


"""simple docstring"""






import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer


class 			__lowerCamelCase     :

     '''simple docstring'''


     def __init__(					self			, __UpperCAmelCase			, __UpperCAmelCase=13			, __UpperCAmelCase=7			, __UpperCAmelCase=True			, __UpperCAmelCase=True			, __UpperCAmelCase=False			, __UpperCAmelCase=True			, __UpperCAmelCase=99			, __UpperCAmelCase=32			, __UpperCAmelCase=5			, __UpperCAmelCase=4			, __UpperCAmelCase=37			, __UpperCAmelCase="gelu"			, __UpperCAmelCase=0.1			, __UpperCAmelCase=0.1			, __UpperCAmelCase=512			, __UpperCAmelCase=16			, __UpperCAmelCase=2			, __UpperCAmelCase=0.02			, __UpperCAmelCase=3			, __UpperCAmelCase=4			, __UpperCAmelCase=None			, )				->							Optional[int]:
          _a					= parent
          _a					= batch_size
          _a					= seq_length
          _a					= is_training
          _a					= use_input_mask
          _a					= use_token_type_ids
          _a					= use_labels
          _a					= vocab_size
          _a					= hidden_size
          _a					= num_hidden_layers
          _a					= num_attention_heads
          _a					= intermediate_size
          _a					= hidden_act
          _a					= hidden_dropout_prob
          _a					= attention_probs_dropout_prob
          _a					= max_position_embeddings
          _a					= type_vocab_size
          _a					= type_sequence_label_size
          _a					= initializer_range
          _a					= num_labels
          _a					= num_choices
          _a					= scope


     def 							_UpperCAmelCase					(					self							)				->							Any:
          _a					= ids_tensor([self.batch_size, self.seq_length]			, self.vocab_size							)

          _a					= None
          if self.use_input_mask:
               _a					= random_attention_mask([self.batch_size, self.seq_length]							)

          _a					= None
          if self.use_token_type_ids:
               _a					= ids_tensor([self.batch_size, self.seq_length]			, self.type_vocab_size							)

          _a					= None
          _a					= None
          _a					= None
          if self.use_labels:
               _a					= ids_tensor([self.batch_size]			, self.type_sequence_label_size							)
               _a					= ids_tensor([self.batch_size, self.seq_length]			, self.num_labels							)
               _a					= ids_tensor([self.batch_size]			, self.num_choices							)

          _a					= self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


     def 							_UpperCAmelCase					(					self							)				->							List[str]:
          return LlamaConfig(
              vocab_size=self.vocab_size			, hidden_size=self.hidden_size			, num_hidden_layers=self.num_hidden_layers			, num_attention_heads=self.num_attention_heads			, intermediate_size=self.intermediate_size			, hidden_act=self.hidden_act			, hidden_dropout_prob=self.hidden_dropout_prob			, attention_probs_dropout_prob=self.attention_probs_dropout_prob			, max_position_embeddings=self.max_position_embeddings			, type_vocab_size=self.type_vocab_size			, is_decoder=UpperCAmelCase__			, initializer_range=self.initializer_range			, )


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							)				->							Any:
          _a					= LlamaModel(config=UpperCAmelCase__							)
          model.to(UpperCAmelCase__							)
          model.eval()
          _a					= model(UpperCAmelCase__			, attention_mask=UpperCAmelCase__							)
          _a					= model(UpperCAmelCase__							)
          self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, self.seq_length, self.hidden_size)							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, )				->							Dict:
          _a					= True
          _a					= LlamaModel(UpperCAmelCase__							)
          model.to(UpperCAmelCase__							)
          model.eval()
          _a					= model(
              UpperCAmelCase__			, attention_mask=UpperCAmelCase__			, encoder_hidden_states=UpperCAmelCase__			, encoder_attention_mask=UpperCAmelCase__			, )
          _a					= model(
              UpperCAmelCase__			, attention_mask=UpperCAmelCase__			, encoder_hidden_states=UpperCAmelCase__			, )
          _a					= model(UpperCAmelCase__			, attention_mask=UpperCAmelCase__							)
          self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, self.seq_length, self.hidden_size)							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, )				->							Any:
          _a					= LlamaForCausalLM(config=UpperCAmelCase__							)
          model.to(UpperCAmelCase__							)
          model.eval()
          _a					= model(UpperCAmelCase__			, attention_mask=UpperCAmelCase__			, labels=UpperCAmelCase__							)
          self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.seq_length, self.vocab_size)							)


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, )				->							Any:
          _a					= True
          _a					= True
          _a					= LlamaForCausalLM(config=UpperCAmelCase__							)
          model.to(UpperCAmelCase__							)
          model.eval()

