



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 							A     (						__UpperCAmelCase      )			->			Union[str, Any]:



						'''simple docstring'''






						UpperCAmelCase_					=	task_cls()
						UpperCAmelCase_					=	asdict(__UpperCAmelCase      )
						UpperCAmelCase_					=	task_template_from_dict(__UpperCAmelCase      )
						assert task == reloaded







class 				a_    :

						def       __a			(       self      :Union[str, Any])						->   Dict:
												UpperCAmelCase_					=	LanguageModeling(text_column='''input_text''')
												assert {"input_text": "text"} == task.column_mapping




						def       __a			(       self      :Optional[int])						->   Any:
												UpperCAmelCase_					=	Features({'''text''': Value('''string''')})
												UpperCAmelCase_					=	{'''text_column''': '''input_text'''}
												UpperCAmelCase_					=	LanguageModeling.from_dict(_lowercase)
												assert "language-modeling" == task.task
												assert input_schema == task.input_schema







class 				a_    (						_snake_case							):

						def       __a			(       self      :List[str])						->   Optional[int]:
												UpperCAmelCase_					=	sorted(['''pos''', '''neg'''])

						def       __a			(       self      :Union[str, Any])						->   Tuple:
												UpperCAmelCase_					=	TextClassification(text_column='''input_text'''  ,		label_column='''input_label''')
												self.assertDictEqual({'''input_text''': '''text''', '''input_label''': '''labels'''}  ,		task.column_mapping)

						def       __a			(       self      :Optional[Any])						->   List[str]:
												UpperCAmelCase_					=	Features({'''text''': Value('''string''')})
												# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
												UpperCAmelCase_					=	Features({'''labels''': ClassLabel})
												UpperCAmelCase_					=	{'''text_column''': '''input_text''', '''label_column''': '''input_labels'''}
												UpperCAmelCase_					=	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      :Dict)						->   List[str]:
												UpperCAmelCase_					=	TextClassification(text_column='''input_text'''  ,		label_column='''input_label''')
												self.assertEqual(task.label_schema['''labels''']  ,		_lowercase)
												UpperCAmelCase_					=	task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels)}))
												self.assertEqual(task.label_schema['''labels''']  ,		ClassLabel(names=self.labels))







class 				a_    (						_snake_case							):

						def       __a			(       self      :List[str])						->   int:
												UpperCAmelCase_					=	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      :Optional[Any])						->   List[Any]:
												UpperCAmelCase_					=	Features({'''question''': Value('''string'''), '''context''': Value('''string''')})
												UpperCAmelCase_					=	Features(
												    {
												        '''answers''': Sequence(
												            {
												                '''text''': Value('''string'''),
												                '''answer_start''': Value('''int32'''),
												            })
												    })
												UpperCAmelCase_					=	{
												    '''context_column''': '''input_input_context''',
												    '''question_column''': '''input_question''',
												    '''answers_column''': '''input_answers''',
												}
												UpperCAmelCase_					=	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_    (						_snake_case							):

						def       __a			(       self      :List[Any])						->   str:
												UpperCAmelCase_					=	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])						->   Any:
												UpperCAmelCase_					=	Features({'''text''': Value('''string''')})
												UpperCAmelCase_					=	Features({'''summary''': Value('''string''')})
												UpperCAmelCase_					=	{'''text_column''': '''input_text''', '''summary_column''': '''input_summary'''}
												UpperCAmelCase_					=	Summarization.from_dict(_lowercase)
												self.assertEqual('''summarization'''  ,		task.task)
												self.assertEqual(_lowercase  ,		task.input_schema)
												self.assertEqual(_lowercase  ,		task.label_schema)







class 				a_    (						_snake_case							):

						def       __a			(       self      :Optional[int])						->   int:
												UpperCAmelCase_					=	AutomaticSpeechRecognition(audio_column='''input_audio'''  ,		transcription_column='''input_transcription''')
												self.assertDictEqual({'''input_audio''': '''audio''', '''input_transcription''': '''transcription'''}  ,		task.column_mapping)




						def       __a			(       self      :Optional[int])						->   Optional[Any]:
												UpperCAmelCase_					=	Features({'''audio''': Audio()})
												UpperCAmelCase_					=	Features({'''transcription''': Value('''string''')})
												UpperCAmelCase_					=	{
												    '''audio_column''': '''input_audio''',
												    '''transcription_column''': '''input_transcription''',
												}
												UpperCAmelCase_					=	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_    (						_snake_case							):

						def       __a			(       self      :Dict)						->   Any:
												UpperCAmelCase_					=	sorted(['''pos''', '''neg'''])

						def       __a			(       self      :Dict)						->   Union[str, Any]:
												UpperCAmelCase_					=	AudioClassification(audio_column='''input_audio'''  ,		label_column='''input_label''')
												self.assertDictEqual({'''input_audio''': '''audio''', '''input_label''': '''labels'''}  ,		task.column_mapping)

						def       __a			(       self      :Tuple)						->   Union[str, Any]:
												UpperCAmelCase_					=	Features({'''audio''': Audio()})
												UpperCAmelCase_					=	Features({'''labels''': ClassLabel})
												UpperCAmelCase_					=	{
												    '''audio_column''': '''input_image''',
												    '''label_column''': '''input_label''',
												}
												UpperCAmelCase_					=	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      :Dict)						->   Dict:
												UpperCAmelCase_					=	AudioClassification(audio_column='''input_audio'''  ,		label_column='''input_label''')
												self.assertEqual(task.label_schema['''labels''']  ,		_lowercase)
												UpperCAmelCase_					=	task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels)}))
												self.assertEqual(task.label_schema['''labels''']  ,		ClassLabel(names=self.labels))







