import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil







class 	__snake_case      (   UpperCAmelCase_      ):
 def 		UpperCAmelCase__				(     self  )		->							Optional[int]:
       '''simple docstring'''





       UpperCAmelCase			:       Optional[Any]			     =pa.array(TypedSequence([1, 2, 3]  )  )
       self.assertEqual(arr.type						,      pa.intaa()  )
 def 		UpperCAmelCase__				(     self  )		->							int:
       '''simple docstring'''





       with self.assertRaises(lowerCAmelCase__  ):
             UpperCAmelCase			:       Optional[int]			     =pa.array(TypedSequence([1, 2, 3]  )						,      type=pa.intaa()  )
 def 		UpperCAmelCase__				(     self  )		->							Dict:
       '''simple docstring'''





       with self.assertRaises(lowerCAmelCase__  ):
             UpperCAmelCase			:       Dict			     =pa.array(TypedSequence([1, 2, 3]						,      try_type=Value('''bool'''  )						,      type=Value('''int64'''  )  )  )
 def 		UpperCAmelCase__				(     self  )		->							str:
       '''simple docstring'''





       UpperCAmelCase			:       Dict			     =pa.array(TypedSequence([1, 2, 3]						,      type=Value('''int32'''  )  )  )
       self.assertEqual(arr.type						,      pa.intaa()  )
 def 		UpperCAmelCase__				(     self  )		->							Optional[Any]:
       '''simple docstring'''





       with self.assertRaises((TypeError, pa.lib.ArrowInvalid)  ):
             UpperCAmelCase			:       List[str]			     =pa.array(TypedSequence(['''foo''', '''bar''']						,      type=Value('''int64'''  )  )  )
 def 		UpperCAmelCase__				(     self  )		->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase			:       Optional[int]			     =pa.array(TypedSequence([1, 2, 3]						,      try_type=Value('''int32'''  )  )  )
       self.assertEqual(arr.type						,      pa.intaa()  )
 def 		UpperCAmelCase__				(     self  )		->							Any:
       '''simple docstring'''





       UpperCAmelCase			:       Dict			     =pa.array(TypedSequence(['''foo''', '''bar''']						,      try_type=Value('''int64'''  )  )  )
       self.assertEqual(arr.type						,      pa.string()  )
 def 		UpperCAmelCase__				(     self  )		->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase			:       int			     =pa.array(TypedSequence([[[1, 2, 3]]]						,      type=ArrayaD((1, 3)						,      '''int64'''  )  )  )
       self.assertEqual(arr.type						,      ArrayaDExtensionType((1, 3)						,      '''int64'''  )  )
 def 		UpperCAmelCase__				(     self  )		->							Optional[int]:
       '''simple docstring'''





       with self.assertRaises((TypeError, pa.lib.ArrowInvalid)  ):
             UpperCAmelCase			:       List[str]			     =pa.array(TypedSequence(['''foo''', '''bar''']						,      type=ArrayaD((1, 3)						,      '''int64'''  )  )  )
 def 		UpperCAmelCase__				(     self  )		->							List[str]:
       '''simple docstring'''





       UpperCAmelCase			:       Optional[int]			     =pa.array(TypedSequence([[[1, 2, 3]]]						,      try_type=ArrayaD((1, 3)						,      '''int64'''  )  )  )
       self.assertEqual(arr.type						,      ArrayaDExtensionType((1, 3)						,      '''int64'''  )  )
 def 		UpperCAmelCase__				(     self  )		->							Optional[int]:
       '''simple docstring'''





       UpperCAmelCase			:       List[Any]			     =pa.array(TypedSequence(['''foo''', '''bar''']						,      try_type=ArrayaD((1, 3)						,      '''int64'''  )  )  )
       self.assertEqual(arr.type						,      pa.string()  )






 @require_pil
 def 		UpperCAmelCase__				(     self  )		->							int:
       '''simple docstring'''





       import PIL.Image

       UpperCAmelCase			:       Tuple			     =PIL.Image.fromarray(np.arange(10						,      dtype=np.uinta  ).reshape(2						,      5  )  )
       with patch(
           '''datasets.arrow_writer.cast_to_python_objects'''						,      side_effect=lowerCAmelCase__  ) as mock_cast_to_python_objects:
             UpperCAmelCase			:       Tuple			     =pa.array(TypedSequence([{'''path''': None, '''bytes''': b'''image_bytes'''}, pil_image]						,      type=Image()  )  )
             UpperCAmelCase			:       Optional[int]			     =mock_cast_to_python_objects.call_args_list[-1]
             self.assertIn('''optimize_list_casting'''						,      lowerCAmelCase__  )
             self.assertFalse(kwargs['''optimize_list_casting''']  )





def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase  )->     Tuple:






