





from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


__lowercase				    =      {
    """id""": """5733be284776f41900661182""",
    """title""": """University_of_Notre_Dame""",
    """context""": """Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.""",
    """question""": """To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?""",
    """answers""": {"""text""": ["""Saint Bernadette Soubirous"""], """answer_start""": [515]},
}


@pytest.mark.parametrize(
    '''task_cls'''  ,				[
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]  ,				)
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[str]):



  '''simple docstring'''






  __lowerCAmelCase								=					task_cls()
  __lowerCAmelCase								=					asdict(__lowerCAmelCase)
  __lowerCAmelCase								=					task_template_from_dict(__lowerCAmelCase)
  assert task == reloaded

class 	__snake_case					:






    '''simple docstring'''





    def 						A		( self    :		List[Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					LanguageModeling(text_column='''input_text'''				)
      assert {"input_text": "text"} == task.column_mapping





    def 						A		( self    :		Dict				):

      """simple docstring"""





      __lowerCAmelCase								=					Features({'''text''': Value('''string'''				)}				)
      __lowerCAmelCase								=					{"text_column": "input_text"}
      __lowerCAmelCase								=					LanguageModeling.from_dict(lowerCAmelCase__				)
      assert "language-modeling" == task.task
      assert input_schema == task.input_schema


class 	__snake_case					(      SCREAMING_SNAKE_CASE__ ):






    '''simple docstring'''





    def 						A		( self    :		Any				):

      """simple docstring"""





      __lowerCAmelCase								=					sorted(['''pos''', '''neg''']				)





    def 						A		( self    :		Dict				):

      """simple docstring"""





      __lowerCAmelCase								=					TextClassification(text_column='''input_text'''      ,    label_column='''input_label'''				)
      self.assertDictEqual({'''input_text''': '''text''', '''input_label''': '''labels'''}      ,    task.column_mapping				)





    def 						A		( self    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					Features({'''text''': Value('''string'''				)}				)
      # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
      __lowerCAmelCase								=					Features({'''labels''': ClassLabel}				)
      __lowerCAmelCase								=					{"text_column": "input_text", "label_column": "input_labels"}
      __lowerCAmelCase								=					TextClassification.from_dict(lowerCAmelCase__				)
      self.assertEqual('''text-classification'''      ,    task.task				)
      self.assertEqual(lowerCAmelCase__      ,    task.input_schema				)
      self.assertEqual(lowerCAmelCase__      ,    task.label_schema				)





    def 						A		( self    :		Optional[Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					TextClassification(text_column='''input_text'''      ,    label_column='''input_label'''				)
      self.assertEqual(task.label_schema['''labels''']      ,    lowerCAmelCase__				)
      __lowerCAmelCase								=					task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels				)}				)				)
      self.assertEqual(task.label_schema['''labels''']      ,    ClassLabel(names=self.labels				)				)


class 	__snake_case					(      SCREAMING_SNAKE_CASE__ ):






    '''simple docstring'''





    def 						A		( self    :		List[Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					QuestionAnsweringExtractive(
          context_column='''input_context'''      ,    question_column='''input_question'''      ,    answers_column='''input_answers'''				)
      self.assertDictEqual(
          {'''input_context''': '''context''', '''input_question''': '''question''', '''input_answers''': '''answers'''}      ,    task.column_mapping				)





    def 						A		( self    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					Features({'''question''': Value('''string'''				), '''context''': Value('''string'''				)}				)
      __lowerCAmelCase								=					Features(
          {
              '''answers''': Sequence(
                  {
                      '''text''': Value('''string'''				),
                      '''answer_start''': Value('''int32'''				),
                  }				)
          }				)
      __lowerCAmelCase								=					{
          "context_column": "input_input_context",
          "question_column": "input_question",
          "answers_column": "input_answers",
      }
      __lowerCAmelCase								=					QuestionAnsweringExtractive.from_dict(lowerCAmelCase__				)
      self.assertEqual('''question-answering-extractive'''      ,    task.task				)
      self.assertEqual(lowerCAmelCase__      ,    task.input_schema				)
      self.assertEqual(lowerCAmelCase__      ,    task.label_schema				)


class 	__snake_case					(      SCREAMING_SNAKE_CASE__ ):






