

"""simple docstring"""


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Dict:
      # load base model
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	StableDiffusionPipeline.from_pretrained(__lowerCAmelCase  ,					torch_dtype=torch.floataa							)

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

      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[]

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

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

            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	[]
            if "lora_down" in key:
                  pair_keys.append(key.replace("""lora_down"""  ,					"""lora_up"""							)							)
                  pair_keys.append(__lowerCAmelCase							)
            else:
                  pair_keys.append(__lowerCAmelCase							)
                  pair_keys.append(key.replace("""lora_up"""  ,					"""lora_down"""							)							)

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

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

      return pipeline


if __name__ == "__main__":
  a					:Optional[Any]    = 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					:int    = parser.parse_args()

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

  a					:Optional[int]    = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

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







"""simple docstring"""


import unittest

import numpy as np

from transformers import RoFormerConfig, 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.roformer.modeling_flax_roformer import (
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
  )

class      __a							(unittest.TestCase):






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a=13	,    _a=7	,    _a=True	,    _a=True	,    _a=True	,    _a=True	,    _a=99	,    _a=32	,    _a=5	,    _a=4	,    _a=37	,    _a="gelu"	,    _a=0.1	,    _a=0.1	,    _a=512	,    _a=16	,    _a=2	,    _a=0.02	,    _a=4	,    )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	parent
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	batch_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	seq_length
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	is_training
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	use_attention_mask
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	use_token_type_ids
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	use_labels
           SCREAMING_SNAKE_CASE__   :      int      		=	vocab_size
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	hidden_size
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	num_hidden_layers
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	num_attention_heads
           SCREAMING_SNAKE_CASE__   :      Dict      		=	intermediate_size
           SCREAMING_SNAKE_CASE__   :      int      		=	hidden_act
           SCREAMING_SNAKE_CASE__   :      Dict      		=	hidden_dropout_prob
           SCREAMING_SNAKE_CASE__   :      str      		=	attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	max_position_embeddings
           SCREAMING_SNAKE_CASE__   :      Dict      		=	type_vocab_size
           SCREAMING_SNAKE_CASE__   :      Any      		=	type_sequence_label_size
           SCREAMING_SNAKE_CASE__   :      int      		=	initializer_range
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	num_choices

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	None
           if self.use_attention_mask:
                 SCREAMING_SNAKE_CASE__   :      int      		=	random_attention_mask([self.batch_size, self.seq_length]  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	None
           if self.use_token_type_ids:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	RoFormerConfig(
               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=_a	,    initializer_range=self.initializer_range	,    )

           return config, input_ids, token_type_ids, attention_mask





     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      List[str]      		=	config_and_inputs
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
           return config, inputs_dict





@require_flax
class      __a							(UpperCamelCase_   ,	unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Any       = True

     _SCREAMING_SNAKE_CASE						:Optional[Any]       = (
         (
             FlaxRoFormerModel,
             FlaxRoFormerForMaskedLM,
             FlaxRoFormerForSequenceClassification,
             FlaxRoFormerForTokenClassification,
             FlaxRoFormerForMultipleChoice,
             FlaxRoFormerForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FlaxRoFormerModelTester(self  )





     @slow
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""	,    from_pt=_a  )
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	model(np.ones((1, 1)  )  )
                 self.assertIsNotNone(_a  )





@require_flax
class      __a							(unittest.TestCase):






     '''simple docstring'''

     @slow
     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	jnp.array([[0, 1, 2, 3, 4, 5]]  )
           SCREAMING_SNAKE_CASE__   :      str      		=	model(_a  )[0]

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	50_000

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	(1, 6, vocab_size)
           self.assertEqual(output.shape	,    _a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	jnp.array(
               [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]  )

           self.assertTrue(jnp.allclose(output[:, :3, :3]	,    _a	,    atol=1E-4  )  )




