

"""simple docstring"""




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

import numpy as np



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      np.array:




   """simple docstring"""
   lowerCamelCase__						=			f'{sampling_rate}'
   lowerCamelCase__						=			"1"
   lowerCamelCase__						=			"f32le"
   lowerCamelCase__						=			[
       "ffmpeg",
       "-i",
       "pipe:0",
       "-ac",
       ac,
       "-ar",
       ar,
       "-f",
       format_for_conversion,
       "-hide_banner",
       "-loglevel",
       "quiet",
       "pipe:1",
   ]

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



def 	UpperCAmelCase__   (				A__	,    A__	,    A__ = "f32le"	,    )      ->      Any:




   """simple docstring"""
   lowerCamelCase__						=			f'{sampling_rate}'
   lowerCamelCase__						=			"1"
   if format_for_conversion == "s16le":
      lowerCamelCase__						=			2
   elif format_for_conversion == "f32le":
      lowerCamelCase__						=			4
   else:
      raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'   )

   lowerCamelCase__						=			platform.system()
   if system == "Linux":
      lowerCamelCase__						=			"alsa"
      lowerCamelCase__						=			"default"
   elif system == "Darwin":
      lowerCamelCase__						=			"avfoundation"
      lowerCamelCase__						=			":0"
   elif system == "Windows":
      lowerCamelCase__						=			"dshow"
      lowerCamelCase__						=			"default"

   lowerCamelCase__						=			[
       "ffmpeg",
       "-f",
       format_,
       "-i",
       input_,
       "-ac",
       ac,
       "-ar",
       ar,
       "-f",
       format_for_conversion,
       "-fflags",
       "nobuffer",
       "-hide_banner",
       "-loglevel",
       "quiet",
       "pipe:1",
   ]
   lowerCamelCase__						=			int(round(sampling_rate * chunk_length_s   )   ) * size_of_sample
   lowerCamelCase__						=			_ffmpeg_stream(A__	,    A__   )
   for item in iterator:
      yield item



def 	UpperCAmelCase__   (				A__	,    A__	,    A__ = None	,    A__ = None	,    A__ = "f32le"	,    )      ->      int:




   """simple docstring"""
   if stream_chunk_s is not None:
      lowerCamelCase__						=			stream_chunk_s
   else:
      lowerCamelCase__						=			chunk_length_s

   lowerCamelCase__						=			ffmpeg_microphone(A__	,    A__	,    format_for_conversion=A__   )
   if format_for_conversion == "s16le":
      lowerCamelCase__						=			np.intaa
      lowerCamelCase__						=			2
   elif format_for_conversion == "f32le":
      lowerCamelCase__						=			np.floataa
      lowerCamelCase__						=			4
   else:
      raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'   )

   if stride_length_s is None:
      lowerCamelCase__						=			chunk_length_s / 6
   lowerCamelCase__						=			int(round(sampling_rate * chunk_length_s   )   ) * size_of_sample
   if isinstance(A__	,    (int, float)   ):
      lowerCamelCase__						=			[stride_length_s, stride_length_s]

   lowerCamelCase__						=			int(round(sampling_rate * stride_length_s[0]   )   ) * size_of_sample
   lowerCamelCase__						=			int(round(sampling_rate * stride_length_s[1]   )   ) * size_of_sample
   lowerCamelCase__						=			datetime.datetime.now()
   lowerCamelCase__						=			datetime.timedelta(seconds=A__   )
   for item in chunk_bytes_iter(A__	,    A__	,    stride=(stride_left, stride_right)	,    stream=A__   ):
      # Put everything back in numpy scale
      lowerCamelCase__						=			np.frombuffer(item["raw"]	,    dtype=A__   )
      lowerCamelCase__						=			(
          item["stride"][0] // size_of_sample,
          item["stride"][1] // size_of_sample,
      )
      lowerCamelCase__						=			sampling_rate
      audio_time += delta
      if datetime.datetime.now() > audio_time + 10 * delta:
         # We're late !! SKIP
         continue
      yield item



def 	UpperCAmelCase__   (				A__	,    A__	,    A__	,    A__ = False   )      ->      Any:




   """simple docstring"""
   lowerCamelCase__						=			b""
   lowerCamelCase__					,       lowerCamelCase__						=			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}'   )
   lowerCamelCase__						=			0
   for raw in iterator:
      acc += raw
      if stream and len(A__   ) < chunk_len:
         lowerCamelCase__						=			(_stride_left, 0)
         yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
      else:
         while len(A__   ) >= chunk_len:
            # We are flushing the accumulator
            lowerCamelCase__						=			(_stride_left, stride_right)
            lowerCamelCase__						=			{"raw": acc[:chunk_len], "stride": stride}
            if stream:
               lowerCamelCase__						=			False
            yield item
            lowerCamelCase__						=			stride_left
            lowerCamelCase__						=			acc[chunk_len - stride_left - stride_right :]
    # Last chunk
   if len(A__   ) > stride_left:
      lowerCamelCase__						=			{"raw": acc, "stride": (_stride_left, 0)}
      if stream:
         lowerCamelCase__						=			False
      yield item



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      List[Any]:




   """simple docstring"""
   lowerCamelCase__						=			2**24  # 16Mo
   try:
      with subprocess.Popen(A__	,    stdout=subprocess.PIPE	,    bufsize=A__   ) as ffmpeg_process:
         while True:
            lowerCamelCase__						=			ffmpeg_process.stdout.read(A__   )
            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







"""simple docstring"""




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


SCREAMING_SNAKE_CASE_     :			Optional[List[str]]	  =			None
SCREAMING_SNAKE_CASE_     :			Optional[int]	  =			'''<''' 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
SCREAMING_SNAKE_CASE_     :			int	  =			[
    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 		_A       :
      __a								=	True
      __a								=	None
      # Automatically constructed
      __a								=	"PIL.Image.Image"
      __a								=	pa.struct({'bytes': pa.binary(), 'path': pa.string()}					)
      __a								=	field(default='Image'				,       init=__a				,       repr=__a					)





      def __call__(  self		)   ->					Dict:
         return self.pa_type





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					dict:
         if config.PIL_AVAILABLE:
            import PIL.Image
         else:
            raise ImportError("To support encoding images, please install 'Pillow'."		)

         if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			np.array(SCREAMING_SNAKE_CASE__		)

         if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            return {"path": value, "bytes": None}
         elif isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            return {"path": None, "bytes": value}
         elif isinstance(SCREAMING_SNAKE_CASE__    ,						np.ndarray		):
            # convert the image array to PNG/TIFF bytes
            return encode_np_array(SCREAMING_SNAKE_CASE__		)
         elif isinstance(SCREAMING_SNAKE_CASE__    ,						PIL.Image.Image		):
            # convert the PIL image to bytes (default format is PNG/TIFF)
            return encode_pil_image(SCREAMING_SNAKE_CASE__		)
         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 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None		)   ->					"PIL.Image.Image":
         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:
            lowerCamelCase__						=			{}

         lowerCamelCase__					,       lowerCamelCase__						=			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(SCREAMING_SNAKE_CASE__		):
                  lowerCamelCase__						=			PIL.Image.open(SCREAMING_SNAKE_CASE__		)
               else:
                  lowerCamelCase__						=			path.split("::"		)[-1]
                  try:
                     lowerCamelCase__						=			string_to_dict(SCREAMING_SNAKE_CASE__    ,						config.HUB_DATASETS_URL		)["repo_id"]
                     lowerCamelCase__						=			token_per_repo_id.get(SCREAMING_SNAKE_CASE__		)
                  except ValueError:
                     lowerCamelCase__						=			None
                  with xopen(SCREAMING_SNAKE_CASE__    ,						"rb"    ,						use_auth_token=SCREAMING_SNAKE_CASE__		) as f:
                     lowerCamelCase__						=			BytesIO(f.read()		)
                  lowerCamelCase__						=			PIL.Image.open(bytes_		)
         else:
            lowerCamelCase__						=			PIL.Image.open(BytesIO(bytes_		)		)
         image.load()  # to avoid "Too many open files" errors
         return image





      def 					_lowerCamelCase  (  self		)   ->					Union["FeatureType", Dict[str, "FeatureType"]]:
         from .features import Value

