






"""simple docstring"""


import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


_UpperCamelCase				:    Optional[Any]     =						logging.get_logger(__name__)






class 						snake_case  (  UpperCAmelCase       ):
    __magic_name__         =     '''semantic'''





    def __init__(							self	:					Dict				,		A	:					Optional[Any]=1_0_0_0_0				,		A	:					Optional[int]=True				,		A	:					str=7_6_8				,		A	:					List[str]=False				,		A	:					Optional[int]=False				,		A	:					Dict=False				,		A	:					List[str]=False				,		A	:					Tuple=0.7				,		A	:					Optional[int]=True				,		A	:					Optional[Any]=1_0_0_4_8				,		A	:					Union[str, Any]=1_2_9_5_9_5				,		A	:					Optional[Any]=1_2_9_5_9_9				,		A	:					Dict=1_0_0_0_0				,		A	:					str=2_5_6				,		A	:					Union[str, Any]=49.9				,		**A	:					Optional[Any]				,		):


      '''simple docstring'''




      super().__init__(
          temperature=A				,		do_sample=A				,		eos_token_id=A				,		renormalize_logits=A				,		max_new_tokens=A				,		output_scores=A				,		return_dict_in_generate=A				,		output_hidden_states=A				,		output_attentions=A				,		**A				,		)

      a     :			int				=   text_encoding_offset
      a     :			Optional[int]				=   text_pad_token
      a     :			Tuple				=   eos_token_id
      a     :			Tuple				=   semantic_infer_token
      a     :			int				=   semantic_vocab_size
      a     :			Optional[int]				=   max_input_semantic_length
      a     :			str				=   semantic_rate_hz






class 						snake_case  (  UpperCAmelCase       ):
    __magic_name__         =     '''coarse_acoustics'''





    def __init__(							self	:					Optional[Any]				,		A	:					Dict=True				,		A	:					List[str]=False				,		A	:					int=False				,		A	:					Optional[Any]=False				,		A	:					List[str]=False				,		A	:					List[Any]=0.7				,		A	:					List[str]=True				,		A	:					int=1_2_0_4_8				,		A	:					Dict=7_5				,		A	:					Union[str, Any]=2				,		A	:					Dict=1_2_0_5_0				,		A	:					Union[str, Any]=2_5_6				,		A	:					int = 6_3_0				,		A	:					int = 6_0				,		**A	:					Optional[int]				,		):


      '''simple docstring'''




      super().__init__(
          temperature=A				,		do_sample=A				,		renormalize_logits=A				,		output_scores=A				,		return_dict_in_generate=A				,		output_hidden_states=A				,		output_attentions=A				,		**A				,		)

      a     :			Optional[int]				=   coarse_semantic_pad_token
      a     :			Dict				=   coarse_rate_hz
      a     :			int				=   n_coarse_codebooks
      a     :			Any				=   coarse_infer_token
      a     :			Tuple				=   max_coarse_input_length
      a     :			int				=   max_coarse_history
      a     :			Optional[int]				=   sliding_window_len






class 						snake_case  (  UpperCAmelCase       ):
    __magic_name__         =     '''fine_acoustics'''





    def __init__(							self	:					int				,		A	:					Tuple=0.5				,		A	:					List[Any]=5_1_2				,		A	:					str=1_0_2_4				,		A	:					Union[str, Any]=8				,		**A	:					List[Any]				,		):


      '''simple docstring'''




      super().__init__(temperature=A			)

      a     :			str				=   max_fine_history_length
      a     :			List[str]				=   max_fine_input_length
      a     :			str				=   n_fine_codebooks






class 						snake_case  (  UpperCAmelCase       ):
    __magic_name__         =     '''bark'''
    __magic_name__         =     True





    def __init__(							self	:					Optional[Any]				,		A	:					Dict = None				,		A	:					Dict = None				,		A	:					Dict = None				,		A	:					Union[str, Any]=2_4_0_0_0				,		A	:					Tuple=1_0_2_4				,		**A	:					Any				,		):