      '''simple docstring'''





      UpperCAmelCase			:       Dict			     =pa.BufferReader(_UpperCAmelCase  ) if isinstance(_UpperCAmelCase						,    pa.Buffer  ) else pa.memory_map(_UpperCAmelCase  )
      UpperCAmelCase			:       Optional[int]			     =pa.ipc.open_stream(_UpperCAmelCase  )
      UpperCAmelCase			:       pa.Table			     =f.read_all()
      assert len(pa_table.to_batches()  ) == expected_num_chunks
      assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
      del pa_table





@pytest.mark.parametrize('''writer_batch_size'''						,    [None, 1, 10]  )
@pytest.mark.parametrize(
    '''fields'''						,    [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]  )
def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase  )->     Any:






      '''simple docstring'''





      UpperCAmelCase			:       Optional[int]			     =pa.BufferOutputStream()
      UpperCAmelCase			:       Optional[Any]			     =pa.schema(_UpperCAmelCase  ) if fields else None
      with ArrowWriter(stream=_UpperCAmelCase						,    schema=_UpperCAmelCase						,    writer_batch_size=_UpperCAmelCase  ) as writer:
            writer.write({'''col_1''': '''foo''', '''col_2''': 1}  )
            writer.write({'''col_1''': '''bar''', '''col_2''': 2}  )
            UpperCAmelCase			:       Union[str, Any]			     =writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            UpperCAmelCase			:       str			     ={"col_1": pa.string(), "col_2": pa.intaa()}
      assert writer._schema == pa.schema(_UpperCAmelCase						,    metadata=writer._schema.metadata  )
      _check_output(output.getvalue()						,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1  )





def      lowerCAmelCase_       ( )->     Tuple:






      '''simple docstring'''





      UpperCAmelCase			:       Optional[Any]			     =pa.BufferOutputStream()
      UpperCAmelCase			:       int			     =Features({'''labels''': ClassLabel(names=['''neg''', '''pos''']  )}  )
      with ArrowWriter(stream=_UpperCAmelCase						,    features=_UpperCAmelCase  ) as writer:
            writer.write({'''labels''': 0}  )
            writer.write({'''labels''': 1}  )
            UpperCAmelCase			:       Optional[Any]			     =writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      assert writer._schema == features.arrow_schema
      assert writer._schema.metadata == features.arrow_schema.metadata
      UpperCAmelCase			:       Any			     =pa.BufferReader(output.getvalue()  )
      UpperCAmelCase			:       Optional[int]			     =pa.ipc.open_stream(_UpperCAmelCase  )
      UpperCAmelCase			:       pa.Table			     =f.read_all()
      UpperCAmelCase			:       str			     =pa_table.schema
      assert pa_table.num_rows == 2
      assert schema == features.arrow_schema
      assert schema.metadata == features.arrow_schema.metadata
      assert features == Features.from_arrow_schema(_UpperCAmelCase  )





@pytest.mark.parametrize('''writer_batch_size'''						,    [None, 1, 10]  )
def      lowerCAmelCase_       ( __lowerCAmelCase  )->     str:






      '''simple docstring'''





      UpperCAmelCase			:       str			     =pa.BufferOutputStream()
      with ArrowWriter(
          stream=_UpperCAmelCase						,    writer_batch_size=_UpperCAmelCase						,    hash_salt='''split_name'''						,    check_duplicates=_UpperCAmelCase						,    ) as writer:
            with pytest.raises(_UpperCAmelCase  ):
                  writer.write({'''col_1''': '''foo''', '''col_2''': 1}						,    key=[1, 2]  )
                  UpperCAmelCase			:       Union[str, Any]			     =writer.finalize()





@pytest.mark.parametrize('''writer_batch_size'''						,    [None, 2, 10]  )
def      lowerCAmelCase_       ( __lowerCAmelCase  )->     Union[str, Any]:






      '''simple docstring'''





