




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


a_		,    a_		,    a_   		=     False, False, False

@dataclass
class  _lowerCamelCase						:

      """simple docstring"""

      lowerCAmelCase__			: Optional[int]    =					None
      lowerCAmelCase__			: bool    =					True
      lowerCAmelCase__			: bool    =					True
      lowerCAmelCase__			: Optional[str]    =					None
      # Automatically constructed
      lowerCAmelCase__			: ClassVar[str]    =					"dict"
      lowerCAmelCase__			: ClassVar[Any]    =					pa.struct({"bytes": pa.binary(), "path": pa.string()}   )
      lowerCAmelCase__			: str    =					field(default="Audio"							,			init=_SCREAMING_SNAKE_CASE							,			repr=_SCREAMING_SNAKE_CASE   )







      def __call__(     self					: List[str]				):
          return self.pa_type







      def       snake_case	(     self					: str ,       snake_case					: 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(snake_case ,       snake_case				):
              return {"bytes": None, "path": value}
          elif isinstance(snake_case ,       snake_case				):
              return {"bytes": value, "path": None}
          elif "array" in value:
              # convert the audio array to wav bytes
              __UpperCamelCase  		=						BytesIO()
              sf.write(snake_case ,       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!)
                      __UpperCamelCase  		=						np.frombuffer(value['''bytes'''] ,       dtype=np.intaa				).astype(np.floataa				) / 32767
                  else:
                      __UpperCamelCase  		=						np.memmap(value['''path'''] ,       dtype='''h''' ,       mode='''r'''				).astype(np.floataa				) / 32767

                  __UpperCamelCase  		=						BytesIO(bytes()				)
                  sf.write(snake_case ,       snake_case ,       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					: str ,       snake_case					: dict ,       snake_case					: 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.'''				)

          __UpperCamelCase		,      __UpperCamelCase  		=						(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

          __UpperCamelCase  		=						xsplitext(snake_case				)[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:
              __UpperCamelCase  		=						token_per_repo_id or {}
              __UpperCamelCase  		=						path.split('''::'''				)[-1]
              try:
                  __UpperCamelCase  		=						string_to_dict(snake_case ,       config.HUB_DATASETS_URL				)['''repo_id''']
                  __UpperCamelCase  		=						token_per_repo_id[repo_id]
              except (ValueError, KeyError):
                  __UpperCamelCase  		=						None

              with xopen(snake_case ,       '''rb''' ,       use_auth_token=snake_case				) as f:
                  __UpperCamelCase		,      __UpperCamelCase  		=						sf.read(snake_case				)

          else:
              __UpperCamelCase		,      __UpperCamelCase  		=						sf.read(snake_case				)

          __UpperCamelCase  		=						array.T
          if self.mono:
              __UpperCamelCase  		=						librosa.to_mono(snake_case				)
          if self.sampling_rate and self.sampling_rate != sampling_rate:
              __UpperCamelCase  		=						librosa.resample(snake_case ,       orig_sr=snake_case ,       target_sr=self.sampling_rate				)
              __UpperCamelCase  		=						self.sampling_rate

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







      def       snake_case	(     self					: str				):
          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[int] ,       snake_case					: Union[pa.StringArray, pa.StructArray]				):
          if pa.types.is_string(storage.type				):
              __UpperCamelCase  		=						pa.array([None] * len(snake_case				) ,       type=pa.binary()				)
              __UpperCamelCase  		=						pa.StructArray.from_arrays([bytes_array, storage] ,       ['''bytes''', '''path'''] ,       mask=storage.is_null()				)
          elif pa.types.is_binary(storage.type				):
              __UpperCamelCase  		=						pa.array([None] * len(snake_case				) ,       type=pa.string()				)
              __UpperCamelCase  		=						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'''				):
              __UpperCamelCase  		=						pa.array([Audio().encode_example(snake_case				) 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:
                  __UpperCamelCase  		=						storage.field('''bytes'''				)
              else:
                  __UpperCamelCase  		=						pa.array([None] * len(snake_case				) ,       type=pa.binary()				)
              if storage.type.get_field_index('''path'''				) >= 0:
                  __UpperCamelCase  		=						storage.field('''path'''				)
              else:
                  __UpperCamelCase  		=						pa.array([None] * len(snake_case				) ,       type=pa.string()				)
              __UpperCamelCase  		=						pa.StructArray.from_arrays([bytes_array, path_array] ,       ['''bytes''', '''path'''] ,       mask=storage.is_null()				)
          return array_cast(snake_case ,       self.pa_type				)







      def       snake_case	(     self					: Optional[int] ,       snake_case					: pa.StructArray				):

          @no_op_if_value_is_null
          def path_to_bytes(snake_case					: Union[str, Any]				):
              with xopen(snake_case ,       '''rb'''				) as f:
                  __UpperCamelCase  		=						f.read()
              return bytes_

          __UpperCamelCase  		=						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() ,       )
          __UpperCamelCase  		=						pa.array(
              [os.path.basename(snake_case				) if path is not None else None for path in storage.field('''path'''				).to_pylist()] ,       type=pa.string() ,       )
          __UpperCamelCase  		=						pa.StructArray.from_arrays([bytes_array, path_array] ,       ['''bytes''', '''path'''] ,       mask=bytes_array.is_null()				)
          return array_cast(snake_case ,       self.pa_type				)












from typing import TYPE_CHECKING

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


a_   		=     {
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     ["LongformerTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     [
     "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LongformerForMaskedLM",
     "LongformerForMultipleChoice",
     "LongformerForQuestionAnswering",
     "LongformerForSequenceClassification",
     "LongformerForTokenClassification",
     "LongformerModel",
     "LongformerPreTrainedModel",
     "LongformerSelfAttention",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     [
     "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFLongformerForMaskedLM",
     "TFLongformerForMultipleChoice",
     "TFLongformerForQuestionAnswering",
     "TFLongformerForSequenceClassification",
     "TFLongformerForTokenClassification",
     "TFLongformerModel",
     "TFLongformerPreTrainedModel",
     "TFLongformerSelfAttention",
 ]


if TYPE_CHECKING:
 from .configuration_longformer import (
     LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     LongformerConfig,
     LongformerOnnxConfig,
 )
 from .tokenization_longformer import LongformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_longformer_fast import LongformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longformer import (
      LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongformerForMaskedLM,
      LongformerForMultipleChoice,
      LongformerForQuestionAnswering,
      LongformerForSequenceClassification,
      LongformerForTokenClassification,
      LongformerModel,
      LongformerPreTrainedModel,
      LongformerSelfAttention,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_longformer import (
      TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLongformerForMaskedLM,
      TFLongformerForMultipleChoice,
      TFLongformerForQuestionAnswering,
      TFLongformerForSequenceClassification,
      TFLongformerForTokenClassification,
      TFLongformerModel,
      TFLongformerPreTrainedModel,
      TFLongformerSelfAttention,
  )

else:
 import sys

 a_   		=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