      '''simple docstring'''




      if semantic_config is None:
        a     :			Tuple				=   {}
        logger.info('semantic_config is None. initializing the semantic model with default values.'			)

      if coarse_acoustics_config is None:
        a     :			Union[str, Any]				=   {}
        logger.info('coarse_acoustics_config is None. initializing the coarse model with default values.'			)

      if fine_acoustics_config is None:
        a     :			str				=   {}
        logger.info('fine_acoustics_config is None. initializing the fine model with default values.'			)

      a     :			str				=   BarkSemanticGenerationConfig(**A			)
      a     :			str				=   BarkCoarseGenerationConfig(**A			)
      a     :			str				=   BarkFineGenerationConfig(**A			)

      a     :			Union[str, Any]				=   sample_rate
      a     :			str				=   codebook_size





    @classmethod
    def 				lowerCamelCase__      (							cls	:					str				,		A	:					BarkSemanticGenerationConfig				,		A	:					BarkCoarseGenerationConfig				,		A	:					BarkFineGenerationConfig				,		**A	:					Any				,		):


      '''simple docstring'''




      return cls(
          semantic_config=semantic_config.to_dict()				,		coarse_acoustics_config=coarse_acoustics_config.to_dict()				,		fine_acoustics_config=fine_acoustics_config.to_dict()				,		**A				,		)





    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      a     :			List[Any]				=   copy.deepcopy(self.__dict__			)

      a     :			List[Any]				=   self.semantic_config.to_dict()
      a     :			List[str]				=   self.coarse_acoustics_config.to_dict()
      a     :			Optional[Any]				=   self.fine_acoustics_config.to_dict()

      a     :			Optional[int]				=   self.__class__.model_type
      return output












"""simple docstring"""


import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def 			snake_case  (A_      :Dict	,			A_      :Tuple      ):







  '''simple docstring'''

  assert isinstance(A_	,			A_      )
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize('keep_in_memory'	,			[False, True]      )
def 			snake_case  (A_      :str	,			A_      :List[Any]	,			A_      :Optional[Any]      ):







  '''simple docstring'''

  a     :			List[Any]				=   tmp_path / 'cache'
  a     :			str				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    a     :			Union[str, Any]				=   CsvDatasetReader(A_	,			cache_dir=A_	,			keep_in_memory=A_      ).read()
  _check_csv_dataset(A_	,			A_      )




@pytest.mark.parametrize(
    'features'	,			[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]	,			)
def 			snake_case  (A_      :List[str]	,			A_      :Union[str, Any]	,			A_      :Union[str, Any]      ):







  '''simple docstring'''

  a     :			str				=   tmp_path / 'cache'
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  a     :			Optional[int]				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  a     :			Dict				=   features.copy() if features else default_expected_features
  a     :			Dict				=   (
      Features({feature: Value(A_      ) for feature, dtype in features.items()}      ) if features is not None else None
  )
  a     :			Tuple				=   CsvDatasetReader(A_	,			features=A_	,			cache_dir=A_      ).read()
  _check_csv_dataset(A_	,			A_      )




@pytest.mark.parametrize('split'	,			[None, NamedSplit('train'      ), 'train', 'test']      )
def 			snake_case  (A_      :List[str]	,			A_      :str	,			A_      :int      ):







  '''simple docstring'''

  a     :			str				=   tmp_path / 'cache'
  a     :			Tuple				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  a     :			List[str]				=   CsvDatasetReader(A_	,			cache_dir=A_	,			split=A_      ).read()
  _check_csv_dataset(A_	,			A_      )
  assert dataset.split == split if split else "train"




@pytest.mark.parametrize('path_type'	,			[str, list]      )
def 			snake_case  (A_      :int	,			A_      :List[Any]	,			A_      :str      ):







  '''simple docstring'''

  if issubclass(A_	,			A_      ):
    a     :			Dict				=   csv_path
  elif issubclass(A_	,			A_      ):
    a     :			Optional[Any]				=   [csv_path]
  a     :			Tuple				=   tmp_path / 'cache'
  a     :			str				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  a     :			Any				=   CsvDatasetReader(A_	,			cache_dir=A_      ).read()
  _check_csv_dataset(A_	,			A_      )




def 			snake_case  (A_      :List[str]	,			A_      :Dict	,			A_      :Optional[Any]=("train",)      ):