      UpperCAmelCase			:       Tuple			     =pa.BufferOutputStream()
      with ArrowWriter(
          stream=_UpperCAmelCase						,    writer_batch_size=_UpperCAmelCase						,    hash_salt='''split_name'''						,    check_duplicates=_UpperCAmelCase						,    ) as writer:
            with pytest.raises(_UpperCAmelCase  ):
                  writer.write({'''col_1''': '''foo''', '''col_2''': 1}						,    key=10  )
                  writer.write({'''col_1''': '''bar''', '''col_2''': 2}						,    key=10  )
                  UpperCAmelCase			:       Tuple			     =writer.finalize()





@pytest.mark.parametrize('''writer_batch_size'''						,    [None, 2, 10]  )
def      lowerCAmelCase_       ( __lowerCAmelCase  )->     Optional[int]:






      '''simple docstring'''





      UpperCAmelCase			:       int			     =pa.BufferOutputStream()
      with ArrowWriter(
          stream=_UpperCAmelCase						,    writer_batch_size=_UpperCAmelCase						,    hash_salt='''split_name'''						,    check_duplicates=_UpperCAmelCase						,    ) as writer:
            writer.write({'''col_1''': '''foo''', '''col_2''': 1}						,    key=1  )
            writer.write({'''col_1''': '''bar''', '''col_2''': 2}						,    key=2  )
            UpperCAmelCase			:       Optional[Any]			     =writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      _check_output(output.getvalue()						,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1  )





@pytest.mark.parametrize('''writer_batch_size'''						,    [None, 1, 10]  )
@pytest.mark.parametrize(
    '''fields'''						,    [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]  )
def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase  )->     Tuple:






      '''simple docstring'''





      UpperCAmelCase			:       Tuple			     =pa.BufferOutputStream()
      UpperCAmelCase			:       List[Any]			     =pa.schema(_UpperCAmelCase  ) if fields else None
      with ArrowWriter(stream=_UpperCAmelCase						,    schema=_UpperCAmelCase						,    writer_batch_size=_UpperCAmelCase  ) as writer:
            writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}  )
            writer.write_batch({'''col_1''': [], '''col_2''': []}  )
            UpperCAmelCase			:       Union[str, Any]			     =writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            UpperCAmelCase			:       List[str]			     ={"col_1": pa.string(), "col_2": pa.intaa()}
      assert writer._schema == pa.schema(_UpperCAmelCase						,    metadata=writer._schema.metadata  )
      _check_output(output.getvalue()						,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1  )





@pytest.mark.parametrize('''writer_batch_size'''						,    [None, 1, 10]  )
@pytest.mark.parametrize(
    '''fields'''						,    [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]  )
def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase  )->     str:






      '''simple docstring'''





      UpperCAmelCase			:       Any			     =pa.BufferOutputStream()
      UpperCAmelCase			:       str			     =pa.schema(_UpperCAmelCase  ) if fields else None
      with ArrowWriter(stream=_UpperCAmelCase						,    schema=_UpperCAmelCase						,    writer_batch_size=_UpperCAmelCase  ) as writer:
            writer.write_table(pa.Table.from_pydict({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}  )  )
            UpperCAmelCase			:       Optional[Any]			     =writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            UpperCAmelCase			:       Dict			     ={"col_1": pa.string(), "col_2": pa.intaa()}
      assert writer._schema == pa.schema(_UpperCAmelCase						,    metadata=writer._schema.metadata  )
      _check_output(output.getvalue()						,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1  )





@pytest.mark.parametrize('''writer_batch_size'''						,    [None, 1, 10]  )
@pytest.mark.parametrize(
    '''fields'''						,    [None, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}, {'''col_1''': pa.string(), '''col_2''': pa.intaa()}]  )
def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase  )->     Union[str, Any]:






      '''simple docstring'''





