

'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
						from PIL import Image

						from transformers import OwlViTImageProcessor, OwlViTProcessor



@require_vision
class 					lowercase						(  unittest.TestCase ):


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_		:      List[str]       	=					tempfile.mkdtemp()

								# fmt: off
								UpperCAmelCase_		:      List[Any]       	=					['', 'l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
								# fmt: on
								UpperCAmelCase_		:      List[str]       	=					dict(zip(_snake_case    ,			range(len(_snake_case))))
								UpperCAmelCase_		:      Tuple       	=					['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
								UpperCAmelCase_		:      int       	=					{'unk_token': '<unk>'}

								UpperCAmelCase_		:      Any       	=					os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['vocab_file'])
								UpperCAmelCase_		:      List[Any]       	=					os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['merges_file'])
								with open(self.vocab_file    ,			'w'    ,			encoding='utf-8') as fp:
									fp.write(json.dumps(_snake_case) + '\n')
								with open(self.merges_file    ,			'w'    ,			encoding='utf-8') as fp:
									fp.write('\n'.join(_snake_case))

								UpperCAmelCase_		:      Optional[int]       	=					{
								    'do_resize': True,
								    'size': 20,
								    'do_center_crop': True,
								    'crop_size': 18,
								    'do_normalize': True,
								    'image_mean': [0.48_145_466, 0.4_578_275, 0.40_821_073],
								    'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
								}
								UpperCAmelCase_		:      Tuple       	=					os.path.join(self.tmpdirname    ,			_snake_case)
								with open(self.image_processor_file    ,			'w'    ,			encoding='utf-8') as fp:
									json.dump(_snake_case    ,			_snake_case)


							def 						_snake_case							(			self    ,			**_snake_case)							->				Dict:
								return CLIPTokenizer.from_pretrained(self.tmpdirname    ,			pad_token='!'    ,			**_snake_case)


							def 						_snake_case							(			self    ,			**_snake_case)							->				int:
								return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,			pad_token='!'    ,			**_snake_case)


							def 						_snake_case							(			self    ,			**_snake_case)							->				Optional[Any]:
								return OwlViTImageProcessor.from_pretrained(self.tmpdirname    ,			**_snake_case)


							def 						_snake_case							(			self)							->				List[Any]:
								shutil.rmtree(self.tmpdirname)


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      List[str]       	=					[np.random.randint(255    ,			size=(3, 30, 400)    ,			dtype=np.uinta)]

								UpperCAmelCase_		:      List[str]       	=					[Image.fromarray(np.moveaxis(_snake_case    ,			0    ,			-1)) for x in image_inputs]

								return image_inputs


							def 						_snake_case							(			self)							->				List[Any]:
								UpperCAmelCase_		:      Union[str, Any]       	=					self.get_tokenizer()
								UpperCAmelCase_		:      Dict       	=					self.get_rust_tokenizer()
								UpperCAmelCase_		:      List[Any]       	=					self.get_image_processor()

								UpperCAmelCase_		:      Optional[int]       	=					OwlViTProcessor(tokenizer=_snake_case    ,			image_processor=_snake_case)
								processor_slow.save_pretrained(self.tmpdirname)
								UpperCAmelCase_		:      Optional[Any]       	=					OwlViTProcessor.from_pretrained(self.tmpdirname    ,			use_fast=_snake_case)

								UpperCAmelCase_		:      Union[str, Any]       	=					OwlViTProcessor(tokenizer=_snake_case    ,			image_processor=_snake_case)
								processor_fast.save_pretrained(self.tmpdirname)
								UpperCAmelCase_		:      int       	=					OwlViTProcessor.from_pretrained(self.tmpdirname)

								self.assertEqual(processor_slow.tokenizer.get_vocab()    ,			tokenizer_slow.get_vocab())
								self.assertEqual(processor_fast.tokenizer.get_vocab()    ,			tokenizer_fast.get_vocab())
								self.assertEqual(tokenizer_slow.get_vocab()    ,			tokenizer_fast.get_vocab())
								self.assertIsInstance(processor_slow.tokenizer    ,			_snake_case)
								self.assertIsInstance(processor_fast.tokenizer    ,			_snake_case)

