



import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__UpperCAmelCase	       =	{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}







@is_pipeline_test
class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : Dict       					=  MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       UpperCAmelCase__    : List[str]       					=  TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

       if model_mapping is not None:
              UpperCAmelCase__    : Optional[Any]       					=  {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
       if tf_model_mapping is not None:
              UpperCAmelCase__    : Dict       					=  {
                  config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
              }
       @require_torch
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  List[str]						     =     pipeline(
              task='text-classification',      model='hf-internal-testing/tiny-random-distilbert',      framework='pt'	)

          UpperCamelCase   :  str						     =     text_classifier('This is great !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'LABEL_0', 'score': 0.5_04}]	)

          UpperCamelCase   :  Dict						     =     text_classifier('This is great !',      top_k=2	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}]	)

          UpperCamelCase   :  List[Any]						     =     text_classifier(['This is great !', 'This is bad'],      top_k=2	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      [
                  [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
                  [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
              ],      )

          UpperCamelCase   :  List[Any]						     =     text_classifier('This is great !',      top_k=1	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'LABEL_0', 'score': 0.5_04}]	)

          # Legacy behavior
          UpperCamelCase   :  str						     =     text_classifier('This is great !',      return_all_scores=SCREAMING_SNAKE_CASE_	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'LABEL_0', 'score': 0.5_04}]	)

          UpperCamelCase   :  Tuple						     =     text_classifier('This is great !',      return_all_scores=SCREAMING_SNAKE_CASE_	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      [[{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}]]	)

          UpperCamelCase   :  List[Any]						     =     text_classifier(['This is great !', 'Something else'],      return_all_scores=SCREAMING_SNAKE_CASE_	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      [
                  [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
                  [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
              ],      )

          UpperCamelCase   :  Optional[int]						     =     text_classifier(['This is great !', 'Something else'],      return_all_scores=SCREAMING_SNAKE_CASE_	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      [
                  {'label': 'LABEL_0', 'score': 0.5_04},
                  {'label': 'LABEL_0', 'score': 0.5_04},
              ],      )
       @require_torch
       def  snake_case_	(      self	)					->		Optional[Any]:
          import torch

          UpperCamelCase   :  str						     =     pipeline(
              task='text-classification',      model='hf-internal-testing/tiny-random-distilbert',      framework='pt',      device=torch.device('cpu'	),      )

          UpperCamelCase   :  List[Any]						     =     text_classifier('This is great !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'LABEL_0', 'score': 0.5_04}]	)
       @require_tf
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  List[Any]						     =     pipeline(
              task='text-classification',      model='hf-internal-testing/tiny-random-distilbert',      framework='tf'	)

          UpperCamelCase   :  str						     =     text_classifier('This is great !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'LABEL_0', 'score': 0.5_04}]	)
       @slow
       @require_torch
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  str						     =     pipeline('text-classification'	)

          UpperCamelCase   :  str						     =     text_classifier('This is great !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'POSITIVE', 'score': 1.0}]	)
          UpperCamelCase   :  List[str]						     =     text_classifier('This is bad !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'NEGATIVE', 'score': 1.0}]	)
          UpperCamelCase   :  int						     =     text_classifier('Birds are a type of animal'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'POSITIVE', 'score': 0.9_88}]	)
       @slow
       @require_tf
       def  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  int						     =     pipeline('text-classification',      framework='tf'	)

          UpperCamelCase   :  Tuple						     =     text_classifier('This is great !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'POSITIVE', 'score': 1.0}]	)
          UpperCamelCase   :  Union[str, Any]						     =     text_classifier('This is bad !'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'NEGATIVE', 'score': 1.0}]	)
          UpperCamelCase   :  int						     =     text_classifier('Birds are a type of animal'	)
          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': 'POSITIVE', 'score': 0.9_88}]	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          UpperCamelCase   :  str						     =     TextClassificationPipeline(model=SCREAMING_SNAKE_CASE_,      tokenizer=SCREAMING_SNAKE_CASE_	)
          return text_classifier, ["HuggingFace is in", "This is another test"]






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Any:
          UpperCamelCase   :  List[str]						     =     text_classifier.model
          # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
          UpperCamelCase   :  List[str]						     =     'HuggingFace is in'
          UpperCamelCase   :  Tuple						     =     text_classifier(SCREAMING_SNAKE_CASE_	)

          self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': ANY(SCREAMING_SNAKE_CASE_	), 'score': ANY(SCREAMING_SNAKE_CASE_	)}]	)
          self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()	)

          UpperCamelCase   :  List[Any]						     =     ['HuggingFace is in ', 'Paris is in France']
          UpperCamelCase   :  List[str]						     =     text_classifier(SCREAMING_SNAKE_CASE_	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': ANY(SCREAMING_SNAKE_CASE_	), 'score': ANY(SCREAMING_SNAKE_CASE_	)}, {'label': ANY(SCREAMING_SNAKE_CASE_	), 'score': ANY(SCREAMING_SNAKE_CASE_	)}],      )
          self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()	)
          self.assertTrue(outputs[1]['label'] in model.config.idalabel.values()	)

          # Forcing to get all results with `top_k=None`
          # This is NOT the legacy format
          UpperCamelCase   :  Optional[int]						     =     text_classifier(SCREAMING_SNAKE_CASE_,      top_k=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  str						     =     len(model.config.idalabel.values()	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      [[{'label': ANY(SCREAMING_SNAKE_CASE_	), 'score': ANY(SCREAMING_SNAKE_CASE_	)}] * N, [{'label': ANY(SCREAMING_SNAKE_CASE_	), 'score': ANY(SCREAMING_SNAKE_CASE_	)}] * N],      )

          UpperCamelCase   :  str						     =     {'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
          UpperCamelCase   :  Union[str, Any]						     =     text_classifier(SCREAMING_SNAKE_CASE_	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      {'label': ANY(SCREAMING_SNAKE_CASE_	), 'score': ANY(SCREAMING_SNAKE_CASE_	)},      )
          self.assertTrue(outputs['label'] in model.config.idalabel.values()	)

          # This might be used a text pair, but tokenizer + pipe interaction
          # makes it hard to understand that it's not using the pair properly
          # https://github.com/huggingface/transformers/issues/17305
          # We disabled this usage instead as it was outputting wrong outputs.
          UpperCamelCase   :  List[str]						     =     [['HuggingFace is in ', 'Paris is in France']]
          with self.assertRaises(SCREAMING_SNAKE_CASE_	):
             text_classifier(SCREAMING_SNAKE_CASE_	)

          # This used to be valid for doing text pairs
          # We're keeping it working because of backward compatibility
          UpperCamelCase   :  List[str]						     =     text_classifier([[['HuggingFace is in ', 'Paris is in France']]]	)
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_	),      [{'label': ANY(SCREAMING_SNAKE_CASE_	), 'score': ANY(SCREAMING_SNAKE_CASE_	)}],      )
          self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()	)








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)



