

class SCREAMING_SNAKE_CASE	:






			"""simple docstring"""




			def __init__(     self				):

				"""simple docstring"""


				snake_case_   		=			{}  # Mapping from char to TrieNode
				snake_case_   		=			False


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				for word in words:
					self.insert(__UpperCamelCase				)


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self
				for char in word:
					if char not in curr.nodes:
						snake_case_   		=			TrieNode()
					snake_case_   		=			curr.nodes[char]
				snake_case_   		=			True


			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_   		=			self
				for char in word:
					if char not in curr.nodes:
						return False
					snake_case_   		=			curr.nodes[char]
				return curr.is_leaf







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				def _delete(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase				) -> bool:
					if index == len(__UpperCamelCase				):
						# If word does not exist
						if not curr.is_leaf:
							return False
						snake_case_   		=			False
						return len(curr.nodes				) == 0
					snake_case_   		=			word[index]
					snake_case_   		=			curr.nodes.get(__UpperCamelCase				)
					# If char not in current trie node
					if not char_node:
						return False
					# Flag to check if node can be deleted
					snake_case_   		=			_delete(__UpperCamelCase			,					__UpperCamelCase			,					index + 1				)
					if delete_curr:
						del curr.nodes[char]
						return len(curr.nodes				) == 0
					return delete_curr

				_delete(self			,					__UpperCamelCase			,					0				)







def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	if node.is_leaf:
		print(lowercase__      ,			end=' '						)

	for key, value in node.nodes.items():
		print_words(lowercase__      ,			word + key						)





def a():





	'''simple docstring'''
	snake_case_   		=			'banana bananas bandana band apple all beast'.split()
	snake_case_   		=			TrieNode()
	root.insert_many(lowercase__						)
	# print_words(root, "")
	assert all(root.find(lowercase__						) for word in words						)
	assert root.find('banana'						)
	assert not root.find('bandanas'						)
	assert not root.find('apps'						)
	assert root.find('apple'						)
	assert root.find('all'						)
	root.delete('all'						)
	assert not root.find('all'						)
	root.delete('banana'						)
	assert not root.find('banana'						)
	assert root.find('bananas'						)
	return True





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	print(str(lowercase__						)      ,			'works!' if passes else 'doesn\'t work :('						)





def a():





	'''simple docstring'''
	assert test_trie()





def a():





	'''simple docstring'''
	print_results('Testing trie functionality'      ,			test_trie()						)


if __name__ == "__main__":
		main()



from jiwer import compute_measures

import datasets


A									=  '\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

A									=  '\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

A									=  '\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,    _KWARGS_DESCRIPTION				)
class SCREAMING_SNAKE_CASE	(		datasets.Metric				):






			"""simple docstring"""




			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return datasets.MetricInfo(
				    description=_DESCRIPTION			,					citation=_CITATION			,					inputs_description=_KWARGS_DESCRIPTION			,					features=datasets.Features(
				        {
				            'predictions': datasets.Value('string'			,					id='sequence'				),
				            'references': datasets.Value('string'			,					id='sequence'				),
				        }				)			,					codebase_urls=['https://github.com/jitsi/jiwer/']			,					reference_urls=[
				        'https://en.wikipedia.org/wiki/Word_error_rate',
				    ]			,					)







			def       __lowerCAmelCase			(     self			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					__UpperCamelCase=False				):

				"""simple docstring"""


				if concatenate_texts:
					return compute_measures(__UpperCamelCase			,					__UpperCamelCase				)["wer"]
				else:
					snake_case_   		=			0
					snake_case_   		=			0
					for prediction, reference in zip(__UpperCamelCase			,					__UpperCamelCase				):
						snake_case_   		=			compute_measures(__UpperCamelCase			,					__UpperCamelCase				)
						incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
						total += measures["substitutions"] + measures["deletions"] + measures["hits"]
					return incorrect / total