								self.assertEqual(processor_slow.image_processor.to_json_string()    ,			image_processor.to_json_string())
								self.assertEqual(processor_fast.image_processor.to_json_string()    ,			image_processor.to_json_string())
								self.assertIsInstance(processor_slow.image_processor    ,			_snake_case)
								self.assertIsInstance(processor_fast.image_processor    ,			_snake_case)


							def 						_snake_case							(			self)							->				List[Any]:
								UpperCAmelCase_		:      Union[str, Any]       	=					OwlViTProcessor(tokenizer=self.get_tokenizer()    ,			image_processor=self.get_image_processor())
								processor.save_pretrained(self.tmpdirname)

								UpperCAmelCase_		:      Optional[int]       	=					self.get_tokenizer(bos_token='(BOS)'    ,			eos_token='(EOS)')
								UpperCAmelCase_		:      Any       	=					self.get_image_processor(do_normalize=_snake_case)

								UpperCAmelCase_		:      int       	=					OwlViTProcessor.from_pretrained(
								    self.tmpdirname    ,			bos_token='(BOS)'    ,			eos_token='(EOS)'    ,			do_normalize=_snake_case)

								self.assertEqual(processor.tokenizer.get_vocab()    ,			tokenizer_add_kwargs.get_vocab())
								self.assertIsInstance(processor.tokenizer    ,			_snake_case)

								self.assertEqual(processor.image_processor.to_json_string()    ,			image_processor_add_kwargs.to_json_string())
								self.assertIsInstance(processor.image_processor    ,			_snake_case)


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      Union[str, Any]       	=					self.get_image_processor()
								UpperCAmelCase_		:      Optional[Any]       	=					self.get_tokenizer()

								UpperCAmelCase_		:      List[Any]       	=					OwlViTProcessor(tokenizer=_snake_case    ,			image_processor=_snake_case)

								UpperCAmelCase_		:      List[str]       	=					self.prepare_image_inputs()

								UpperCAmelCase_		:      str       	=					image_processor(_snake_case    ,			return_tensors='np')
								UpperCAmelCase_		:      Union[str, Any]       	=					processor(images=_snake_case    ,			return_tensors='np')

								for key in input_image_proc.keys():
									self.assertAlmostEqual(input_image_proc[key].sum()    ,			input_processor[key].sum()    ,			delta=1e-2)


							def 						_snake_case							(			self)							->				Union[str, Any]:
								UpperCAmelCase_		:      Any       	=					self.get_image_processor()
								UpperCAmelCase_		:      List[str]       	=					self.get_tokenizer()

								UpperCAmelCase_		:      str       	=					OwlViTProcessor(tokenizer=_snake_case    ,			image_processor=_snake_case)

								UpperCAmelCase_		:      Union[str, Any]       	=					'lower newer'

								UpperCAmelCase_		:      Union[str, Any]       	=					processor(text=_snake_case    ,			return_tensors='np')

								UpperCAmelCase_		:      Dict       	=					tokenizer(_snake_case    ,			return_tensors='np')

								for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key][0].tolist()    ,			encoded_processor[key][0].tolist())


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      List[str]       	=					self.get_image_processor()
								UpperCAmelCase_		:      Optional[int]       	=					self.get_tokenizer()

								UpperCAmelCase_		:      List[Any]       	=					OwlViTProcessor(tokenizer=_snake_case    ,			image_processor=_snake_case)

								UpperCAmelCase_		:      Tuple       	=					'lower newer'
								UpperCAmelCase_		:      Union[str, Any]       	=					self.prepare_image_inputs()

								UpperCAmelCase_		:      str       	=					processor(text=_snake_case    ,			images=_snake_case)

								self.assertListEqual(list(inputs.keys())    ,			['input_ids', 'attention_mask', 'pixel_values'])

								# test if it raises when no input is passed
								with pytest.raises(_snake_case):
									processor()


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      str       	=					'google/owlvit-base-patch32'
								UpperCAmelCase_		:      Any       	=					OwlViTProcessor.from_pretrained(_snake_case)

