


"""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


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







      def __call__(   self			:				Any     ):
        return self.pa_type







      def 					_lowerCAmelCase							(   self			:				str		,	_lowercase			:				List[Any]     ):
        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__     :     Any										=       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			:				Optional[Any]		,	_lowercase			:				List[str]		,	_lowercase			:				Optional[Any]=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:
          lowerCAmelCase__     :     List[Any]										=       {}

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







      def 					_lowerCAmelCase							(   self			:				str     ):
        from .features import Value

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







      def 					_lowerCAmelCase							(   self			:				List[Any]		,	_lowercase			:				Optional[Any]     ):
        if pa.types.is_string(storage.type     ):
          lowerCAmelCase__     :     int										=       pa.array([None] * len(SCREAMING_SNAKE_CASE__     )		,	type=pa.binary()     )
          lowerCAmelCase__     :     Any										=       pa.StructArray.from_arrays([bytes_array, storage]		,	["bytes", "path"]		,	mask=storage.is_null()     )
        elif pa.types.is_binary(storage.type     ):
          lowerCAmelCase__     :     Dict										=       pa.array([None] * len(SCREAMING_SNAKE_CASE__     )		,	type=pa.string()     )
          lowerCAmelCase__     :     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:
            lowerCAmelCase__     :     List[str]										=       storage.field("bytes"     )
          else:
            lowerCAmelCase__     :     Tuple										=       pa.array([None] * len(SCREAMING_SNAKE_CASE__     )		,	type=pa.binary()     )
          if storage.type.get_field_index("path"     ) >= 0:
            lowerCAmelCase__     :     Tuple										=       storage.field("path"     )
          else:
            lowerCAmelCase__     :     List[str]										=       pa.array([None] * len(SCREAMING_SNAKE_CASE__     )		,	type=pa.string()     )
          lowerCAmelCase__     :     Any										=       pa.StructArray.from_arrays([bytes_array, path_array]		,	["bytes", "path"]		,	mask=storage.is_null()     )
        elif pa.types.is_list(storage.type     ):
          lowerCAmelCase__     :     int										=       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__     :     Optional[Any]										=       pa.array([None] * len(SCREAMING_SNAKE_CASE__     )		,	type=pa.string()     )
          lowerCAmelCase__     :     Any										=       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			:				Union[str, Any]		,	_lowercase			:				Optional[int]     ):

        @no_op_if_value_is_null
        def path_to_bytes(_lowercase			:				List[str]     ):
          with xopen(SCREAMING_SNAKE_CASE__		,	"rb"     ) as f:
            lowerCAmelCase__     :     List[str]										=       f.read()
          return bytes_

        lowerCAmelCase__     :     List[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()		,	)
        lowerCAmelCase__     :     Any										=       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__     :     List[Any]										=       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      lowercase__						(	)       ->  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()
    lowerCAmelCase__     :     List[Any]										=       list(set(PIL.Image.OPEN.keys()					) & set(PIL.Image.SAVE.keys()					)					)
  return _IMAGE_COMPRESSION_FORMATS





def      lowercase__						(	lowerCamelCase  :	"PIL.Image.Image"					)       ->  bytes:
  lowerCAmelCase__     :     str										=       BytesIO()
  if image.format in list_image_compression_formats():
    lowerCAmelCase__     :     List[str]										=       image.format
  else:
    lowerCAmelCase__     :     Tuple										=       "PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
  image.save(UpperCAmelCase_					,						format=UpperCAmelCase_					)
  return buffer.getvalue()





def      lowercase__						(	lowerCamelCase  :	"PIL.Image.Image"					)       ->  dict:





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





def      lowercase__						(	lowerCamelCase  :	np.ndarray					)       ->  dict:





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

  lowerCAmelCase__     :     Union[str, Any]										=       array.dtype
  lowerCAmelCase__     :     List[str]										=       dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
  lowerCAmelCase__     :     Dict										=       dtype.kind
  lowerCAmelCase__     :     Any										=       dtype.itemsize

  lowerCAmelCase__     :     Union[str, Any]										=       None

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







def      lowercase__						(	lowerCamelCase  :	Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]					)       ->  List[dict]:





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

  if objs:
    lowerCAmelCase__					,		lowerCAmelCase__     :     List[Any]										=       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					):
      lowerCAmelCase__     :     Union[str, Any]										=       no_op_if_value_is_null(UpperCAmelCase_					)
      return [obj_to_image_dict_func(UpperCAmelCase_					) for obj in objs]
    elif isinstance(UpperCAmelCase_					,						PIL.Image.Image					):
      lowerCAmelCase__     :     Dict										=       no_op_if_value_is_null(UpperCAmelCase_					)
      return [obj_to_image_dict_func(UpperCAmelCase_					) for obj in objs]
    else:
      return objs
  else:
    return objs





"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase     =							{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							[
			    """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

			UpperCamelCase     =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