  '''simple docstring'''

  assert isinstance(A_	,			A_      )
  for split in splits:
    a     :			List[Any]				=   dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize('keep_in_memory'	,			[False, True]      )
def 			snake_case  (A_      :str	,			A_      :Any	,			A_      :List[str]      ):







  '''simple docstring'''

  a     :			Any				=   tmp_path / 'cache'
  a     :			int				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    a     :			str				=   CsvDatasetReader({'train': csv_path}	,			cache_dir=A_	,			keep_in_memory=A_      ).read()
  _check_csv_datasetdict(A_	,			A_      )




@pytest.mark.parametrize(
    'features'	,			[
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]	,			)
def 			snake_case  (A_      :Optional[int]	,			A_      :List[Any]	,			A_      :Tuple      ):







  '''simple docstring'''

  a     :			Dict				=   tmp_path / 'cache'
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  a     :			Any				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  a     :			Tuple				=   features.copy() if features else default_expected_features
  a     :			Optional[int]				=   (
      Features({feature: Value(A_      ) for feature, dtype in features.items()}      ) if features is not None else None
  )
  a     :			Any				=   CsvDatasetReader({'train': csv_path}	,			features=A_	,			cache_dir=A_      ).read()
  _check_csv_datasetdict(A_	,			A_      )




@pytest.mark.parametrize('split'	,			[None, NamedSplit('train'      ), 'train', 'test']      )
def 			snake_case  (A_      :List[str]	,			A_      :Optional[Any]	,			A_      :str      ):







  '''simple docstring'''

  if split:
    a     :			List[Any]				=   {split: csv_path}
  else:
    a     :			str				=   {'train': csv_path, 'test': csv_path}
  a     :			Union[str, Any]				=   tmp_path / 'cache'
  a     :			Tuple				=   {'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
  a     :			List[str]				=   CsvDatasetReader(A_	,			cache_dir=A_      ).read()
  _check_csv_datasetdict(A_	,			A_	,			splits=list(path.keys()      )      )
  assert all(dataset[split].split == split for split in path.keys()      )




def 			snake_case  (A_      :Dict      ):







  '''simple docstring'''

  with open(A_	,			encoding='utf-8'      ) as csvfile:
    yield from csv.reader(A_      )




def 			snake_case  (A_      :int	,			A_      :Optional[int]      ):







  '''simple docstring'''

  a     :			str				=   tmp_path / 'cache'
  a     :			List[str]				=   os.path.join(A_	,			'tmp.csv'      )
  a     :			Union[str, Any]				=   CsvDatasetReader({'train': csv_path}	,			cache_dir=A_      ).read()
  CsvDatasetWriter(dataset['train']	,			A_	,			num_proc=1      ).write()

  a     :			int				=   iter_csv_file(A_      )
  a     :			Optional[Any]				=   iter_csv_file(A_      )

  for rowa, rowa in zip(A_	,			A_      ):
    assert rowa == rowa




def 			snake_case  (A_      :Tuple	,			A_      :Tuple      ):







  '''simple docstring'''

  a     :			Union[str, Any]				=   tmp_path / 'cache'
  a     :			List[Any]				=   os.path.join(A_	,			'tmp.csv'      )
  a     :			Union[str, Any]				=   CsvDatasetReader({'train': csv_path}	,			cache_dir=A_      ).read()
  CsvDatasetWriter(dataset['train']	,			A_	,			num_proc=2      ).write()

  a     :			Dict				=   iter_csv_file(A_      )
  a     :			List[Any]				=   iter_csv_file(A_      )

  for rowa, rowa in zip(A_	,			A_      ):
    assert rowa == rowa






def 			snake_case  (A_      :Union[str, Any]	,			A_      :List[Any]      ):







  '''simple docstring'''

  a     :			List[Any]				=   tmp_path / 'cache'
  a     :			Dict				=   os.path.join(A_	,			'tmp.csv'      )
  a     :			Union[str, Any]				=   CsvDatasetReader({'train': csv_path}	,			cache_dir=A_      ).read()
  with pytest.raises(A_      ):
    CsvDatasetWriter(dataset['train']	,			A_	,			num_proc=0      )