    '''simple docstring'''





    def 						A		( self    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					Summarization(text_column='''input_text'''      ,    summary_column='''input_summary'''				)
      self.assertDictEqual({'''input_text''': '''text''', '''input_summary''': '''summary'''}      ,    task.column_mapping				)





    def 						A		( self    :		Optional[Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					Features({'''text''': Value('''string'''				)}				)
      __lowerCAmelCase								=					Features({'''summary''': Value('''string'''				)}				)
      __lowerCAmelCase								=					{"text_column": "input_text", "summary_column": "input_summary"}
      __lowerCAmelCase								=					Summarization.from_dict(lowerCAmelCase__				)
      self.assertEqual('''summarization'''      ,    task.task				)
      self.assertEqual(lowerCAmelCase__      ,    task.input_schema				)
      self.assertEqual(lowerCAmelCase__      ,    task.label_schema				)


class 	__snake_case					(      SCREAMING_SNAKE_CASE__ ):






    '''simple docstring'''





    def 						A		( self    :		Union[str, Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					AutomaticSpeechRecognition(audio_column='''input_audio'''      ,    transcription_column='''input_transcription'''				)
      self.assertDictEqual({'''input_audio''': '''audio''', '''input_transcription''': '''transcription'''}      ,    task.column_mapping				)





    def 						A		( self    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					Features({'''audio''': Audio()}				)
      __lowerCAmelCase								=					Features({'''transcription''': Value('''string'''				)}				)
      __lowerCAmelCase								=					{
          "audio_column": "input_audio",
          "transcription_column": "input_transcription",
      }
      __lowerCAmelCase								=					AutomaticSpeechRecognition.from_dict(lowerCAmelCase__				)
      self.assertEqual('''automatic-speech-recognition'''      ,    task.task				)
      self.assertEqual(lowerCAmelCase__      ,    task.input_schema				)
      self.assertEqual(lowerCAmelCase__      ,    task.label_schema				)


class 	__snake_case					(      SCREAMING_SNAKE_CASE__ ):






    '''simple docstring'''





    def 						A		( self    :		Dict				):

      """simple docstring"""





      __lowerCAmelCase								=					sorted(['''pos''', '''neg''']				)





    def 						A		( self    :		int				):

      """simple docstring"""





      __lowerCAmelCase								=					AudioClassification(audio_column='''input_audio'''      ,    label_column='''input_label'''				)
      self.assertDictEqual({'''input_audio''': '''audio''', '''input_label''': '''labels'''}      ,    task.column_mapping				)





    def 						A		( self    :		Optional[int]				):

      """simple docstring"""





      __lowerCAmelCase								=					Features({'''audio''': Audio()}				)
      __lowerCAmelCase								=					Features({'''labels''': ClassLabel}				)
      __lowerCAmelCase								=					{
          "audio_column": "input_image",
          "label_column": "input_label",
      }
      __lowerCAmelCase								=					AudioClassification.from_dict(lowerCAmelCase__				)
      self.assertEqual('''audio-classification'''      ,    task.task				)
      self.assertEqual(lowerCAmelCase__      ,    task.input_schema				)
      self.assertEqual(lowerCAmelCase__      ,    task.label_schema				)





    def 						A		( self    :		Optional[int]				):

      """simple docstring"""





      __lowerCAmelCase								=					AudioClassification(audio_column='''input_audio'''      ,    label_column='''input_label'''				)
      self.assertEqual(task.label_schema['''labels''']      ,    lowerCAmelCase__				)
      __lowerCAmelCase								=					task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels				)}				)				)
      self.assertEqual(task.label_schema['''labels''']      ,    ClassLabel(names=self.labels				)				)


class 	__snake_case					(      SCREAMING_SNAKE_CASE__ ):






