



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 		_lowercase		(       unittest.TestCase	):

			def 		A							(      self			:       str							)       ->				Optional[int]:


				"""simple docstring"""






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

			def 		A							(      self			:       Any							,   **__lowerCAmelCase			:       Dict							)       ->				Union[str, Any]:


				"""simple docstring"""






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

			def 		A							(      self			:       int							)       ->				Dict:


				"""simple docstring"""






				shutil.rmtree(self.tmpdirname							)

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


				"""simple docstring"""






				a						=		self.get_tokenizer()

				a						=		BarkProcessor(tokenizer=__lowerCAmelCase							)

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

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

			@slow
			def 		A							(      self			:       List[str]							)       ->				Dict:


				"""simple docstring"""






				a						=		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							,   )

				a						=		self.get_tokenizer(bos_token="(BOS)"							,   eos_token="(EOS)"							)

				a						=		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			:       Optional[Any]							)       ->				Dict:


				"""simple docstring"""






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

				a						=		35
				a						=		2
				a						=		8

				a						=		{
				    "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
				a						=		processor(text=self.input_string							,   voice_preset=__lowerCAmelCase							)

				a						=		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
				a						=		os.path.join(self.tmpdirname							,   "file.npz"							)
				np.savez(__lowerCAmelCase							,   **__lowerCAmelCase							)
				a						=		processor(text=self.input_string							,   voice_preset=__lowerCAmelCase							)
				a						=		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
				a						=		processor(text=self.input_string							,   voice_preset=self.voice_preset							)




			def 		A							(      self			:       Optional[Any]							)       ->				List[Any]:


				"""simple docstring"""






				a						=		self.get_tokenizer()

				a						=		BarkProcessor(tokenizer=__lowerCAmelCase							)

				a						=		processor(text=self.input_string							)

				a						=		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()							)






A_		:     Any        =     [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_		:     Tuple        =     [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_		:     Optional[int]        =     {
    0: '''Sunday''',
    1: '''Monday''',
    2: '''Tuesday''',
    3: '''Wednesday''',
    4: '''Thursday''',
    5: '''Friday''',
    6: '''Saturday''',
}


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int ,					UpperCAmelCase__ :int ,					UpperCAmelCase__ :int					):






 '''simple docstring'''

 assert len(str(UpperCAmelCase__					)					) > 2, "year should be in YYYY format"
 assert 1 <= month <= 12, "month should be between 1 to 12"
 assert 1 <= day <= 31, "day should be between 1 to 31"

 # Doomsday algorithm:
 a						=		year // 1_00
 a						=		(5 * (century % 4) + 2) % 7
 a						=		year % 1_00
 a						=		centurian % 12
 a						=		(
     (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
 ) % 7
 a						=		(
     DOOMSDAY_NOT_LEAP[month - 1]
     if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
     else DOOMSDAY_LEAP[month - 1]
 )
 a						=		(dooms_day + day - day_anchor) % 7
 return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