          # first forward pass
          _a					= model(
              UpperCAmelCase__			, attention_mask=UpperCAmelCase__			, encoder_hidden_states=UpperCAmelCase__			, encoder_attention_mask=UpperCAmelCase__			, use_cache=UpperCAmelCase__			, )
          _a					= outputs.past_key_values

          # create hypothetical multiple next token and extent to next_input_ids
          _a					= ids_tensor((self.batch_size, 3)			, config.vocab_size							)
          _a					= ids_tensor((self.batch_size, 3)			, vocab_size=2							)

          # append to next input_ids and
          _a					= torch.cat([input_ids, next_tokens]			, dim=-1							)
          _a					= torch.cat([input_mask, next_mask]			, dim=-1							)

          _a					= model(
              UpperCAmelCase__			, attention_mask=UpperCAmelCase__			, encoder_hidden_states=UpperCAmelCase__			, encoder_attention_mask=UpperCAmelCase__			, output_hidden_states=UpperCAmelCase__			, )['''hidden_states'''][0]
          _a					= model(
              UpperCAmelCase__			, attention_mask=UpperCAmelCase__			, encoder_hidden_states=UpperCAmelCase__			, encoder_attention_mask=UpperCAmelCase__			, past_key_values=UpperCAmelCase__			, output_hidden_states=UpperCAmelCase__			, )['''hidden_states'''][0]

          # select random slice
          _a					= ids_tensor((1,)			, output_from_past.shape[-1]							).item()
          _a					= output_from_no_past[:, -3:, random_slice_idx].detach()
          _a					= output_from_past[:, :, random_slice_idx].detach()

          self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(UpperCAmelCase__			, UpperCAmelCase__			, atol=1e-3							)							)





     def 							_UpperCAmelCase					(					self							)				->							List[Any]:
          _a					= self.prepare_config_and_inputs()
          (
              (
              _a							
          )		,					(
              _a							
          )		,					(
              _a							
          )		,					(
              _a							
          )		,					(
              _a							
          )		,					(
              _a							
          )		,					(
              _a							
          )		,												
          )					= config_and_inputs
          _a					= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict







@require_torch
class 			__lowerCamelCase     (    UpperCAmelCase__     ,    UpperCAmelCase__     ,    UpperCAmelCase__     ,    unittest.TestCase ):

     '''simple docstring'''
     A_				:		Union[str, Any]			=						(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
     A_				:		Tuple			=						(LlamaForCausalLM,) if is_torch_available() else ()
     A_				:		Any			=						(
         {
             'feature-extraction': LlamaModel,
             'text-classification': LlamaForSequenceClassification,
             'text-generation': LlamaForCausalLM,
             'zero-shot': LlamaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     A_				:		Optional[Any]			=						False
     A_				:		str			=						False


     def 							_UpperCAmelCase					(					self							)				->							int:
          _a					= LlamaModelTester(self							)
          _a					= ConfigTester(self			, config_class=UpperCAmelCase__			, hidden_size=37							)


     def 							_UpperCAmelCase					(					self							)				->							Optional[int]:
          self.config_tester.run_common_tests()


     def 							_UpperCAmelCase					(					self							)				->							List[str]:
          _a					= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*UpperCAmelCase__							)


     def 							_UpperCAmelCase					(					self							)				->							Tuple:
          _a					= self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
               _a					= type
               self.model_tester.create_and_check_model(*UpperCAmelCase__							)


     def 							_UpperCAmelCase					(					self							)				->							Optional[int]:
          _a		,					_a					= self.model_tester.prepare_config_and_inputs_for_common()
          _a					= 3
          _a					= input_dict['''input_ids''']
          _a					= input_ids.ne(1							).to(UpperCAmelCase__							)
          _a					= ids_tensor([self.model_tester.batch_size]			, self.model_tester.type_sequence_label_size							)
          _a					= LlamaForSequenceClassification(UpperCAmelCase__							)
          model.to(UpperCAmelCase__							)
          model.eval()
          _a					= model(UpperCAmelCase__			, attention_mask=UpperCAmelCase__			, labels=UpperCAmelCase__							)
          self.assertEqual(result.logits.shape			, (self.model_tester.batch_size, self.model_tester.num_labels)							)


