


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


snake_case       :					str        =  {
    '''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''': [5_15]},
}
@pytest.mark.parametrize(
    '''task_cls'''				,						[
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]				,						)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Dict		):




      """simple docstring"""





      a		:Tuple											= task_cls()
      a		:Dict											= asdict(UpperCAmelCase_		)
      a		:Dict											= task_template_from_dict(UpperCAmelCase_		)
      assert task == reloaded







class 	_snake_case						:




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Union[str, Any]											= LanguageModeling(text_column='''input_text'''					)
       assert {"input_text": "text"} == task.column_mapping




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:str											= Features({'''text''': Value('''string'''					)}					)
       a		:str											= {'''text_column''': '''input_text'''}
       a		:Union[str, Any]											= LanguageModeling.from_dict(_lowerCamelCase					)
       assert "language-modeling" == task.task
       assert input_schema == task.input_schema







class 	_snake_case						(   _snake_case							):




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= sorted(['''pos''', '''neg''']					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[int]											= TextClassification(text_column='''input_text'''      ,						label_column='''input_label'''					)
       self.assertDictEqual({'''input_text''': '''text''', '''input_label''': '''labels'''}      ,						task.column_mapping					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= Features({'''text''': Value('''string'''					)}					)
       # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
       a		:List[str]											= Features({'''labels''': ClassLabel}					)
       a		:Any											= {'''text_column''': '''input_text''', '''label_column''': '''input_labels'''}
       a		:Any											= TextClassification.from_dict(_lowerCamelCase					)
       self.assertEqual('''text-classification'''      ,						task.task					)
       self.assertEqual(_lowerCamelCase      ,						task.input_schema					)
       self.assertEqual(_lowerCamelCase      ,						task.label_schema					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Any											= TextClassification(text_column='''input_text'''      ,						label_column='''input_label'''					)
       self.assertEqual(task.label_schema['''labels''']      ,						_lowerCamelCase					)
       a		:str											= task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels					)}					)					)
       self.assertEqual(task.label_schema['''labels''']      ,						ClassLabel(names=self.labels					)					)







class 	_snake_case						(   _snake_case							):




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		: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  SCREAMING_SNAKE_CASE__       (	self					):
       a		:str											= Features({'''question''': Value('''string'''					), '''context''': Value('''string'''					)}					)
       a		:Tuple											= Features(
           {
               '''answers''': Sequence(
                   {
                       '''text''': Value('''string'''					),
                       '''answer_start''': Value('''int32'''					),
                   }					)
           }					)
       a		:Optional[int]											= {
           '''context_column''': '''input_input_context''',
           '''question_column''': '''input_question''',
           '''answers_column''': '''input_answers''',
       }
       a		:List[str]											= 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						(   _snake_case							):




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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= Features({'''text''': Value('''string'''					)}					)
       a		:List[str]											= Features({'''summary''': Value('''string'''					)}					)
       a		:Tuple											= {'''text_column''': '''input_text''', '''summary_column''': '''input_summary'''}
       a		:Any											= Summarization.from_dict(_lowerCamelCase					)
       self.assertEqual('''summarization'''      ,						task.task					)
       self.assertEqual(_lowerCamelCase      ,						task.input_schema					)
       self.assertEqual(_lowerCamelCase      ,						task.label_schema					)







class 	_snake_case						(   _snake_case							):




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:List[Any]											= AutomaticSpeechRecognition(audio_column='''input_audio'''      ,						transcription_column='''input_transcription'''					)
       self.assertDictEqual({'''input_audio''': '''audio''', '''input_transcription''': '''transcription'''}      ,						task.column_mapping					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:List[str]											= Features({'''audio''': Audio()}					)
       a		:Union[str, Any]											= Features({'''transcription''': Value('''string'''					)}					)
       a		:Any											= {
           '''audio_column''': '''input_audio''',
           '''transcription_column''': '''input_transcription''',
       }
       a		:List[str]											= 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						(   _snake_case							):




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[Any]											= sorted(['''pos''', '''neg''']					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:List[str]											= AudioClassification(audio_column='''input_audio'''      ,						label_column='''input_label'''					)
       self.assertDictEqual({'''input_audio''': '''audio''', '''input_label''': '''labels'''}      ,						task.column_mapping					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:List[str]											= Features({'''audio''': Audio()}					)
       a		:str											= Features({'''labels''': ClassLabel}					)
       a		:Dict											= {
           '''audio_column''': '''input_image''',
           '''label_column''': '''input_label''',
       }
       a		:Any											= AudioClassification.from_dict(_lowerCamelCase					)
       self.assertEqual('''audio-classification'''      ,						task.task					)
       self.assertEqual(_lowerCamelCase      ,						task.input_schema					)
       self.assertEqual(_lowerCamelCase      ,						task.label_schema					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:List[str]											= AudioClassification(audio_column='''input_audio'''      ,						label_column='''input_label'''					)
       self.assertEqual(task.label_schema['''labels''']      ,						_lowerCamelCase					)
       a		:List[Any]											= task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels					)}					)					)
       self.assertEqual(task.label_schema['''labels''']      ,						ClassLabel(names=self.labels					)					)







class 	_snake_case						(   _snake_case							):