    '''simple docstring'''





    def 						A		( self    :		List[Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					sorted(['''pos''', '''neg''']				)





    def 						A		( self    :		Any				):

      """simple docstring"""





      __lowerCAmelCase								=					ImageClassification(image_column='''input_image'''      ,    label_column='''input_label'''				)
      self.assertDictEqual({'''input_image''': '''image''', '''input_label''': '''labels'''}      ,    task.column_mapping				)





    def 						A		( self    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					Features({'''image''': Image()}				)
      __lowerCAmelCase								=					Features({'''labels''': ClassLabel}				)
      __lowerCAmelCase								=					{
          "image_column": "input_image",
          "label_column": "input_label",
      }
      __lowerCAmelCase								=					ImageClassification.from_dict(lowerCAmelCase__				)
      self.assertEqual('''image-classification'''      ,    task.task				)
      self.assertEqual(lowerCAmelCase__      ,    task.input_schema				)
      self.assertEqual(lowerCAmelCase__      ,    task.label_schema				)





    def 						A		( self    :		Dict				):

      """simple docstring"""





      __lowerCAmelCase								=					ImageClassification(image_column='''input_image'''      ,    label_column='''input_label'''				)
      self.assertEqual(task.label_schema['''labels''']      ,    lowerCAmelCase__				)
      __lowerCAmelCase								=					task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels				)}				)				)
      self.assertEqual(task.label_schema['''labels''']      ,    ClassLabel(names=self.labels				)				)


class 	__snake_case					(      SCREAMING_SNAKE_CASE__ ):






    '''simple docstring'''





    def 						A		( self    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					DatasetInfo(task_templates=QuestionAnsweringExtractive()				)
      __lowerCAmelCase								=					Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}      ,    info=lowerCAmelCase__				)
      assert isinstance(dataset.info.task_templates      ,    lowerCAmelCase__				)
      assert len(dataset.info.task_templates				) == 1

      def keep_task(snake_case    :		List[str]				):
        return x

      def dont_keep_task(snake_case    :		int				):
        __lowerCAmelCase								=					deepcopy(lowerCAmelCase__				)
        __lowerCAmelCase								=					0
        return out

      __lowerCAmelCase								=					dataset.map(lowerCAmelCase__				)
      assert mapped_dataset.info.task_templates == dataset.info.task_templates
      # reload from cache
      __lowerCAmelCase								=					dataset.map(lowerCAmelCase__				)
      assert mapped_dataset.info.task_templates == dataset.info.task_templates

      __lowerCAmelCase								=					dataset.map(lowerCAmelCase__				)
      assert mapped_dataset.info.task_templates == []
      # reload from cache
      __lowerCAmelCase								=					dataset.map(lowerCAmelCase__				)
      assert mapped_dataset.info.task_templates == []





    def 						A		( self    :		Optional[Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					Features({'''text''': Value('''string'''				), '''label''': ClassLabel(names=('''pos''', '''neg''')				)}				)
      __lowerCAmelCase								=					TextClassification(text_column='''text'''      ,    label_column='''label'''				)
      __lowerCAmelCase								=					DatasetInfo(features=lowerCAmelCase__      ,    task_templates=lowerCAmelCase__				)
      __lowerCAmelCase								=					Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']}      ,    info=lowerCAmelCase__				)

      def process(snake_case    :		Optional[Any]				):
        return example

      __lowerCAmelCase								=					dataset.remove_columns('''label'''				)
      __lowerCAmelCase								=					modified_dataset.map(lowerCAmelCase__				)
      assert mapped_dataset.info.task_templates == []






"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class  _SCREAMING_SNAKE_CASE       (     SCREAMING_SNAKE_CASE__			,				unittest.TestCase				):



 '''simple docstring'''





