




"""simple docstring"""

from collections import defaultdict




def       snake_case__	(       lowerCAmelCase   :		str		):
					__snake_case          =	defaultdict(lowerCAmelCase		)
					# Creating a dictionary containing count of each word
					for word in sentence.split():
										occurrence[word] += 1
					return occurrence


if __name__ == "__main__":
		for word, count in word_occurrence("INPUT STRING").items():
				print(f'{word}: {count}')








"""simple docstring"""

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__ :    Union[str, Any] 					=							{
    "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       snake_case__	(       lowerCAmelCase   :		Optional[int]		):
					__snake_case          =	task_cls()
					__snake_case          =	asdict(lowerCAmelCase		)
					__snake_case          =	task_template_from_dict(lowerCAmelCase		)
					assert task == reloaded
class     A__      :




	def 							a_		(   self  :	Tuple	)					-> int:
						__snake_case          =	LanguageModeling(text_column="""input_text"""	)
						assert {"input_text": "text"} == task.column_mapping




	def 							a_		(   self  :	Union[str, Any]	)					-> List[Any]:
						__snake_case          =	Features({"""text""": Value("""string"""	)}	)
						__snake_case          =	{"""text_column""": """input_text"""}
						__snake_case          =	LanguageModeling.from_dict(lowercase	)
						assert "language-modeling" == task.task
						assert input_schema == task.input_schema

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	str	)					-> Any:
						__snake_case          =	sorted(["""pos""", """neg"""]	)




	def 							a_		(   self  :	str	)					-> Union[str, Any]:
						__snake_case          =	TextClassification(text_column="""input_text"""      ,    label_column="""input_label"""	)
						self.assertDictEqual({"""input_text""": """text""", """input_label""": """labels"""}      ,    task.column_mapping	)




	def 							a_		(   self  :	int	)					-> List[Any]:
						__snake_case          =	Features({"""text""": Value("""string"""	)}	)
						# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
						__snake_case          =	Features({"""labels""": ClassLabel}	)
						__snake_case          =	{"""text_column""": """input_text""", """label_column""": """input_labels"""}
						__snake_case          =	TextClassification.from_dict(lowercase	)
						self.assertEqual("""text-classification"""      ,    task.task	)
						self.assertEqual(lowercase      ,    task.input_schema	)
						self.assertEqual(lowercase      ,    task.label_schema	)




	def 							a_		(   self  :	Union[str, Any]	)					-> List[Any]:
						__snake_case          =	TextClassification(text_column="""input_text"""      ,    label_column="""input_label"""	)
						self.assertEqual(task.label_schema["""labels"""]      ,    lowercase	)
						__snake_case          =	task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels	)}	)	)
						self.assertEqual(task.label_schema["""labels"""]      ,    ClassLabel(names=self.labels	)	)

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	Any	)					-> Tuple:
						__snake_case          =	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  :	int	)					-> Tuple:
						__snake_case          =	Features({"""question""": Value("""string"""	), """context""": Value("""string"""	)}	)
						__snake_case          =	Features(
						    {
						        """answers""": Sequence(
						            {
						                """text""": Value("""string"""	),
						                """answer_start""": Value("""int32"""	),
						            }	)
						    }	)
						__snake_case          =	{
						    """context_column""": """input_input_context""",
						    """question_column""": """input_question""",
						    """answers_column""": """input_answers""",
						}
						__snake_case          =	QuestionAnsweringExtractive.from_dict(lowercase	)
						self.assertEqual("""question-answering-extractive"""      ,    task.task	)
						self.assertEqual(lowercase      ,    task.input_schema	)
						self.assertEqual(lowercase      ,    task.label_schema	)

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	Any	)					-> Union[str, Any]:
						__snake_case          =	Summarization(text_column="""input_text"""      ,    summary_column="""input_summary"""	)
						self.assertDictEqual({"""input_text""": """text""", """input_summary""": """summary"""}      ,    task.column_mapping	)




	def 							a_		(   self  :	List[Any]	)					-> Dict:
						__snake_case          =	Features({"""text""": Value("""string"""	)}	)
						__snake_case          =	Features({"""summary""": Value("""string"""	)}	)
						__snake_case          =	{"""text_column""": """input_text""", """summary_column""": """input_summary"""}
						__snake_case          =	Summarization.from_dict(lowercase	)
						self.assertEqual("""summarization"""      ,    task.task	)
						self.assertEqual(lowercase      ,    task.input_schema	)
						self.assertEqual(lowercase      ,    task.label_schema	)

