





import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 					A_					(   UpperCAmelCase       ,						unittest.TestCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			Any  			=	RobertaTokenizer
       SCREAMING_SNAKE_CASE_   :			Union[str, Any]  			=	RobertaTokenizerFast
       SCREAMING_SNAKE_CASE_   :			int  			=	True
       SCREAMING_SNAKE_CASE_   :			Any  			=	{'''cls_token''': '''<s>'''}







       def  __UpperCAmelCase			(     self						:						List[Any]	)    ->       List[str]:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         _lowercase						    =			[
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '<unk>',
         ]
         _lowercase						    =			dict(zip(__A    ,range(len(__A	)	)	)	)
         _lowercase						    =			['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         _lowercase						    =			{'unk_token': '<unk>'}

         _lowercase						    =			os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']	)
         _lowercase						    =			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(__A	) + '\n'	)
         with open(self.merges_file    ,'w'    ,encoding='utf-8'	) as fp:
           fp.write('\n'.join(__A	)	)







       def  __UpperCAmelCase			(     self						:						List[str]    ,**__A						:						List[Any]	)    ->       Union[str, Any]:
         kwargs.update(self.special_tokens_map	)
         return self.tokenizer_class.from_pretrained(self.tmpdirname    ,**__A	)







       def  __UpperCAmelCase			(     self						:						Union[str, Any]    ,**__A						:						Any	)    ->       str:
         kwargs.update(self.special_tokens_map	)
         return RobertaTokenizerFast.from_pretrained(self.tmpdirname    ,**__A	)







       def  __UpperCAmelCase			(     self						:						str    ,__A						:						List[str]	)    ->       List[Any]:
         _lowercase						    =			'lower newer'
         _lowercase						    =			'lower newer'
         return input_text, output_text







       def  __UpperCAmelCase			(     self						:						Tuple	)    ->       Union[str, Any]:
         _lowercase						    =			self.tokenizer_class(self.vocab_file    ,self.merges_file    ,**self.special_tokens_map	)
         _lowercase						    =			'lower newer'
         _lowercase						    =			['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
         _lowercase						    =			tokenizer.tokenize(__A	)  # , add_prefix_space=True)
         self.assertListEqual(__A    ,__A	)

         _lowercase						    =			tokens + [tokenizer.unk_token]
         _lowercase						    =			[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__A	)    ,__A	)







       def  __UpperCAmelCase			(     self						:						Dict	)    ->       Optional[int]:
         _lowercase						    =			self.get_tokenizer()

         self.assertListEqual(tokenizer.encode('Hello world!'    ,add_special_tokens=__A	)    ,[0, 3_1414, 232, 328, 2]	)
         self.assertListEqual(
             tokenizer.encode('Hello world! cécé herlolip 418'    ,add_special_tokens=__A	)    ,[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]    ,)







       @slow
       def  __UpperCAmelCase			(     self						:						Optional[Any]	)    ->       Optional[Any]:
         _lowercase						    =			self.tokenizer_class.from_pretrained('roberta-base'	)

         _lowercase						    =			tokenizer.encode('sequence builders'    ,add_special_tokens=__A	)
         _lowercase						    =			tokenizer.encode('multi-sequence build'    ,add_special_tokens=__A	)

         _lowercase						    =			tokenizer.encode(
             'sequence builders'    ,add_special_tokens=__A    ,add_prefix_space=__A	)
         _lowercase						    =			tokenizer.encode(
             'sequence builders'    ,'multi-sequence build'    ,add_special_tokens=__A    ,add_prefix_space=__A	)

         _lowercase						    =			tokenizer.build_inputs_with_special_tokens(__A	)
         _lowercase						    =			tokenizer.build_inputs_with_special_tokens(__A    ,__A	)

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode







       def  __UpperCAmelCase			(     self						:						List[Any]	)    ->       List[Any]:
         _lowercase						    =			self.get_tokenizer()

