


import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
       from .features import FeatureType


lowercase_   ,lowercase_   ,lowercase_	   =     False, False, False


@dataclass
class 	A_ :




      '''simple docstring'''


      __snake_case 	=       None
      __snake_case 	=       True
      __snake_case 	=       True
      __snake_case 	=       None
      # Automatically constructed
      __snake_case 	=       """dict"""
      __snake_case 	=       pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}   )
      __snake_case 	=       field(default="""Audio""" ,				init=__UpperCamelCase ,				repr=__UpperCamelCase   )




      def __call__(    self:	int    ):
       return self.pa_type




      def 					_snake_case     (    self:	Tuple						,				a:	Union[str, bytes, dict]    ):
       try:
        import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
       except ImportError as err:
        raise ImportError('To support encoding audio data, please install \'soundfile\'.'    ) from err
       if isinstance(a						,				a    ):
        return {"bytes": None, "path": value}
       elif isinstance(a						,				a    ):
        return {"bytes": value, "path": None}
       elif "array" in value:
        # convert the audio array to wav bytes
        __lowerCamelCase		:							List[str]		 =						BytesIO()
        sf.write(a						,				value['array']						,				value['sampling_rate']						,				format='wav'    )
        return {"bytes": buffer.getvalue(), "path": None}
       elif value.get('path'    ) is not None and os.path.isfile(value['path']    ):
        # we set "bytes": None to not duplicate the data if they're already available locally
        if value["path"].endswith('pcm'    ):
         # "PCM" only has raw audio bytes
         if value.get('sampling_rate'    ) is None:
          # At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
          raise KeyError('To use PCM files, please specify a \'sampling_rate\' in Audio object'    )
         if value.get('bytes'    ):
          # If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
          __lowerCamelCase		:							int		 =						np.frombuffer(value['bytes']						,				dtype=np.intaa    ).astype(np.floataa    ) / 3_2767
         else:
          __lowerCamelCase		:							Dict		 =						np.memmap(value['path']						,				dtype='h'						,				mode='r'    ).astype(np.floataa    ) / 3_2767

         __lowerCamelCase		:							Dict		 =						BytesIO(bytes()    )
         sf.write(a						,				a						,				value['sampling_rate']						,				format='wav'    )
         return {"bytes": buffer.getvalue(), "path": None}
        else:
         return {"bytes": None, "path": value.get('path'    )}
       elif value.get('bytes'    ) is not None or value.get('path'    ) is not None:
        # store the audio bytes, and path is used to infer the audio format using the file extension
        return {"bytes": value.get('bytes'    ), "path": value.get('path'    )}
       else:
        raise ValueError(
            F'An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'    )




      def 					_snake_case     (    self:	Tuple						,				a:	dict						,				a:	Optional[Dict[str, Union[str, bool, None]]] = None    ):
       if not self.decode:
        raise RuntimeError('Decoding is disabled for this feature. Please use Audio(decode=True) instead.'    )

       __lowerCamelCase		:							Optional[int]		 =						(value['path'], BytesIO(value['bytes']    )) if value['bytes'] is not None else (value['path'], None)
       if path is None and file is None:
        raise ValueError(F'An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.'    )

       try:
        import librosa
        import soundfile as sf
       except ImportError as err:
        raise ImportError('To support decoding audio files, please install \'librosa\' and \'soundfile\'.'    ) from err

       __lowerCamelCase		:							Dict		 =						xsplitext(a    )[1][1:].lower() if path is not None else None
       if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
        raise RuntimeError(
            'Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '
            'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '    )
       elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
        raise RuntimeError(
            'Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '
            'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '    )

       if file is None:
        __lowerCamelCase		:							Union[str, Any]		 =						token_per_repo_id or {}
        __lowerCamelCase		:							str		 =						path.split('::'    )[-1]
        try:
         __lowerCamelCase		:							Optional[Any]		 =						string_to_dict(a						,				config.HUB_DATASETS_URL    )['repo_id']
         __lowerCamelCase		:							List[str]		 =						token_per_repo_id[repo_id]
        except (ValueError, KeyError):
         __lowerCamelCase		:							Optional[int]		 =						None

        with xopen(a						,				'rb'						,				use_auth_token=a    ) as f:
         __lowerCamelCase		:							Optional[int]		 =						sf.read(a    )

       else:
        __lowerCamelCase		:							str		 =						sf.read(a    )

