




class 			UpperCAmelCase_      :




							'''simple docstring'''



							def __init__(					self       :	int       ,     a       :	str = ""       ,     a       :	bool = False			)			->					Tuple:
										# Mapping from the first character of the prefix of the node
										SCREAMING_SNAKE_CASE           =					{}

										# A node will be a leaf if the tree contains its word
										SCREAMING_SNAKE_CASE           =					is_leaf

										SCREAMING_SNAKE_CASE           =					prefix





							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	str			)			->					Any:
										SCREAMING_SNAKE_CASE           =					0
										for q, w in zip(self.prefix       ,     a			):
													if q != w:
																break

													x += 1

										return self.prefix[:x], self.prefix[x:], word[x:]





							def    _UpperCAmelCase     (					self       :	str       ,     a       :	list[str]			)			->					Optional[Any]:
										for word in words:
													self.insert(a			)





							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	str			)			->					Optional[Any]:
										# Case 1: If the word is the prefix of the node
										# Solution: We set the current node as leaf
										if self.prefix == word:
													SCREAMING_SNAKE_CASE           =					True

										# Case 2: The node has no edges that have a prefix to the word
										# Solution: We create an edge from the current node to a new one
										# containing the word
										elif word[0] not in self.nodes:
													SCREAMING_SNAKE_CASE           =					RadixNode(prefix=a       ,     is_leaf=a			)

										else:
													SCREAMING_SNAKE_CASE           =					self.nodes[word[0]]
													SCREAMING_SNAKE_CASE           =					incoming_node.match(
													    a			)

													# Case 3: The node prefix is equal to the matching
													# Solution: We insert remaining word on the next node
													if remaining_prefix == "":
																self.nodes[matching_string[0]].insert(a			)

													# Case 4: The word is greater equal to the matching
													# Solution: Create a node in between both nodes, change
													# prefixes and add the new node for the remaining word
													else:
																SCREAMING_SNAKE_CASE           =					remaining_prefix

																SCREAMING_SNAKE_CASE           =					self.nodes[matching_string[0]]
																SCREAMING_SNAKE_CASE           =					RadixNode(a       ,     a			)
																SCREAMING_SNAKE_CASE           =					aux_node

																if remaining_word == "":
																			SCREAMING_SNAKE_CASE           =					True
																else:
																			self.nodes[matching_string[0]].insert(a			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]       ,     a       :	str			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					self.nodes.get(word[0]       ,     a			)
										if not incoming_node:
													return False
										else:
													SCREAMING_SNAKE_CASE           =					incoming_node.match(
													    a			)
													# If there is remaining prefix, the word can't be on the tree
													if remaining_prefix != "":
																return False
													# This applies when the word and the prefix are equal
													elif remaining_word == "":
																return incoming_node.is_leaf
													# We have word remaining so we check the next node
													else:
																return incoming_node.find(a			)





							def    _UpperCAmelCase     (					self       :	int       ,     a       :	str			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					self.nodes.get(word[0]       ,     a			)
										if not incoming_node:
													return False
										else:
													SCREAMING_SNAKE_CASE           =					incoming_node.match(
													    a			)
													# If there is remaining prefix, the word can't be on the tree
													if remaining_prefix != "":
																return False
													# We have word remaining so we check the next node
													elif remaining_word != "":
																return incoming_node.delete(a			)
													else:
																# If it is not a leaf, we don't have to delete
																if not incoming_node.is_leaf:
																			return False
																else:
																			# We delete the nodes if no edges go from it
																			if len(incoming_node.nodes			) == 0:
																						del self.nodes[word[0]]
																						# We merge the current node with its only child
																						if len(self.nodes			) == 1 and not self.is_leaf:
																									SCREAMING_SNAKE_CASE           =					list(self.nodes.values()			)[0]
																									SCREAMING_SNAKE_CASE           =					merging_node.is_leaf
																									self.prefix += merging_node.prefix
																									SCREAMING_SNAKE_CASE           =					merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																			elif len(incoming_node.nodes			) > 1:
																						SCREAMING_SNAKE_CASE           =					False
																			# If there is 1 edge, we merge it with its child
																			else:
																						SCREAMING_SNAKE_CASE           =					list(incoming_node.nodes.values()			)[0]
																						SCREAMING_SNAKE_CASE           =					merging_node.is_leaf
																						incoming_node.prefix += merging_node.prefix
																						SCREAMING_SNAKE_CASE           =					merging_node.nodes

																			return True







							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	int = 0			)			->					int:
										if self.prefix != "":
													print("""-""" * height       ,     self.prefix       ,     """  (leaf)""" if self.is_leaf else """"""			)

										for value in self.nodes.values():
													value.print_tree(height + 1			)





def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					"banana bananas bandana band apple all beast".split()
			SCREAMING_SNAKE_CASE           =					RadixNode()
			root.insert_many(lowerCamelCase__   )

			assert all(root.find(lowerCamelCase__   ) for word in words   )
			assert not root.find("""bandanas"""   )
			assert not root.find("""apps"""   )
			root.delete("""all"""   )
			assert not root.find("""all"""   )
			root.delete("""banana"""   )
			assert not root.find("""banana"""   )
			assert root.find("""bananas"""   )

			return True
def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			assert test_trie()
def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					RadixNode()
			SCREAMING_SNAKE_CASE           =					"banana bananas bandanas bandana band apple all beast".split()
			root.insert_many(lowerCamelCase__   )

			print("""Words:"""					,	lowerCamelCase__   )
			print("""Tree:"""   )
			root.print_tree()


if __name__ == "__main__":
		main()









import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					args.log_outputs
			SCREAMING_SNAKE_CASE           =					"""_""".join(args.dataset.split("""/"""   ) + [args.config, args.split]   )

