





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 		snake_case_   (							unittest.TestCase						):







	'''simple docstring'''




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

			'''simple docstring'''




			__lowercase											=     'ylacombe/bark-small'
			__lowercase											=     tempfile.mkdtemp()
			__lowercase											=     'en_speaker_1'
			__lowercase											=     'This is a test string'
			__lowercase											=     'speaker_embeddings_path.json'
			__lowercase											=     'speaker_embeddings'




	def    UpperCAmelCase			(						self							:     Tuple					,      **__lowerCamelCase							:     Any   )       -> Union[str, Any]:

			'''simple docstring'''




			return AutoTokenizer.from_pretrained(self.checkpoint					,      **__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[Any]   )       -> Tuple:

			'''simple docstring'''




			shutil.rmtree(self.tmpdirname   )




	def    UpperCAmelCase			(						self							:     List[str]   )       -> Any:

			'''simple docstring'''




			__lowercase											=     self.get_tokenizer()

			__lowercase											=     BarkProcessor(tokenizer=__lowerCamelCase   )

			processor.save_pretrained(self.tmpdirname   )
			__lowercase											=     BarkProcessor.from_pretrained(self.tmpdirname   )

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




	@slow
	def    UpperCAmelCase			(						self							:     Optional[Any]   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     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					,      )

			__lowercase											=     self.get_tokenizer(bos_token='(BOS)'					,      eos_token='(EOS)'   )

			__lowercase											=     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    UpperCAmelCase			(						self							:     str   )       -> str:

			'''simple docstring'''




			__lowercase											=     BarkProcessor.from_pretrained(
			    pretrained_processor_name_or_path=self.checkpoint					,      speaker_embeddings_dict_path=self.speaker_embeddings_dict_path					,      )

			__lowercase											=     35
			__lowercase											=     2
			__lowercase											=     8

			__lowercase											=     {
			    '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
			__lowercase											=     processor(text=self.input_string					,      voice_preset=__lowerCamelCase   )

			__lowercase											=     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
			__lowercase											=     os.path.join(self.tmpdirname					,      'file.npz'   )
			np.savez(__lowerCamelCase					,      **__lowerCamelCase   )
			__lowercase											=     processor(text=self.input_string					,      voice_preset=__lowerCamelCase   )
			__lowercase											=     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
			__lowercase											=     processor(text=self.input_string					,      voice_preset=self.voice_preset   )






	def    UpperCAmelCase			(						self							:     Dict   )       -> Any:

			'''simple docstring'''




			__lowercase											=     self.get_tokenizer()

			__lowercase											=     BarkProcessor(tokenizer=__lowerCamelCase   )

			__lowercase											=     processor(text=self.input_string   )

			__lowercase											=     tokenizer(
			    self.input_string					,      padding='max_length'					,      max_length=256					,      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()   )











from __future__ import annotations



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    list[tuple[int, int]]:
		__lowercase						,      __lowercase											=     position
		__lowercase											=     [
		    (y + 1, x + 2),
		    (y - 1, x + 2),
		    (y + 1, x - 2),
		    (y - 1, x - 2),
		    (y + 2, x + 1),
		    (y + 2, x - 1),
		    (y - 2, x + 1),
		    (y - 2, x - 1),
		]
		__lowercase											=     []

		for position in positions:
				__lowercase						,      __lowercase											=     position
				if 0 <= y_test < n and 0 <= x_test < n:
						permissible_positions.append(snake_case							)

		return permissible_positions



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    bool:
		return not any(elem == 0 for row in board for elem in row							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    bool:

		if is_complete(snake_case							):
				return True

		for position in get_valid_pos(snake_case						,   len(snake_case							)							):
				__lowercase						,      __lowercase											=     position

				if board[y][x] == 0:
						__lowercase											=     curr + 1
						if open_knight_tour_helper(snake_case						,   snake_case						,   curr + 1							):
								return True
						__lowercase											=     0

		return False



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    list[list[int]]:
		__lowercase											=     [[0 for i in range(snake_case							)] for j in range(snake_case							)]

		for i in range(snake_case							):
				for j in range(snake_case							):
						__lowercase											=     1
						if open_knight_tour_helper(snake_case						,   (i, j)						,   1							):
								return board
						__lowercase											=     0

		__lowercase											=     F"Open Kight Tour cannot be performed on a board of size {n}"
		raise ValueError(snake_case							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