      UpperCAmelCase			:       Tuple			     =pa.BufferOutputStream()
      UpperCAmelCase			:       Any			     =pa.schema(_UpperCAmelCase  ) if fields else None
      with ArrowWriter(stream=_UpperCAmelCase						,    schema=_UpperCAmelCase						,    writer_batch_size=_UpperCAmelCase  ) as writer:
            writer.write_row(pa.Table.from_pydict({'''col_1''': ['''foo'''], '''col_2''': [1]}  )  )
            writer.write_row(pa.Table.from_pydict({'''col_1''': ['''bar'''], '''col_2''': [2]}  )  )
            UpperCAmelCase			:       Union[str, Any]			     =writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      if not fields:
            UpperCAmelCase			:       List[str]			     ={"col_1": pa.string(), "col_2": pa.intaa()}
      assert writer._schema == pa.schema(_UpperCAmelCase						,    metadata=writer._schema.metadata  )
      _check_output(output.getvalue()						,    expected_num_chunks=num_examples if writer_batch_size == 1 else 1  )





def      lowerCAmelCase_       ( )->     Tuple:






      '''simple docstring'''





      with tempfile.TemporaryDirectory() as tmp_dir:
            UpperCAmelCase			:       Optional[int]			     ={"col_1": pa.string(), "col_2": pa.intaa()}
            UpperCAmelCase			:       Any			     =os.path.join(_UpperCAmelCase						,    '''test.arrow'''  )
            with ArrowWriter(path=_UpperCAmelCase						,    schema=pa.schema(_UpperCAmelCase  )  ) as writer:
                  writer.write_batch({'''col_1''': ['''foo''', '''bar'''], '''col_2''': [1, 2]}  )
                  UpperCAmelCase			:       List[Any]			     =writer.finalize()
            assert num_examples == 2
            assert num_bytes > 0
            assert writer._schema == pa.schema(_UpperCAmelCase						,    metadata=writer._schema.metadata  )
            _check_output(_UpperCAmelCase						,    1  )





def      lowerCAmelCase_       ( __lowerCAmelCase  )->     Dict:






      '''simple docstring'''





      if pa.types.is_list(_UpperCAmelCase  ):
            return get_base_dtype(arr_type.value_type  )
      else:
            return arr_type





def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase  )->     List[str]:






      '''simple docstring'''





      if isinstance(lst[0]						,    _UpperCAmelCase  ):
            change_first_primitive_element_in_list(lst[0]						,    _UpperCAmelCase  )
      else:
            UpperCAmelCase			:       Optional[int]			     =value





@pytest.mark.parametrize('''optimized_int_type, expected_dtype'''						,    [(None, pa.intaa()), (Value('''int32'''  ), pa.intaa())]  )
@pytest.mark.parametrize('''sequence'''						,    [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]  )
def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase  )->     Optional[int]:






      '''simple docstring'''





      UpperCAmelCase			:       Tuple			     =pa.array(TypedSequence(_UpperCAmelCase						,    optimized_int_type=_UpperCAmelCase  )  )
      assert get_base_dtype(arr.type  ) == expected_dtype





@pytest.mark.parametrize(
    '''col, expected_dtype'''						,    [
        ('''attention_mask''', pa.inta()),
        ('''special_tokens_mask''', pa.inta()),
        ('''token_type_ids''', pa.inta()),
        ('''input_ids''', pa.intaa()),
        ('''other''', pa.intaa()),
    ]						,    )
@pytest.mark.parametrize('''sequence'''						,    [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]  )
def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase  )->     Optional[int]:






      '''simple docstring'''





      UpperCAmelCase			:       List[str]			     =pa.array(OptimizedTypedSequence(_UpperCAmelCase						,    col=_UpperCAmelCase  )  )
      assert get_base_dtype(arr.type  ) == expected_dtype

      # not in range
      if col != "other":
            # avoids errors due to in-place modifications
            UpperCAmelCase			:       Optional[int]			     =copy.deepcopy(_UpperCAmelCase  )
            UpperCAmelCase			:       str			     =np.iinfo(expected_dtype.to_pandas_dtype()  ).max + 1
            change_first_primitive_element_in_list(_UpperCAmelCase						,    _UpperCAmelCase  )
            UpperCAmelCase			:       int			     =pa.array(OptimizedTypedSequence(_UpperCAmelCase						,    col=_UpperCAmelCase  )  )
            assert get_base_dtype(arr.type  ) == pa.intaa()





@pytest.mark.parametrize('''raise_exception'''						,    [False, True]  )
def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase  )->     Optional[int]:






      '''simple docstring'''





      UpperCAmelCase			:       Any			     =str(tmp_path / '''dataset-train.arrow'''  )
      try:
            with ArrowWriter(path=_UpperCAmelCase  ) as writer:
                  if raise_exception:
                        raise pa.lib.ArrowInvalid()
                  else:
                        writer.stream.close()
      except pa.lib.ArrowInvalid:
            pass
      finally:
            assert writer.stream.closed





def      lowerCAmelCase_       ( __lowerCAmelCase  )->     int:






