


from ...processing_utils import ProcessorMixin




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  Optional[int]      ='SpeechT5FeatureExtractor'
      UpperCamelCase__							:  Dict      ='SpeechT5Tokenizer'






      def __init__(					self						:     Optional[int]			,   lowercase_						:     str			,   lowercase_						:     int )					->						Optional[int]:
             """simple docstring"""




             super().__init__(lowercase_			,   lowercase_ )






      def __call__(					self						:     List[str]			,   *lowercase_						:     Union[str, Any]			,   **lowercase_						:     int )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=kwargs.pop('audio'			,   lowercase_ )
             _lowerCamelCase       :		Any		=kwargs.pop('text'			,   lowercase_ )
             _lowerCamelCase       :		Optional[int]		=kwargs.pop('text_target'			,   lowercase_ )
             _lowerCamelCase       :		str		=kwargs.pop('audio_target'			,   lowercase_ )
             _lowerCamelCase       :		str		=kwargs.pop('sampling_rate'			,   lowercase_ )

             if audio is not None and text is not None:
                    raise ValueError(
                        'Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?' )
             if audio_target is not None and text_target is not None:
                    raise ValueError(
                        'Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?' )
             if audio is None and audio_target is None and text is None and text_target is None:
                    raise ValueError(
                        'You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.' )

             if audio is not None:
                    _lowerCamelCase       :		str		=self.feature_extractor(lowercase_			,   *lowercase_			,   sampling_rate=lowercase_			,   **lowercase_ )
             elif text is not None:
                    _lowerCamelCase       :		List[str]		=self.tokenizer(lowercase_			,   **lowercase_ )
             else:
                    _lowerCamelCase       :		Union[str, Any]		=None

             if audio_target is not None:
                    _lowerCamelCase       :		Any		=self.feature_extractor(audio_target=lowercase_			,   *lowercase_			,   sampling_rate=lowercase_			,   **lowercase_ )
                    _lowerCamelCase       :		int		=targets['input_values']
             elif text_target is not None:
                    _lowerCamelCase       :		Union[str, Any]		=self.tokenizer(lowercase_			,   **lowercase_ )
                    _lowerCamelCase       :		List[Any]		=targets['input_ids']
             else:
                    _lowerCamelCase       :		List[Any]		=None

             if inputs is None:
                    return targets

             if targets is not None:
                    _lowerCamelCase       :		Dict		=labels

                    _lowerCamelCase       :		List[Any]		=targets.get('attention_mask' )
                    if decoder_attention_mask is not None:
                           _lowerCamelCase       :		int		=decoder_attention_mask

             return inputs






      def        lowerCamelCase       (					self						:     List[str]			,   *lowercase_						:     Optional[Any]			,   **lowercase_						:     List[str] )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		Any		=kwargs.pop('input_values'			,   lowercase_ )
             _lowerCamelCase       :		int		=kwargs.pop('input_ids'			,   lowercase_ )
             _lowerCamelCase       :		Tuple		=kwargs.pop('labels'			,   lowercase_ )

             if input_values is not None and input_ids is not None:
                    raise ValueError('Cannot process both `input_values` and `input_ids` inputs.' )
             if input_values is None and input_ids is None and labels is None:
                    raise ValueError(
                        'You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.' )

             if input_values is not None:
                    _lowerCamelCase       :		Optional[Any]		=self.feature_extractor.pad(lowercase_			,   *lowercase_			,   **lowercase_ )
             elif input_ids is not None:
                    _lowerCamelCase       :		List[Any]		=self.tokenizer.pad(lowercase_			,   **lowercase_ )
             else:
                    _lowerCamelCase       :		Union[str, Any]		=None

             if labels is not None:
                    if "input_ids" in labels or (isinstance(lowercase_			,   lowercase_ ) and "input_ids" in labels[0]):
                           _lowerCamelCase       :		Optional[int]		=self.tokenizer.pad(lowercase_			,   **lowercase_ )
                           _lowerCamelCase       :		Tuple		=targets['input_ids']
                    else:
                           _lowerCamelCase       :		Optional[Any]		=self.feature_extractor.feature_size
                           _lowerCamelCase       :		Optional[Any]		=self.feature_extractor.num_mel_bins
                           _lowerCamelCase       :		Optional[Any]		=self.feature_extractor.pad(lowercase_			,   *lowercase_			,   **lowercase_ )
                           _lowerCamelCase       :		Dict		=feature_size_hack
                           _lowerCamelCase       :		Dict		=targets['input_values']
             else:
                    _lowerCamelCase       :		Optional[Any]		=None

             if inputs is None:
                    return targets

             if targets is not None:
                    _lowerCamelCase       :		str		=labels

