




import numpy as np




class 			UpperCAmelCase_      :




							'''simple docstring'''



							def __init__(					self       :	List[Any]			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					(0, 0)
										SCREAMING_SNAKE_CASE           =					None
										SCREAMING_SNAKE_CASE           =					0
										SCREAMING_SNAKE_CASE           =					0
										SCREAMING_SNAKE_CASE           =					0





							def __eq__(					self       :	List[Any]       ,     a       :	List[Any]			)			->					str:
										return self.position == cell.position







							def    _UpperCAmelCase     (					self       :	List[str]			)			->					Optional[int]:
										print(self.position			)





class 			UpperCAmelCase_      :




							'''simple docstring'''



							def __init__(					self       :	str       ,     a       :	List[Any]=(5, 5)			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					np.zeros(a			)
										SCREAMING_SNAKE_CASE           =					world_size[0]
										SCREAMING_SNAKE_CASE           =					world_size[1]





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Dict:
										print(self.w			)







							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	Tuple			)			->					List[str]:
										SCREAMING_SNAKE_CASE           =					[
										    (-1, -1),
										    (-1, 0),
										    (-1, 1),
										    (0, -1),
										    (0, 1),
										    (1, -1),
										    (1, 0),
										    (1, 1),
										]
										SCREAMING_SNAKE_CASE           =					cell.position[0]
										SCREAMING_SNAKE_CASE           =					cell.position[1]
										SCREAMING_SNAKE_CASE           =					[]
										for n in neughbour_cord:
													SCREAMING_SNAKE_CASE           =					current_x + n[0]
													SCREAMING_SNAKE_CASE           =					current_y + n[1]
													if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																SCREAMING_SNAKE_CASE           =					Cell()
																SCREAMING_SNAKE_CASE           =					(x, y)
																SCREAMING_SNAKE_CASE           =					cell
																neighbours.append(a			)
										return neighbours





def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					[]
			SCREAMING_SNAKE_CASE           =					[]
			_open.append(SCREAMING_SNAKE_CASE   )

			while _open:
						SCREAMING_SNAKE_CASE           =					np.argmin([n.f for n in _open]   )
						SCREAMING_SNAKE_CASE           =					_open[min_f]
						_closed.append(_open.pop(SCREAMING_SNAKE_CASE   )   )
						if current == goal:
									break
						for n in world.get_neigbours(SCREAMING_SNAKE_CASE   ):
									for c in _closed:
												if c == n:
															continue
									SCREAMING_SNAKE_CASE           =					current.g + 1
									SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					n.position
									SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					goal.position
									SCREAMING_SNAKE_CASE           =					(ya - ya) ** 2 + (xa - xa) ** 2
									SCREAMING_SNAKE_CASE           =					n.h + n.g

									for c in _open:
												if c == n and c.f < n.f:
															continue
									_open.append(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					[]
			while current.parent is not None:
						path.append(current.position   )
						SCREAMING_SNAKE_CASE           =					current.parent
			path.append(current.position   )
			return path[::-1]


if __name__ == "__main__":
		__A						:							int							=  Gridworld()
		# Start position and goal
		__A						:							Optional[Any]							=  Cell()
		__A						:							Optional[Any]							=  (0, 0)
		__A						:							int							=  Cell()
		__A						:							int							=  (4, 4)
		print(f'''path from {start.position} to {goal.position}''')
		__A						:							List[str]							=  astar(world, start, goal)
		#   Just for visual reasons.
		for i in s:
				__A						:							str							=  1
		print(world.w)












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)