      '''simple docstring'''





      UpperCAmelCase			:       str			     ="mock://dataset-train.arrow"
      with ArrowWriter(path=_UpperCAmelCase						,    storage_options=mockfs.storage_options  ) as writer:
            assert isinstance(writer._fs						,    type(_UpperCAmelCase  )  )
            assert writer._fs.storage_options == mockfs.storage_options
            writer.write({'''col_1''': '''foo''', '''col_2''': 1}  )
            writer.write({'''col_1''': '''bar''', '''col_2''': 2}  )
            UpperCAmelCase			:       Any			     =writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      assert mockfs.exists(_UpperCAmelCase  )





def      lowerCAmelCase_       ( )->     List[str]:






      '''simple docstring'''





      UpperCAmelCase			:       List[str]			     =pa.BufferOutputStream()
      with ParquetWriter(stream=_UpperCAmelCase  ) as writer:
            writer.write({'''col_1''': '''foo''', '''col_2''': 1}  )
            writer.write({'''col_1''': '''bar''', '''col_2''': 2}  )
            UpperCAmelCase			:       Dict			     =writer.finalize()
      assert num_examples == 2
      assert num_bytes > 0
      UpperCAmelCase			:       int			     =pa.BufferReader(output.getvalue()  )
      UpperCAmelCase			:       pa.Table			     =pq.read_table(_UpperCAmelCase  )
      assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}





@require_pil
@pytest.mark.parametrize('''embed_local_files'''						,    [False, True]  )
def      lowerCAmelCase_       ( __lowerCAmelCase						,    __lowerCAmelCase  )->     Any:






      '''simple docstring'''





      import PIL.Image

      UpperCAmelCase			:       Any			     =str(tmp_path / '''test_image_rgb.jpg'''  )
      PIL.Image.fromarray(np.zeros((5, 5)						,    dtype=np.uinta  )  ).save(_UpperCAmelCase						,    format='''png'''  )
      UpperCAmelCase			:       Any			     =pa.BufferOutputStream()
      with ParquetWriter(
          stream=_UpperCAmelCase						,    features=Features({'''image''': Image()}  )						,    embed_local_files=_UpperCAmelCase  ) as writer:
            writer.write({'''image''': image_path}  )
            writer.finalize()
      UpperCAmelCase			:       Union[str, Any]			     =pa.BufferReader(output.getvalue()  )
      UpperCAmelCase			:       pa.Table			     =pq.read_table(_UpperCAmelCase  )
      UpperCAmelCase			:       int			     =pa_table.to_pydict()
      if embed_local_files:
            assert isinstance(out['''image'''][0]['''path''']						,    _UpperCAmelCase  )
            with open(_UpperCAmelCase						,    '''rb'''  ) as f:
                  assert out["image"][0]["bytes"] == f.read()
      else:
            assert out["image"][0]["path"] == image_path
            assert out["image"][0]["bytes"] is None





def      lowerCAmelCase_       ( )->     Dict:






      '''simple docstring'''





      UpperCAmelCase			:       Dict			     =pa.schema([pa.field('''col_1'''						,    pa.string()						,    nullable=_UpperCAmelCase  )]  )
      UpperCAmelCase			:       int			     =pa.BufferOutputStream()
      with ArrowWriter(stream=_UpperCAmelCase  ) as writer:
            writer._build_writer(inferred_schema=_UpperCAmelCase  )
            assert writer._schema == pa.schema([pa.field('''col_1'''						,    pa.string()  )]  )



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:  Optional[int]   	=						{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Any   	=						[
						    """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """WavLMForAudioFrameClassification""",
						    """WavLMForCTC""",
						    """WavLMForSequenceClassification""",
						    """WavLMForXVector""",
						    """WavLMModel""",
						    """WavLMPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_wavlm import (
												    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    WavLMForAudioFrameClassification,
												    WavLMForCTC,
												    WavLMForSequenceClassification,
												    WavLMForXVector,
												    WavLMModel,
												    WavLMPreTrainedModel,
												)

else:
						import sys

						lowerCAmelCase							:  Union[str, Any]   	=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