         _lowercase						    =			'Encode this sequence.'
         _lowercase						    =			tokenizer.byte_encoder[' '.encode('utf-8'	)[0]]

         # Testing encoder arguments
         _lowercase						    =			tokenizer.encode(__A    ,add_special_tokens=__A    ,add_prefix_space=__A	)
         _lowercase						    =			tokenizer.convert_ids_to_tokens(encoded[0]	)[0]
         self.assertNotEqual(__A    ,__A	)

         _lowercase						    =			tokenizer.encode(__A    ,add_special_tokens=__A    ,add_prefix_space=__A	)
         _lowercase						    =			tokenizer.convert_ids_to_tokens(encoded[0]	)[0]
         self.assertEqual(__A    ,__A	)

         tokenizer.add_special_tokens({'bos_token': '<s>'}	)
         _lowercase						    =			tokenizer.encode(__A    ,add_special_tokens=__A	)
         _lowercase						    =			tokenizer.convert_ids_to_tokens(encoded[1]	)[0]
         self.assertNotEqual(__A    ,__A	)

         # Testing spaces after special tokens
         _lowercase						    =			'<mask>'
         tokenizer.add_special_tokens(
             {'mask_token': AddedToken(__A    ,lstrip=__A    ,rstrip=__A	)}	)  # mask token has a left space
         _lowercase						    =			tokenizer.convert_tokens_to_ids(__A	)

         _lowercase						    =			'Encode <mask> sequence'
         _lowercase						    =			'Encode <mask>sequence'

         _lowercase						    =			tokenizer.encode(__A	)
         _lowercase						    =			encoded.index(__A	)
         _lowercase						    =			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]	)[0]
         self.assertEqual(__A    ,__A	)

         _lowercase						    =			tokenizer.encode(__A	)
         _lowercase						    =			encoded.index(__A	)
         _lowercase						    =			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]	)[0]
         self.assertNotEqual(__A    ,__A	)







       def  __UpperCAmelCase			(     self						:						List[Any]	)    ->       Dict:
         pass







       def  __UpperCAmelCase			(     self						:						Optional[Any]	)    ->       Optional[int]:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
             _lowercase						    =			self.rust_tokenizer_class.from_pretrained(__A    ,**__A	)
             _lowercase						    =			self.tokenizer_class.from_pretrained(__A    ,**__A	)
             _lowercase						    =			'A, <mask> AllenNLP sentence.'
             _lowercase						    =			tokenizer_r.encode_plus(__A    ,add_special_tokens=__A    ,return_token_type_ids=__A	)
             _lowercase						    =			tokenizer_p.encode_plus(__A    ,add_special_tokens=__A    ,return_token_type_ids=__A	)

             # token_type_ids should put 0 everywhere
             self.assertEqual(sum(tokens_r['token_type_ids']	)    ,sum(tokens_p['token_type_ids']	)	)

             # attention_mask should put 1 everywhere, so sum over length should be 1
             self.assertEqual(
                 sum(tokens_r['attention_mask']	) / len(tokens_r['attention_mask']	)    ,sum(tokens_p['attention_mask']	) / len(tokens_p['attention_mask']	)    ,)

             _lowercase						    =			tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']	)
             _lowercase						    =			tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']	)

             # Rust correctly handles the space before the mask while python doesnt
             self.assertSequenceEqual(tokens_p['input_ids']    ,[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]	)
             self.assertSequenceEqual(tokens_r['input_ids']    ,[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]	)

             self.assertSequenceEqual(
                 __A    ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']	)
             self.assertSequenceEqual(
                 __A    ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']	)







       def  __UpperCAmelCase			(     self						:						int	)    ->       Any:
         for trim_offsets, add_prefix_space in itertools.product([True, False]    ,repeat=2	):
           _lowercase						    =			self.rust_tokenizer_class.from_pretrained(
               self.tmpdirname    ,use_fast=__A    ,add_prefix_space=__A    ,trim_offsets=__A	)

