"""simple docstring"""






from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
	import tensorflow as tf


_A   					=    logging.get_logger(__name__)

@dataclass
class lowerCamelCase		(UpperCAmelCase_      ):






					'''simple docstring'''


					a   	=		[
					    "no_inference",
					    "no_cuda",
					    "no_tpu",
					    "no_speed",
					    "no_memory",
					    "no_env_print",
					    "no_multi_process",
					]



					def __init__(						self			:				str   , **_snake_case			:				Union[str, Any]		)							->       str:
									for deprecated_arg in self.deprecated_args:
													if deprecated_arg in kwargs:
																	SCREAMING_SNAKE_CASE__		  =				deprecated_arg[3:]
																	SCREAMING_SNAKE_CASE__		  =				not kwargs.pop(_lowercase		)
																	logger.warning(
																	    F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																	    F""" {positive_arg}={kwargs[positive_arg]}"""		)
									SCREAMING_SNAKE_CASE__		  =				kwargs.pop("tpu_name"   , self.tpu_name		)
									SCREAMING_SNAKE_CASE__		  =				kwargs.pop("device_idx"   , self.device_idx		)
									SCREAMING_SNAKE_CASE__		  =				kwargs.pop("eager_mode"   , self.eager_mode		)
									SCREAMING_SNAKE_CASE__		  =				kwargs.pop("use_xla"   , self.use_xla		)
									super().__init__(**_lowercase		)

					a   	=		field(
					    default=UpperCAmelCase_    ,    metadata={"help": "Name of TPU"}    ,    )
					a   	=		field(
					    default=0    ,    metadata={"help": "CPU / GPU device index. Defaults to 0."}    ,    )
					a   	=		field(default=UpperCAmelCase_    ,    metadata={"help": "Benchmark models in eager model."}      )
					a   	=		field(
					    default=UpperCAmelCase_    ,    metadata={
					        "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
					    }    ,    )



					@cached_property
					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Union[str, Any]:
									requires_backends(self   , ["tf"]		)
									SCREAMING_SNAKE_CASE__		  =				None
									if self.tpu:
													try:
																	if self.tpu_name:
																					SCREAMING_SNAKE_CASE__		  =				tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name		)
																	else:
																					SCREAMING_SNAKE_CASE__		  =				tf.distribute.cluster_resolver.TPUClusterResolver()
													except ValueError:
																	SCREAMING_SNAKE_CASE__		  =				None
									return tpu



					@cached_property
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       List[Any]:
									requires_backends(self   , ["tf"]		)
									if self.is_tpu:
													tf.config.experimental_connect_to_cluster(self._setup_tpu		)
													tf.tpu.experimental.initialize_tpu_system(self._setup_tpu		)

													SCREAMING_SNAKE_CASE__		  =				tf.distribute.TPUStrategy(self._setup_tpu		)
									else:
													# currently no multi gpu is allowed
													if self.is_gpu:
																	# TODO: Currently only single GPU is supported
																	tf.config.set_visible_devices(self.gpu_list[self.device_idx]   , "GPU"		)
																	SCREAMING_SNAKE_CASE__		  =				tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""		)
													else:
																	tf.config.set_visible_devices([]   , "GPU"		)  # disable GPU
																	SCREAMING_SNAKE_CASE__		  =				tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""		)

									return strategy



					@property
					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       int:
									requires_backends(self   , ["tf"]		)
									return self._setup_tpu is not None



					@property
					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Any:
									requires_backends(self   , ["tf"]		)
									return self._setup_strategy



					@property
					def 			lowerCAmelCase_					(						self			:				str		)							->       List[str]:
									requires_backends(self   , ["tf"]		)
									return tf.config.list_physical_devices("GPU"		)



					@property
					def 			lowerCAmelCase_					(						self			:				Any		)							->       Union[str, Any]:
									requires_backends(self   , ["tf"]		)
									if self.cuda:
													return len(self.gpu_list		)
									return 0



					@property
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       int:
									return self.n_gpu > 0





"""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 						SCREAMING_SNAKE_CASE					(  )							->   List[str]:
				SCREAMING_SNAKE_CASE__		  =				"https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
				SCREAMING_SNAKE_CASE__		  =				Image.open(requests.get(__UpperCAmelCase  ,					stream=__UpperCAmelCase       ).raw       ).convert("RGB"       )

				return image




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   int:
				SCREAMING_SNAKE_CASE__		  =				[]
				# 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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase       )							->   int:
				SCREAMING_SNAKE_CASE__		  =				dct.pop(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				val




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Dict:
				for i in range(config.vision_config.num_hidden_layers       ):
								# read in original q and v biases
								SCREAMING_SNAKE_CASE__		  =				state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""       )
								SCREAMING_SNAKE_CASE__		  =				state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""       )

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




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

				SCREAMING_SNAKE_CASE__		  =				BlipaConfig(vision_config=__UpperCAmelCase  ,					text_config=__UpperCAmelCase       )

				return config, image_size




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

				SCREAMING_SNAKE_CASE__		  =				BlipaForConditionalGeneration(__UpperCAmelCase       ).eval()

				SCREAMING_SNAKE_CASE__		  =				{
				    "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"),
				}

				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				model_name_to_original[model_name]

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

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

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

				# read in qv biases
				read_in_q_v_bias(__UpperCAmelCase  ,					__UpperCAmelCase       )

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

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

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

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

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

				print("Generating a caption..."       )
				SCREAMING_SNAKE_CASE__		  =				""
				SCREAMING_SNAKE_CASE__		  =				tokenizer(__UpperCAmelCase  ,					return_tensors="pt"       ).input_ids.to(__UpperCAmelCase       )

				SCREAMING_SNAKE_CASE__		  =				original_model.generate({"image": original_pixel_values}       )
				SCREAMING_SNAKE_CASE__		  =				hf_model.generate(
				    __UpperCAmelCase  ,					__UpperCAmelCase  ,					do_sample=__UpperCAmelCase  ,					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:"  ,					__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				input_ids.shape[1]
				SCREAMING_SNAKE_CASE__		  =				processor.batch_decode(outputs[:, prompt_length:]  ,					skip_special_tokens=__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				[text.strip() for text in output_text]
				print("HF generation:"  ,					__UpperCAmelCase       )

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

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


if __name__ == "__main__":
						_A   					=    argparse.ArgumentParser()
						_A   					=    [
						    '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',
						)

						_A   					=    parser.parse_args()

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




