






import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
			import PIL.Image

			from .features import FeatureType


A_		:		Optional[List[str]]		     =			None
A_		:		Union[str, Any]		     =			'<' if sys.byteorder == 'little' else '>'
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
A_		:		Any		     =			[
    np.dtype('|b1'),
    np.dtype('|u1'),
    np.dtype('<u2'),
    np.dtype('>u2'),
    np.dtype('<i2'),
    np.dtype('>i2'),
    np.dtype('<u4'),
    np.dtype('>u4'),
    np.dtype('<i4'),
    np.dtype('>i4'),
    np.dtype('<f4'),
    np.dtype('>f4'),
    np.dtype('<f8'),
    np.dtype('>f8'),
]




@dataclass
class     _lowerCAmelCase:





							"""simple docstring"""







							a    :		bool									=True
							a    :		Optional[str]									=None
							# Automatically constructed
							a    :		ClassVar[str]									="PIL.Image.Image"
							a    :		ClassVar[Any]									=pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()} )
							a    :		str									=field(default='''Image'''							,     init=UpperCAmelCase_							,     repr=UpperCAmelCase_ )





							def __call__( self							):
									return self.pa_type





							def       _a   ( self  ,      _lowerCamelCase							):
									if config.PIL_AVAILABLE:
											import PIL.Image
									else:
											raise ImportError('To support encoding images, please install \'Pillow\'.'							)

									if isinstance(_lowerCamelCase  ,      _lowerCamelCase							):
											UpperCamelCase_: Dict   				=    np.array(_lowerCamelCase							)

									if isinstance(_lowerCamelCase  ,      _lowerCamelCase							):
											return {"path": value, "bytes": None}
									elif isinstance(_lowerCamelCase  ,      _lowerCamelCase							):
											return {"path": None, "bytes": value}
									elif isinstance(_lowerCamelCase  ,      np.ndarray							):
											# convert the image array to PNG/TIFF bytes
											return encode_np_array(_lowerCamelCase							)
									elif isinstance(_lowerCamelCase  ,      PIL.Image.Image							):
											# convert the PIL image to bytes (default format is PNG/TIFF)
											return encode_pil_image(_lowerCamelCase							)
									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
											return {"bytes": None, "path": value.get('path'							)}
									elif value.get('bytes'							) is not None or value.get('path'							) is not None:
											# store the image bytes, and path is used to infer the image format using the file extension
											return {"bytes": value.get('bytes'							), "path": value.get('path'							)}
									else:
											raise ValueError(
											    f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''							)





							def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase=None							):
									if not self.decode:
											raise RuntimeError('Decoding is disabled for this feature. Please use Image(decode=True) instead.'							)

									if config.PIL_AVAILABLE:
											import PIL.Image
									else:
											raise ImportError('To support decoding images, please install \'Pillow\'.'							)

									if token_per_repo_id is None:
											UpperCamelCase_: List[Any]   				=    {}

									UpperCamelCase_: Any   				=    value['path'], value['bytes']
									if bytes_ is None:
											if path is None:
													raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.'''							)
											else:
													if is_local_path(_lowerCamelCase							):
															UpperCamelCase_: List[Any]   				=    PIL.Image.open(_lowerCamelCase							)
													else:
															UpperCamelCase_: Optional[Any]   				=    path.split('::'							)[-1]
															try:
																	UpperCamelCase_: Any   				=    string_to_dict(_lowerCamelCase  ,      config.HUB_DATASETS_URL							)['repo_id']
																	UpperCamelCase_: Tuple   				=    token_per_repo_id.get(_lowerCamelCase							)
															except ValueError:
																	UpperCamelCase_: Tuple   				=    None
															with xopen(_lowerCamelCase  ,      'rb'  ,      use_auth_token=_lowerCamelCase							) as f:
																	UpperCamelCase_: Dict   				=    BytesIO(f.read()							)
															UpperCamelCase_: int   				=    PIL.Image.open(bytes_							)
									else:
											UpperCamelCase_: Optional[int]   				=    PIL.Image.open(BytesIO(bytes_							)							)
									image.load()  # to avoid "Too many open files" errors
									return image





							def       _a   ( self							):
									from .features import Value

									return (
									    self
									    if self.decode
									    else {
									        "bytes": Value('binary'							),
									        "path": Value('string'							),
									    }
									)





