

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        SCREAMING_SNAKE_CASE__	(  )  ->					List[str]:
	__lowerCamelCase    :    Optional[Any] 	=			'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
	__lowerCamelCase    :    List[str] 	=			Image.open(requests.get(lowerCamelCase__     ,			stream=lowerCamelCase__      ).raw      ).convert('RGB'      )

	return image






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					int:
	__lowerCamelCase    :    int 	=			[]
	# 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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Any:
	__lowerCamelCase    :    List[Any] 	=			dct.pop(lowerCamelCase__      )
	__lowerCamelCase    :    Tuple 	=			val






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

		# next, set bias in the state dict
		__lowerCamelCase    :    Any 	=			torch.cat((q_bias, torch.zeros_like(lowerCamelCase__     ,			requires_grad=lowerCamelCase__      ), v_bias)      )
		__lowerCamelCase    :    List[str] 	=			qkv_bias






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:
	__lowerCamelCase    :    List[str] 	=			3_6_4 if 'coco' in model_name else 2_2_4
	__lowerCamelCase    :    Union[str, Any] 	=			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    :    Any 	=			TaConfig.from_pretrained('google/flan-t5-xl'     ,			dense_act_fn='gelu'     ,			bos_token_id=1      ).to_dict()
	elif "t5-xxl" in model_name:
		__lowerCamelCase    :    List[Any] 	=			TaConfig.from_pretrained('google/flan-t5-xxl'     ,			dense_act_fn='gelu'     ,			bos_token_id=1      ).to_dict()
	elif "vicuna-7b" in model_name:
		__lowerCamelCase    :    int 	=			LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'     ,			vocab_size=3_2_0_0_1      ).to_dict()
	elif "vicuna-13b" in model_name:
		__lowerCamelCase    :    Union[str, Any] 	=			LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf'     ,			vocab_size=3_2_0_0_1      ).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    :    int 	=			InstructBlipQFormerConfig(vocab_size=3_0_5_2_3      ).to_dict()
	__lowerCamelCase    :    Any 	=			InstructBlipConfig(vision_config=lowerCamelCase__     ,			text_config=lowerCamelCase__     ,			qformer_config=lowerCamelCase__      )

	return config, image_size






@torch.no_grad()
def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=None     ,			lowerCamelCase__=False      )  ->					Tuple:
	__lowerCamelCase    :    List[str] 	=			AutoTokenizer.from_pretrained('bert-base-uncased'     ,			truncation_side='left'      )
	qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}      )

	if "t5" in model_name:
		__lowerCamelCase    :    Any 	=			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    :    str 	=			LlamaTokenizerFast.from_pretrained(
		    'huggyllama/llama-7b'     ,			truncation_side='left'     ,			bos_token='</s>'     ,			unk_token='</s>'      )
		tokenizer.add_special_tokens({'pad_token': '[PAD]'}      )

	__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			get_blipa_config(lowerCamelCase__      )
	__lowerCamelCase    :    List[str] 	=			InstructBlipForConditionalGeneration(lowerCamelCase__      ).eval()

	__lowerCamelCase    :    List[Any] 	=			{
	    '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    :    Optional[int] 	=			model_name_to_original[model_name]

	# load original model
	print('Loading original model...'      )
	__lowerCamelCase    :    Dict 	=			'cuda:1' if torch.cuda.is_available() else 'cpu'
	__lowerCamelCase    :    int 	=			'cuda:2' if torch.cuda.is_available() else 'cpu'
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Optional[int] 	=			load_model_and_preprocess(
	    name=lowerCamelCase__     ,			model_type=lowerCamelCase__     ,			is_eval=lowerCamelCase__     ,			device=lowerCamelCase__      )
	original_model.eval()
	print('Done!'      )

	# update state dict keys
	__lowerCamelCase    :    Union[str, Any] 	=			original_model.state_dict()
	__lowerCamelCase    :    List[str] 	=			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    :    str 	=			state_dict.pop(lowerCamelCase__      )
		if key.startswith('Qformer.bert'      ):
			__lowerCamelCase    :    int 	=			key.replace('Qformer.bert'     ,			'qformer'      )
		if "attention.self" in key:
			__lowerCamelCase    :    Union[str, Any] 	=			key.replace('self'     ,			'attention'      )
		if "llm_proj" in key:
			__lowerCamelCase    :    int 	=			key.replace('llm_proj'     ,			'language_projection'      )
		if "t5_proj" in key:
			__lowerCamelCase    :    Optional[int] 	=			key.replace('t5_proj'     ,			'language_projection'      )
		if key.startswith('llm_model'      ):
			__lowerCamelCase    :    Dict 	=			key.replace('llm_model'     ,			'language_model'      )
		if key.startswith('t5'      ):
			__lowerCamelCase    :    Dict 	=			key.replace('t5'     ,			'language'      )
		__lowerCamelCase    :    Any 	=			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    :    Optional[int] 	=			load_demo_image()
	__lowerCamelCase    :    Dict 	=			'What is unusual about this image?'

