





'''simple docstring'''

import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list


@require_torchaudio
@require_sentencepiece
class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''


    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			"""laion/clap-htsat-unfused"""
        _SCREAMING_SNAKE_CASE									=			tempfile.mkdtemp()


    def 			snake_case_(		self ,		**A       )		->   int:
        return RobertaTokenizer.from_pretrained(self.checkpoint ,		**A       )


    def 			snake_case_(		self ,		**A       )		->   Optional[int]:
        return ClapFeatureExtractor.from_pretrained(self.checkpoint ,		**A       )


    def 			snake_case_(		self       )		->   Dict:
        shutil.rmtree(self.tmpdirname       )


    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer()
        _SCREAMING_SNAKE_CASE									=			self.get_feature_extractor()

        _SCREAMING_SNAKE_CASE									=			ClapProcessor(tokenizer=A ,		feature_extractor=A       )

        processor.save_pretrained(self.tmpdirname       )
        _SCREAMING_SNAKE_CASE									=			ClapProcessor.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 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			ClapProcessor(tokenizer=self.get_tokenizer() ,		feature_extractor=self.get_feature_extractor()       )
        processor.save_pretrained(self.tmpdirname       )

        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer(bos_token="""(BOS)""" ,		eos_token="""(EOS)"""       )
        _SCREAMING_SNAKE_CASE									=			self.get_feature_extractor(do_normalize=A ,		padding_value=1.0       )

        _SCREAMING_SNAKE_CASE									=			ClapProcessor.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 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			self.get_feature_extractor()
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer()

        _SCREAMING_SNAKE_CASE									=			ClapProcessor(tokenizer=A ,		feature_extractor=A       )

        _SCREAMING_SNAKE_CASE									=			floats_list((3, 1000)       )

        _SCREAMING_SNAKE_CASE									=			feature_extractor(A ,		return_tensors="""np"""       )
        _SCREAMING_SNAKE_CASE									=			processor(audios=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 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self.get_feature_extractor()
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer()

        _SCREAMING_SNAKE_CASE									=			ClapProcessor(tokenizer=A ,		feature_extractor=A       )

        _SCREAMING_SNAKE_CASE									=			"""This is a test string"""

        _SCREAMING_SNAKE_CASE									=			processor(text=A       )

        _SCREAMING_SNAKE_CASE									=			tokenizer(A       )

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


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			self.get_feature_extractor()
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer()

        _SCREAMING_SNAKE_CASE									=			ClapProcessor(tokenizer=A ,		feature_extractor=A       )

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

        _SCREAMING_SNAKE_CASE									=			processor.batch_decode(A       )
        _SCREAMING_SNAKE_CASE									=			tokenizer.batch_decode(A       )

        self.assertListEqual(A ,		A       )





    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			self.get_feature_extractor()
        _SCREAMING_SNAKE_CASE									=			self.get_tokenizer()

        _SCREAMING_SNAKE_CASE									=			ClapProcessor(tokenizer=A ,		feature_extractor=A       )

        self.assertListEqual(
            processor.model_input_names[2:] ,		feature_extractor.model_input_names ,		msg="""`processor` and `feature_extractor` model input names do not match""" ,		)










'''simple docstring'''

import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

lowercase_					= """Usage of script: script_name <size_of_canvas:int>"""

lowercase_					= [0] * 100 + [1] * 10
random.shuffle(choice)





def lowerCamelCase						(     __lowerCamelCase :    int    )						->list[list[bool]]:
    _SCREAMING_SNAKE_CASE									=			[[False for i in range(__lowerCamelCase    )] for j in range(__lowerCamelCase    )]
    return canvas





def lowerCamelCase						(     __lowerCamelCase :    list[list[bool]]    )						->None:
    for i, row in enumerate(__lowerCamelCase    ):
        for j, _ in enumerate(__lowerCamelCase    ):
            _SCREAMING_SNAKE_CASE									=			bool(random.getrandbits(1    )    )





def lowerCamelCase						(     __lowerCamelCase :    list[list[bool]]    )						->list[list[bool]]:
    _SCREAMING_SNAKE_CASE									=			np.array(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			np.array(create_canvas(current_canvas.shape[0]    )    )
    for r, row in enumerate(__lowerCamelCase    ):
        for c, pt in enumerate(__lowerCamelCase    ):
            _SCREAMING_SNAKE_CASE									=			__judge_point(
                __lowerCamelCase					,       current_canvas[r - 1 : r + 2, c - 1 : c + 2]    )

    _SCREAMING_SNAKE_CASE									=			next_gen_canvas
    del next_gen_canvas  # cleaning memory as we move on.
    _SCREAMING_SNAKE_CASE									=			current_canvas.tolist()
    return return_canvas





def lowerCamelCase						(     __lowerCamelCase :    bool					,       __lowerCamelCase :    list[list[bool]]    )						->bool:
    _SCREAMING_SNAKE_CASE									=			0
    _SCREAMING_SNAKE_CASE									=			0
    # finding dead or alive neighbours count.
    for i in neighbours:
        for status in i:
            if status:
                alive += 1
            else:
                dead += 1

    # handling duplicate entry for focus pt.
    if pt:
        alive -= 1
    else:
        dead -= 1

    # running the rules of game here.
    _SCREAMING_SNAKE_CASE									=			pt
    if pt:
        if alive < 2:
            _SCREAMING_SNAKE_CASE									=			False
        elif alive == 2 or alive == 3:
            _SCREAMING_SNAKE_CASE									=			True
        elif alive > 3:
            _SCREAMING_SNAKE_CASE									=			False
    else:
        if alive == 3:
            _SCREAMING_SNAKE_CASE									=			True

    return state


if __name__ == "__main__":
      if len(sys.argv) != 2:
            raise Exception(usage_doc)

      lowercase_					= int(sys.argv[1])
      # main working structure of this module.
      lowercase_					= create_canvas(canvas_size)
      seed(c)
      lowercase_				,			lowercase_					= plt.subplots()
      fig.show()
      lowercase_					= ListedColormap(["""w""", """k"""])
      try:
            while True:
                  lowercase_					= run(c)
                  ax.matshow(c, cmap=cmap)
                  fig.canvas.draw()
                  ax.cla()
      except KeyboardInterrupt:
            # do nothing.
            pass



