}.layer_norm1.bias''')     )
						rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.weight''', f'''vision_model.encoder.layers.{i}.layer_norm2.weight''')     )
						rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.bias''', f'''vision_model.encoder.layers.{i}.layer_norm2.bias''')     )
						rename_keys.append((f'''visual_encoder.blocks.{i}.attn.qkv.weight''', f'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')     )
						rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.weight''', f'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)     )
						rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.bias''', f'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')     )
						rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')     )
						rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')     )
						rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')     )
						rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')     )

			# QFormer
			rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight")     )
			rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias")     )

			# fmt: on
			return rename_keys







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]					,  __lowerCamelCase  :     Optional[int]					,  __lowerCamelCase  :     Any     )   ->    str:
			a__     = dct.pop(__lowerCamelCase     )
			a__     = val







def    _lowerCamelCase  (__lowerCamelCase  :     Any					,  __lowerCamelCase  :     int     )   ->    str:
			for i in range(config.vision_config.num_hidden_layers     ):
						# read in original q and v biases
						a__     = state_dict.pop(f'''visual_encoder.blocks.{i}.attn.q_bias'''     )
						a__     = state_dict.pop(f'''visual_encoder.blocks.{i}.attn.v_bias'''     )

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







def    _lowerCamelCase  (__lowerCamelCase  :     Any					,  __lowerCamelCase  :     Tuple     )   ->    Dict:
			a__     = 364 if "coco" in model_name else 224
			a__     = BlipaVisionConfig(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 "opt-2.7b" in model_name:
						a__     = OPTConfig.from_pretrained("facebook/opt-2.7b"					,  eos_token_id=__lowerCamelCase     ).to_dict()
			elif "opt-6.7b" in model_name:
						a__     = OPTConfig.from_pretrained("facebook/opt-6.7b"					,  eos_token_id=__lowerCamelCase     ).to_dict()
			elif "t5-xl" in model_name:
						a__     = TaConfig.from_pretrained("google/flan-t5-xl"					,  dense_act_fn="gelu"					,  bos_token_id=1     ).to_dict()
			elif "t5-xxl" in model_name:
						a__     = TaConfig.from_pretrained("google/flan-t5-xxl"					,  dense_act_fn="gelu"					,  bos_token_id=1     ).to_dict()

			a__     = BlipaConfig(vision_config=__lowerCamelCase					,  text_config=__lowerCamelCase     )

			return config, image_size







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

			a__     = BlipaForConditionalGeneration(__lowerCamelCase     ).eval()

			a__     = {
			    "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"),
			}

			a__   ,				a__     = model_name_to_original[model_name]

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

			# update state dict keys
			a__     = original_model.state_dict()
			a__     = 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():
						a__     = state_dict.pop(__lowerCamelCase     )
						if key.startswith("Qformer.bert"     ):
									a__     = key.replace("Qformer.bert"					,  "qformer"     )
						if "attention.self" in key:
									a__     = key.replace("self"					,  "attention"     )
						if "opt_proj" in key:
									a__     = key.replace("opt_proj"					,  "language_projection"     )
						if "t5_proj" in key:
									a__     = key.replace("t5_proj"					,  "language_projection"     )
						if key.startswith("opt"     ):
									a__     = key.replace("opt"					,  "language"     )
						if key.startswith("t5"     ):
									a__     = key.replace("t5"					,  "language"     )
						a__     = val

			# read in qv biases
			read_in_q_v_bias(__lowerCamelCase					,  __lowerCamelCase     )

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

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

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

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

			original_model.to(__lowerCamelCase     )
			hf_model.to(__lowerCamelCase     )
			with torch.no_grad():
						if "opt" in model_name:
									a__     = original_model({"image": original_pixel_values, "text_input": [""]}     ).logits
									a__     = hf_model(__lowerCamelCase					,  __lowerCamelCase     ).logits
						else:
									a__     = original_model(
									    {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}     ).logits
									a__     = input_ids.masked_fill(input_ids == tokenizer.pad_token_id					,  -100     )
									a__     = hf_model(__lowerCamelCase					,  __lowerCamelCase					,  labels=__lowerCamelCase     ).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":
						a__     = torch.tensor(
						    [[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]]					,  device=__lowerCamelCase     )
						assert torch.allclose(logits[0, :3, :3]					,  __lowerCamelCase					,  atol=1e-4     )
			elif model_name == "blip2-flan-t5-xl-coco":
						a__     = torch.tensor(
						    [[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]]					,  device=__lowerCamelCase     )
			else:
						# cast to same type
						a__     = logits.dtype
						assert torch.allclose(original_logits.to(__lowerCamelCase     )					,  __lowerCamelCase					,  atol=1e-2     )
			print("Looks ok!"     )