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Union[str, Any]											= sorted(['''pos''', '''neg''']					)




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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:List[Any]											= Features({'''image''': Image()}					)
       a		:Dict											= Features({'''labels''': ClassLabel}					)
       a		:Optional[Any]											= {
           '''image_column''': '''input_image''',
           '''label_column''': '''input_label''',
       }
       a		:Optional[Any]											= ImageClassification.from_dict(_lowerCamelCase					)
       self.assertEqual('''image-classification'''      ,						task.task					)
       self.assertEqual(_lowerCamelCase      ,						task.input_schema					)
       self.assertEqual(_lowerCamelCase      ,						task.label_schema					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:str											= ImageClassification(image_column='''input_image'''      ,						label_column='''input_label'''					)
       self.assertEqual(task.label_schema['''labels''']      ,						_lowerCamelCase					)
       a		:Any											= task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels					)}					)					)
       self.assertEqual(task.label_schema['''labels''']      ,						ClassLabel(names=self.labels					)					)







class 	_snake_case						(   _snake_case							):




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Any											= DatasetInfo(task_templates=QuestionAnsweringExtractive()					)
       a		:Dict											= 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(_lowerCamelCase					):
             return x

       def dont_keep_task(_lowerCamelCase					):
             a		:Tuple											= deepcopy(_lowerCamelCase					)
             a		:Tuple											= 0
             return out

       a		:Optional[int]											= dataset.map(_lowerCamelCase					)
       assert mapped_dataset.info.task_templates == dataset.info.task_templates
       # reload from cache
       a		:Any											= dataset.map(_lowerCamelCase					)
       assert mapped_dataset.info.task_templates == dataset.info.task_templates

       a		:List[str]											= dataset.map(_lowerCamelCase					)
       assert mapped_dataset.info.task_templates == []
       # reload from cache
       a		:Union[str, Any]											= dataset.map(_lowerCamelCase					)
       assert mapped_dataset.info.task_templates == []




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:List[str]											= Features({'''text''': Value('''string'''					), '''label''': ClassLabel(names=('''pos''', '''neg''')					)}					)
       a		:Union[str, Any]											= TextClassification(text_column='''text'''      ,						label_column='''label'''					)
       a		:Tuple											= DatasetInfo(features=_lowerCamelCase      ,						task_templates=_lowerCamelCase					)
       a		:Tuple											= Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']}      ,						info=_lowerCamelCase					)

       def process(_lowerCamelCase					):
             return example

       a		:List[str]											= dataset.remove_columns('''label'''					)
       a		:Optional[Any]											= modified_dataset.map(_lowerCamelCase					)
       assert mapped_dataset.info.task_templates == []









import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class 	_snake_case						(   unittest.TestCase							):
 # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
 SCREAMING_SNAKE_CASE__   = (ScoreSdeVeScheduler,)
 SCREAMING_SNAKE_CASE__   = ()




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= 4
       a		:List[str]											= 3
       a		:Any											= 8
       a		:Any											= 8

       a		:List[str]											= torch.rand((batch_size, num_channels, height, width)					)

       return sample




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= 4
       a		:Union[str, Any]											= 3
       a		:Optional[Any]											= 8
       a		:Tuple											= 8

       a		:Dict											= batch_size * num_channels * height * width
       a		:Optional[int]											= torch.arange(_lowerCamelCase					)
       a		:List[Any]											= sample.reshape(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					)
       a		:Union[str, Any]											= sample / num_elems
       a		:Dict											= sample.permute(3      ,						0      ,						1      ,						2					)

       return sample




 def  SCREAMING_SNAKE_CASE__       (	self					):
       def model(_lowerCamelCase      ,						_lowerCamelCase      ,						*_lowerCamelCase					):
             return sample * t / (t + 1)

       return model




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       a		:str											= {
           '''num_train_timesteps''': 2000,
           '''snr''': 0.15,
           '''sigma_min''': 0.01,
           '''sigma_max''': 1348,
           '''sampling_eps''': 1e-5,
       }

