



import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
    import torch

    from transformers import AutoModelForCausalLM







@require_torch
class     lowerCAmelCase_		(  unittest.TestCase							):
       def  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  Optional[int]						     =     AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'	)
          UpperCamelCase   :  Tuple						     =     AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'	).to(_lowerCamelCase	)
          UpperCamelCase   :  Dict						     =     -1

          UpperCamelCase   :  List[str]						     =     ids_tensor((1, 5),      vocab_size=model.config.vocab_size	).to(_lowerCamelCase	)
          UpperCamelCase   :  Any						     =     model.generate(_lowerCamelCase,      max_new_tokens=10,      do_sample=_lowerCamelCase	)
          UpperCamelCase   :  List[str]						     =     tokenizer.decode(greedy_ids[0]	)

          with CaptureStdout() as cs:
             UpperCamelCase   :  List[str]						     =     TextStreamer(_lowerCamelCase	)
             model.generate(_lowerCamelCase,      max_new_tokens=10,      do_sample=_lowerCamelCase,      streamer=_lowerCamelCase	)
          # The greedy text should be printed to stdout, except for the final "\n" in the streamer
          UpperCamelCase   :  Dict						     =     cs.out[:-1]

          self.assertEqual(_lowerCamelCase,      _lowerCamelCase	)
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  Optional[int]						     =     AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'	)
          UpperCamelCase   :  List[str]						     =     AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'	).to(_lowerCamelCase	)
          UpperCamelCase   :  Dict						     =     -1

          UpperCamelCase   :  List[str]						     =     ids_tensor((1, 5),      vocab_size=model.config.vocab_size	).to(_lowerCamelCase	)
          UpperCamelCase   :  Optional[int]						     =     model.generate(_lowerCamelCase,      max_new_tokens=10,      do_sample=_lowerCamelCase	)
          UpperCamelCase   :  str						     =     tokenizer.decode(greedy_ids[0]	)

          UpperCamelCase   :  int						     =     TextIteratorStreamer(_lowerCamelCase	)
          UpperCamelCase   :  List[Any]						     =     {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
          UpperCamelCase   :  List[Any]						     =     Thread(target=model.generate,      kwargs=_lowerCamelCase	)
          thread.start()
          UpperCamelCase   :  List[Any]						     =     ''''''
          for new_text in streamer:
             streamer_text += new_text

          self.assertEqual(_lowerCamelCase,      _lowerCamelCase	)
       def  snake_case_	(      self	)					->		Any:
          UpperCamelCase   :  List[str]						     =     AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'	)
          UpperCamelCase   :  List[Any]						     =     AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'	).to(_lowerCamelCase	)
          UpperCamelCase   :  List[str]						     =     -1

          UpperCamelCase   :  Any						     =     ids_tensor((1, 5),      vocab_size=model.config.vocab_size	).to(_lowerCamelCase	)
          UpperCamelCase   :  Tuple						     =     model.generate(_lowerCamelCase,      max_new_tokens=10,      do_sample=_lowerCamelCase	)
          UpperCamelCase   :  Tuple						     =     greedy_ids[:, input_ids.shape[1] :]
          UpperCamelCase   :  Tuple						     =     tokenizer.decode(new_greedy_ids[0]	)

          with CaptureStdout() as cs:
             UpperCamelCase   :  Any						     =     TextStreamer(_lowerCamelCase,      skip_prompt=_lowerCamelCase	)
             model.generate(_lowerCamelCase,      max_new_tokens=10,      do_sample=_lowerCamelCase,      streamer=_lowerCamelCase	)
          # The greedy text should be printed to stdout, except for the final "\n" in the streamer
          UpperCamelCase   :  Any						     =     cs.out[:-1]

          self.assertEqual(_lowerCamelCase,      _lowerCamelCase	)
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  List[Any]						     =     AutoTokenizer.from_pretrained('distilgpt2'	)
          UpperCamelCase   :  Tuple						     =     AutoModelForCausalLM.from_pretrained('distilgpt2'	).to(_lowerCamelCase	)
          UpperCamelCase   :  List[Any]						     =     -1