							def       _a   ( self  ,      _lowerCamelCase							):
									if pa.types.is_string(storage.type							):
											UpperCamelCase_: int   				=    pa.array([None] * len(_lowerCamelCase							)  ,      type=pa.binary()							)
											UpperCamelCase_: int   				=    pa.StructArray.from_arrays([bytes_array, storage]  ,      ['bytes', 'path']  ,      mask=storage.is_null()							)
									elif pa.types.is_binary(storage.type							):
											UpperCamelCase_: str   				=    pa.array([None] * len(_lowerCamelCase							)  ,      type=pa.string()							)
											UpperCamelCase_: Optional[int]   				=    pa.StructArray.from_arrays([storage, path_array]  ,      ['bytes', 'path']  ,      mask=storage.is_null()							)
									elif pa.types.is_struct(storage.type							):
											if storage.type.get_field_index('bytes'							) >= 0:
													UpperCamelCase_: Union[str, Any]   				=    storage.field('bytes'							)
											else:
													UpperCamelCase_: Dict   				=    pa.array([None] * len(_lowerCamelCase							)  ,      type=pa.binary()							)
											if storage.type.get_field_index('path'							) >= 0:
													UpperCamelCase_: List[Any]   				=    storage.field('path'							)
											else:
													UpperCamelCase_: Dict   				=    pa.array([None] * len(_lowerCamelCase							)  ,      type=pa.string()							)
											UpperCamelCase_: List[Any]   				=    pa.StructArray.from_arrays([bytes_array, path_array]  ,      ['bytes', 'path']  ,      mask=storage.is_null()							)
									elif pa.types.is_list(storage.type							):
											UpperCamelCase_: int   				=    pa.array(
											    [encode_np_array(np.array(_lowerCamelCase							)							)['bytes'] if arr is not None else None for arr in storage.to_pylist()]  ,      type=pa.binary()  ,      )
											UpperCamelCase_: Optional[Any]   				=    pa.array([None] * len(_lowerCamelCase							)  ,      type=pa.string()							)
											UpperCamelCase_: Optional[Any]   				=    pa.StructArray.from_arrays(
											    [bytes_array, path_array]  ,      ['bytes', 'path']  ,      mask=bytes_array.is_null()							)
									return array_cast(_lowerCamelCase  ,      self.pa_type							)





							def       _a   ( self  ,      _lowerCamelCase							):

									@no_op_if_value_is_null
									def path_to_bytes(_lowerCamelCase							):
											with xopen(_lowerCamelCase  ,      'rb'							) as f:
													UpperCamelCase_: Dict   				=    f.read()
											return bytes_

									UpperCamelCase_: Any   				=    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_: Tuple   				=    pa.array(
									    [os.path.basename(_lowerCamelCase							) if path is not None else None for path in storage.field('path'							).to_pylist()]  ,      type=pa.string()  ,      )
									UpperCamelCase_: List[str]   				=    pa.StructArray.from_arrays([bytes_array, path_array]  ,      ['bytes', 'path']  ,      mask=bytes_array.is_null()							)
									return array_cast(_lowerCamelCase  ,      self.pa_type							)







def   snake_case ()	->    List[str]:
		if config.PIL_AVAILABLE:
				import PIL.Image
		else:
				raise ImportError('To support encoding images, please install \'Pillow\'.'  )

		global _IMAGE_COMPRESSION_FORMATS
		if _IMAGE_COMPRESSION_FORMATS is None:
				PIL.Image.init()
				UpperCamelCase_: Union[str, Any]   				=    list(set(PIL.Image.OPEN.keys()  ) & set(PIL.Image.SAVE.keys()  )  )
		return _IMAGE_COMPRESSION_FORMATS

def   snake_case (UpperCAmelCase__  )	->    bytes:
		UpperCamelCase_: Optional[int]   				=    BytesIO()
		if image.format in list_image_compression_formats():
				UpperCamelCase_: Dict   				=    image.format
		else:
				UpperCamelCase_: List[Any]   				=    'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
		image.save(UpperCAmelCase__     ,   format=UpperCAmelCase__  )
		return buffer.getvalue()

def   snake_case (UpperCAmelCase__  )	->    dict:
		if hasattr(UpperCAmelCase__     ,   'filename'  ) and image.filename != "":
				return {"path": image.filename, "bytes": None}
		else:
				return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__  )}

def   snake_case (UpperCAmelCase__  )	->    dict:
		if config.PIL_AVAILABLE:
				import PIL.Image
		else:
				raise ImportError('To support encoding images, please install \'Pillow\'.'  )

		UpperCamelCase_: Optional[Any]   				=    array.dtype
		UpperCamelCase_: Any   				=    dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
		UpperCamelCase_: Optional[Any]   				=    dtype.kind
		UpperCamelCase_: str   				=    dtype.itemsize

