code: 
  
  
  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
  
  
  UpperCamelCase__				       =			{
      '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   UpperCamelCase__   (    UpperCAmelCase_			)						->							Any:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				str           =			task_cls()
         _lowercase					:				Optional[Any]           =			asdict(UpperCAmelCase_			)
         _lowercase					:				Any           =			task_template_from_dict(UpperCAmelCase_			)
         assert task == reloaded
  
  class 					UpperCAmelCase__   :
  
  
  
       '''simple docstring'''
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Dict		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			LanguageModeling(text_column='''input_text'''		)
              assert {"input_text": "text"} == task.column_mapping
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Tuple		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[Any]           =			Features({'''text''': Value('''string'''		)}		)
              _lowercase					:				Any           =			{'''text_column''': '''input_text'''}
              _lowercase					:				Any           =			LanguageModeling.from_dict(UpperCamelCase		)
              assert "language-modeling" == task.task
              assert input_schema == task.input_schema
  
  
  
  
  class 					UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			sorted(['''pos''', '''neg''']		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			TextClassification(text_column='''input_text'''	,      label_column='''input_label'''		)
              self.assertDictEqual({'''input_text''': '''text''', '''input_label''': '''labels'''}	,      task.column_mapping		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Union[str, Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Tuple           =			Features({'''text''': Value('''string'''		)}		)
              # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
              _lowercase					:				Dict           =			Features({'''labels''': ClassLabel}		)
              _lowercase					:				Any           =			{'''text_column''': '''input_text''', '''label_column''': '''input_labels'''}
              _lowercase					:				Dict           =			TextClassification.from_dict(UpperCamelCase		)
              self.assertEqual('''text-classification'''	,      task.task		)
              self.assertEqual(UpperCamelCase	,      task.input_schema		)
              self.assertEqual(UpperCamelCase	,      task.label_schema		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Union[str, Any]           =			TextClassification(text_column='''input_text'''	,      label_column='''input_label'''		)
              self.assertEqual(task.label_schema['''labels''']	,      UpperCamelCase		)
              _lowercase					:				Any           =			task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels		)}		)		)
              self.assertEqual(task.label_schema['''labels''']	,      ClassLabel(names=self.labels		)		)
  
  
  
  
  class 					UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def   lowerCAmelCase_   ( self       :       str		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				str           =			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   lowerCAmelCase_   ( self       :       Dict		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			Features({'''question''': Value('''string'''		), '''context''': Value('''string'''		)}		)
              _lowercase					:				Optional[int]           =			Features(
                  {
                      '''answers''': Sequence(
                          {
                              '''text''': Value('''string'''		),
                              '''answer_start''': Value('''int32'''		),
                          }		)
                  }		)
              _lowercase					:				Tuple           =			{
                  '''context_column''': '''input_input_context''',
                  '''question_column''': '''input_question''',
                  '''answers_column''': '''input_answers''',
              }
              _lowercase					:				int           =			QuestionAnsweringExtractive.from_dict(UpperCamelCase		)
              self.assertEqual('''question-answering-extractive'''	,      task.task		)
              self.assertEqual(UpperCamelCase	,      task.input_schema		)
              self.assertEqual(UpperCamelCase	,      task.label_schema		)
  
  
  
  
  class 					UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Dict           =			Summarization(text_column='''input_text'''	,      summary_column='''input_summary'''		)
              self.assertDictEqual({'''input_text''': '''text''', '''input_summary''': '''summary'''}	,      task.column_mapping		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Tuple		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[Any]           =			Features({'''text''': Value('''string'''		)}		)
              _lowercase					:				Optional[Any]           =			Features({'''summary''': Value('''string'''		)}		)
              _lowercase					:				Optional[int]           =			{'''text_column''': '''input_text''', '''summary_column''': '''input_summary'''}
              _lowercase					:				str           =			Summarization.from_dict(UpperCamelCase		)
              self.assertEqual('''summarization'''	,      task.task		)
              self.assertEqual(UpperCamelCase	,      task.input_schema		)
              self.assertEqual(UpperCamelCase	,      task.label_schema		)
  