                    _lowerCamelCase       :		Optional[Any]		=targets.get('attention_mask' )
                    if decoder_attention_mask is not None:
                           _lowerCamelCase       :		Optional[int]		=decoder_attention_mask

             return inputs






      def        lowerCamelCase       (					self						:     Any			,   *lowercase_						:     Tuple			,   **lowercase_						:     Tuple )					->						Tuple:
             """simple docstring"""




             return self.tokenizer.batch_decode(*lowercase_			,   **lowercase_ )






      def        lowerCamelCase       (					self						:     str			,   *lowercase_						:     Optional[int]			,   **lowercase_						:     Any )					->						Tuple:
             """simple docstring"""




             return self.tokenizer.decode(*lowercase_			,   **lowercase_ )






import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


lowerCamelCase											=       datasets.utils.logging.get_logger(__name__)

lowerCamelCase											=       ['names', 'prefix']
lowerCamelCase											=       ['warn_bad_lines', 'error_bad_lines', 'mangle_dupe_cols']
lowerCamelCase											=       ['encoding_errors', 'on_bad_lines']
lowerCamelCase											=       ['date_format']




@dataclass
class A  (   datasets.BuilderConfig     ):
      UpperCamelCase__							:  str      =","
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  Optional[Union[int, List[int], str]]      ="infer"
      UpperCamelCase__							:  Optional[List[str]]      =None
      UpperCamelCase__							:  Optional[List[str]]      =None
      UpperCamelCase__							:  Optional[Union[int, str, List[int], List[str]]]      =None
      UpperCamelCase__							:  Optional[Union[List[int], List[str]]]      =None
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  Optional[Literal["c", "python", "pyarrow"]]      =None
      UpperCamelCase__							:  Dict[Union[int, str], Callable[[Any], Any]]      =None
      UpperCamelCase__							:  Optional[list]      =None
      UpperCamelCase__							:  Optional[list]      =None
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  Optional[Union[int, List[int]]]      =None
      UpperCamelCase__							:  Optional[int]      =None
      UpperCamelCase__							:  Optional[Union[str, List[str]]]      =None
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  str      ="."
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  str      ='"'
      UpperCamelCase__							:  int      =0
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  int      =0
      UpperCamelCase__							:  bool      =True
      UpperCamelCase__							:  bool      =False
      UpperCamelCase__							:  Optional[str]      =None
      UpperCamelCase__							:  int      =10000
      UpperCamelCase__							:  Optional[datasets.Features]      =None
      UpperCamelCase__							:  Optional[str]      ="strict"
      UpperCamelCase__							:  Literal["error", "warn", "skip"]      ="error"
      UpperCamelCase__							:  Optional[str]      =None






      def        lowerCamelCase       (					self						:     List[str] )					->						str:
             """simple docstring"""




             if self.delimiter is not None:
                    _lowerCamelCase       :		Union[str, Any]		=self.delimiter
             if self.column_names is not None:
                    _lowerCamelCase       :		Dict		=self.column_names






      @property
      def        lowerCamelCase       (					self						:     int )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		Any		={
                 'sep': self.sep,
                 'header': self.header,
                 'names': self.names,
                 'index_col': self.index_col,
                 'usecols': self.usecols,
                 'prefix': self.prefix,
                 'mangle_dupe_cols': self.mangle_dupe_cols,
                 'engine': self.engine,
                 'converters': self.converters,
                 'true_values': self.true_values,
                 'false_values': self.false_values,
                 'skipinitialspace': self.skipinitialspace,
                 'skiprows': self.skiprows,
                 'nrows': self.nrows,
                 'na_values': self.na_values,
                 'keep_default_na': self.keep_default_na,
                 'na_filter': self.na_filter,
                 'verbose': self.verbose,
                 'skip_blank_lines': self.skip_blank_lines,
                 'thousands': self.thousands,
                 'decimal': self.decimal,
                 'lineterminator': self.lineterminator,
                 'quotechar': self.quotechar,
                 'quoting': self.quoting,
                 'escapechar': self.escapechar,
                 'comment': self.comment,
                 'encoding': self.encoding,
                 'dialect': self.dialect,
                 'error_bad_lines': self.error_bad_lines,
                 'warn_bad_lines': self.warn_bad_lines,
                 'skipfooter': self.skipfooter,
                 'doublequote': self.doublequote,
                 'memory_map': self.memory_map,
                 'float_precision': self.float_precision,
                 'chunksize': self.chunksize,
                 'encoding_errors': self.encoding_errors,
                 'on_bad_lines': self.on_bad_lines,
                 'date_format': self.date_format,
             }