       __lowerCamelCase		:							Any		 =						array.T
       if self.mono:
        __lowerCamelCase		:							List[str]		 =						librosa.to_mono(a    )
       if self.sampling_rate and self.sampling_rate != sampling_rate:
        __lowerCamelCase		:							List[Any]		 =						librosa.resample(a						,				orig_sr=a						,				target_sr=self.sampling_rate    )
        __lowerCamelCase		:							Optional[Any]		 =						self.sampling_rate

       return {"path": path, "array": array, "sampling_rate": sampling_rate}




      def 					_snake_case     (    self:	Tuple    ):
       from .features import Value

       if self.decode:
        raise ValueError('Cannot flatten a decoded Audio feature.'    )
       return {
           "bytes": Value('binary'    ),
           "path": Value('string'    ),
       }




      def 					_snake_case     (    self:	Optional[Any]						,				a:	Union[pa.StringArray, pa.StructArray]    ):
       if pa.types.is_string(storage.type    ):
        __lowerCamelCase		:							Dict		 =						pa.array([None] * len(a    )						,				type=pa.binary()    )
        __lowerCamelCase		:							List[str]		 =						pa.StructArray.from_arrays([bytes_array, storage]						,				['bytes', 'path']						,				mask=storage.is_null()    )
       elif pa.types.is_binary(storage.type    ):
        __lowerCamelCase		:							str		 =						pa.array([None] * len(a    )						,				type=pa.string()    )
        __lowerCamelCase		:							Any		 =						pa.StructArray.from_arrays([storage, path_array]						,				['bytes', 'path']						,				mask=storage.is_null()    )
       elif pa.types.is_struct(storage.type    ) and storage.type.get_all_field_indices('array'    ):
        __lowerCamelCase		:							Union[str, Any]		 =						pa.array([Audio().encode_example(a    ) if x is not None else None for x in storage.to_pylist()]    )
       elif pa.types.is_struct(storage.type    ):
        if storage.type.get_field_index('bytes'    ) >= 0:
         __lowerCamelCase		:							Optional[Any]		 =						storage.field('bytes'    )
        else:
         __lowerCamelCase		:							List[str]		 =						pa.array([None] * len(a    )						,				type=pa.binary()    )
        if storage.type.get_field_index('path'    ) >= 0:
         __lowerCamelCase		:							List[str]		 =						storage.field('path'    )
        else:
         __lowerCamelCase		:							Optional[Any]		 =						pa.array([None] * len(a    )						,				type=pa.string()    )
        __lowerCamelCase		:							Optional[Any]		 =						pa.StructArray.from_arrays([bytes_array, path_array]						,				['bytes', 'path']						,				mask=storage.is_null()    )
       return array_cast(a						,				self.pa_type    )




      def 					_snake_case     (    self:	Optional[Any]						,				a:	pa.StructArray    ):

       @no_op_if_value_is_null
       def path_to_bytes(a:	Any    ):
        with xopen(a						,				'rb'    ) as f:
         __lowerCamelCase		:							List[str]		 =						f.read()
        return bytes_

       __lowerCamelCase		:							Dict		 =						pa.array(
           [
               (path_to_bytes(x['path']    ) if x['bytes'] is None else x['bytes']) if x is not None else None
               for x in storage.to_pylist()
           ]						,				type=pa.binary()						,				)
       __lowerCamelCase		:							List[Any]		 =						pa.array(
           [os.path.basename(a    ) if path is not None else None for path in storage.field('path'    ).to_pylist()]						,				type=pa.string()						,				)
       __lowerCamelCase		:							Optional[Any]		 =						pa.StructArray.from_arrays([bytes_array, path_array]						,				['bytes', 'path']						,				mask=bytes_array.is_null()    )
       return array_cast(a						,				self.pa_type    )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase_	   =     {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     ['ConvNextFeatureExtractor']
       lowercase_	   =     ['ConvNextImageProcessor']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'ConvNextForImageClassification',
           'ConvNextModel',
           'ConvNextPreTrainedModel',
           'ConvNextBackbone',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'TFConvNextForImageClassification',
           'TFConvNextModel',
           'TFConvNextPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_convnext import ConvNextFeatureExtractor
              from .image_processing_convnext import ConvNextImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_convnext import (
                  CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ConvNextBackbone,
                  ConvNextForImageClassification,
                  ConvNextModel,
                  ConvNextPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
       import sys

       lowercase_	   =     _LazyModule(__name__, globals()['__file__'], _import_structure)