     def 							_UpperCAmelCase					(					self							)				->							List[str]:
          _a		,					_a					= self.model_tester.prepare_config_and_inputs_for_common()
          _a					= 3
          _a					= '''single_label_classification'''
          _a					= input_dict['''input_ids''']
          _a					= input_ids.ne(1							).to(UpperCAmelCase__							)
          _a					= ids_tensor([self.model_tester.batch_size]			, self.model_tester.type_sequence_label_size							)
          _a					= LlamaForSequenceClassification(UpperCAmelCase__							)
          model.to(UpperCAmelCase__							)
          model.eval()
          _a					= model(UpperCAmelCase__			, attention_mask=UpperCAmelCase__			, labels=UpperCAmelCase__							)
          self.assertEqual(result.logits.shape			, (self.model_tester.batch_size, self.model_tester.num_labels)							)


     def 							_UpperCAmelCase					(					self							)				->							Tuple:
          _a		,					_a					= self.model_tester.prepare_config_and_inputs_for_common()
          _a					= 3
          _a					= '''multi_label_classification'''
          _a					= input_dict['''input_ids''']
          _a					= input_ids.ne(1							).to(UpperCAmelCase__							)
          _a					= ids_tensor(
              [self.model_tester.batch_size, config.num_labels]			, self.model_tester.type_sequence_label_size							).to(torch.float							)
          _a					= LlamaForSequenceClassification(UpperCAmelCase__							)
          model.to(UpperCAmelCase__							)
          model.eval()
          _a					= model(UpperCAmelCase__			, attention_mask=UpperCAmelCase__			, labels=UpperCAmelCase__							)
          self.assertEqual(result.logits.shape			, (self.model_tester.batch_size, self.model_tester.num_labels)							)


     @unittest.skip('''LLaMA buffers include complex numbers, which breaks this test'''							)
     def 							_UpperCAmelCase					(					self							)				->							int:
          pass





     @parameterized.expand([('''linear''',), ('''dynamic''',)]							)
     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase							)				->							List[str]:
          _a		,					_a					= self.model_tester.prepare_config_and_inputs_for_common()
          _a					= ids_tensor([1, 10]			, config.vocab_size							)
          _a					= ids_tensor([1, int(config.max_position_embeddings * 1.5							)]			, config.vocab_size							)

          set_seed(42							)  # Fixed seed at init time so the two models get the same random weights
          _a					= LlamaModel(UpperCAmelCase__							)
          original_model.to(UpperCAmelCase__							)
          original_model.eval()
          _a					= original_model(UpperCAmelCase__							).last_hidden_state
          _a					= original_model(UpperCAmelCase__							).last_hidden_state

          set_seed(42							)  # Fixed seed at init time so the two models get the same random weights
          _a					= {'''type''': scaling_type, '''factor''': 10.0}
          _a					= LlamaModel(UpperCAmelCase__							)
          scaled_model.to(UpperCAmelCase__							)
          scaled_model.eval()
          _a					= scaled_model(UpperCAmelCase__							).last_hidden_state
          _a					= scaled_model(UpperCAmelCase__							).last_hidden_state

          # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
          # maximum sequence length, so the outputs for the short input should match.
          if scaling_type == "dynamic":
               self.assertTrue(torch.allclose(UpperCAmelCase__			, UpperCAmelCase__			, atol=1e-5							)							)
          else:
               self.assertFalse(torch.allclose(UpperCAmelCase__			, UpperCAmelCase__			, atol=1e-5							)							)

          # The output should be different for long inputs
          self.assertFalse(torch.allclose(UpperCAmelCase__			, UpperCAmelCase__			, atol=1e-5							)							)







@require_torch
class 			__lowerCamelCase     (    unittest.TestCase ):

     '''simple docstring'''


     @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''							)
     @slow
     def 							_UpperCAmelCase					(					self							)				->							Union[str, Any]:
          _a					= [1, 306, 4658, 278, 6593, 310, 2834, 338]
          _a					= LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-7b-hf'''			, device_map='''auto'''							)
          _a					= model(torch.tensor([input_ids]							)							)
          # Expected mean on dim = -1
          _a					= torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]							)
          torch.testing.assert_close(out.mean(-1							)			, UpperCAmelCase__			, atol=1e-2			, rtol=1e-2							)
          # slicing logits[0, 0, 0:30]
          # fmt: off
          _a					= torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]							)
          # fmt: on
          torch.testing.assert_close(out[0, 0, :30]			, UpperCAmelCase__			, atol=1e-5			, rtol=1e-5							)


