


'''simple docstring'''


import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile

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

import datasets
import datasets.config




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      ):
      A_						:  Optional[Any]          =				10
      A_						:  Any          =				datasets.Features(
          {
              """tokens""": datasets.Sequence(datasets.Value("""string""")),
              """labels""": datasets.Sequence(datasets.ClassLabel(names=["""negative""", """positive"""])),
              """answers""": datasets.Sequence(
                  {
                      """text""": datasets.Value("""string"""),
                      """answer_start""": datasets.Value("""int32"""),
                  }),
              """id""": datasets.Value("""int64"""),
          })
      A_						:  str          =				datasets.Dataset.from_dict(
          {
              """tokens""": [["""foo"""] * 5] * n,
              """labels""": [[1] * 5] * n,
              """answers""": [{"""answer_start""": [97], """text""": ["""1976"""]}] * 10,
              """id""": list(range(_A)),
          }							,						features=_A							,						)
      return dataset




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      Tuple):
      A_						:  Union[str, Any]          =				str(tmp_path_factory.mktemp("""data""") / """file.arrow""")
      dataset.map(cache_file_name=_A)
      return filename


# FILE_CONTENT + files


__magic_name__			=      '''\
    Text data.
    Second line of data.'''




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Tuple):
      A_						:  Union[str, Any]          =				tmp_path_factory.mktemp("""data""") / """file.txt"""
      A_						:  Dict          =				FILE_CONTENT
      with open(_A							,						"""w""") as f:
            f.write(_A)
      return filename




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      str):
      import bza

      A_						:  Dict          =				tmp_path_factory.mktemp("""data""") / """file.txt.bz2"""
      A_						:  Dict          =				bytes(_A							,						"""utf-8""")
      with bza.open(_A							,						"""wb""") as f:
            f.write(_A)
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      List[str]):
      import gzip

      A_						:  Dict          =				str(tmp_path_factory.mktemp("""data""") / """file.txt.gz""")
      A_						:  List[str]          =				bytes(_A							,						"""utf-8""")
      with gzip.open(_A							,						"""wb""") as f:
            f.write(_A)
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      List[Any]):

      if datasets.config.LZ4_AVAILABLE:
            import lza.frame

            A_						:  Any          =				tmp_path_factory.mktemp("""data""") / """file.txt.lz4"""
            A_						:  Tuple          =				bytes(_A							,						"""utf-8""")
            with lza.frame.open(_A							,						"""wb""") as f:
                  f.write(_A)
            return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Any							,						lowerCamelCase  :      List[Any]):

      if datasets.config.PY7ZR_AVAILABLE:
            import pyazr

            A_						:  Any          =				tmp_path_factory.mktemp("""data""") / """file.txt.7z"""
            with pyazr.SevenZipFile(_A							,						"""w""") as archive:
                  archive.write(_A							,						arcname=os.path.basename(_A))
            return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Dict							,						lowerCamelCase  :      List[str]):
      import tarfile

      A_						:  Optional[Any]          =				tmp_path_factory.mktemp("""data""") / """file.txt.tar"""
      with tarfile.TarFile(_A							,						"""w""") as f:
            f.add(_A							,						arcname=os.path.basename(_A))
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      List[str]):
      import lzma

      A_						:  List[Any]          =				tmp_path_factory.mktemp("""data""") / """file.txt.xz"""
      A_						:  List[Any]          =				bytes(_A							,						"""utf-8""")
      with lzma.open(_A							,						"""wb""") as f:
            f.write(_A)
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      List[str]):
      import zipfile