			# load metric
			SCREAMING_SNAKE_CASE           =					load_metric("""wer"""   )
			SCREAMING_SNAKE_CASE           =					load_metric("""cer"""   )

			# compute metrics
			SCREAMING_SNAKE_CASE           =					wer.compute(references=result["""target"""]					,	predictions=result["""prediction"""]   )
			SCREAMING_SNAKE_CASE           =					cer.compute(references=result["""target"""]					,	predictions=result["""prediction"""]   )

			# print & log results
			SCREAMING_SNAKE_CASE           =					f"""WER: {wer_result}\nCER: {cer_result}"""
			print(SCREAMING_SNAKE_CASE   )

			with open(f"""{dataset_id}_eval_results.txt"""					,	"""w"""   ) as f:
						f.write(SCREAMING_SNAKE_CASE   )

			# log all results in text file. Possibly interesting for analysis
			if log_outputs is not None:
						SCREAMING_SNAKE_CASE           =					f"""log_{dataset_id}_predictions.txt"""
						SCREAMING_SNAKE_CASE           =					f"""log_{dataset_id}_targets.txt"""

						with open(SCREAMING_SNAKE_CASE					,	"""w"""   ) as p, open(SCREAMING_SNAKE_CASE					,	"""w"""   ) as t:
									# mapping function to write output
									def write_to_file(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
												p.write(f"""{i}""" + """\n"""   )
												p.write(batch["""prediction"""] + """\n"""   )
												t.write(f"""{i}""" + """\n"""   )
												t.write(batch["""target"""] + """\n"""   )

									result.map(SCREAMING_SNAKE_CASE					,	with_indices=SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

			SCREAMING_SNAKE_CASE           =					re.sub(SCREAMING_SNAKE_CASE					,	""""""					,	text.lower()   )

			# In addition, we can normalize the target text, e.g. removing new lines characters etc...
			# note that order is important here!
			SCREAMING_SNAKE_CASE           =					["""\n\n""", """\n""", """   """, """  """]

			for t in token_sequences_to_ignore:
						SCREAMING_SNAKE_CASE           =					""" """.join(text.split(SCREAMING_SNAKE_CASE   )   )

			return text
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			# load dataset
			SCREAMING_SNAKE_CASE           =					load_dataset(args.dataset					,	args.config					,	split=args.split					,	use_auth_token=SCREAMING_SNAKE_CASE   )

			# for testing: only process the first two examples as a test
			# dataset = dataset.select(range(10))

			# load processor
			SCREAMING_SNAKE_CASE           =					AutoFeatureExtractor.from_pretrained(args.model_id   )
			SCREAMING_SNAKE_CASE           =					feature_extractor.sampling_rate

			# resample audio
			SCREAMING_SNAKE_CASE           =					dataset.cast_column("""audio"""					,	Audio(sampling_rate=SCREAMING_SNAKE_CASE   )   )

			# load eval pipeline
			if args.device is None:
						SCREAMING_SNAKE_CASE           =					0 if torch.cuda.is_available() else -1
			SCREAMING_SNAKE_CASE           =					pipeline("""automatic-speech-recognition"""					,	model=args.model_id					,	device=args.device   )

			# map function to decode audio
			def map_to_pred(SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE           =					asr(
						    batch["""audio"""]["""array"""]					,	chunk_length_s=args.chunk_length_s					,	stride_length_s=args.stride_length_s   )

						SCREAMING_SNAKE_CASE           =					prediction["""text"""]
						SCREAMING_SNAKE_CASE           =					normalize_text(batch["""sentence"""]   )
						return batch

			# run inference on all examples
			SCREAMING_SNAKE_CASE           =					dataset.map(SCREAMING_SNAKE_CASE					,	remove_columns=dataset.column_names   )

			# compute and log_results
			# do not change function below
			log_results(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
		__A						:							List[Any]							=  argparse.ArgumentParser()

		parser.add_argument(
		    """--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
		)
		parser.add_argument(
		    """--dataset""",
		    type=str,
		    required=True,
		    help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
		)
		parser.add_argument(
		    """--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'`  for Common Voice"""
		)
		parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
		parser.add_argument(
		    """--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
		)
		parser.add_argument(
		    """--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
		)
		parser.add_argument(
		    """--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
		)
		parser.add_argument(
		    """--device""",
		    type=int,
		    default=None,
		    help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
		)
		__A						:							Tuple							=  parser.parse_args()

		main(args)






