

'''simple docstring'''







import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


__lowerCamelCase										=			get_logger(__name__)


class     A__					(					enum.Enum	):
    lowercase		    =						"all_checks"
    lowercase		    =						"basic_checks"
    lowercase		    =						"no_checks"


class     A__					(					_snake_case	):
    pass


class     A__					(					_snake_case	):
    pass


class     A__					(					_snake_case	):
    pass


class     A__					(					_snake_case	):
    pass

def 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__=None )	->	Tuple:



       if expected_checksums is None:
              logger.info("""Unable to verify checksums.""" )
              return
       if len(set(_UpperCamelCase ) - set(_UpperCamelCase ) ) > 0:
              raise ExpectedMoreDownloadedFiles(str(set(_UpperCamelCase ) - set(_UpperCamelCase ) ) )
       if len(set(_UpperCamelCase ) - set(_UpperCamelCase ) ) > 0:
              raise UnexpectedDownloadedFile(str(set(_UpperCamelCase ) - set(_UpperCamelCase ) ) )
       A_		    =					[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
       A_		    =					""" for """ + verification_name if verification_name is not None else """"""
       if len(_UpperCamelCase ) > 0:
              raise NonMatchingChecksumError(
                  F'''Checksums didn\'t match{for_verification_name}:\n'''
                  F'''{bad_urls}\n'''
                  """Set `verification_mode='no_checks'` to skip checksums verification and ignore this error""" )
       logger.info("""All the checksums matched successfully""" + for_verification_name )


class     A__					(					_snake_case	):
    pass


class     A__					(					_snake_case	):
    pass


class     A__					(					_snake_case	):
    pass


class     A__					(					_snake_case	):
    pass

def 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__ )	->	Dict:



       if expected_splits is None:
              logger.info("""Unable to verify splits sizes.""" )
              return
       if len(set(_UpperCamelCase ) - set(_UpperCamelCase ) ) > 0:
              raise ExpectedMoreSplits(str(set(_UpperCamelCase ) - set(_UpperCamelCase ) ) )
       if len(set(_UpperCamelCase ) - set(_UpperCamelCase ) ) > 0:
              raise UnexpectedSplits(str(set(_UpperCamelCase ) - set(_UpperCamelCase ) ) )
       A_		    =					[
           {"""expected""": expected_splits[name], """recorded""": recorded_splits[name]}
           for name in expected_splits
           if expected_splits[name].num_examples != recorded_splits[name].num_examples
       ]
       if len(_UpperCamelCase ) > 0:
              raise NonMatchingSplitsSizesError(str(_UpperCamelCase ) )
       logger.info("""All the splits matched successfully.""" )
def 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__ = True )	->	dict:



       if record_checksum:
              A_		    =					shaaaa()
              with open(_UpperCamelCase,				"""rb""" ) as f:
                     for chunk in iter(lambda: f.read(1 << 20 ),				b"""""" ):
                            m.update(_UpperCamelCase )
                     A_		    =					m.hexdigest()
       else:
              A_		    =					None
       return {"num_bytes": os.path.getsize(_UpperCamelCase ), "checksum": checksum}







def 					UpperCAmelCase__			(					UpperCAmelCase__ )	->	Dict:



       if dataset_size and config.IN_MEMORY_MAX_SIZE:
              return dataset_size < config.IN_MEMORY_MAX_SIZE
       else:
              return False




"""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      lowerCamelCase__   (     unittest.TestCase				):







					"""simple docstring"""


					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					"""ylacombe/bark-small"""
												__UpperCAmelCase   :						List[Any]      							=					tempfile.mkdtemp()
												__UpperCAmelCase   :						Optional[Any]      							=					"""en_speaker_1"""
												__UpperCAmelCase   :						Union[str, Any]      							=					"""This is a test string"""
												__UpperCAmelCase   :						Dict      							=					"""speaker_embeddings_path.json"""
												__UpperCAmelCase   :						Any      							=					"""speaker_embeddings"""






					def lowerCamelCase__							(       self    : Dict ,       **UpperCamelCase    : Union[str, Any]  ):
												'''simple docstring'''






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






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												shutil.rmtree(self.tmpdirname  )






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					self.get_tokenizer()

												__UpperCAmelCase   :						Any      							=					BarkProcessor(tokenizer=UpperCamelCase  )

												processor.save_pretrained(self.tmpdirname  )
												__UpperCAmelCase   :						Union[str, Any]      							=					BarkProcessor.from_pretrained(self.tmpdirname  )

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






					@slow
					def lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					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   :						Tuple      							=					self.get_tokenizer(bos_token="""(BOS)""" ,       eos_token="""(EOS)"""  )

												__UpperCAmelCase   :						Any      							=					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    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					BarkProcessor.from_pretrained(
												    pretrained_processor_name_or_path=self.checkpoint ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,       )

												__UpperCAmelCase   :						List[str]      							=					35
												__UpperCAmelCase   :						Tuple      							=					2
												__UpperCAmelCase   :						Union[str, Any]      							=					8

												__UpperCAmelCase   :						Optional[Any]      							=					{
												    """semantic_prompt""": np.ones(UpperCamelCase  ),
												    """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=UpperCamelCase  )

												__UpperCAmelCase   :						Union[str, Any]      							=					inputs["""history_prompt"""]
												for key in voice_preset:
																			self.assertListEqual(voice_preset[key].tolist() ,       processed_voice_preset.get(UpperCamelCase ,       np.array([]  )  ).tolist()  )

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

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

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






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					self.get_tokenizer()

												__UpperCAmelCase   :						Union[str, Any]      							=					BarkProcessor(tokenizer=UpperCamelCase  )

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

												__UpperCAmelCase   :						Tuple      							=					tokenizer(
												    self.input_string ,       padding="""max_length""" ,       max_length=256 ,       add_special_tokens=UpperCamelCase ,       return_attention_mask=UpperCamelCase ,       return_token_type_ids=UpperCamelCase ,       )

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