      A_						:  Optional[Any]          =				tmp_path_factory.mktemp("""data""") / """file.txt.zip"""
      with zipfile.ZipFile(_A							,						"""w""") as f:
            f.write(_A							,						arcname=os.path.basename(_A))
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Optional[int]):

      if datasets.config.ZSTANDARD_AVAILABLE:
            import zstandard as zstd

            A_						:  List[str]          =				tmp_path_factory.mktemp("""data""") / """file.txt.zst"""
            A_						:  Tuple          =				bytes(_A							,						"""utf-8""")
            with zstd.open(_A							,						"""wb""") as f:
                  f.write(_A)
            return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      int):
      A_						:  Dict          =				tmp_path_factory.mktemp("""data""") / """file.xml"""
      A_						:  int          =				textwrap.dedent(
          """\
    <?xml version=\"1.0\" encoding=\"UTF-8\" ?>
    <tmx version=\"1.4\">
      <header segtype=\"sentence\" srclang=\"ca\" />
      <body>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>
        </tu>
        <tu>
          <tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>
          <tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>
        </tu>
      </body>
    </tmx>""")
      with open(_A							,						"""w""") as f:
            f.write(_A)
      return filename


__magic_name__			=      [
    {'''col_1''': '''0''', '''col_2''': 0, '''col_3''': 0.0},
    {'''col_1''': '''1''', '''col_2''': 1, '''col_3''': 1.0},
    {'''col_1''': '''2''', '''col_2''': 2, '''col_3''': 2.0},
    {'''col_1''': '''3''', '''col_2''': 3, '''col_3''': 3.0},
]
__magic_name__			=      [
    {'''col_1''': '''4''', '''col_2''': 4, '''col_3''': 4.0},
    {'''col_1''': '''5''', '''col_2''': 5, '''col_3''': 5.0},
]
__magic_name__			=      {
    '''col_1''': ['''0''', '''1''', '''2''', '''3'''],
    '''col_2''': [0, 1, 2, 3],
    '''col_3''': [0.0, 1.0, 2.0, 3.0],
}

__magic_name__			=      [
    {'''col_3''': 0.0, '''col_1''': '''0''', '''col_2''': 0},
    {'''col_3''': 1.0, '''col_1''': '''1''', '''col_2''': 1},
]

__magic_name__			=      [
    {'''col_1''': '''s0''', '''col_2''': 0, '''col_3''': 0.0},
    {'''col_1''': '''s1''', '''col_2''': 1, '''col_3''': 1.0},
    {'''col_1''': '''s2''', '''col_2''': 2, '''col_3''': 2.0},
    {'''col_1''': '''s3''', '''col_2''': 3, '''col_3''': 3.0},
]




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      ):
      return DATA_DICT_OF_LISTS




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      List[Any]):
      A_						:  str          =				datasets.Dataset.from_dict(_A)
      A_						:  Union[str, Any]          =				str(tmp_path_factory.mktemp("""data""") / """dataset.arrow""")
      dataset.map(cache_file_name=_A)
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]):
      A_						:  Any          =				str(tmp_path_factory.mktemp("""data""") / """dataset.sqlite""")
      with contextlib.closing(sqlitea.connect(_A)) as con:
            A_						:  Union[str, Any]          =				con.cursor()
            cur.execute("""CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)""")
            for item in DATA:
                  cur.execute("""INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)"""							,						tuple(item.values()))
            con.commit()
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]):
      A_						:  Tuple          =				str(tmp_path_factory.mktemp("""data""") / """dataset.csv""")
      with open(_A							,						"""w"""							,						newline="""""") as f:
            A_						:  List[Any]          =				csv.DictWriter(_A							,						fieldnames=["""col_1""", """col_2""", """col_3"""])
            writer.writeheader()
            for item in DATA:
                  writer.writerow(_A)
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      List[Any]):
      A_						:  Optional[int]          =				str(tmp_path_factory.mktemp("""data""") / """dataset2.csv""")
      with open(_A							,						"""w"""							,						newline="""""") as f:
            A_						:  Dict          =				csv.DictWriter(_A							,						fieldnames=["""col_1""", """col_2""", """col_3"""])
            writer.writeheader()
            for item in DATA:
                  writer.writerow(_A)
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      int							,						lowerCamelCase  :      List[Any]):
      import bza