								UpperCAmelCase_		:      List[Any]       	=					['cat', 'nasa badge']
								UpperCAmelCase_		:      Any       	=					processor(text=_snake_case)

								UpperCAmelCase_		:      int       	=					16
								self.assertListEqual(list(inputs.keys())    ,			['input_ids', 'attention_mask'])
								self.assertEqual(inputs['input_ids'].shape    ,			(2, seq_length))

								# test if it raises when no input is passed
								with pytest.raises(_snake_case):
									processor()


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      str       	=					'google/owlvit-base-patch32'
								UpperCAmelCase_		:      List[str]       	=					OwlViTProcessor.from_pretrained(_snake_case)

								UpperCAmelCase_		:      str       	=					[['cat', 'nasa badge'], ['person']]
								UpperCAmelCase_		:      List[Any]       	=					processor(text=_snake_case)

								UpperCAmelCase_		:      List[str]       	=					16
								UpperCAmelCase_		:      Optional[int]       	=					len(_snake_case)
								UpperCAmelCase_		:      List[Any]       	=					max([len(_snake_case) for texts in input_texts])

								self.assertListEqual(list(inputs.keys())    ,			['input_ids', 'attention_mask'])
								self.assertEqual(inputs['input_ids'].shape    ,			(batch_size * num_max_text_queries, seq_length))

								# test if it raises when no input is passed
								with pytest.raises(_snake_case):
									processor()


							def 						_snake_case							(			self)							->				Dict:
								UpperCAmelCase_		:      Union[str, Any]       	=					'google/owlvit-base-patch32'
								UpperCAmelCase_		:      List[Any]       	=					OwlViTProcessor.from_pretrained(_snake_case)

								UpperCAmelCase_		:      str       	=					['cat', 'nasa badge']
								UpperCAmelCase_		:      str       	=					processor(text=_snake_case)

								UpperCAmelCase_		:      Dict       	=					16
								UpperCAmelCase_		:      int       	=					inputs['input_ids']
								UpperCAmelCase_		:      Optional[int]       	=					[
								    [4_9406, 2368, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
								    [4_9406, 6841, 1_1301, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
								]

								self.assertListEqual(list(inputs.keys())    ,			['input_ids', 'attention_mask'])
								self.assertEqual(inputs['input_ids'].shape    ,			(2, seq_length))
								self.assertListEqual(list(input_ids[0])    ,			predicted_ids[0])
								self.assertListEqual(list(input_ids[1])    ,			predicted_ids[1])


							def 						_snake_case							(			self)							->				Any:
								UpperCAmelCase_		:      Tuple       	=					self.get_image_processor()
								UpperCAmelCase_		:      List[Any]       	=					self.get_tokenizer()

								UpperCAmelCase_		:      int       	=					OwlViTProcessor(tokenizer=_snake_case    ,			image_processor=_snake_case)

								UpperCAmelCase_		:      Optional[Any]       	=					self.prepare_image_inputs()
								UpperCAmelCase_		:      Optional[int]       	=					self.prepare_image_inputs()

								UpperCAmelCase_		:      str       	=					processor(images=_snake_case    ,			query_images=_snake_case)

								self.assertListEqual(list(inputs.keys())    ,			['query_pixel_values', 'pixel_values'])

								# test if it raises when no input is passed
								with pytest.raises(_snake_case):
									processor()





							def 						_snake_case							(			self)							->				List[str]:
								UpperCAmelCase_		:      Union[str, Any]       	=					self.get_image_processor()
								UpperCAmelCase_		:      Tuple       	=					self.get_tokenizer()

								UpperCAmelCase_		:      str       	=					OwlViTProcessor(tokenizer=_snake_case    ,			image_processor=_snake_case)

								UpperCAmelCase_		:      int       	=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								UpperCAmelCase_		:      Union[str, Any]       	=					processor.batch_decode(_snake_case)
								UpperCAmelCase_		:      Union[str, Any]       	=					tokenizer.batch_decode(_snake_case)

								self.assertListEqual(_snake_case    ,			_snake_case)









'''simple docstring'''


