





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


A__:  int       					=					get_tests_dir('''fixtures/test_sentencepiece.model''')




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

      """simple docstring"""


      def 	UpperCAmelCase_   (						self:	List[Any]			):
          '''simple docstring'''


          UpperCamelCase__:					int												=      tempfile.mkdtemp()

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

          UpperCamelCase__:					str												=      SpeechaTextTokenizer.from_pretrained(self.tmpdirname			)
          tokenizer.save_pretrained(self.tmpdirname			)

          UpperCamelCase__:					Optional[int]												=      {
              "feature_size": 24,
              "num_mel_bins": 24,
              "padding_value": 0.0,
              "sampling_rate": 1_6000,
              "return_attention_mask": False,
              "do_normalize": True,
          }
          save_json(__lowerCamelCase					,  save_dir / FEATURE_EXTRACTOR_NAME			)






      def 	UpperCAmelCase_   (						self:	List[Any]					,  **__lowerCamelCase:	int			):
          '''simple docstring'''


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






      def 	UpperCAmelCase_   (						self:	Dict					,  **__lowerCamelCase:	int			):
          '''simple docstring'''


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






      def 	UpperCAmelCase_   (						self:	Union[str, Any]			):
          '''simple docstring'''


          shutil.rmtree(self.tmpdirname			)






      def 	UpperCAmelCase_   (						self:	List[str]			):
          '''simple docstring'''


          UpperCamelCase__:					int												=      self.get_tokenizer()
          UpperCamelCase__:					Union[str, Any]												=      self.get_feature_extractor()

          UpperCamelCase__:					Union[str, Any]												=      SpeechaTextProcessor(tokenizer=__lowerCamelCase					,  feature_extractor=__lowerCamelCase			)

          processor.save_pretrained(self.tmpdirname			)
          UpperCamelCase__:					Tuple												=      SpeechaTextProcessor.from_pretrained(self.tmpdirname			)

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

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






      def 	UpperCAmelCase_   (						self:	str			):
          '''simple docstring'''


          UpperCamelCase__:					str												=      SpeechaTextProcessor(
              tokenizer=self.get_tokenizer()					,  feature_extractor=self.get_feature_extractor()			)
          processor.save_pretrained(self.tmpdirname			)

          UpperCamelCase__:					List[str]												=      self.get_tokenizer(bos_token="(BOS)"					,  eos_token="(EOS)"			)
          UpperCamelCase__:					Dict												=      self.get_feature_extractor(do_normalize=__lowerCamelCase					,  padding_value=1.0			)

          UpperCamelCase__:					List[str]												=      SpeechaTextProcessor.from_pretrained(
              self.tmpdirname					,  bos_token="(BOS)"					,  eos_token="(EOS)"					,  do_normalize=__lowerCamelCase					,  padding_value=1.0			)

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

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






      def 	UpperCAmelCase_   (						self:	Optional[Any]			):
          '''simple docstring'''


          UpperCamelCase__:					Union[str, Any]												=      self.get_feature_extractor()
          UpperCamelCase__:					List[Any]												=      self.get_tokenizer()

          UpperCamelCase__:					List[Any]												=      SpeechaTextProcessor(tokenizer=__lowerCamelCase					,  feature_extractor=__lowerCamelCase			)

          UpperCamelCase__:					Optional[Any]												=      floats_list((3, 1000)			)

          UpperCamelCase__:					Tuple												=      feature_extractor(__lowerCamelCase					,  return_tensors="np"			)
          UpperCamelCase__:					Optional[int]												=      processor(__lowerCamelCase					,  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:	Union[str, Any]			):
          '''simple docstring'''


          UpperCamelCase__:					int												=      self.get_feature_extractor()
          UpperCamelCase__:					List[Any]												=      self.get_tokenizer()

          UpperCamelCase__:					Optional[int]												=      SpeechaTextProcessor(tokenizer=__lowerCamelCase					,  feature_extractor=__lowerCamelCase			)

          UpperCamelCase__:					int												=      "This is a test string"

          UpperCamelCase__:					Tuple												=      processor(text=__lowerCamelCase			)

          UpperCamelCase__:					Optional[int]												=      tokenizer(__lowerCamelCase			)

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






      def 	UpperCAmelCase_   (						self:	List[str]			):
          '''simple docstring'''


          UpperCamelCase__:					Union[str, Any]												=      self.get_feature_extractor()
          UpperCamelCase__:					int												=      self.get_tokenizer()

          UpperCamelCase__:					str												=      SpeechaTextProcessor(tokenizer=__lowerCamelCase					,  feature_extractor=__lowerCamelCase			)

          UpperCamelCase__:					Union[str, Any]												=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          UpperCamelCase__:					str												=      processor.batch_decode(__lowerCamelCase			)
          UpperCamelCase__:					Optional[int]												=      tokenizer.batch_decode(__lowerCamelCase			)

          self.assertListEqual(__lowerCamelCase					,  __lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	Dict			):
          '''simple docstring'''


          UpperCamelCase__:					Any												=      self.get_feature_extractor()
          UpperCamelCase__:					Optional[Any]												=      self.get_tokenizer()

          UpperCamelCase__:					List[Any]												=      SpeechaTextProcessor(tokenizer=__lowerCamelCase					,  feature_extractor=__lowerCamelCase			)

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








from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
A__:  Tuple       					=					[[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 							lowerCAmelCase_				(    A_):
    return [int(A_) for c in F"{ruleset:08}"[:8]]



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



def 							lowerCAmelCase_				(    A_):
    UpperCamelCase__:					int												=      Image.new("RGB"  ,(len(cells[0]), len(A_)))
    UpperCamelCase__:					Dict												=      img.load()
    # Generates image
    for w in range(img.width):
        for h in range(img.height):
            UpperCamelCase__:					List[str]												=      2_55 - int(2_55 * cells[h][w])
            UpperCamelCase__:					Union[str, Any]												=      (color, color, color)
    return img


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