      A_						:  Optional[Any]          =				tmp_path_factory.mktemp("""data""") / """dataset.csv.bz2"""
      with open(_A							,						"""rb""") as f:
            A_						:  str          =				f.read()
      # data = bytes(FILE_CONTENT, "utf-8")
      with bza.open(_A							,						"""wb""") as f:
            f.write(_A)
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Any							,						lowerCamelCase  :      Tuple							,						lowerCamelCase  :      int):
      A_						:  Union[str, Any]          =				tmp_path_factory.mktemp("""data""") / """dataset.csv.zip"""
      with zipfile.ZipFile(_A							,						"""w""") as f:
            f.write(_A							,						arcname=os.path.basename(_A))
            f.write(_A							,						arcname=os.path.basename(_A))
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      List[Any]):
      A_						:  Tuple          =				tmp_path_factory.mktemp("""data""") / """dataset.csv.zip"""
      with zipfile.ZipFile(_A							,						"""w""") as f:
            f.write(_A							,						arcname=os.path.basename(csv_path.replace(""".csv"""							,						""".CSV""")))
            f.write(_A							,						arcname=os.path.basename(csva_path.replace(""".csv"""							,						""".CSV""")))
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Any							,						lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      Tuple):
      A_						:  int          =				tmp_path_factory.mktemp("""data""") / """dataset_with_dir.csv.zip"""
      with zipfile.ZipFile(_A							,						"""w""") as f:
            f.write(_A							,						arcname=os.path.join("""main_dir"""							,						os.path.basename(_A)))
            f.write(_A							,						arcname=os.path.join("""main_dir"""							,						os.path.basename(_A)))
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Tuple):
      A_						:  int          =				str(tmp_path_factory.mktemp("""data""") / """dataset.parquet""")
      A_						:  Any          =				pa.schema(
          {
              """col_1""": pa.string(),
              """col_2""": pa.intaa(),
              """col_3""": pa.floataa(),
          })
      with open(_A							,						"""wb""") as f:
            A_						:  Optional[Any]          =				pq.ParquetWriter(_A							,						schema=_A)
            A_						:  Any          =				pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(_A))] for k in DATA[0]}							,						schema=_A)
            writer.write_table(_A)
            writer.close()
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Optional[Any]):
      A_						:  int          =				str(tmp_path_factory.mktemp("""data""") / """dataset.json""")
      A_						:  Union[str, Any]          =				{"""data""": DATA}
      with open(_A							,						"""w""") as f:
            json.dump(_A							,						_A)
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      int):
      A_						:  Union[str, Any]          =				str(tmp_path_factory.mktemp("""data""") / """dataset.json""")
      A_						:  List[str]          =				{"""data""": DATA_DICT_OF_LISTS}
      with open(_A							,						"""w""") as f:
            json.dump(_A							,						_A)
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Any):
      A_						:  Optional[int]          =				str(tmp_path_factory.mktemp("""data""") / """dataset.jsonl""")
      with open(_A							,						"""w""") as f:
            for item in DATA:
                  f.write(json.dumps(_A) + """\n""")
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      List[Any]):
      A_						:  Dict          =				str(tmp_path_factory.mktemp("""data""") / """dataset2.jsonl""")
      with open(_A							,						"""w""") as f:
            for item in DATA:
                  f.write(json.dumps(_A) + """\n""")
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Any):
      A_						:  Any          =				str(tmp_path_factory.mktemp("""data""") / """dataset_312.jsonl""")
      with open(_A							,						"""w""") as f:
            for item in DATA_312:
                  f.write(json.dumps(_A) + """\n""")
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Any):
      A_						:  List[Any]          =				str(tmp_path_factory.mktemp("""data""") / """dataset-str.jsonl""")
      with open(_A							,						"""w""") as f:
            for item in DATA_STR:
                  f.write(json.dumps(_A) + """\n""")
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      List[Any]):
      import gzip