			print("Generating a caption..."     )
			a__     = ""
			a__     = tokenizer(__lowerCamelCase					,  return_tensors="pt"     ).input_ids.to(__lowerCamelCase     )

			a__     = original_model.generate({"image": original_pixel_values}     )
			a__     = hf_model.generate(
			    __lowerCamelCase					,  __lowerCamelCase					,  do_sample=__lowerCamelCase					,  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:"					,  __lowerCamelCase     )
			a__     = input_ids.shape[1]
			a__     = processor.batch_decode(outputs[:, prompt_length:]					,  skip_special_tokens=__lowerCamelCase     )
			a__     = [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'''nielsr/{model_name}'''     )
						hf_model.push_to_hub(f'''nielsr/{model_name}'''     )


if __name__ == "__main__":
		lowerCAmelCase_   :   Optional[Any]       =					argparse.ArgumentParser()
		lowerCAmelCase_   :   Tuple       =					[
		    "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",
		)

		lowerCAmelCase_   :   int       =					parser.parse_args()

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










'''simple docstring'''


# Copyright 2021 The HuggingFace Team. 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.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


lowerCAmelCase_   :   List[Any]       =					[
    "EAGER",
    "AOT_EAGER",
    "INDUCTOR",
    "NVFUSER",
    "AOT_NVFUSER",
    "AOT_CUDAGRAPHS",
    "OFI",
    "FX2TRT",
    "ONNXRT",
    "IPEX",
]







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple					,  __lowerCamelCase  :     Tuple=None					,  __lowerCamelCase  :     Tuple=None					,  __lowerCamelCase  :     List[Any]=None     )   ->    Any:
			a__     = True
			while ask_again:
						a__     = input(__lowerCamelCase     )
						try:
									if default is not None and len(__lowerCamelCase     ) == 0:
												return default
									return convert_value(__lowerCamelCase     ) if convert_value is not None else result
						except Exception:
									if error_message is not None:
												print(__lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     Dict					,  __lowerCamelCase  :     List[Any]=[]					,  __lowerCamelCase  :     str=None					,  __lowerCamelCase  :     List[Any]=0     )   ->    List[Any]:
			a__     = BulletMenu(__lowerCamelCase					,  __lowerCamelCase     )
			a__     = menu.run(default_choice=__lowerCamelCase     )
			return convert_value(__lowerCamelCase     ) if convert_value is not None else result







def    _lowerCamelCase  (__lowerCamelCase  :     Tuple     )   ->    Optional[Any]:
			a__     = int(__lowerCamelCase     )
			return ComputeEnvironment(["LOCAL_MACHINE", "AMAZON_SAGEMAKER"][value]     )







def    _lowerCamelCase  (__lowerCamelCase  :     List[str]     )   ->    Optional[int]:
			a__     = int(__lowerCamelCase     )
			return DistributedType(["NO", "MULTI_CPU", "MULTI_XPU", "MULTI_GPU", "MULTI_NPU", "TPU"][value]     )







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    Optional[Any]:
			a__     = int(__lowerCamelCase     )
			return DynamoBackend(DYNAMO_BACKENDS[value]     ).value







def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    str:
			a__     = int(__lowerCamelCase     )
			return PrecisionType(["no", "fp16", "bf16", "fp8"][value]     )







def    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    Optional[Any]:
			a__     = int(__lowerCamelCase     )
			return SageMakerDistributedType(["NO", "DATA_PARALLEL", "MODEL_PARALLEL"][value]     )







def    _lowerCamelCase  (__lowerCamelCase  :     Any     )   ->    List[Any]:
			return {"yes": True, "no": False}[value.lower()]






class       UpperCamelCase__  (		argparse.RawDescriptionHelpFormatter     ):







	def 						__a   (				self			:   Dict , lowerCamelCase			:   int , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   List[Any] , lowerCamelCase			:   Dict		):




				'''simple docstring'''
				a__     = super()._format_usage(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase		)
				a__     = usage.replace("<command> [<args>] " , ""		)
				return usage