class 				a_    (						_snake_case							):

						def       __a			(       self      :List[str])						->   Any:
												UpperCAmelCase_					=	sorted(['''pos''', '''neg'''])

						def       __a			(       self      :int)						->   Optional[Any]:
												UpperCAmelCase_					=	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:
												UpperCAmelCase_					=	Features({'''image''': Image()})
												UpperCAmelCase_					=	Features({'''labels''': ClassLabel})
												UpperCAmelCase_					=	{
												    '''image_column''': '''input_image''',
												    '''label_column''': '''input_label''',
												}
												UpperCAmelCase_					=	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])						->   Optional[int]:
												UpperCAmelCase_					=	ImageClassification(image_column='''input_image'''  ,		label_column='''input_label''')
												self.assertEqual(task.label_schema['''labels''']  ,		_lowercase)
												UpperCAmelCase_					=	task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels)}))
												self.assertEqual(task.label_schema['''labels''']  ,		ClassLabel(names=self.labels))







class 				a_    (						_snake_case							):

						def       __a			(       self      :Union[str, Any])						->   Optional[int]:
												UpperCAmelCase_					=	DatasetInfo(task_templates=QuestionAnsweringExtractive())
												UpperCAmelCase_					=	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      :Optional[Any]):
																		UpperCAmelCase_					=	deepcopy(_lowercase)
																		UpperCAmelCase_					=	0
																		return out

												UpperCAmelCase_					=	dataset.map(_lowercase)
												assert mapped_dataset.info.task_templates == dataset.info.task_templates
												# reload from cache
												UpperCAmelCase_					=	dataset.map(_lowercase)
												assert mapped_dataset.info.task_templates == dataset.info.task_templates

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




						def       __a			(       self      :Tuple)						->   List[str]:
												UpperCAmelCase_					=	Features({'''text''': Value('''string'''), '''label''': ClassLabel(names=('''pos''', '''neg'''))})
												UpperCAmelCase_					=	TextClassification(text_column='''text'''  ,		label_column='''label''')
												UpperCAmelCase_					=	DatasetInfo(features=_lowercase  ,		task_templates=_lowercase)
												UpperCAmelCase_					=	Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']}  ,		info=_lowercase)

												def process(_lowercase      :Union[str, Any]):
																		return example

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










from __future__ import annotations

import sys







class 				a_    :

						def __init__(       self      :Dict  ,		_lowercase      :str  ,		_lowercase      :int)						->   List[Any]:
												UpperCAmelCase_					=	letter
												UpperCAmelCase_					=	freq
												UpperCAmelCase_					=	{}




						def __repr__(       self      :str)						->   str:
												return f"{self.letter}:{self.freq}"







class 				a_    :

						def __init__(       self      :List[Any]  ,		_lowercase      :int  ,		_lowercase      :Letter | TreeNode  ,		_lowercase      :Letter | TreeNode)						->   Dict:
												UpperCAmelCase_					=	freq
												UpperCAmelCase_					=	left
												UpperCAmelCase_					=	right







def 							A     (						__UpperCAmelCase      )			->			list[Letter]:



						'''simple docstring'''






						UpperCAmelCase_					=	{}
						with open(__UpperCAmelCase      ) as f:
												while True:
																		UpperCAmelCase_					=	f.read(1      )
																		if not c:
																								break
																		UpperCAmelCase_					=	chars[c] + 1 if c in chars else 1
						return sorted((Letter(__UpperCAmelCase				,   __UpperCAmelCase      ) for c, f in chars.items())				,   key=lambda __UpperCAmelCase      : x.freq      )







def 							A     (						__UpperCAmelCase      )			->			Letter | TreeNode:



						'''simple docstring'''






						UpperCAmelCase_					=	letters  # type: ignore
						while len(__UpperCAmelCase      ) > 1:
												UpperCAmelCase_					=	response.pop(0      )
												UpperCAmelCase_					=	response.pop(0      )
												UpperCAmelCase_					=	left.freq + right.freq
												UpperCAmelCase_					=	TreeNode(__UpperCAmelCase				,   __UpperCAmelCase				,   __UpperCAmelCase      )
												response.append(__UpperCAmelCase      )
												response.sort(key=lambda __UpperCAmelCase      : x.freq      )
						return response[0]







def 							A     (						__UpperCAmelCase				,   __UpperCAmelCase      )			->			list[Letter]:



						'''simple docstring'''






						if isinstance(__UpperCAmelCase				,   __UpperCAmelCase      ):
												UpperCAmelCase_					=	bitstring
												return [root]
						UpperCAmelCase_					=	root  # type: ignore
						UpperCAmelCase_					=	[]
						letters += traverse_tree(treenode.left				,   bitstring + '''0'''      )
						letters += traverse_tree(treenode.right				,   bitstring + '''1'''      )
						return letters







def 							A     (						__UpperCAmelCase      )			->			None:



						'''simple docstring'''






						UpperCAmelCase_					=	parse_file(__UpperCAmelCase      )
						UpperCAmelCase_					=	build_tree(__UpperCAmelCase      )
						UpperCAmelCase_					=	{
						    k: v for letter in traverse_tree(__UpperCAmelCase				,   ''''''      ) for k, v in letter.bitstring.items()
						}
						print(f"Huffman Coding  of {file_path}: "      )
						with open(__UpperCAmelCase      ) as f:
												while True:
																		UpperCAmelCase_					=	f.read(1      )
																		if not c:
																								break
																		print(letters[c]				,   end=''' '''      )
						print()


if __name__ == "__main__":
							# pass the file path to the huffman function
							huffman(sys.argv[1])