      A_						:  Dict          =				str(tmp_path_factory.mktemp("""data""") / """dataset.txt.gz""")
      with open(_A							,						"""rb""") as orig_file:
            with gzip.open(_A							,						"""wb""") as zipped_file:
                  zipped_file.writelines(_A)
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      List[Any]):
      import gzip

      A_						:  List[Any]          =				str(tmp_path_factory.mktemp("""data""") / """dataset.jsonl.gz""")
      with open(_A							,						"""rb""") as orig_file:
            with gzip.open(_A							,						"""wb""") as zipped_file:
                  zipped_file.writelines(_A)
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      str							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      Optional[int]):
      A_						:  List[Any]          =				tmp_path_factory.mktemp("""data""") / """dataset.jsonl.zip"""
      with zipfile.ZipFile(_A							,						"""w""") as f:
            f.write(_A							,						arcname=os.path.basename(_A))
            f.write(_A							,						arcname=os.path.basename(_A))
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      str							,						lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      Any):
      A_						:  Optional[int]          =				tmp_path_factory.mktemp("""data""") / """dataset_nested.jsonl.zip"""
      with zipfile.ZipFile(_A							,						"""w""") as f:
            f.write(_A							,						arcname=os.path.join("""nested"""							,						os.path.basename(_A)))
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      int							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      List[Any]):
      A_						:  Optional[Any]          =				tmp_path_factory.mktemp("""data""") / """dataset_with_dir.jsonl.zip"""
      with zipfile.ZipFile(_A							,						"""w""") as f:
            f.write(_A							,						arcname=os.path.join("""main_dir"""							,						os.path.basename(_A)))
            f.write(_A							,						arcname=os.path.join("""main_dir"""							,						os.path.basename(_A)))
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      List[str]							,						lowerCamelCase  :      Optional[Any]):
      A_						:  int          =				tmp_path_factory.mktemp("""data""") / """dataset.jsonl.tar"""
      with tarfile.TarFile(_A							,						"""w""") as f:
            f.add(_A							,						arcname=os.path.basename(_A))
            f.add(_A							,						arcname=os.path.basename(_A))
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      Dict							,						lowerCamelCase  :      List[Any]):
      A_						:  int          =				tmp_path_factory.mktemp("""data""") / """dataset_nested.jsonl.tar"""
      with tarfile.TarFile(_A							,						"""w""") as f:
            f.add(_A							,						arcname=os.path.join("""nested"""							,						os.path.basename(_A)))
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Tuple):
      A_						:  List[str]          =				["""0""", """1""", """2""", """3"""]
      A_						:  str          =				str(tmp_path_factory.mktemp("""data""") / """dataset.txt""")
      with open(_A							,						"""w""") as f:
            for item in data:
                  f.write(item + """\n""")
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      int):
      A_						:  List[Any]          =				["""0""", """1""", """2""", """3"""]
      A_						:  Union[str, Any]          =				str(tmp_path_factory.mktemp("""data""") / """dataset2.txt""")
      with open(_A							,						"""w""") as f:
            for item in data:
                  f.write(item + """\n""")
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Tuple):
      A_						:  Union[str, Any]          =				["""0""", """1""", """2""", """3"""]
      A_						:  Dict          =				tmp_path_factory.mktemp("""data""") / """dataset.abc"""
      with open(_A							,						"""w""") as f:
            for item in data:
                  f.write(item + """\n""")
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      Dict							,						lowerCamelCase  :      Optional[Any]):
      A_						:  int          =				tmp_path_factory.mktemp("""data""") / """dataset.text.zip"""
      with zipfile.ZipFile(_A							,						"""w""") as f:
            f.write(_A							,						arcname=os.path.basename(_A))
            f.write(_A							,						arcname=os.path.basename(_A))
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Dict							,						lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      List[Any]):
      A_						:  Dict          =				tmp_path_factory.mktemp("""data""") / """dataset_with_dir.text.zip"""
      with zipfile.ZipFile(_A							,						"""w""") as f:
            f.write(_A							,						arcname=os.path.join("""main_dir"""							,						os.path.basename(_A)))
            f.write(_A							,						arcname=os.path.join("""main_dir"""							,						os.path.basename(_A)))
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      Dict):
      A_						:  Optional[Any]          =				tmp_path_factory.mktemp("""data""") / """dataset.ext.zip"""
      with zipfile.ZipFile(_A							,						"""w""") as f:
            f.write(_A							,						arcname=os.path.basename("""unsupported.ext"""))
            f.write(_A							,						arcname=os.path.basename("""unsupported_2.ext"""))
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      int):
      A_						:  Optional[Any]          =				"""\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""])
      A_						:  List[Any]          =				str(tmp_path_factory.mktemp("""data""") / """dataset_with_unicode_new_lines.txt""")
      with open(_A							,						"""w"""							,						encoding="""utf-8""") as f:
            f.write(_A)
      return path