             # some kwargs must not be passed if they don't have a default value
             # some others are deprecated and we can also not pass them if they are the default value
             for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
                    if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()			,   lowercase_ ):
                           del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
             if not (datasets.config.PANDAS_VERSION.major >= 2):
                    for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
                           del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
             if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
                    for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
                           del pd_read_csv_kwargs[pd_read_csv_parameter]

             return pd_read_csv_kwargs




class A  (   datasets.ArrowBasedBuilder     ):
      UpperCamelCase__							:  List[str]      =CsvConfig






      def        lowerCamelCase       (					self						:     Any )					->						Tuple:
             """simple docstring"""




             return datasets.DatasetInfo(features=self.config.features )






      def        lowerCamelCase       (					self						:     Tuple			,   lowercase_						:     Any )					->						List[Any]:
             """simple docstring"""




             if not self.config.data_files:
                    raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}''' )
             _lowerCamelCase       :		int		=dl_manager.download_and_extract(self.config.data_files )
             if isinstance(lowercase_			,   (str, list, tuple) ):
                    _lowerCamelCase       :		List[Any]		=data_files
                    if isinstance(lowercase_			,   lowercase_ ):
                           _lowerCamelCase       :		Union[str, Any]		=[files]
                    _lowerCamelCase       :		Union[str, Any]		=[dl_manager.iter_files(lowercase_ ) for file in files]
                    return [datasets.SplitGenerator(name=datasets.Split.TRAIN			,   gen_kwargs={'files': files} )]
             _lowerCamelCase       :		Optional[int]		=[]
             for split_name, files in data_files.items():
                    if isinstance(lowercase_			,   lowercase_ ):
                           _lowerCamelCase       :		Dict		=[files]
                    _lowerCamelCase       :		Optional[int]		=[dl_manager.iter_files(lowercase_ ) for file in files]
                    splits.append(datasets.SplitGenerator(name=lowercase_			,   gen_kwargs={'files': files} ) )
             return splits






      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     pa.Table )					->						pa.Table:
             """simple docstring"""




             if self.config.features is not None:
                    _lowerCamelCase       :		List[str]		=self.config.features.arrow_schema
                    if all(not require_storage_cast(lowercase_ ) for feature in self.config.features.values() ):
                           # cheaper cast
                           _lowerCamelCase       :		Optional[int]		=pa.Table.from_arrays([pa_table[field.name] for field in schema]			,   schema=lowercase_ )
                    else:
                           # more expensive cast; allows str <-> int/float or str to Audio for example
                           _lowerCamelCase       :		List[Any]		=table_cast(lowercase_			,   lowercase_ )
             return pa_table






      def        lowerCamelCase       (					self						:     Optional[Any]			,   lowercase_						:     List[Any] )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		str		=self.config.features.arrow_schema if self.config.features else None
             # dtype allows reading an int column as str
             _lowerCamelCase       :		Union[str, Any]		=(
                 {
                     name: dtype.to_pandas_dtype() if not require_storage_cast(lowercase_ ) else object
                     for name, dtype, feature in zip(schema.names			,   schema.types			,   self.config.features.values() )
                 }
                 if schema is not None
                 else None
             )
             for file_idx, file in enumerate(itertools.chain.from_iterable(lowercase_ ) ):
                    _lowerCamelCase       :		Union[str, Any]		=pd.read_csv(lowercase_			,   iterator=lowercase_			,   dtype=lowercase_			,   **self.config.pd_read_csv_kwargs )
                    try:
                           for batch_idx, df in enumerate(lowercase_ ):
                                  _lowerCamelCase       :		Tuple		=pa.Table.from_pandas(lowercase_ )
                                  # Uncomment for debugging (will print the Arrow table size and elements)
                                  # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                  # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                  yield (file_idx, batch_idx), self._cast_table(lowercase_ )
                    except ValueError as e:
                           logger.error(F'''Failed to read file \'{file}\' with error {type(lowercase_ )}: {e}''' )
                           raise


