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


UpperCamelCase__							,							UpperCamelCase__							,							UpperCamelCase__											=					False, False, False





@dataclass
class      A							:
   __UpperCAmelCase : Optional[int]      =			None
   __UpperCAmelCase : bool      =			True
   __UpperCAmelCase : bool      =			True
   __UpperCAmelCase : Optional[str]      =			None
   # Automatically constructed
   __UpperCAmelCase : ClassVar[str]      =			"dict"
   __UpperCAmelCase : ClassVar[Any]      =			pa.struct({'bytes': pa.binary(), 'path': pa.string()}     )
   __UpperCAmelCase : str      =			field(default='Audio'		,				init=UpperCAmelCase_		,				repr=UpperCAmelCase_     )


   def __call__(self     :						int		)						->				Dict:
         """simple docstring"""



         return self.pa_type


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						Union[str, bytes, dict]		)						->				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
               UpperCAmelCase__								=  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!)
                           UpperCAmelCase__								=  np.frombuffer(value["bytes"]       ,  dtype=np.intaa		).astype(np.floataa		) / 3_2_7_6_7
                     else:
                           UpperCAmelCase__								=  np.memmap(value["path"]       ,  dtype="h"       ,  mode="r"		).astype(np.floataa		) / 3_2_7_6_7

                     UpperCAmelCase__								=  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 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						dict       ,  __UpperCAmelCase     :						Optional[Dict[str, Union[str, bool, None]]] = None		)						->				dict:
         """simple docstring"""



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

               with xopen(__UpperCAmelCase       ,  "rb"       ,  use_auth_token=__UpperCAmelCase		) as f:
                     UpperCAmelCase__		,      UpperCAmelCase__								=  sf.read(__UpperCAmelCase		)

         else:
               UpperCAmelCase__		,      UpperCAmelCase__								=  sf.read(__UpperCAmelCase		)

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

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


   def 			lowercase_      (self     :						Optional[int]		)						->				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 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						Union[pa.StringArray, pa.StructArray]		)						->				pa.StructArray:
         """simple docstring"""



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





   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						pa.StructArray		)						->				pa.StructArray:
         """simple docstring"""



         @no_op_if_value_is_null
         def path_to_bytes(__UpperCAmelCase     :						Any		):
               with xopen(__UpperCAmelCase       ,  "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(__UpperCAmelCase		) 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(__UpperCAmelCase       ,  self.pa_type		)









from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch





@require_torch
@require_tf
class     lowerCamelCase__(					__lowerCamelCase):
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						SMALL_MODEL_IDENTIFIER
             __lowerCamelCase   =						"""pt"""
             __lowerCamelCase   =						"""tf"""
      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       UpperCamelCase_: Optional[Any]   ):
             __lowerCamelCase   =						AutoModel.from_pretrained(self.test_model   )
             model_pt.save_pretrained(UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: Union[str, Any]   ):
             __lowerCamelCase   =						TFAutoModel.from_pretrained(self.test_model    ,       from_pt=UpperCamelCase_   )
             model_tf.save_pretrained(UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						"""mock_framework"""

             # Framework provided - return whatever the user provides
             __lowerCamelCase   =						FeaturesManager.determine_framework(self.test_model    ,       UpperCamelCase_   )
             self.assertEqual(UpperCamelCase_    ,       UpperCamelCase_   )

             # Local checkpoint and framework provided - return provided framework
             # PyTorch checkpoint
             with TemporaryDirectory() as local_pt_ckpt:
                    self._setup_pt_ckpt(UpperCamelCase_   )
                    __lowerCamelCase   =						FeaturesManager.determine_framework(UpperCamelCase_    ,       UpperCamelCase_   )
                    self.assertEqual(UpperCamelCase_    ,       UpperCamelCase_   )

             # TensorFlow checkpoint
             with TemporaryDirectory() as local_tf_ckpt:
                    self._setup_tf_ckpt(UpperCamelCase_   )
                    __lowerCamelCase   =						FeaturesManager.determine_framework(UpperCamelCase_    ,       UpperCamelCase_   )
                    self.assertEqual(UpperCamelCase_    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: int   ):
             # PyTorch checkpoint
             with TemporaryDirectory() as local_pt_ckpt:
                    self._setup_pt_ckpt(UpperCamelCase_   )
                    __lowerCamelCase   =						FeaturesManager.determine_framework(UpperCamelCase_   )
                    self.assertEqual(UpperCamelCase_    ,       self.framework_pt   )

             # TensorFlow checkpoint
             with TemporaryDirectory() as local_tf_ckpt:
                    self._setup_tf_ckpt(UpperCamelCase_   )
                    __lowerCamelCase   =						FeaturesManager.determine_framework(UpperCamelCase_   )
                    self.assertEqual(UpperCamelCase_    ,       self.framework_tf   )

             # Invalid local checkpoint
             with TemporaryDirectory() as local_invalid_ckpt:
                    with self.assertRaises(UpperCamelCase_   ):
                           __lowerCamelCase   =						FeaturesManager.determine_framework(UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						MagicMock(return_value=UpperCamelCase_   )
             with patch("""transformers.onnx.features.is_tf_available"""    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						FeaturesManager.determine_framework(self.test_model   )
                    self.assertEqual(UpperCamelCase_    ,       self.framework_pt   )

             # PyTorch not in environment -> use TensorFlow
             __lowerCamelCase   =						MagicMock(return_value=UpperCamelCase_   )
             with patch("""transformers.onnx.features.is_torch_available"""    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						FeaturesManager.determine_framework(self.test_model   )
                    self.assertEqual(UpperCamelCase_    ,       self.framework_tf   )

             # Both in environment -> use PyTorch
             __lowerCamelCase   =						MagicMock(return_value=UpperCamelCase_   )
             __lowerCamelCase   =						MagicMock(return_value=UpperCamelCase_   )
             with patch("""transformers.onnx.features.is_tf_available"""    ,       UpperCamelCase_   ), patch(
                 """transformers.onnx.features.is_torch_available"""    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						FeaturesManager.determine_framework(self.test_model   )
                    self.assertEqual(UpperCamelCase_    ,       self.framework_pt   )

             # Both not in environment -> raise error
             __lowerCamelCase   =						MagicMock(return_value=UpperCamelCase_   )
             __lowerCamelCase   =						MagicMock(return_value=UpperCamelCase_   )
             with patch("""transformers.onnx.features.is_tf_available"""    ,       UpperCamelCase_   ), patch(
                 """transformers.onnx.features.is_torch_available"""    ,       UpperCamelCase_   ):
                    with self.assertRaises(UpperCamelCase_   ):
                           __lowerCamelCase   =						FeaturesManager.determine_framework(self.test_model   )

