

"""simple docstring"""





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def   _A  (  UpperCamelCase_     :       Optional[Any],			UpperCamelCase_     :       Any,			UpperCamelCase_     :       Dict,			UpperCamelCase_     :       Optional[int],			UpperCamelCase_     :       List[Any])	->						str:
   '''simple docstring'''


   __lowercase								=						StableDiffusionPipeline.from_pretrained(UpperCamelCase_,			torch_dtype=torch.floataa)

   # load LoRA weight from .safetensors
   __lowercase								=						load_file(UpperCamelCase_)

   __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(UpperCamelCase_) > -1:
         try:
            __lowercase								=						curr_layer.__getattr__(UpperCamelCase_)
            if len(UpperCamelCase_) > 0:
               __lowercase								=						layer_infos.pop(0)
            elif len(UpperCamelCase_) == 0:
               break
         except Exception:
            if len(UpperCamelCase_) > 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(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:
         __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(UpperCamelCase_,			UpperCamelCase_).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(UpperCamelCase_,			UpperCamelCase_)

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

   return pipeline


if __name__ == "__main__":
   _a       =							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       =							parser.parse_args()

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

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

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







"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers.models.xglm.modeling_tf_xglm import (
       TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFXGLMForCausalLM,
       TFXGLMModel,
   )




@require_tf
class 					_lowerCAmelCase  :







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	XGLMConfig
 __UpperCAmelCase	:					Optional[Any]   		=	{}
 __UpperCAmelCase	:					Union[str, Any]   		=	"gelu"


 def __init__(			self							:			Optional[int],					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Optional[int]=1_4,					UpperCAmelCase__							:			str=7,					UpperCAmelCase__							:			Optional[Any]=True,					UpperCAmelCase__							:			List[Any]=True,					UpperCAmelCase__							:			int=True,					UpperCAmelCase__							:			List[str]=9_9,					UpperCAmelCase__							:			Union[str, Any]=3_2,					UpperCAmelCase__							:			Union[str, Any]=2,					UpperCAmelCase__							:			Union[str, Any]=4,					UpperCAmelCase__							:			Tuple=3_7,					UpperCAmelCase__							:			List[Any]="gelu",					UpperCAmelCase__							:			List[str]=0.1,					UpperCAmelCase__							:			Optional[int]=0.1,					UpperCAmelCase__							:			Tuple=5_1_2,					UpperCAmelCase__							:			Optional[Any]=0.02,					):
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						seq_length
    __lowercase								=						is_training
    __lowercase								=						use_input_mask
    __lowercase								=						use_labels
    __lowercase								=						vocab_size
    __lowercase								=						d_model
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						ffn_dim
    __lowercase								=						activation_function
    __lowercase								=						activation_dropout
    __lowercase								=						attention_dropout
    __lowercase								=						max_position_embeddings
    __lowercase								=						initializer_range
    __lowercase								=						None
    __lowercase								=						0
    __lowercase								=						2
    __lowercase								=						1


 def 					_lowercase (			self							:			Union[str, Any]	):
    return XGLMConfig.from_pretrained("facebook/xglm-564M"	)


 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						tf.clip_by_value(
        ids_tensor([self.batch_size, self.seq_length],					self.vocab_size	),					clip_value_min=0,					clip_value_max=3	)

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

    __lowercase								=						self.get_config()

    __lowercase								=						floats_tensor([self.num_hidden_layers, self.num_attention_heads],					2	)

    return (
        config,
        input_ids,
        input_mask,
        head_mask,
    )


 def 					_lowercase (			self							:			List[Any]	):
    return XGLMConfig(
        vocab_size=self.vocab_size,					d_model=self.hidden_size,					num_layers=self.num_hidden_layers,					attention_heads=self.num_attention_heads,					ffn_dim=self.ffn_dim,					activation_function=self.activation_function,					activation_dropout=self.activation_dropout,					attention_dropout=self.attention_dropout,					max_position_embeddings=self.max_position_embeddings,					initializer_range=self.initializer_range,					use_cache=UpperCAmelCase__,					bos_token_id=self.bos_token_id,					eos_token_id=self.eos_token_id,					pad_token_id=self.pad_token_id,					return_dict=UpperCAmelCase__,					)



 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						self.prepare_config_and_inputs()

    (
        (
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,(
        __lowercase
    )		,
    )								=						config_and_inputs

