
'''simple docstring'''
import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
						from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


lowerCAmelCase :  List[Any]      =      get_tests_dir('fixtures/test_sentencepiece.model')


@require_torch
@require_torchaudio
@require_sentencepiece
class        SCREAMING_SNAKE_CASE__	(			unittest.TestCase):







				def 		UpperCAmelCase_  (    self			)->						List[str]:







											'''simple docstring'''






											UpperCamelCase					       =			tempfile.mkdtemp()

											UpperCamelCase					       =			['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
											UpperCamelCase					       =			dict(zip(A_		,							range(len(A_			)			)			)			)
											UpperCamelCase					       =			Path(self.tmpdirname			)
											save_json(A_		,							save_dir / VOCAB_FILES_NAMES['vocab_file']			)
											if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																		copyfile(A_		,							save_dir / VOCAB_FILES_NAMES['spm_file']			)

											UpperCamelCase					       =			SpeechaTextTokenizer.from_pretrained(self.tmpdirname			)
											tokenizer.save_pretrained(self.tmpdirname			)

											UpperCamelCase					       =			{
											    'feature_size': 24,
											    'num_mel_bins': 24,
											    'padding_value': 0.0,
											    'sampling_rate': 16000,
											    'return_attention_mask': False,
											    'do_normalize': True,
											}
											save_json(A_		,							save_dir / FEATURE_EXTRACTOR_NAME			)







				def 		UpperCAmelCase_  (    self		,							**A_			)->						Tuple:







											'''simple docstring'''






											return SpeechaTextTokenizer.from_pretrained(self.tmpdirname		,							**A_			)







				def 		UpperCAmelCase_  (    self		,							**A_			)->						Optional[int]:







											'''simple docstring'''






											return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname		,							**A_			)







				def 		UpperCAmelCase_  (    self			)->						Optional[Any]:







											'''simple docstring'''






											shutil.rmtree(self.tmpdirname			)







				def 		UpperCAmelCase_  (    self			)->						List[Any]:







											'''simple docstring'''






											UpperCamelCase					       =			self.get_tokenizer()
											UpperCamelCase					       =			self.get_feature_extractor()

											UpperCamelCase					       =			SpeechaTextProcessor(tokenizer=A_		,							feature_extractor=A_			)

											processor.save_pretrained(self.tmpdirname			)
											UpperCamelCase					       =			SpeechaTextProcessor.from_pretrained(self.tmpdirname			)

											self.assertEqual(processor.tokenizer.get_vocab()		,							tokenizer.get_vocab()			)
											self.assertIsInstance(processor.tokenizer		,							A_			)

											self.assertEqual(processor.feature_extractor.to_json_string()		,							feature_extractor.to_json_string()			)
											self.assertIsInstance(processor.feature_extractor		,							A_			)







				def 		UpperCAmelCase_  (    self			)->						List[Any]:







											'''simple docstring'''






											UpperCamelCase					       =			SpeechaTextProcessor(
											    tokenizer=self.get_tokenizer()		,							feature_extractor=self.get_feature_extractor()			)
											processor.save_pretrained(self.tmpdirname			)

											UpperCamelCase					       =			self.get_tokenizer(bos_token='(BOS)'		,							eos_token='(EOS)'			)
											UpperCamelCase					       =			self.get_feature_extractor(do_normalize=A_		,							padding_value=1.0			)

											UpperCamelCase					       =			SpeechaTextProcessor.from_pretrained(
											    self.tmpdirname		,							bos_token='(BOS)'		,							eos_token='(EOS)'		,							do_normalize=A_		,							padding_value=1.0			)

											self.assertEqual(processor.tokenizer.get_vocab()		,							tokenizer_add_kwargs.get_vocab()			)
											self.assertIsInstance(processor.tokenizer		,							A_			)

											self.assertEqual(processor.feature_extractor.to_json_string()		,							feature_extractor_add_kwargs.to_json_string()			)
											self.assertIsInstance(processor.feature_extractor		,							A_			)







				def 		UpperCAmelCase_  (    self			)->						Dict:







											'''simple docstring'''