     @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''							)
     @slow
     def 							_UpperCAmelCase					(					self							)				->							str:
          _a					= [1, 306, 4658, 278, 6593, 310, 2834, 338]
          _a					= LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-hf'''			, device_map='''auto'''							)
          _a					= model(torch.tensor(UpperCAmelCase__							)							)
          # Expected mean on dim = -1
          _a					= torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]							)
          torch.testing.assert_close(out.mean(-1							)			, UpperCAmelCase__			, atol=1e-2			, rtol=1e-2							)
          # slicing logits[0, 0, 0:30]
          # fmt: off
          _a					= torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]							)
          # fmt: on
          torch.testing.assert_close(out[0, 0, :30]			, UpperCAmelCase__			, atol=1e-5			, rtol=1e-5							)


     @unittest.skip('''Logits are not exactly the same, once we fix the instabalities somehow, will update!'''							)
     @slow
     def 							_UpperCAmelCase					(					self							)				->							Any:
          _a					= [1, 306, 4658, 278, 6593, 310, 2834, 338]
          _a					= LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''			, device_map='''auto'''							)
          _a					= model(torch.tensor(UpperCAmelCase__							)							)
          # Expected mean on dim = -1
          _a					= torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]							)
          torch.testing.assert_close(out.mean(-1							)			, UpperCAmelCase__			, atol=1e-2			, rtol=1e-2							)
          # slicing logits[0, 0, 0:30]
          # fmt: off
          _a					= torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]							)
          # fmt: on
          torch.testing.assert_close(out.mean(-1							)			, UpperCAmelCase__			, atol=1e-2			, rtol=1e-2							)


     @unittest.skip(
         '''Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'''							)
     @slow
     def 							_UpperCAmelCase					(					self							)				->							Dict:
          _a					= [1, 306, 4658, 278, 6593, 310, 2834, 338]
          _a					= LlamaForCausalLM.from_pretrained('''meta-llama/Llama-2-70b-hf'''			, device_map='''auto'''							)
          _a					= model(torch.tensor(UpperCAmelCase__							)							)

          _a					= torch.tensor(
              [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]			, dtype=torch.floataa							)
          torch.testing.assert_close(out.mean(-1							)			, UpperCAmelCase__			, atol=1e-2			, rtol=1e-2							)
          # fmt: off
          _a					= torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]							)
          # fmt: on
          torch.testing.assert_close(out[0, 0, :30]			, UpperCAmelCase__			, atol=1e-5			, rtol=1e-5							)





     @unittest.skip('''Model is curently gated'''							)
     @slow
     def 							_UpperCAmelCase					(					self							)				->							Optional[Any]:
          _a					= '''Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'''
          _a					= '''Simply put, the theory of relativity states that '''
          _a					= LlamaTokenizer.from_pretrained('''meta-llama/Llama-2-13b-chat-hf'''							)
          _a					= tokenizer.encode(UpperCAmelCase__			, return_tensors='''pt'''							)
          _a					= LlamaForCausalLM.from_pretrained(
              '''meta-llama/Llama-2-13b-chat-hf'''			, device_map='''sequential'''			, use_safetensors=UpperCAmelCase__							)

          # greedy generation outputs
          _a					= model.generate(UpperCAmelCase__			, max_new_tokens=64			, top_p=UpperCAmelCase__			, temperature=1			, do_sample=UpperCAmelCase__							)
          _a					= tokenizer.decode(generated_ids[0]			, skip_special_tokens=UpperCAmelCase__							)
          self.assertEqual(UpperCAmelCase__			, UpperCAmelCase__							)





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)   ->       List[Any]:



       """simple docstring"""

       A__       =    StableDiffusionPipeline.from_pretrained(lowercase_ ,				torch_dtype=torch.floataa						)

       # load LoRA weight from .safetensors
       A__       =    load_file(lowercase_						)

       A__       =    []

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

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

              A__       =    []
              if "lora_down" in key:
                     pair_keys.append(key.replace('''lora_down''' ,				'''lora_up'''						)						)
                     pair_keys.append(lowercase_						)
              else:
                     pair_keys.append(lowercase_						)
                     pair_keys.append(key.replace('''lora_up''' ,				'''lora_down'''						)						)

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

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

       return pipeline


if __name__ == "__main__":
  _lowerCamelCase	:		Tuple							       =   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	:		Tuple							       =   parser.parse_args()

  _lowerCamelCase	:		List[Any]							       =   args.base_model_path
  _lowerCamelCase	:		Optional[int]							       =   args.checkpoint_path
  _lowerCamelCase	:		Dict							       =   args.dump_path
  _lowerCamelCase	:		Optional[Any]							       =   args.lora_prefix_unet
  _lowerCamelCase	:		Optional[int]							       =   args.lora_prefix_text_encoder
  _lowerCamelCase	:		List[Any]							       =   args.alpha

  _lowerCamelCase	:		int							       =   convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

  _lowerCamelCase	:		Tuple							       =   pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