	# create processor
	__lowerCamelCase    :    Union[str, Any] 	=			BlipImageProcessor(
	    size={'height': image_size, 'width': image_size}     ,			image_mean=lowerCamelCase__     ,			image_std=lowerCamelCase__      )
	__lowerCamelCase    :    str 	=			InstructBlipProcessor(
	    image_processor=lowerCamelCase__     ,			tokenizer=lowerCamelCase__     ,			qformer_tokenizer=lowerCamelCase__     ,			)
	__lowerCamelCase    :    Optional[Any] 	=			processor(images=lowerCamelCase__     ,			text=lowerCamelCase__     ,			return_tensors='pt'      ).to(lowerCamelCase__      )

	# make sure processor creates exact same pixel values
	__lowerCamelCase    :    Union[str, Any] 	=			vis_processors['eval'](lowerCamelCase__      ).unsqueeze(0      ).to(lowerCamelCase__      )
	__lowerCamelCase    :    int 	=			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    :    Union[str, Any] 	=			original_model({'image': original_pixel_values, 'text_input': [prompt]}      ).logits
			__lowerCamelCase    :    Tuple 	=			hf_model(**lowerCamelCase__      ).logits
		else:
			__lowerCamelCase    :    Dict 	=			original_model(
			    {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}      ).logits
			__lowerCamelCase    :    List[Any] 	=			tokenizer('\n'     ,			return_tensors='pt'      ).input_ids.to(lowerCamelCase__      )
			__lowerCamelCase    :    List[str] 	=			label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id     ,			-1_0_0      )
			__lowerCamelCase    :    Optional[Any] 	=			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    :    Optional[int] 	=			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    :    Optional[int] 	=			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    :    List[Any] 	=			hf_model.generate(
	    **lowerCamelCase__     ,			do_sample=lowerCamelCase__     ,			num_beams=5     ,			max_length=2_5_6     ,			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    :    Union[str, Any] 	=			2
	print('Original generation:'     ,			lowerCamelCase__      )
	__lowerCamelCase    :    int 	=			processor.batch_decode(lowerCamelCase__     ,			skip_special_tokens=lowerCamelCase__      )
	__lowerCamelCase    :    Optional[int] 	=			[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)







import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
					import onnxruntime as ort






@nightly
@require_onnxruntime
@require_torch_gpu
class 	A_		(	unittest.TestCase    ):
							@property
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								return (
								    "CUDAExecutionProvider",
								    {
								        "gpu_mem_limit": "15000000000",  # 15GB
								        "arena_extend_strategy": "kSameAsRequested",
								    },
								)
							@property
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[int] 	=			ort.SessionOptions()
								__lowerCamelCase    :    Tuple 	=			False
								return options


							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    Tuple 	=			load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo.png')
								__lowerCamelCase    :    List[str] 	=			load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png')
								__lowerCamelCase    :    Optional[int] 	=			load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy')

								# using the PNDM scheduler by default
								__lowerCamelCase    :    Dict 	=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
								    'CompVis/stable-diffusion-v1-4'       ,revision='onnx'       ,safety_checker=SCREAMING_SNAKE_CASE__       ,feature_extractor=SCREAMING_SNAKE_CASE__       ,provider=self.gpu_provider       ,sess_options=self.gpu_options       ,)
								pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Union[str, Any] 	=			'A red cat sitting on a park bench'

								__lowerCamelCase    :    Any 	=			np.random.RandomState(0)
								__lowerCamelCase    :    List[Any] 	=			pipe(
								    prompt=SCREAMING_SNAKE_CASE__       ,image=SCREAMING_SNAKE_CASE__       ,mask_image=SCREAMING_SNAKE_CASE__       ,strength=0.75       ,guidance_scale=7.5       ,num_inference_steps=1_5       ,generator=SCREAMING_SNAKE_CASE__       ,output_type='np'       ,)

								__lowerCamelCase    :    Union[str, Any] 	=			output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)
								assert np.abs(expected_image - image).max() < 1E-2