       config.update(**_lowerCamelCase					)
       return config




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=0      ,						**_lowerCamelCase					):
       a		:Optional[int]											= dict(self.forward_default_kwargs					)

       for scheduler_class in self.scheduler_classes:
             a		:Union[str, Any]											= self.dummy_sample
             a		:Tuple											= 0.1 * sample

             a		:Union[str, Any]											= self.get_scheduler_config(**_lowerCamelCase					)
             a		:Union[str, Any]											= scheduler_class(**_lowerCamelCase					)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCamelCase					)
                   a		:Optional[int]											= scheduler_class.from_pretrained(_lowerCamelCase					)

             a		:List[str]											= scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Dict											= new_scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

             a		:int											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Optional[Any]											= new_scheduler.step_correct(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler correction are not identical"




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=0      ,						**_lowerCamelCase					):
       a		:Dict											= dict(self.forward_default_kwargs					)
       kwargs.update(_lowerCamelCase					)

       for scheduler_class in self.scheduler_classes:
             a		:int											= self.dummy_sample
             a		:str											= 0.1 * sample

             a		:Optional[Any]											= self.get_scheduler_config()
             a		:Union[str, Any]											= scheduler_class(**_lowerCamelCase					)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCamelCase					)
                   a		:Union[str, Any]											= scheduler_class.from_pretrained(_lowerCamelCase					)

             a		:Union[str, Any]											= scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Optional[int]											= new_scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

             a		:List[str]											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Tuple											= new_scheduler.step_correct(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler correction are not identical"




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for timesteps in [10, 100, 1000]:
             self.check_over_configs(num_train_timesteps=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]      ,						[1, 100, 1000]					):
             self.check_over_configs(sigma_min=_lowerCamelCase      ,						sigma_max=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for t in [0.1, 0.5, 0.75]:
             self.check_over_forward(time_step=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Dict											= dict(self.forward_default_kwargs					)

       a		:Optional[Any]											= self.scheduler_classes[0]
       a		:Union[str, Any]											= self.get_scheduler_config()
       a		:List[Any]											= scheduler_class(**_lowerCamelCase					)

       a		:Union[str, Any]											= 3

       a		:List[Any]											= self.dummy_model()
       a		:Dict											= self.dummy_sample_deter

       scheduler.set_sigmas(_lowerCamelCase					)
       scheduler.set_timesteps(_lowerCamelCase					)
       a		:str											= torch.manual_seed(0					)

       for i, t in enumerate(scheduler.timesteps					):
             a		:Optional[int]											= scheduler.sigmas[i]

             for _ in range(scheduler.config.correct_steps					):
                   with torch.no_grad():
                         a		:Dict											= model(_lowerCamelCase      ,						_lowerCamelCase					)
                   a		:int											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase      ,						**_lowerCamelCase					).prev_sample

             with torch.no_grad():
                   a		:Any											= model(_lowerCamelCase      ,						_lowerCamelCase					)

             a		:Optional[Any]											= scheduler.step_pred(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase      ,						**_lowerCamelCase					)
             a							,       a		:Any											= output.prev_sample, output.prev_sample_mean

       a		:Dict											= torch.sum(torch.abs(_lowerCamelCase					)					)
       a		:int											= torch.mean(torch.abs(_lowerCamelCase					)					)

       assert np.isclose(result_sum.item()      ,						143_7275_8528.0					)
       assert np.isclose(result_mean.item()      ,						1871_4530.0					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:str											= dict(self.forward_default_kwargs					)

       a		:Union[str, Any]											= kwargs.pop('''num_inference_steps'''      ,						_lowerCamelCase					)

       for scheduler_class in self.scheduler_classes:
             a		:Optional[int]											= self.get_scheduler_config()
             a		:str											= scheduler_class(**_lowerCamelCase					)

             a		:List[str]											= self.dummy_sample
             a		:List[Any]											= 0.1 * sample

             if num_inference_steps is not None and hasattr(_lowerCamelCase      ,						'''set_timesteps'''					):
                   scheduler.set_timesteps(_lowerCamelCase					)
             elif num_inference_steps is not None and not hasattr(_lowerCamelCase      ,						'''set_timesteps'''					):
                   a		:Dict											= num_inference_steps

             a		:Tuple											= scheduler.step_pred(_lowerCamelCase      ,						0      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Tuple											= scheduler.step_pred(_lowerCamelCase      ,						1      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             self.assertEqual(output_a.shape      ,						sample.shape					)
             self.assertEqual(output_a.shape      ,						output_a.shape					)





