

'''simple docstring'''


import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax.numpy as jnp

						from transformers.models.distilbert.modeling_flax_distilbert import (
						    FlaxDistilBertForMaskedLM,
						    FlaxDistilBertForMultipleChoice,
						    FlaxDistilBertForQuestionAnswering,
						    FlaxDistilBertForSequenceClassification,
						    FlaxDistilBertForTokenClassification,
						    FlaxDistilBertModel,
						)



class 					lowercase						(  unittest.TestCase ):


							def __init__(			self    ,			_snake_case    ,			_snake_case=13    ,			_snake_case=7    ,			_snake_case=True    ,			_snake_case=True    ,			_snake_case=True    ,			_snake_case=True    ,			_snake_case=99    ,			_snake_case=32    ,			_snake_case=5    ,			_snake_case=4    ,			_snake_case=37    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=512    ,			_snake_case=16    ,			_snake_case=2    ,			_snake_case=0.02    ,			_snake_case=4    ,			)							->				Dict:
								UpperCAmelCase_		:      Tuple       	=					parent
								UpperCAmelCase_		:      str       	=					batch_size
								UpperCAmelCase_		:      Any       	=					seq_length
								UpperCAmelCase_		:      Tuple       	=					is_training
								UpperCAmelCase_		:      Dict       	=					use_attention_mask
								UpperCAmelCase_		:      Tuple       	=					use_token_type_ids
								UpperCAmelCase_		:      List[Any]       	=					use_labels
								UpperCAmelCase_		:      List[str]       	=					vocab_size
								UpperCAmelCase_		:      Union[str, Any]       	=					hidden_size
								UpperCAmelCase_		:      Union[str, Any]       	=					num_hidden_layers
								UpperCAmelCase_		:      Dict       	=					num_attention_heads
								UpperCAmelCase_		:      List[str]       	=					intermediate_size
								UpperCAmelCase_		:      Optional[int]       	=					hidden_act
								UpperCAmelCase_		:      List[str]       	=					hidden_dropout_prob
								UpperCAmelCase_		:      Dict       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      str       	=					max_position_embeddings
								UpperCAmelCase_		:      int       	=					type_vocab_size
								UpperCAmelCase_		:      Optional[int]       	=					type_sequence_label_size
								UpperCAmelCase_		:      int       	=					initializer_range
								UpperCAmelCase_		:      str       	=					num_choices


							def 						_snake_case							(			self)							->				int:
								UpperCAmelCase_		:      Any       	=					ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size)

								UpperCAmelCase_		:      List[str]       	=					None
								if self.use_attention_mask:
									UpperCAmelCase_		:      int       	=					random_attention_mask([self.batch_size, self.seq_length])

								UpperCAmelCase_		:      Optional[Any]       	=					DistilBertConfig(
								    vocab_size=self.vocab_size    ,			dim=self.hidden_size    ,			n_layers=self.num_hidden_layers    ,			n_heads=self.num_attention_heads    ,			hidden_dim=self.intermediate_size    ,			hidden_act=self.hidden_act    ,			dropout=self.hidden_dropout_prob    ,			attention_dropout=self.attention_probs_dropout_prob    ,			max_position_embeddings=self.max_position_embeddings    ,			initializer_range=self.initializer_range    ,			tie_weights_=_snake_case    ,			)

								return config, input_ids, attention_mask





							def 						_snake_case							(			self)							->				Optional[Any]:
								UpperCAmelCase_		:      List[Any]       	=					self.prepare_config_and_inputs()
								UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      List[str]       	=					config_and_inputs
								UpperCAmelCase_		:      Optional[Any]       	=					{'input_ids': input_ids, 'attention_mask': attention_mask}
								return config, inputs_dict



@require_flax
class 					lowercase						(  a_,					unittest.TestCase ):
							_lowerCamelCase		:      str=   (
							    (
							        FlaxDistilBertModel,
							        FlaxDistilBertForMaskedLM,
							        FlaxDistilBertForMultipleChoice,
							        FlaxDistilBertForQuestionAnswering,
							        FlaxDistilBertForSequenceClassification,
							        FlaxDistilBertForTokenClassification,
							        FlaxDistilBertForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)


							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      List[str]       	=					FlaxDistilBertModelTester(self)





							@slow
							def 						_snake_case							(			self)							->				List[Any]:
								for model_class_name in self.all_model_classes:
									UpperCAmelCase_		:      Tuple       	=					model_class_name.from_pretrained('distilbert-base-uncased')
									UpperCAmelCase_		:      Tuple       	=					model(np.ones((1, 1)))
									self.assertIsNotNone(_snake_case)



@require_flax
class 					lowercase						(  unittest.TestCase ):


							@slow
							def 						_snake_case							(			self)							->				str:
								UpperCAmelCase_		:      str       	=					FlaxDistilBertModel.from_pretrained('distilbert-base-uncased')
								UpperCAmelCase_		:      int       	=					np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
								UpperCAmelCase_		:      Dict       	=					np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
								UpperCAmelCase_		:      List[str]       	=					model(_snake_case    ,			attention_mask=_snake_case)[0]
								UpperCAmelCase_		:      List[str]       	=					(1, 11, 768)
								self.assertEqual(output.shape    ,			_snake_case)
								UpperCAmelCase_		:      Union[str, Any]       	=					np.array([[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]])

								self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]    ,			_snake_case    ,			atol=1e-4))









'''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)