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





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					pa.StructArray:
         if pa.types.is_string(storage.type		):
            lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.binary()		)
            lowerCamelCase__						=			pa.StructArray.from_arrays([bytes_array, storage]    ,						["bytes", "path"]    ,						mask=storage.is_null()		)
         elif pa.types.is_binary(storage.type		):
            lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.string()		)
            lowerCamelCase__						=			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:
               lowerCamelCase__						=			storage.field("bytes"		)
            else:
               lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.binary()		)
            if storage.type.get_field_index("path"		) >= 0:
               lowerCamelCase__						=			storage.field("path"		)
            else:
               lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.string()		)
            lowerCamelCase__						=			pa.StructArray.from_arrays([bytes_array, path_array]    ,						["bytes", "path"]    ,						mask=storage.is_null()		)
         elif pa.types.is_list(storage.type		):
            lowerCamelCase__						=			pa.array(
                [encode_np_array(np.array(SCREAMING_SNAKE_CASE__		)		)["bytes"] if arr is not None else None for arr in storage.to_pylist()]    ,						type=pa.binary()    ,						)
            lowerCamelCase__						=			pa.array([None] * len(SCREAMING_SNAKE_CASE__		)    ,						type=pa.string()		)
            lowerCamelCase__						=			pa.StructArray.from_arrays(
                [bytes_array, path_array]    ,						["bytes", "path"]    ,						mask=bytes_array.is_null()		)
         return array_cast(SCREAMING_SNAKE_CASE__    ,						self.pa_type		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					pa.StructArray:

         @no_op_if_value_is_null
         def path_to_bytes(SCREAMING_SNAKE_CASE__		):
            with xopen(SCREAMING_SNAKE_CASE__    ,						"rb"		) as f:
               lowerCamelCase__						=			f.read()
            return bytes_

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






def 	UpperCAmelCase__   (				)      ->      List[str]:




   """simple docstring"""
   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()
      lowerCamelCase__						=			list(set(PIL.Image.OPEN.keys()   ) & set(PIL.Image.SAVE.keys()   )   )
   return _IMAGE_COMPRESSION_FORMATS



def 	UpperCAmelCase__   (				A__   )      ->      bytes:




   """simple docstring"""
   lowerCamelCase__						=			BytesIO()
   if image.format in list_image_compression_formats():
      lowerCamelCase__						=			image.format
   else:
      lowerCamelCase__						=			"PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
   image.save(A__	,    format=A__   )
   return buffer.getvalue()



def 	UpperCAmelCase__   (				A__   )      ->      dict:




   """simple docstring"""
   if hasattr(A__	,    "filename"   ) and image.filename != "":
      return {"path": image.filename, "bytes": None}
   else:
      return {"path": None, "bytes": image_to_bytes(A__   )}



def 	UpperCAmelCase__   (				A__   )      ->      dict:




   """simple docstring"""
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError("To support encoding images, please install 'Pillow'."   )

   lowerCamelCase__						=			array.dtype
   lowerCamelCase__						=			dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
   lowerCamelCase__						=			dtype.kind
   lowerCamelCase__						=			dtype.itemsize

   lowerCamelCase__						=			None

   # Multi-channel array case (only np.dtype("|u1") is allowed)
   if array.shape[2:]:
      lowerCamelCase__						=			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:
      lowerCamelCase__						=			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:
         lowerCamelCase__						=			dtype_byteorder + dtype_kind + str(A__   )
         lowerCamelCase__						=			np.dtype(A__   )
         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}'   )
   lowerCamelCase__						=			PIL.Image.fromarray(array.astype(A__   )   )
   return {"path": None, "bytes": image_to_bytes(A__   )}



def 	UpperCAmelCase__   (				A__   )      ->      List[dict]:




   """simple docstring"""
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError("To support encoding images, please install 'Pillow'."   )

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