  
  
  
  class 					UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[str]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[Any]           =			AutomaticSpeechRecognition(audio_column='''input_audio'''	,      transcription_column='''input_transcription'''		)
              self.assertDictEqual({'''input_audio''': '''audio''', '''input_transcription''': '''transcription'''}	,      task.column_mapping		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Dict		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[str]           =			Features({'''audio''': Audio()}		)
              _lowercase					:				List[Any]           =			Features({'''transcription''': Value('''string'''		)}		)
              _lowercase					:				Optional[Any]           =			{
                  '''audio_column''': '''input_audio''',
                  '''transcription_column''': '''input_transcription''',
              }
              _lowercase					:				Union[str, Any]           =			AutomaticSpeechRecognition.from_dict(UpperCamelCase		)
              self.assertEqual('''automatic-speech-recognition'''	,      task.task		)
              self.assertEqual(UpperCamelCase	,      task.input_schema		)
              self.assertEqual(UpperCamelCase	,      task.label_schema		)
  
  
  
  
  class 					UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Tuple		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Any           =			sorted(['''pos''', '''neg''']		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Dict           =			AudioClassification(audio_column='''input_audio'''	,      label_column='''input_label'''		)
              self.assertDictEqual({'''input_audio''': '''audio''', '''input_label''': '''labels'''}	,      task.column_mapping		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			Features({'''audio''': Audio()}		)
              _lowercase					:				Optional[int]           =			Features({'''labels''': ClassLabel}		)
              _lowercase					:				Union[str, Any]           =			{
                  '''audio_column''': '''input_image''',
                  '''label_column''': '''input_label''',
              }
              _lowercase					:				Optional[int]           =			AudioClassification.from_dict(UpperCamelCase		)
              self.assertEqual('''audio-classification'''	,      task.task		)
              self.assertEqual(UpperCamelCase	,      task.input_schema		)
              self.assertEqual(UpperCamelCase	,      task.label_schema		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       str		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				str           =			AudioClassification(audio_column='''input_audio'''	,      label_column='''input_label'''		)
              self.assertEqual(task.label_schema['''labels''']	,      UpperCamelCase		)
              _lowercase					:				Optional[Any]           =			task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels		)}		)		)
              self.assertEqual(task.label_schema['''labels''']	,      ClassLabel(names=self.labels		)		)
  
  
  
  
  class 					UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Union[str, Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[Any]           =			sorted(['''pos''', '''neg''']		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[str]           =			ImageClassification(image_column='''input_image'''	,      label_column='''input_label'''		)
              self.assertDictEqual({'''input_image''': '''image''', '''input_label''': '''labels'''}	,      task.column_mapping		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Union[str, Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				int           =			Features({'''image''': Image()}		)
              _lowercase					:				Optional[int]           =			Features({'''labels''': ClassLabel}		)
              _lowercase					:				int           =			{
                  '''image_column''': '''input_image''',
                  '''label_column''': '''input_label''',
              }
              _lowercase					:				Tuple           =			ImageClassification.from_dict(UpperCamelCase		)
              self.assertEqual('''image-classification'''	,      task.task		)
              self.assertEqual(UpperCamelCase	,      task.input_schema		)
              self.assertEqual(UpperCamelCase	,      task.label_schema		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				int           =			ImageClassification(image_column='''input_image'''	,      label_column='''input_label'''		)
              self.assertEqual(task.label_schema['''labels''']	,      UpperCamelCase		)
              _lowercase					:				List[Any]           =			task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels		)}		)		)
              self.assertEqual(task.label_schema['''labels''']	,      ClassLabel(names=self.labels		)		)
  