    __lowercase								=						{
        "input_ids": input_ids,
        "head_mask": head_mask,
    }

    return config, inputs_dict







@require_tf
class 					_lowerCAmelCase  (	lowercase     ,lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Union[str, Any]   		=	(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
 __UpperCAmelCase	:					List[str]   		=	(TFXGLMForCausalLM,) if is_tf_available() else ()
 __UpperCAmelCase	:					Any   		=	(
     {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
 )
 __UpperCAmelCase	:					Optional[Any]   		=	False
 __UpperCAmelCase	:					List[str]   		=	False
 __UpperCAmelCase	:					int   		=	False


 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						TFXGLMModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__,					n_embd=3_7	)


 def 					_lowercase (			self							:			Any	):
    self.config_tester.run_common_tests()


 @slow
 def 					_lowercase (			self							:			List[str]	):
    for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowercase								=						TFXGLMModel.from_pretrained(UpperCAmelCase__	)
       self.assertIsNotNone(UpperCAmelCase__	)



 @unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."	)
 def 					_lowercase (			self							:			int	):
    super().test_resize_token_embeddings()







@require_tf
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @slow
 def 					_lowercase (			self							:			Dict,					UpperCAmelCase__							:			Optional[int]=True	):
    __lowercase								=						TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"	)
    __lowercase								=						tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]],					dtype=tf.intaa	)  # The dog
    # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
    # fmt: off
    __lowercase								=						[2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
    # fmt: on
    __lowercase								=						model.generate(UpperCAmelCase__,					do_sample=UpperCAmelCase__,					num_beams=1	)
    if verify_outputs:
       self.assertListEqual(output_ids[0].numpy().tolist(),					UpperCAmelCase__	)


 @slow
 def 					_lowercase (			self							:			List[Any]	):
    __lowercase								=						XGLMTokenizer.from_pretrained("facebook/xglm-564M"	)
    __lowercase								=						TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"	)

    tf.random.set_seed(0	)
    __lowercase								=						tokenizer("Today is a nice day and",					return_tensors="tf"	)
    __lowercase								=						tokenized.input_ids
    # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
    with tf.device(":/CPU:0"	):
       __lowercase								=						model.generate(UpperCAmelCase__,					do_sample=UpperCAmelCase__,					seed=[7, 0]	)
    __lowercase								=						tokenizer.decode(output_ids[0],					skip_special_tokens=UpperCAmelCase__	)

    __lowercase								=						(
        "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
    )
    self.assertEqual(UpperCAmelCase__,					UpperCAmelCase__	)



 @slow
 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"	)
    __lowercase								=						XGLMTokenizer.from_pretrained("facebook/xglm-564M"	)

    __lowercase								=						"left"

    # use different length sentences to test batching
    __lowercase								=						[
        "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
        "left-padding, such as in batched generation. The output for the sequence below should be the same "
        "regardless of whether left padding is applied or not. When",
        "Hello, my dog is a little",
    ]

    __lowercase								=						tokenizer(UpperCAmelCase__,					return_tensors="tf",					padding=UpperCAmelCase__	)
    __lowercase								=						inputs["input_ids"]

    __lowercase								=						model.generate(input_ids=UpperCAmelCase__,					attention_mask=inputs["attention_mask"],					max_new_tokens=1_2	)

    __lowercase								=						tokenizer(sentences[0],					return_tensors="tf"	).input_ids
    __lowercase								=						model.generate(input_ids=UpperCAmelCase__,					max_new_tokens=1_2	)

    __lowercase								=						tokenizer(sentences[1],					return_tensors="tf"	).input_ids
    __lowercase								=						model.generate(input_ids=UpperCAmelCase__,					max_new_tokens=1_2	)

    __lowercase								=						tokenizer.batch_decode(UpperCAmelCase__,					skip_special_tokens=UpperCAmelCase__	)
    __lowercase								=						tokenizer.decode(output_non_padded[0],					skip_special_tokens=UpperCAmelCase__	)
    __lowercase								=						tokenizer.decode(output_padded[0],					skip_special_tokens=UpperCAmelCase__	)

    __lowercase								=						[
        "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
        "left-padding, such as in batched generation. The output for the sequence below should be the same "
        "regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
        "a single",
        "Hello, my dog is a little bit of a shy one, but he is very friendly",
    ]
    self.assertListEqual(UpperCAmelCase__,					UpperCAmelCase__	)
    self.assertListEqual(UpperCAmelCase__,					[non_padded_sentence, padded_sentence]	)