		UpperCamelCase_: int   				=    None

		# Multi-channel array case (only np.dtype("|u1") is allowed)
		if array.shape[2:]:
				UpperCamelCase_: Optional[Any]   				=    np.dtype('|u1'  )
				if dtype_kind not in ["u", "i"]:
						raise TypeError(
						    F'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.'''  )
				if dtype is not dest_dtype:
						warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''''  )
				# Exact match
		elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
				UpperCamelCase_: Optional[int]   				=    dtype
		else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
				while dtype_itemsize >= 1:
						UpperCamelCase_: Optional[int]   				=    dtype_byteorder + dtype_kind + str(UpperCAmelCase__  )
						UpperCamelCase_: Union[str, Any]   				=    np.dtype(UpperCAmelCase__  )
						if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
								warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''''  )
								break
						else:
								dtype_itemsize //= 2

		if dest_dtype is None:
				raise TypeError(
				    F'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}'''  )
		UpperCamelCase_: Any   				=    PIL.Image.fromarray(array.astype(UpperCAmelCase__  )  )
		return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__  )}

def   snake_case (UpperCAmelCase__  )	->    List[dict]:
		if config.PIL_AVAILABLE:
				import PIL.Image
		else:
				raise ImportError('To support encoding images, please install \'Pillow\'.'  )

		if objs:
				UpperCamelCase_: Dict   				=    first_non_null_value(UpperCAmelCase__  )
				if isinstance(UpperCAmelCase__     ,   UpperCAmelCase__  ):
						return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
				if isinstance(UpperCAmelCase__     ,   np.ndarray  ):
						UpperCamelCase_: Tuple   				=    no_op_if_value_is_null(UpperCAmelCase__  )
						return [obj_to_image_dict_func(UpperCAmelCase__  ) for obj in objs]
				elif isinstance(UpperCAmelCase__     ,   PIL.Image.Image  ):
						UpperCamelCase_: List[Any]   				=    no_op_if_value_is_null(UpperCAmelCase__  )
						return [obj_to_image_dict_func(UpperCAmelCase__  ) for obj in objs]
				else:
						return objs
		else:
				return objs







import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    np.array:
  UpperCamelCase_: Dict   				=    F'''{sampling_rate}'''
  UpperCamelCase_: Any   				=    '1'
  UpperCamelCase_: Any   				=    'f32le'
  UpperCamelCase_: Union[str, Any]   				=    [
      'ffmpeg',
      '-i',
      'pipe:0',
      '-ac',
      ac,
      '-ar',
      ar,
      '-f',
      format_for_conversion,
      '-hide_banner',
      '-loglevel',
      'quiet',
      'pipe:1',
  ]

  try:
    with subprocess.Popen(UpperCAmelCase__     ,   stdin=subprocess.PIPE     ,   stdout=subprocess.PIPE  ) as ffmpeg_process:
      UpperCamelCase_: Optional[Any]   				=    ffmpeg_process.communicate(UpperCAmelCase__  )
  except FileNotFoundError as error:
    raise ValueError('ffmpeg was not found but is required to load audio files from filename'  ) from error
  UpperCamelCase_: Union[str, Any]   				=    output_stream[0]
  UpperCamelCase_: List[str]   				=    np.frombuffer(UpperCAmelCase__     ,   np.floataa  )
  if audio.shape[0] == 0:
    raise ValueError('Malformed soundfile'  )
  return audio

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = "f32le"     ,   )	->    Tuple:
  UpperCamelCase_: Any   				=    F'''{sampling_rate}'''
  UpperCamelCase_: Union[str, Any]   				=    '1'
  if format_for_conversion == "s16le":
    UpperCamelCase_: Optional[Any]   				=    2
  elif format_for_conversion == "f32le":
    UpperCamelCase_: Any   				=    4
  else:
    raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''  )

  UpperCamelCase_: int   				=    platform.system()
  if system == "Linux":
    UpperCamelCase_: Tuple   				=    'alsa'
    UpperCamelCase_: List[str]   				=    'default'
  elif system == "Darwin":
    UpperCamelCase_: int   				=    'avfoundation'
    UpperCamelCase_: Union[str, Any]   				=    ':0'
  elif system == "Windows":
    UpperCamelCase_: Tuple   				=    'dshow'
    UpperCamelCase_: Dict   				=    'default'

