




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


lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_    		=  False, False, False
@dataclass
class  __a   :


     """simple docstring"""


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




     def __call__(      self     :					Union[str, Any]		)						->  Optional[int]:


         '''simple docstring'''





         return self.pa_type




     def     __A							(      self     :					Any							,_UpperCamelCase     :					str		)						->  dict:


         '''simple docstring'''





         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(_UpperCamelCase							,_UpperCamelCase		):
             return {"bytes": None, "path": value}
         elif isinstance(_UpperCamelCase							,_UpperCamelCase		):
             return {"bytes": value, "path": None}
         elif "array" in value:
             # convert the audio array to wav bytes
             SCREAMING_SNAKE_CASE__        =BytesIO()
             sf.write(_UpperCamelCase							,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!)
                     SCREAMING_SNAKE_CASE__        =np.frombuffer(value["""bytes"""]							,dtype=np.intaa		).astype(np.floataa		) / 3_2_7_6_7
                 else:
                     SCREAMING_SNAKE_CASE__        =np.memmap(value["""path"""]							,dtype="""h"""							,mode="""r"""		).astype(np.floataa		) / 3_2_7_6_7

                 SCREAMING_SNAKE_CASE__        =BytesIO(bytes()		)
                 sf.write(_UpperCamelCase							,_UpperCamelCase							,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     __A							(      self     :					List[str]							,_UpperCamelCase     :					Dict							,_UpperCamelCase     :					Dict = None		)						->  dict:


         '''simple docstring'''





         if not self.decode:
             raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead."""		)

         SCREAMING_SNAKE_CASE__        =(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

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

             with xopen(_UpperCamelCase							,"""rb"""							,use_auth_token=_UpperCamelCase		) as f:
                 SCREAMING_SNAKE_CASE__        =sf.read(_UpperCamelCase		)

         else:
             SCREAMING_SNAKE_CASE__        =sf.read(_UpperCamelCase		)

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

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




     def     __A							(      self     :					List[str]		)						->  Union["FeatureType", Dict[str, "FeatureType"]]:


         '''simple docstring'''





         from .features import Value

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




     def     __A							(      self     :					int							,_UpperCamelCase     :					Dict		)						->  pa.StructArray:


         '''simple docstring'''





         if pa.types.is_string(storage.type		):
             SCREAMING_SNAKE_CASE__        =pa.array([None] * len(_UpperCamelCase		)							,type=pa.binary()		)
             SCREAMING_SNAKE_CASE__        =pa.StructArray.from_arrays([bytes_array, storage]							,["""bytes""", """path"""]							,mask=storage.is_null()		)
         elif pa.types.is_binary(storage.type		):
             SCREAMING_SNAKE_CASE__        =pa.array([None] * len(_UpperCamelCase		)							,type=pa.string()		)
             SCREAMING_SNAKE_CASE__        =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"""		):
             SCREAMING_SNAKE_CASE__        =pa.array([Audio().encode_example(_UpperCamelCase		) 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:
                 SCREAMING_SNAKE_CASE__        =storage.field("""bytes"""		)
             else:
                 SCREAMING_SNAKE_CASE__        =pa.array([None] * len(_UpperCamelCase		)							,type=pa.binary()		)
             if storage.type.get_field_index("""path"""		) >= 0:
                 SCREAMING_SNAKE_CASE__        =storage.field("""path"""		)
             else:
                 SCREAMING_SNAKE_CASE__        =pa.array([None] * len(_UpperCamelCase		)							,type=pa.string()		)
             SCREAMING_SNAKE_CASE__        =pa.StructArray.from_arrays([bytes_array, path_array]							,["""bytes""", """path"""]							,mask=storage.is_null()		)
         return array_cast(_UpperCamelCase							,self.pa_type		)




     def     __A							(      self     :					Optional[int]							,_UpperCamelCase     :					Union[str, Any]		)						->  pa.StructArray:


         '''simple docstring'''





         @no_op_if_value_is_null
         def path_to_bytes(_UpperCamelCase     :					List[Any]		):
             with xopen(_UpperCamelCase							,"""rb"""		) as f:
                 SCREAMING_SNAKE_CASE__        =f.read()
             return bytes_

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










"""simple docstring"""
from typing import TYPE_CHECKING

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


UpperCAmelCase				=						{
    """configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncoderDecoderOnnxConfig"""]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase				=						["""VisionEncoderDecoderModel"""]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase				=						["""TFVisionEncoderDecoderModel"""]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase				=						["""FlaxVisionEncoderDecoderModel"""]

if TYPE_CHECKING:
 from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel

else:
 import sys

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