 __UpperCAmelCase		   =							KandinskyPipeline
 __UpperCAmelCase		   =							[
     """prompt""",
     """image_embeds""",
     """negative_image_embeds""",
 ]
 __UpperCAmelCase		   =							["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
 __UpperCAmelCase		   =							[
     """generator""",
     """height""",
     """width""",
     """latents""",
     """guidance_scale""",
     """negative_prompt""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 __UpperCAmelCase		   =							False






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  return 32






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  return 32






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  return self.time_input_dim






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  return self.time_input_dim * 4






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  return 1_00






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Tuple					      =  XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"   )
  return tokenizer






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  torch.manual_seed(0   )
  _UpperCamelCase      :	List[str]					      =  MCLIPConfig(
      numDims=self.cross_attention_dim					,     transformerDimensions=self.text_embedder_hidden_size					,     hidden_size=self.text_embedder_hidden_size					,     intermediate_size=37					,     num_attention_heads=4					,     num_hidden_layers=5					,     vocab_size=10_05					,     )

  _UpperCamelCase      :	Optional[Any]					      =  MultilingualCLIP(lowerCAmelCase__   )
  _UpperCamelCase      :	List[str]					      =  text_encoder.eval()

  return text_encoder






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  torch.manual_seed(0   )

  _UpperCamelCase      :	Dict					      =  {
      "in_channels": 4,
      # Out channels is double in channels because predicts mean and variance
      "out_channels": 8,
      "addition_embed_type": "text_image",
      "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
      "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
      "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
      "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
      "layers_per_block": 1,
      "encoder_hid_dim": self.text_embedder_hidden_size,
      "encoder_hid_dim_type": "text_image_proj",
      "cross_attention_dim": self.cross_attention_dim,
      "attention_head_dim": 4,
      "resnet_time_scale_shift": "scale_shift",
      "class_embed_type": None,
  }

  _UpperCamelCase      :	int					      =  UNetaDConditionModel(**lowerCAmelCase__   )
  return model






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  return {
      "block_out_channels": [32, 64],
      "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
      "in_channels": 3,
      "latent_channels": 4,
      "layers_per_block": 1,
      "norm_num_groups": 8,
      "norm_type": "spatial",
      "num_vq_embeddings": 12,
      "out_channels": 3,
      "up_block_types": [
          "AttnUpDecoderBlock2D",
          "UpDecoderBlock2D",
      ],
      "vq_embed_dim": 4,
  }






 @property
 def lowercase_      (self   ):

  '''simple docstring'''







  torch.manual_seed(0   )
  _UpperCamelCase      :	Dict					      =  VQModel(**self.dummy_movq_kwargs   )
  return model






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	List[str]					      =  self.dummy_text_encoder
  _UpperCamelCase      :	str					      =  self.dummy_tokenizer
  _UpperCamelCase      :	List[str]					      =  self.dummy_unet
  _UpperCamelCase      :	Tuple					      =  self.dummy_movq

  _UpperCamelCase      :	Dict					      =  DDIMScheduler(
      num_train_timesteps=10_00					,     beta_schedule="linear"					,     beta_start=0.0_0085					,     beta_end=0.012					,     clip_sample=lowerCAmelCase__					,     set_alpha_to_one=lowerCAmelCase__					,     steps_offset=1					,     prediction_type="epsilon"					,     thresholding=lowerCAmelCase__					,     )

  _UpperCamelCase      :	Any					      =  {
      "text_encoder": text_encoder,
      "tokenizer": tokenizer,
      "unet": unet,
      "scheduler": scheduler,
      "movq": movq,
  }
  return components






 def lowercase_      (self					,     lowerCAmelCase__					,     lowerCAmelCase__=0   ):

  '''simple docstring'''