  
  
  
  class 					UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Tuple		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				List[Any]           =			DatasetInfo(task_templates=QuestionAnsweringExtractive()		)
              _lowercase					:				Dict           =			Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}	,      info=UpperCamelCase		)
              assert isinstance(dataset.info.task_templates	,      UpperCamelCase		)
              assert len(dataset.info.task_templates		) == 1
  
              def keep_task(UpperCamelCase       :       List[str]		):
                     return x
  
              def dont_keep_task(UpperCamelCase       :       Optional[int]		):
                     _lowercase					:				Union[str, Any]           =			deepcopy(UpperCamelCase		)
                     _lowercase					:				Dict           =			0
                     return out
  
              _lowercase					:				Optional[int]           =			dataset.map(UpperCamelCase		)
              assert mapped_dataset.info.task_templates == dataset.info.task_templates
              # reload from cache
              _lowercase					:				List[str]           =			dataset.map(UpperCamelCase		)
              assert mapped_dataset.info.task_templates == dataset.info.task_templates
  
              _lowercase					:				str           =			dataset.map(UpperCamelCase		)
              assert mapped_dataset.info.task_templates == []
              # reload from cache
              _lowercase					:				Any           =			dataset.map(UpperCamelCase		)
              assert mapped_dataset.info.task_templates == []
  
  
  
  
       def   lowerCAmelCase_   ( self       :       str		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			Features({'''text''': Value('''string'''		), '''label''': ClassLabel(names=('''pos''', '''neg''')		)}		)
              _lowercase					:				Union[str, Any]           =			TextClassification(text_column='''text'''	,      label_column='''label'''		)
              _lowercase					:				List[Any]           =			DatasetInfo(features=UpperCamelCase	,      task_templates=UpperCamelCase		)
              _lowercase					:				Optional[int]           =			Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']}	,      info=UpperCamelCase		)
  
              def process(UpperCamelCase       :       str		):
                     return example
  
              _lowercase					:				Tuple           =			dataset.remove_columns('''label'''		)
              _lowercase					:				Any           =			modified_dataset.map(UpperCamelCase		)
              assert mapped_dataset.info.task_templates == []
style_context: 
  
  
  # limitations under the License.
  
  
  from typing import Optional, Tuple, Union
  
  import torch
  
  from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       List[str]	,      UpperCamelCase       :       Union[str, Any]	,      UpperCamelCase       :       int		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
              self.register_modules(unet=UpperCamelCase	,      scheduler=UpperCamelCase		)
  
  
  
  
       @torch.no_grad()
       def __call__( self       :       Any	,      UpperCamelCase       :       int = 1	,      UpperCamelCase       :       Optional[torch.Generator] = None	,      UpperCamelCase       :       int = 50	,      UpperCamelCase       :       Optional[str] = "pil"	,      UpperCamelCase       :       bool = True	,      **UpperCamelCase       :       int	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			torch.randn(
                  (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,      generator=UpperCamelCase	,      )
              _lowercase					:				Dict           =			image.to(self.device		)
  
              # set step values
              self.scheduler.set_timesteps(UpperCamelCase		)
  
              for t in self.progress_bar(self.scheduler.timesteps		):
                     # 1. predict noise model_output
                     _lowercase					:				Dict           =			self.unet(UpperCamelCase	,      UpperCamelCase		).sample
  
                     # 2. predict previous mean of image x_t-1 and add variance depending on eta
                     # eta corresponds to η in paper and should be between [0, 1]
                     # do x_t -> x_t-1
                     _lowercase					:				Tuple           =			self.scheduler.step(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		).prev_sample
  
              _lowercase					:				Optional[int]           =			(image / 2 + 0.5).clamp(0	,      1		)
              _lowercase					:				int           =			image.cpu().permute(0	,      2	,      3	,      1		).numpy()
              if output_type == "pil":
                     _lowercase					:				List[Any]           =			self.numpy_to_pil(UpperCamelCase		)
  
              if not return_dict:
                     return (image,), "This is a local test"
  
              return ImagePipelineOutput(images=UpperCamelCase		), "This is a local test"