											UpperCamelCase					       =			self.get_feature_extractor()
											UpperCamelCase					       =			self.get_tokenizer()

											UpperCamelCase					       =			SpeechaTextProcessor(tokenizer=A_		,							feature_extractor=A_			)

											UpperCamelCase					       =			floats_list((3, 1000)			)

											UpperCamelCase					       =			feature_extractor(A_		,							return_tensors='np'			)
											UpperCamelCase					       =			processor(A_		,							return_tensors='np'			)

											for key in input_feat_extract.keys():
																		self.assertAlmostEqual(input_feat_extract[key].sum()		,							input_processor[key].sum()		,							delta=1e-2			)







				def 		UpperCAmelCase_  (    self			)->						Dict:







											'''simple docstring'''






											UpperCamelCase					       =			self.get_feature_extractor()
											UpperCamelCase					       =			self.get_tokenizer()

											UpperCamelCase					       =			SpeechaTextProcessor(tokenizer=A_		,							feature_extractor=A_			)

											UpperCamelCase					       =			'This is a test string'

											UpperCamelCase					       =			processor(text=A_			)

											UpperCamelCase					       =			tokenizer(A_			)

											for key in encoded_tok.keys():
																		self.assertListEqual(encoded_tok[key]		,							encoded_processor[key]			)







				def 		UpperCAmelCase_  (    self			)->						Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase					       =			self.get_feature_extractor()
											UpperCamelCase					       =			self.get_tokenizer()

											UpperCamelCase					       =			SpeechaTextProcessor(tokenizer=A_		,							feature_extractor=A_			)

											UpperCamelCase					       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											UpperCamelCase					       =			processor.batch_decode(A_			)
											UpperCamelCase					       =			tokenizer.batch_decode(A_			)

											self.assertListEqual(A_		,							A_			)







				def 		UpperCAmelCase_  (    self			)->						Dict:







											'''simple docstring'''






											UpperCamelCase					       =			self.get_feature_extractor()
											UpperCamelCase					       =			self.get_tokenizer()

											UpperCamelCase					       =			SpeechaTextProcessor(tokenizer=A_		,							feature_extractor=A_			)

											self.assertListEqual(
											    processor.model_input_names		,							feature_extractor.model_input_names		,							msg='`processor` and `feature_extractor` model input names do not match'		,							)






'''simple docstring'''
from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
lowerCAmelCase :  Union[str, Any]      =      [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]







def 						A_(			A		:						int):
							return [int(A) for c in f'''{ruleset:08}'''[:8]]







def 						A_(			A		:						list[list[int]]						,			A		:						list[int]						,			A		:						int):
							UpperCamelCase					       =			len(cells[0])  # 31
							UpperCamelCase					       =			[]
							for i in range(A):
														# Get the neighbors of each cell
														# Handle neighbours outside bounds by using 0 as their value
														UpperCamelCase					       =			0 if i == 0 else cells[time][i - 1]
														UpperCamelCase					       =			0 if i == population - 1 else cells[time][i + 1]
														# Define a new cell and add it to the new generation
														UpperCamelCase					       =			7 - int(f'''{left_neighbor}{cells[time][i]}{right_neighbor}'''						,			2)
														next_generation.append(rule[situation])
							return next_generation







def 						A_(			A		:						list[list[int]]):
							UpperCamelCase					       =			Image.new('RGB'						,			(len(cells[0]), len(A)))
							UpperCamelCase					       =			img.load()
							# Generates image
							for w in range(img.width):
														for h in range(img.height):
																					UpperCamelCase					       =			255 - int(255 * cells[h][w])
																					UpperCamelCase					       =			(color, color, color)
							return img


if __name__ == "__main__":
						lowerCAmelCase :  Optional[int]      =      bin(int(input('Rule:\n').strip()))[2:]
						lowerCAmelCase :  Tuple      =      format_ruleset(int(rule_num))
						for time in range(16):
												CELLS.append(new_generation(CELLS, rule, time))
						lowerCAmelCase :  List[str]      =      generate_image(CELLS)
						# Uncomment to save the image
						# img.save(f"rule_{rule_num}.png")
						img.show()




