'''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


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






       def __call__(      self      ):
              return self.pa_type






       def 			_snake_case						(      self				,     UpperCamelCase_      ):

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

              if isinstance(UpperCamelCase_				,     UpperCamelCase_      ):
                     __UpperCAmelCase  :				Any       				=		np.array(UpperCamelCase_      )

              if isinstance(UpperCamelCase_				,     UpperCamelCase_      ):
                     return {"path": value, "bytes": None}
              elif isinstance(UpperCamelCase_				,     UpperCamelCase_      ):
                     return {"path": None, "bytes": value}
              elif isinstance(UpperCamelCase_				,     np.ndarray      ):
                     # convert the image array to PNG/TIFF bytes
                     return encode_np_array(UpperCamelCase_      )
              elif isinstance(UpperCamelCase_				,     PIL.Image.Image      ):
                     # convert the PIL image to bytes (default format is PNG/TIFF)
                     return encode_pil_image(UpperCamelCase_      )
              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 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_=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  :				Optional[int]       				=		{}

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






       def 			_snake_case						(      self      ):
              from .features import Value

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






       def 			_snake_case						(      self				,     UpperCamelCase_      ):

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







       def 			_snake_case						(      self				,     UpperCamelCase_      ):

              @no_op_if_value_is_null
              def path_to_bytes(UpperCamelCase_      ):
                     with xopen(UpperCamelCase_				,     "rb"      ) as f:
                            __UpperCAmelCase  :				int       				=		f.read()
                     return bytes_

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





def _lowercase	(      )					->       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()
              __UpperCAmelCase  :				Optional[Any]       				=		list(set(PIL.Image.OPEN.keys()						) & set(PIL.Image.SAVE.keys()						)						)
       return _IMAGE_COMPRESSION_FORMATS

def _lowercase	(      lowerCamelCase__						)					->       bytes:





       """simple docstring"""


       __UpperCAmelCase  :				str       				=		BytesIO()
       if image.format in list_image_compression_formats():
              __UpperCAmelCase  :				List[Any]       				=		image.format
       else:
              __UpperCAmelCase  :				str       				=		"PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
       image.save(_UpperCamelCase							,    format=_UpperCamelCase						)
       return buffer.getvalue()

def _lowercase	(      lowerCamelCase__						)					->       dict:





       """simple docstring"""


       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__						)					->       dict:





       """simple docstring"""


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

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

       __UpperCAmelCase  :				Union[str, Any]       				=		None

       # Multi-channel array case (only np.dtype("|u1") is allowed)
       if array.shape[2:]:
              __UpperCAmelCase  :				int       				=		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[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:
                     __UpperCAmelCase  :				Union[str, Any]       				=		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  :				Tuple       				=		PIL.Image.fromarray(array.astype(_UpperCamelCase						)						)
       return {"path": None, "bytes": image_to_bytes(_UpperCamelCase						)}



def _lowercase	(      lowerCamelCase__						)					->       List[dict]:





       """simple docstring"""


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

       if objs:
              __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		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  :				List[str]       				=		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  :				Optional[int]       				=		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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_a   :	Dict 							=   {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Dict 							=   ["ReformerTokenizer"]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	List[Any] 							=   ["ReformerTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	int 							=   [
       "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ReformerAttention",
       "ReformerForMaskedLM",
       "ReformerForQuestionAnswering",
       "ReformerForSequenceClassification",
       "ReformerLayer",
       "ReformerModel",
       "ReformerModelWithLMHead",
       "ReformerPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

   _a   :	Optional[int] 							=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