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      ):
      return os.path.join("""tests"""							,						"""features"""							,						"""data"""							,						"""test_image_rgb.jpg""")




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      ):
      return os.path.join("""tests"""							,						"""features"""							,						"""data"""							,						"""test_audio_44100.wav""")




@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      Dict):
      A_						:  int          =				tmp_path_factory.mktemp("""data""") / """dataset.img.zip"""
      with zipfile.ZipFile(_A							,						"""w""") as f:
            f.write(_A							,						arcname=os.path.basename(_A))
            f.write(_A							,						arcname=os.path.basename(_A).replace(""".jpg"""							,						"""2.jpg"""))
      return path






@pytest.fixture(scope="""session""")
def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]):
      A_						:  Dict          =				tmp_path_factory.mktemp("""data_dir""")

      (data_dir / "subdir").mkdir()
      with open(data_dir / """subdir""" / """train.txt"""							,						"""w""") as f:
            f.write("""foo\n""" * 10)
      with open(data_dir / """subdir""" / """test.txt"""							,						"""w""") as f:
            f.write("""bar\n""" * 10)
      # hidden file
      with open(data_dir / """subdir""" / """.test.txt"""							,						"""w""") as f:
            f.write("""bar\n""" * 10)

      # hidden directory
      (data_dir / ".subdir").mkdir()
      with open(data_dir / """.subdir""" / """train.txt"""							,						"""w""") as f:
            f.write("""foo\n""" * 10)
      with open(data_dir / """.subdir""" / """test.txt"""							,						"""w""") as f:
            f.write("""bar\n""" * 10)

      return data_dir







'''simple docstring'''


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


__magic_name__			=      logging.get_logger(__name__)



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""input_features""", """is_longer"""]






  def __init__(							self :       Dict	,_a :       Optional[int]=64	,_a :       List[Any]=48000	,_a :       str=480	,_a :       Optional[Any]=10	,_a :       Optional[int]=1024	,_a :       Tuple=0.0	,_a :       str=False	,_a :       float = 0	,_a :       float = 14000	,_a :       int = None	,_a :       str = "fusion"	,_a :       str = "repeatpad"	,**_a :       Tuple	,):

        '''simple docstring'''




        super().__init__(
            feature_size=_a	,sampling_rate=_a	,padding_value=_a	,return_attention_mask=_a	,**_a	,)
        A_						:  Tuple          =				top_db
        A_						:  Tuple          =				truncation
        A_						:  Optional[Any]          =				padding
        A_						:  Optional[int]          =				fft_window_size
        A_						:  Dict          =				(fft_window_size >> 1) + 1
        A_						:  Any          =				hop_length
        A_						:  List[Any]          =				max_length_s
        A_						:  Tuple          =				max_length_s * sampling_rate
        A_						:  Tuple          =				sampling_rate
        A_						:  Optional[int]          =				frequency_min
        A_						:  Tuple          =				frequency_max
        A_						:  Tuple          =				mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins	,num_mel_filters=_a	,min_frequency=_a	,max_frequency=_a	,sampling_rate=_a	,norm=_a	,mel_scale="""htk"""	,)
        A_						:  Dict          =				mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins	,num_mel_filters=_a	,min_frequency=_a	,max_frequency=_a	,sampling_rate=_a	,norm="""slaney"""	,mel_scale="""slaney"""	,)






  def        _a							(							self :       int ):

