

'''simple docstring'''





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 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				def  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							Optional[Any]    		="""ylacombe/bark-small"""
										lowercase :							Any    		=tempfile.mkdtemp()
										lowercase :							Optional[int]    		="""en_speaker_1"""
										lowercase :							List[Any]    		="""This is a test string"""
										lowercase :							int    		="""speaker_embeddings_path.json"""
										lowercase :							List[str]    		="""speaker_embeddings"""







				def  lowerCamelCase_	(       self     :		Union[str, Any]		,					**UpperCAmelCase__     :		Any   ):
										'''simple docstring'''
										return AutoTokenizer.from_pretrained(self.checkpoint		,					**__A   )







				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										shutil.rmtree(self.tmpdirname   )







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							str    		=self.get_tokenizer()

										lowercase :							Optional[Any]    		=BarkProcessor(tokenizer=__A   )

										processor.save_pretrained(self.tmpdirname   )
										lowercase :							List[str]    		=BarkProcessor.from_pretrained(self.tmpdirname   )

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







				@slow
				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							Optional[int]    		=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 :							str    		=self.get_tokenizer(bos_token='''(BOS)'''		,					eos_token='''(EOS)'''   )

										lowercase :							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  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										lowercase :							List[str]    		=BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint		,					speaker_embeddings_dict_path=self.speaker_embeddings_dict_path		,					)

										lowercase :							int    		=35
										lowercase :							Union[str, Any]    		=2
										lowercase :							Optional[Any]    		=8

										lowercase :							List[str]    		={
										    """semantic_prompt""": np.ones(__A   ),
										    """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 :							Union[str, Any]    		=processor(text=self.input_string		,					voice_preset=__A   )

										lowercase :							List[str]    		=inputs["""history_prompt"""]
										for key in voice_preset:
																self.assertListEqual(voice_preset[key].tolist()		,					processed_voice_preset.get(__A		,					np.array([]   )   ).tolist()   )

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

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

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







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										lowercase :							str    		=self.get_tokenizer()

										lowercase :							str    		=BarkProcessor(tokenizer=__A   )

										lowercase :							int    		=processor(text=self.input_string   )

										lowercase :							int    		=tokenizer(
										    self.input_string		,					padding='''max_length'''		,					max_length=256		,					add_special_tokens=__A		,					return_attention_mask=__A		,					return_token_type_ids=__A		,					)

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








UpperCAmelCase_							:					dict[str, float]		 =   {
    "joule": 1.0,
    "kilojoule": 1_000,
    "megajoule": 1_000_000,
    "gigajoule": 1_000_000_000,
    "wattsecond": 1.0,
    "watthour": 3_600,
    "kilowatthour": 3_600_000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_186.8,
    "kilocalorie_nutr": 4_186_800.00,
    "electronvolt": 1.6_0217_6634e-19,
    "britishthermalunit_it": 1_055.05_585,
    "footpound": 1.35_5818,
}
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str			,a__    :   float       )					->				float:
	if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
		__A :			Optional[int]		    =			(
		    f"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
		    f"""Valid values are: {", ".join(a__       )}"""
		)
		raise ValueError(a__       )
	return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