           _lowercase						    =			json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()	)
           _lowercase						    =			json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()	)

           self.assertEqual(pre_tokenizer_state['add_prefix_space']    ,__A	)

           self.assertEqual(post_processor_state['add_prefix_space']    ,__A	)
           self.assertEqual(post_processor_state['trim_offsets']    ,__A	)







       def  __UpperCAmelCase			(     self						:						List[str]	)    ->       List[Any]:
         # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
         # `trim_offsets`
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
             _lowercase						    =			'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
             _lowercase						    =			F"""{text_of_1_token} {text_of_1_token}"""

             _lowercase						    =			self.rust_tokenizer_class.from_pretrained(
                 __A    ,use_fast=__A    ,add_prefix_space=__A    ,trim_offsets=__A	)
             _lowercase						    =			tokenizer_r(__A    ,return_offsets_mapping=__A    ,add_special_tokens=__A	)
             self.assertEqual(encoding.offset_mapping[0]    ,(0, len(__A	))	)
             self.assertEqual(
                 encoding.offset_mapping[1]    ,(len(__A	) + 1, len(__A	) + 1 + len(__A	))    ,)

             _lowercase						    =			self.rust_tokenizer_class.from_pretrained(
                 __A    ,use_fast=__A    ,add_prefix_space=__A    ,trim_offsets=__A	)
             _lowercase						    =			tokenizer_r(__A    ,return_offsets_mapping=__A    ,add_special_tokens=__A	)
             self.assertEqual(encoding.offset_mapping[0]    ,(0, len(__A	))	)
             self.assertEqual(
                 encoding.offset_mapping[1]    ,(len(__A	) + 1, len(__A	) + 1 + len(__A	))    ,)

             _lowercase						    =			self.rust_tokenizer_class.from_pretrained(
                 __A    ,use_fast=__A    ,add_prefix_space=__A    ,trim_offsets=__A	)
             _lowercase						    =			tokenizer_r(__A    ,return_offsets_mapping=__A    ,add_special_tokens=__A	)
             self.assertEqual(encoding.offset_mapping[0]    ,(0, len(__A	))	)
             self.assertEqual(
                 encoding.offset_mapping[1]    ,(len(__A	), len(__A	) + 1 + len(__A	))    ,)

             _lowercase						    =			self.rust_tokenizer_class.from_pretrained(
                 __A    ,use_fast=__A    ,add_prefix_space=__A    ,trim_offsets=__A	)
             _lowercase						    =			tokenizer_r(__A    ,return_offsets_mapping=__A    ,add_special_tokens=__A	)
             self.assertEqual(encoding.offset_mapping[0]    ,(0, len(__A	))	)
             self.assertEqual(
                 encoding.offset_mapping[1]    ,(len(__A	), len(__A	) + 1 + len(__A	))    ,)

             _lowercase						    =			F""" {text}"""

             # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
             #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
             # )
             # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
             # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
             # self.assertEqual(
             #     encoding.offset_mapping[1],
             #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
             # )

             _lowercase						    =			self.rust_tokenizer_class.from_pretrained(
                 __A    ,use_fast=__A    ,add_prefix_space=__A    ,trim_offsets=__A	)
             _lowercase						    =			tokenizer_r(__A    ,return_offsets_mapping=__A    ,add_special_tokens=__A	)
             self.assertEqual(encoding.offset_mapping[0]    ,(1, 1 + len(__A	))	)
             self.assertEqual(
                 encoding.offset_mapping[1]    ,(1 + len(__A	) + 1, 1 + len(__A	) + 1 + len(__A	))    ,)

             _lowercase						    =			self.rust_tokenizer_class.from_pretrained(
                 __A    ,use_fast=__A    ,add_prefix_space=__A    ,trim_offsets=__A	)
             _lowercase						    =			tokenizer_r(__A    ,return_offsets_mapping=__A    ,add_special_tokens=__A	)
             self.assertEqual(encoding.offset_mapping[0]    ,(0, 1 + len(__A	))	)
             self.assertEqual(
                 encoding.offset_mapping[1]    ,(1 + len(__A	), 1 + len(__A	) + 1 + len(__A	))    ,)