  _UpperCamelCase      :	Dict					      =  floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(lowerCAmelCase__   )   ).to(lowerCAmelCase__   )
  _UpperCamelCase      :	int					      =  floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(seed + 1   )   ).to(lowerCAmelCase__   )
  if str(lowerCAmelCase__   ).startswith("mps"   ):
   _UpperCamelCase      :	Optional[Any]					      =  torch.manual_seed(lowerCAmelCase__   )
  else:
   _UpperCamelCase      :	List[Any]					      =  torch.Generator(device=lowerCAmelCase__   ).manual_seed(lowerCAmelCase__   )
  _UpperCamelCase      :	Optional[Any]					      =  {
      "prompt": "horse",
      "image_embeds": image_embeds,
      "negative_image_embeds": negative_image_embeds,
      "generator": generator,
      "height": 64,
      "width": 64,
      "guidance_scale": 4.0,
      "num_inference_steps": 2,
      "output_type": "np",
  }
  return inputs






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Optional[Any]					      =  "cpu"

  _UpperCamelCase      :	Any					      =  self.get_dummy_components()

  _UpperCamelCase      :	Tuple					      =  self.pipeline_class(**lowerCAmelCase__   )
  _UpperCamelCase      :	Optional[int]					      =  pipe.to(lowerCAmelCase__   )

  pipe.set_progress_bar_config(disable=lowerCAmelCase__   )

  _UpperCamelCase      :	Union[str, Any]					      =  pipe(**self.get_dummy_inputs(lowerCAmelCase__   )   )
  _UpperCamelCase      :	Union[str, Any]					      =  output.images

  _UpperCamelCase      :	str					      =  pipe(
      **self.get_dummy_inputs(lowerCAmelCase__   )					,     return_dict=lowerCAmelCase__					,     )[0]

  _UpperCamelCase      :	Tuple					      =  image[0, -3:, -3:, -1]
  _UpperCamelCase      :	Optional[int]					      =  image_from_tuple[0, -3:, -3:, -1]

  assert image.shape == (1, 64, 64, 3)

  _UpperCamelCase      :	Union[str, Any]					      =  np.array(
      [0.32_8663, 1.0, 0.2321_6873, 1.0, 0.9271_7564, 0.463_9046, 0.9689_4777, 0.3171_3378, 0.629_3953]   )

  assert (
      np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
  ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
  assert (
      np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
  ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"







@slow
@require_torch_gpu
class  _SCREAMING_SNAKE_CASE       (     unittest.TestCase				):



 '''simple docstring'''





 def lowercase_      (self   ):

  '''simple docstring'''







  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Tuple					      =  load_numpy(
      "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
      "/kandinsky/kandinsky_text2img_cat_fp16.npy"   )

  _UpperCamelCase      :	Optional[int]					      =  KandinskyPriorPipeline.from_pretrained(
      "kandinsky-community/kandinsky-2-1-prior"					,     torch_dtype=torch.floataa   )
  pipe_prior.to(lowerCAmelCase__   )

  _UpperCamelCase      :	List[str]					      =  KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"					,     torch_dtype=torch.floataa   )
  _UpperCamelCase      :	List[Any]					      =  pipeline.to(lowerCAmelCase__   )
  pipeline.set_progress_bar_config(disable=lowerCAmelCase__   )

  _UpperCamelCase      :	Optional[Any]					      =  "red cat, 4k photo"

  _UpperCamelCase      :	str					      =  torch.Generator(device="cuda"   ).manual_seed(0   )
  _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  pipe_prior(
      lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=5					,     negative_prompt=""					,     ).to_tuple()

  _UpperCamelCase      :	Optional[Any]					      =  torch.Generator(device="cuda"   ).manual_seed(0   )
  _UpperCamelCase      :	Any					      =  pipeline(
      lowerCAmelCase__					,     image_embeds=lowerCAmelCase__					,     negative_image_embeds=lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=1_00					,     output_type="np"					,     )

  _UpperCamelCase      :	Optional[Any]					      =  output.images[0]

  assert image.shape == (5_12, 5_12, 3)

  assert_mean_pixel_difference(lowerCAmelCase__					,     lowerCAmelCase__   )