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	Any	)					-> int:
						__snake_case          =	AutomaticSpeechRecognition(audio_column="""input_audio"""      ,    transcription_column="""input_transcription"""	)
						self.assertDictEqual({"""input_audio""": """audio""", """input_transcription""": """transcription"""}      ,    task.column_mapping	)




	def 							a_		(   self  :	Dict	)					-> List[str]:
						__snake_case          =	Features({"""audio""": Audio()}	)
						__snake_case          =	Features({"""transcription""": Value("""string"""	)}	)
						__snake_case          =	{
						    """audio_column""": """input_audio""",
						    """transcription_column""": """input_transcription""",
						}
						__snake_case          =	AutomaticSpeechRecognition.from_dict(lowercase	)
						self.assertEqual("""automatic-speech-recognition"""      ,    task.task	)
						self.assertEqual(lowercase      ,    task.input_schema	)
						self.assertEqual(lowercase      ,    task.label_schema	)

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	int	)					-> Union[str, Any]:
						__snake_case          =	sorted(["""pos""", """neg"""]	)




	def 							a_		(   self  :	Optional[int]	)					-> str:
						__snake_case          =	AudioClassification(audio_column="""input_audio"""      ,    label_column="""input_label"""	)
						self.assertDictEqual({"""input_audio""": """audio""", """input_label""": """labels"""}      ,    task.column_mapping	)




	def 							a_		(   self  :	Union[str, Any]	)					-> Any:
						__snake_case          =	Features({"""audio""": Audio()}	)
						__snake_case          =	Features({"""labels""": ClassLabel}	)
						__snake_case          =	{
						    """audio_column""": """input_image""",
						    """label_column""": """input_label""",
						}
						__snake_case          =	AudioClassification.from_dict(lowercase	)
						self.assertEqual("""audio-classification"""      ,    task.task	)
						self.assertEqual(lowercase      ,    task.input_schema	)
						self.assertEqual(lowercase      ,    task.label_schema	)




	def 							a_		(   self  :	Optional[Any]	)					-> Optional[int]:
						__snake_case          =	AudioClassification(audio_column="""input_audio"""      ,    label_column="""input_label"""	)
						self.assertEqual(task.label_schema["""labels"""]      ,    lowercase	)
						__snake_case          =	task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels	)}	)	)
						self.assertEqual(task.label_schema["""labels"""]      ,    ClassLabel(names=self.labels	)	)

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	Optional[int]	)					-> str:
						__snake_case          =	sorted(["""pos""", """neg"""]	)




	def 							a_		(   self  :	Optional[int]	)					-> List[str]:
						__snake_case          =	ImageClassification(image_column="""input_image"""      ,    label_column="""input_label"""	)
						self.assertDictEqual({"""input_image""": """image""", """input_label""": """labels"""}      ,    task.column_mapping	)




	def 							a_		(   self  :	Optional[Any]	)					-> int:
						__snake_case          =	Features({"""image""": Image()}	)
						__snake_case          =	Features({"""labels""": ClassLabel}	)
						__snake_case          =	{
						    """image_column""": """input_image""",
						    """label_column""": """input_label""",
						}
						__snake_case          =	ImageClassification.from_dict(lowercase	)
						self.assertEqual("""image-classification"""      ,    task.task	)
						self.assertEqual(lowercase      ,    task.input_schema	)
						self.assertEqual(lowercase      ,    task.label_schema	)




	def 							a_		(   self  :	Optional[int]	)					-> Any:
						__snake_case          =	ImageClassification(image_column="""input_image"""      ,    label_column="""input_label"""	)
						self.assertEqual(task.label_schema["""labels"""]      ,    lowercase	)
						__snake_case          =	task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels	)}	)	)
						self.assertEqual(task.label_schema["""labels"""]      ,    ClassLabel(names=self.labels	)	)

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	str	)					-> str:
						__snake_case          =	DatasetInfo(task_templates=QuestionAnsweringExtractive()	)
						__snake_case          =	Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}      ,    info=lowercase	)
						assert isinstance(dataset.info.task_templates      ,    lowercase	)
						assert len(dataset.info.task_templates	) == 1

						def keep_task(lowercase  :	Tuple	):
											return x

						def dont_keep_task(lowercase  :	List[str]	):
											__snake_case          =	deepcopy(lowercase	)
											__snake_case          =	0
											return out

						__snake_case          =	dataset.map(lowercase	)
						assert mapped_dataset.info.task_templates == dataset.info.task_templates
						# reload from cache
						__snake_case          =	dataset.map(lowercase	)
						assert mapped_dataset.info.task_templates == dataset.info.task_templates

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




	def 							a_		(   self  :	Any	)					-> int:
						__snake_case          =	Features({"""text""": Value("""string"""	), """label""": ClassLabel(names=("""pos""", """neg""")	)}	)
						__snake_case          =	TextClassification(text_column="""text"""      ,    label_column="""label"""	)
						__snake_case          =	DatasetInfo(features=lowercase      ,    task_templates=lowercase	)
						__snake_case          =	Dataset.from_dict({"""text""": ["""A sentence."""], """label""": ["""pos"""]}      ,    info=lowercase	)

						def process(lowercase  :	List[Any]	):
											return example

						__snake_case          =	dataset.remove_columns("""label"""	)
						__snake_case          =	modified_dataset.map(lowercase	)
						assert mapped_dataset.info.task_templates == []