        '''simple docstring'''




        A_						:  int          =				copy.deepcopy(self.__dict__ )
        A_						:  Tuple          =				self.__class__.__name__
        if "mel_filters" in output:
              del output["mel_filters"]
        if "mel_filters_slaney" in output:
              del output["mel_filters_slaney"]
        return output






  def        _a							(							self :       Dict	,_a :       np.array	,_a :       Optional[np.array] = None ):

        '''simple docstring'''




        A_						:  List[str]          =				spectrogram(
            _a	,window_function(self.fft_window_size	,"""hann""" )	,frame_length=self.fft_window_size	,hop_length=self.hop_length	,power=2.0	,mel_filters=_a	,log_mel="""dB"""	,)
        return log_mel_spectrogram.T






  def        _a							(							self :       Optional[int]	,_a :       Dict	,_a :       Optional[Any]	,_a :       Optional[int] ):

        '''simple docstring'''




        A_						:  Dict          =				np.array_split(list(range(0	,total_frames - chunk_frames + 1 ) )	,3 )
        if len(ranges[1] ) == 0:
              # if the audio is too short, we just use the first chunk
              A_						:  List[Any]          =				[0]
        if len(ranges[2] ) == 0:
              # if the audio is too short, we just use the first chunk
              A_						:  int          =				[0]
        # randomly choose index for each part
        A_						:  List[str]          =				np.random.choice(ranges[0] )
        A_						:  int          =				np.random.choice(ranges[1] )
        A_						:  Optional[int]          =				np.random.choice(ranges[2] )

        A_						:  Tuple          =				mel[idx_front : idx_front + chunk_frames, :]
        A_						:  Dict          =				mel[idx_middle : idx_middle + chunk_frames, :]
        A_						:  Dict          =				mel[idx_back : idx_back + chunk_frames, :]

        A_						:  Optional[int]          =				torch.tensor(mel[None, None, :] )
        A_						:  Dict          =				torch.nn.functional.interpolate(
            _a	,size=[chunk_frames, 64]	,mode="""bilinear"""	,align_corners=_a )
        A_						:  str          =				mel_shrink[0][0].numpy()
        A_						:  Tuple          =				np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]	,axis=0 )
        return mel_fusion






  def        _a							(							self :       Dict	,_a :       np.array	,_a :       Optional[Any]	,_a :       int	,_a :       Dict ):

        '''simple docstring'''




        if waveform.shape[0] > max_length:
              if truncation == "rand_trunc":
                    A_						:  Dict          =				True
                    # random crop to max_length (for compatibility) -> this should be handled by self.pad
                    A_						:  Tuple          =				len(_a ) - max_length
                    A_						:  Optional[int]          =				np.random.randint(0	,overflow + 1 )
                    A_						:  List[Any]          =				waveform[idx : idx + max_length]
                    A_						:  Optional[Any]          =				self._np_extract_fbank_features(_a	,self.mel_filters_slaney )[None, :]
              elif truncation == "fusion":
                    A_						:  Dict          =				self._np_extract_fbank_features(_a	,self.mel_filters )
                    A_						:  Tuple          =				max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                    A_						:  Optional[int]          =				mel.shape[0]
                    if chunk_frames == total_frames:
                          # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                          # In this case, we just use the whole audio.
                          A_						:  Optional[int]          =				np.stack([mel, mel, mel, mel]	,axis=0 )
                          A_						:  str          =				False
                    else:
                          A_						:  str          =				self._random_mel_fusion(_a	,_a	,_a )
                          A_						:  Optional[Any]          =				True
              else:
                    raise NotImplementedError(f'data_truncating {truncation} not implemented' )