             _lowercase						    =			self.rust_tokenizer_class.from_pretrained(
                 __A    ,use_fast=__A    ,add_prefix_space=__A    ,trim_offsets=__A	)
             _lowercase						    =			tokenizer_r(__A    ,return_offsets_mapping=__A    ,add_special_tokens=__A	)
             self.assertEqual(encoding.offset_mapping[0]    ,(0, 1 + len(__A	))	)
             self.assertEqual(
                 encoding.offset_mapping[1]    ,(1 + len(__A	), 1 + len(__A	) + 1 + len(__A	))    ,)



import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				Any       ,       __magic_name__	:				List[str]       ,       __magic_name__	:				Optional[int]       ,       __magic_name__	:				Tuple	)  ->    List[Any]:
		"""simple docstring"""




		lowercase__	     =						StableDiffusionPipeline.from_pretrained(__magic_name__       ,       torch_dtype=torch.floataa	)

		# load LoRA weight from .safetensors
		lowercase__	     =						load_file(__magic_name__	)

		lowercase__	     =						[]

		# 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:
						lowercase__	     =						key.split("""."""	)[0].split(LORA_PREFIX_TEXT_ENCODER + """_"""	)[-1].split("""_"""	)
						lowercase__	     =						pipeline.text_encoder
				else:
						lowercase__	     =						key.split("""."""	)[0].split(LORA_PREFIX_UNET + """_"""	)[-1].split("""_"""	)
						lowercase__	     =						pipeline.unet

				# find the target layer
				lowercase__	     =						layer_infos.pop(0	)
				while len(__magic_name__	) > -1:
						try:
								lowercase__	     =						curr_layer.__getattr__(__magic_name__	)
								if len(__magic_name__	) > 0:
										lowercase__	     =						layer_infos.pop(0	)
								elif len(__magic_name__	) == 0:
										break
						except Exception:
								if len(__magic_name__	) > 0:
										temp_name += "_" + layer_infos.pop(0	)
								else:
										lowercase__	     =						layer_infos.pop(0	)

				lowercase__	     =						[]
				if "lora_down" in key:
						pair_keys.append(key.replace("""lora_down"""       ,       """lora_up"""	)	)
						pair_keys.append(__magic_name__	)
				else:
						pair_keys.append(__magic_name__	)
						pair_keys.append(key.replace("""lora_up"""       ,       """lora_down"""	)	)

				# update weight
				if len(state_dict[pair_keys[0]].shape	) == 4:
						lowercase__	     =						state_dict[pair_keys[0]].squeeze(3	).squeeze(2	).to(torch.floataa	)
						lowercase__	     =						state_dict[pair_keys[1]].squeeze(3	).squeeze(2	).to(torch.floataa	)
						curr_layer.weight.data += alpha * torch.mm(__magic_name__       ,       __magic_name__	).unsqueeze(2	).unsqueeze(3	)
				else:
						lowercase__	     =						state_dict[pair_keys[0]].to(torch.floataa	)
						lowercase__	     =						state_dict[pair_keys[1]].to(torch.floataa	)
						curr_layer.weight.data += alpha * torch.mm(__magic_name__       ,       __magic_name__	)

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

		return pipeline


if __name__ == "__main__":
	A			:						int        =  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.)')

	A			:						str        =  parser.parse_args()

	A			:						Tuple        =  args.base_model_path
	A			:						List[str]        =  args.checkpoint_path
	A			:						Optional[int]        =  args.dump_path
	A			:						Optional[int]        =  args.lora_prefix_unet
	A			:						Any        =  args.lora_prefix_text_encoder
	A			:						Any        =  args.alpha

	A			:						List[str]        =  convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

	A			:						int        =  pipe.to(args.device)
	pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

