






import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow




@require_torch
class 							_lowerCAmelCase(			unittest.TestCase   ):





       """simple docstring"""







       def      _a       (   self				):
         UpperCamelCase_:   Union[str, Any]        =							'ylacombe/bark-small'
         UpperCamelCase_:   Optional[int]        =							tempfile.mkdtemp()
         UpperCamelCase_:   Dict        =							'en_speaker_1'
         UpperCamelCase_:   List[Any]        =							'This is a test string'
         UpperCamelCase_:   Tuple        =							'speaker_embeddings_path.json'
         UpperCamelCase_:   Tuple        =							'speaker_embeddings'





       def      _a       (   self    ,     **_lowerCamelCase				):
         return AutoTokenizer.from_pretrained(self.checkpoint    ,     **_lowerCamelCase				)





       def      _a       (   self				):
         shutil.rmtree(self.tmpdirname				)





       def      _a       (   self				):
         UpperCamelCase_:   int        =							self.get_tokenizer()

         UpperCamelCase_:   Dict        =							BarkProcessor(tokenizer=_lowerCamelCase				)

         processor.save_pretrained(self.tmpdirname				)
         UpperCamelCase_:   Optional[Any]        =							BarkProcessor.from_pretrained(self.tmpdirname				)

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





       @slow
       def      _a       (   self				):
         UpperCamelCase_:   Dict        =							BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint    ,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,     )
         processor.save_pretrained(
             self.tmpdirname    ,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,     speaker_embeddings_directory=self.speaker_embeddings_directory    ,     )

         UpperCamelCase_:   Optional[int]        =							self.get_tokenizer(bos_token='(BOS)'    ,     eos_token='(EOS)'				)

         UpperCamelCase_:   Optional[int]        =							BarkProcessor.from_pretrained(
             self.tmpdirname    ,     self.speaker_embeddings_dict_path    ,     bos_token='(BOS)'    ,     eos_token='(EOS)'    ,     )

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





       def      _a       (   self				):
         UpperCamelCase_:   Union[str, Any]        =							BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint    ,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,     )

         UpperCamelCase_:   int        =							3_5
         UpperCamelCase_:   Optional[int]        =							2
         UpperCamelCase_:   int        =							8

         UpperCamelCase_:   Union[str, Any]        =							{
             'semantic_prompt': np.ones(_lowerCamelCase				),
             'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)				),
             'fine_prompt': np.ones((nb_codebooks_total, seq_len)				),
         }

         # test providing already loaded voice_preset
         UpperCamelCase_:   Dict        =							processor(text=self.input_string    ,     voice_preset=_lowerCamelCase				)

         UpperCamelCase_:   str        =							inputs['history_prompt']
         for key in voice_preset:
           self.assertListEqual(voice_preset[key].tolist()    ,     processed_voice_preset.get(_lowerCamelCase    ,     np.array([]				)				).tolist()				)

         # test loading voice preset from npz file
         UpperCamelCase_:   Tuple        =							os.path.join(self.tmpdirname    ,     'file.npz'				)
         np.savez(_lowerCamelCase    ,     **_lowerCamelCase				)
         UpperCamelCase_:   List[Any]        =							processor(text=self.input_string    ,     voice_preset=_lowerCamelCase				)
         UpperCamelCase_:   Tuple        =							inputs['history_prompt']

         for key in voice_preset:
           self.assertListEqual(voice_preset[key].tolist()    ,     processed_voice_preset.get(_lowerCamelCase    ,     np.array([]				)				).tolist()				)

         # test loading voice preset from the hub
         UpperCamelCase_:   int        =							processor(text=self.input_string    ,     voice_preset=self.voice_preset				)





       def      _a       (   self				):
         UpperCamelCase_:   Tuple        =							self.get_tokenizer()

         UpperCamelCase_:   str        =							BarkProcessor(tokenizer=_lowerCamelCase				)

         UpperCamelCase_:   List[str]        =							processor(text=self.input_string				)

         UpperCamelCase_:   List[Any]        =							tokenizer(
             self.input_string    ,     padding='max_length'    ,     max_length=2_5_6    ,     add_special_tokens=_lowerCamelCase    ,     return_attention_mask=_lowerCamelCase    ,     return_token_type_ids=_lowerCamelCase    ,     )

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


UpperCAmelCase_							:					dict[tuple[int, int, int], int]		 =   {}
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   int			,a__    :   int       )					->				int:

	# if we are absent twice, or late 3 consecutive days,
	# no further prize strings are possible
	if late == 3 or absent == 2:
		return 0

	# if we have no days left, and have not failed any other rules,
	# we have a prize string
	if days == 0:
		return 1

	# No easy solution, so now we need to do the recursive calculation

	# First, check if the combination is already in the cache, and
	# if yes, return the stored value from there since we already
	# know the number of possible prize strings from this point on
	__A :			List[Any]		    =			(days, absent, late)
	if key in cache:
		return cache[key]

	# now we calculate the three possible ways that can unfold from
	# this point on, depending on our attendance today

	# 1) if we are late (but not absent), the "absent" counter stays as
	# it is, but the "late" counter increases by one
	__A :			Dict		    =			_calculate(days - 1			,a__			,late + 1       )

	# 2) if we are absent, the "absent" counter increases by 1, and the
	# "late" counter resets to 0
	__A :			List[str]		    =			_calculate(days - 1			,absent + 1			,0       )

	# 3) if we are on time, this resets the "late" counter and keeps the
	# absent counter
	__A :			int		    =			_calculate(days - 1			,a__			,0       )

	__A :			Optional[int]		    =			state_late + state_absent + state_ontime

	__A :			Tuple		    =			prizestrings
	return prizestrings
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int = 30       )					->				int:
	return _calculate(a__			,absent=0			,late=0       )


if __name__ == "__main__":
			print(solution())