        else:
              A_						:  Optional[int]          =				False
              # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
              if waveform.shape[0] < max_length:
                    if padding == "repeat":
                          A_						:  int          =				int(max_length / len(_a ) )
                          A_						:  Any          =				np.stack(np.tile(_a	,n_repeat + 1 ) )[:max_length]
                    if padding == "repeatpad":
                          A_						:  List[str]          =				int(max_length / len(_a ) )
                          A_						:  Optional[Any]          =				np.stack(np.tile(_a	,_a ) )
                    A_						:  Any          =				np.pad(_a	,(0, max_length - waveform.shape[0])	,mode="""constant"""	,constant_values=0 )

              if truncation == "fusion":
                    A_						:  List[Any]          =				self._np_extract_fbank_features(_a	,self.mel_filters )
                    A_						:  Optional[Any]          =				np.stack([input_mel, input_mel, input_mel, input_mel]	,axis=0 )
              else:
                    A_						:  Union[str, Any]          =				self._np_extract_fbank_features(_a	,self.mel_filters_slaney )[None, :]

        return input_mel, longer






  def __call__(							self :       List[Any]	,_a :       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,_a :       str = None	,_a :       Optional[str] = None	,_a :       Optional[int] = None	,_a :       Optional[int] = None	,_a :       Optional[Union[str, TensorType]] = None	,**_a :       Any	,):

        '''simple docstring'''




        A_						:  List[str]          =				truncation if truncation is not None else self.truncation
        A_						:  List[Any]          =				padding if padding else self.padding

        if sampling_rate is not None:
              if sampling_rate != self.sampling_rate:
                    raise ValueError(
                        f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
                        f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
                        f' was sampled with {self.sampling_rate} and not {sampling_rate}.' )
        else:
              logger.warning(
                  """It is strongly recommended to pass the `sampling_rate` argument to this function. """
                  """Failing to do so can result in silent errors that might be hard to debug.""" )

        A_						:  Any          =				isinstance(_a	,np.ndarray ) and len(raw_speech.shape ) > 1
        if is_batched_numpy and len(raw_speech.shape ) > 2:
              raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
        A_						:  int          =				is_batched_numpy or (
            isinstance(_a	,(list, tuple) ) and (isinstance(raw_speech[0]	,(np.ndarray, tuple, list) ))
        )

        if is_batched:
              A_						:  Optional[int]          =				[np.asarray(_a	,dtype=np.floataa ) for speech in raw_speech]
        elif not is_batched and not isinstance(_a	,np.ndarray ):
              A_						:  str          =				np.asarray(_a	,dtype=np.floataa )
        elif isinstance(_a	,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
              A_						:  Tuple          =				raw_speech.astype(np.floataa )

        # always return batch
        if not is_batched:
              A_						:  Any          =				[np.asarray(_a )]

        # convert to mel spectrogram, truncate and pad if needed.
        A_						:  str          =				[
            self._get_input_mel(_a	,max_length if max_length else self.nb_max_samples	,_a	,_a )
            for waveform in raw_speech
        ]

        A_						:  int          =				[]
        A_						:  Any          =				[]
        for mel, longer in padded_inputs:
              input_mel.append(_a )
              is_longer.append(_a )

        if truncation == "fusion" and sum(_a ) == 0:
              # if no audio is longer than 10s, then randomly select one audio to be longer
              A_						:  List[Any]          =				np.random.randint(0	,len(_a ) )
              A_						:  List[str]          =				True

        if isinstance(input_mel[0]	,_a ):
              A_						:  Tuple          =				[np.asarray(_a	,dtype=np.floataa ) for feature in input_mel]

        # is_longer is a list of bool
        A_						:  List[str]          =				[[longer] for longer in is_longer]

        A_						:  Optional[Any]          =				{"""input_features""": input_mel, """is_longer""": is_longer}
        A_						:  int          =				BatchFeature(_a )

        if return_tensors is not None:
              A_						:  int          =				input_features.convert_to_tensors(_a )

        return input_features