          UpperCamelCase   :  Union[str, Any]						     =     torch.ones((1, 5),      device=_lowerCamelCase	).long() * model.config.bos_token_id
          with CaptureStdout() as cs:
             UpperCamelCase   :  List[Any]						     =     TextStreamer(_lowerCamelCase,      skip_special_tokens=_lowerCamelCase	)
             model.generate(_lowerCamelCase,      max_new_tokens=1,      do_sample=_lowerCamelCase,      streamer=_lowerCamelCase	)

          # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
          # re-tokenized, must only contain one token
          UpperCamelCase   :  List[str]						     =     cs.out[:-1]  # Remove the final "\n"
          UpperCamelCase   :  List[Any]						     =     tokenizer(_lowerCamelCase,      return_tensors='pt'	)
          self.assertEqual(streamer_text_tokenized.input_ids.shape,      (1, 1)	)






       def  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  str						     =     AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'	)
          UpperCamelCase   :  str						     =     AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'	).to(_lowerCamelCase	)
          UpperCamelCase   :  Union[str, Any]						     =     -1

          UpperCamelCase   :  Union[str, Any]						     =     ids_tensor((1, 5),      vocab_size=model.config.vocab_size	).to(_lowerCamelCase	)
          UpperCamelCase   :  List[str]						     =     TextIteratorStreamer(_lowerCamelCase,      timeout=0.0_01	)
          UpperCamelCase   :  str						     =     {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
          UpperCamelCase   :  List[str]						     =     Thread(target=model.generate,      kwargs=_lowerCamelCase	)
          thread.start()

          # The streamer will timeout after 0.001 seconds, so an exception will be raised
          with self.assertRaises(_lowerCamelCase	):
             UpperCamelCase   :  str						     =     ''''''
             for new_text in streamer:
                streamer_text += new_text








import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def 		UpperCamelCase     (       snake_case__	:   List[Any]	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   Tuple	,					snake_case__	:   Union[str, Any]	,					snake_case__	:   List[Any]     )     ->					Tuple:
   # load base model
   UpperCamelCase   :  Tuple						     =     StableDiffusionPipeline.from_pretrained(snake_case__	,					torch_dtype=torch.floataa     )

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

   UpperCamelCase   :  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:
         UpperCamelCase   :  Optional[Any]						     =     key.split('.'     )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'     )[-1].split('_'     )
         UpperCamelCase   :  Optional[Any]						     =     pipeline.text_encoder
      else:
         UpperCamelCase   :  Tuple						     =     key.split('.'     )[0].split(LORA_PREFIX_UNET + '_'     )[-1].split('_'     )
         UpperCamelCase   :  List[str]						     =     pipeline.unet

      # find the target layer
      UpperCamelCase   :  Optional[Any]						     =     layer_infos.pop(0     )
      while len(snake_case__     ) > -1:
         try:
            UpperCamelCase   :  Dict						     =     curr_layer.__getattr__(snake_case__     )
            if len(snake_case__     ) > 0:
               UpperCamelCase   :  Dict						     =     layer_infos.pop(0     )
            elif len(snake_case__     ) == 0:
               break
         except Exception:
            if len(snake_case__     ) > 0:
               temp_name += "_" + layer_infos.pop(0     )
            else:
               UpperCamelCase   :  Tuple						     =     layer_infos.pop(0     )

      UpperCamelCase   :  List[Any]						     =     []
      if "lora_down" in key:
         pair_keys.append(key.replace('lora_down'	,					'lora_up'     )     )
         pair_keys.append(snake_case__     )
      else:
         pair_keys.append(snake_case__     )
         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   :  Union[str, Any]						     =     state_dict[pair_keys[1]].squeeze(3     ).squeeze(2     ).to(torch.floataa     )
         curr_layer.weight.data += alpha * torch.mm(snake_case__	,					snake_case__     ).unsqueeze(2     ).unsqueeze(3     )
      else:
         UpperCamelCase   :  Dict						     =     state_dict[pair_keys[0]].to(torch.floataa     )
         UpperCamelCase   :  List[Any]						     =     state_dict[pair_keys[1]].to(torch.floataa     )
         curr_layer.weight.data += alpha * torch.mm(snake_case__	,					snake_case__     )

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

   return pipeline


if __name__ == "__main__":
    __UpperCAmelCase	       =	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.)''')

    __UpperCAmelCase	       =	parser.parse_args()

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

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

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