  UpperCamelCase_: Any   				=    [
      'ffmpeg',
      '-f',
      format_,
      '-i',
      input_,
      '-ac',
      ac,
      '-ar',
      ar,
      '-f',
      format_for_conversion,
      '-fflags',
      'nobuffer',
      '-hide_banner',
      '-loglevel',
      'quiet',
      'pipe:1',
  ]
  UpperCamelCase_: Tuple   				=    int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
  UpperCamelCase_: Optional[int]   				=    _ffmpeg_stream(UpperCAmelCase__     ,   UpperCAmelCase__  )
  for item in iterator:
    yield item

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = "f32le"     ,   )	->    Any:
  if stream_chunk_s is not None:
    UpperCamelCase_: List[Any]   				=    stream_chunk_s
  else:
    UpperCamelCase_: Dict   				=    chunk_length_s

  UpperCamelCase_: List[str]   				=    ffmpeg_microphone(UpperCAmelCase__     ,   UpperCAmelCase__     ,   format_for_conversion=UpperCAmelCase__  )
  if format_for_conversion == "s16le":
    UpperCamelCase_: Union[str, Any]   				=    np.intaa
    UpperCamelCase_: List[Any]   				=    2
  elif format_for_conversion == "f32le":
    UpperCamelCase_: str   				=    np.floataa
    UpperCamelCase_: Tuple   				=    4
  else:
    raise ValueError(F'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''  )

  if stride_length_s is None:
    UpperCamelCase_: int   				=    chunk_length_s / 6
  UpperCamelCase_: Tuple   				=    int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
  if isinstance(UpperCAmelCase__     ,   (int, float)  ):
    UpperCamelCase_: Union[str, Any]   				=    [stride_length_s, stride_length_s]

  UpperCamelCase_: Any   				=    int(round(sampling_rate * stride_length_s[0]  )  ) * size_of_sample
  UpperCamelCase_: Dict   				=    int(round(sampling_rate * stride_length_s[1]  )  ) * size_of_sample
  UpperCamelCase_: Optional[int]   				=    datetime.datetime.now()
  UpperCamelCase_: Optional[int]   				=    datetime.timedelta(seconds=UpperCAmelCase__  )
  for item in chunk_bytes_iter(UpperCAmelCase__     ,   UpperCAmelCase__     ,   stride=(stride_left, stride_right)     ,   stream=UpperCAmelCase__  ):
    # Put everything back in numpy scale
    UpperCamelCase_: Tuple   				=    np.frombuffer(item['raw']     ,   dtype=UpperCAmelCase__  )
    UpperCamelCase_: Optional[int]   				=    (
        item['stride'][0] // size_of_sample,
        item['stride'][1] // size_of_sample,
    )
    UpperCamelCase_: int   				=    sampling_rate
    audio_time += delta
    if datetime.datetime.now() > audio_time + 1_0 * delta:
      # We're late !! SKIP
      continue
    yield item

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__ = False  )	->    int:
  UpperCamelCase_: str   				=    b''
  UpperCamelCase_				,UpperCamelCase_: Union[str, Any]   				=    stride
  if stride_left + stride_right >= chunk_len:
    raise ValueError(
        F'''Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'''  )
  UpperCamelCase_: List[str]   				=    0
  for raw in iterator:
    acc += raw
    if stream and len(UpperCAmelCase__  ) < chunk_len:
      UpperCamelCase_: Optional[Any]   				=    (_stride_left, 0)
      yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
    else:
      while len(UpperCAmelCase__  ) >= chunk_len:
        # We are flushing the accumulator
        UpperCamelCase_: int   				=    (_stride_left, stride_right)
        UpperCamelCase_: Optional[Any]   				=    {'raw': acc[:chunk_len], 'stride': stride}
        if stream:
          UpperCamelCase_: Any   				=    False
        yield item
        UpperCamelCase_: Optional[int]   				=    stride_left
        UpperCamelCase_: Optional[Any]   				=    acc[chunk_len - stride_left - stride_right :]
    # Last chunk
  if len(UpperCAmelCase__  ) > stride_left:
    UpperCamelCase_: int   				=    {'raw': acc, 'stride': (_stride_left, 0)}
    if stream:
      UpperCamelCase_: Optional[Any]   				=    False
    yield item

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  UpperCamelCase_: Any   				=    2**2_4  # 16Mo
  try:
    with subprocess.Popen(UpperCAmelCase__     ,   stdout=subprocess.PIPE     ,   bufsize=UpperCAmelCase__  ) as ffmpeg_process:
      while True:
        UpperCamelCase_: Any   				=    ffmpeg_process.stdout.read(UpperCAmelCase__  )
        if raw == b"":
          break
        yield raw
  except FileNotFoundError as error:
    raise ValueError('ffmpeg was not found but is required to stream audio files from filename'  ) from error