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Tuple:
	# load base model
	UpperCAmelCase_		:      List[str]       	=					StableDiffusionPipeline.from_pretrained(UpperCamelCase   ,torch_dtype=torch.floataa  )

	# load LoRA weight from .safetensors
	UpperCAmelCase_		:      Union[str, Any]       	=					load_file(UpperCamelCase  )

	UpperCAmelCase_		:      Any       	=					[]

	# directly update weight in diffusers model
	for key in state_dict:
		# it is suggested to print out the key, it usually will be something like below
		# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

		# as we have set the alpha beforehand, so just skip
		if ".alpha" in key or key in visited:
			continue

		if "text" in key:
			UpperCAmelCase_		:      Optional[Any]       	=					key.split('.'  )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'  )[-1].split('_'  )
			UpperCAmelCase_		:      str       	=					pipeline.text_encoder
		else:
			UpperCAmelCase_		:      List[str]       	=					key.split('.'  )[0].split(LORA_PREFIX_UNET + '_'  )[-1].split('_'  )
			UpperCAmelCase_		:      List[str]       	=					pipeline.unet

		# find the target layer
		UpperCAmelCase_		:      Dict       	=					layer_infos.pop(0  )
		while len(UpperCamelCase  ) > -1:
			try:
				UpperCAmelCase_		:      List[str]       	=					curr_layer.__getattr__(UpperCamelCase  )
				if len(UpperCamelCase  ) > 0:
					UpperCAmelCase_		:      Tuple       	=					layer_infos.pop(0  )
				elif len(UpperCamelCase  ) == 0:
					break
			except Exception:
				if len(UpperCamelCase  ) > 0:
					temp_name += "_" + layer_infos.pop(0  )
				else:
					UpperCAmelCase_		:      List[Any]       	=					layer_infos.pop(0  )

		UpperCAmelCase_		:      str       	=					[]
		if "lora_down" in key:
			pair_keys.append(key.replace('lora_down'   ,'lora_up'  )  )
			pair_keys.append(UpperCamelCase  )
		else:
			pair_keys.append(UpperCamelCase  )
			pair_keys.append(key.replace('lora_up'   ,'lora_down'  )  )

		# update weight
		if len(state_dict[pair_keys[0]].shape  ) == 4:
			UpperCAmelCase_		:      Dict       	=					state_dict[pair_keys[0]].squeeze(3  ).squeeze(2  ).to(torch.floataa  )
			UpperCAmelCase_		:      List[str]       	=					state_dict[pair_keys[1]].squeeze(3  ).squeeze(2  ).to(torch.floataa  )
			curr_layer.weight.data += alpha * torch.mm(UpperCamelCase   ,UpperCamelCase  ).unsqueeze(2  ).unsqueeze(3  )
		else:
			UpperCAmelCase_		:      Optional[int]       	=					state_dict[pair_keys[0]].to(torch.floataa  )
			UpperCAmelCase_		:      Tuple       	=					state_dict[pair_keys[1]].to(torch.floataa  )
			curr_layer.weight.data += alpha * torch.mm(UpperCamelCase   ,UpperCamelCase  )

		# update visited list
		for item in pair_keys:
			visited.append(UpperCamelCase  )

	return pipeline


if __name__ == "__main__":
						lowerCAmelCase__										= argparse.ArgumentParser()

						parser.add_argument(
						    "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
						)
						parser.add_argument(
						    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
						)
						parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
						parser.add_argument(
						    "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
						)
						parser.add_argument(
						    "--lora_prefix_text_encoder",
						    default="lora_te",
						    type=str,
						    help="The prefix of text encoder weight in safetensors",
						)
						parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
						parser.add_argument(
						    "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
						)
						parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")

						lowerCAmelCase__										= parser.parse_args()

						lowerCAmelCase__										= args.base_model_path
						lowerCAmelCase__										= args.checkpoint_path
						lowerCAmelCase__										= args.dump_path
						lowerCAmelCase__										= args.lora_prefix_unet
						lowerCAmelCase__										= args.lora_prefix_text_encoder
						lowerCAmelCase__										= args.alpha

						lowerCAmelCase__										= convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

						lowerCAmelCase__										= pipe.to(args.device)
						pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)






