




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


lowerCamelCase       :	Optional[List[str]]									=     None
lowerCamelCase       :	Optional[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
lowerCamelCase       :	Optional[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:



 '''simple docstring'''


 UpperCamelCase		     =		True
 UpperCamelCase		     =		None
 # Automatically constructed
 UpperCamelCase		     =		"PIL.Image.Image"
 UpperCamelCase		     =		pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}     )
 UpperCamelCase		     =		field(default='''Image'''							,   init=UpperCamelCase							,   repr=UpperCamelCase     )

 def __call__(       self      :						str						)     ->			Optional[int]:

       """simple docstring"""






       return self.pa_type

 def        a__				(       self      :						Optional[Any]		,							A_      :						Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]						)     ->			dict:

       """simple docstring"""






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

       if isinstance(A_		,							A_						):
             lowerCamelCase_	  =					np.array(A_						)

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

       """simple docstring"""






       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(A_						):
                         lowerCamelCase_	  =					PIL.Image.open(A_						)
                   else:
                         lowerCamelCase_	  =					path.split('::'						)[-1]
                         try:
                               lowerCamelCase_	  =					string_to_dict(A_		,							config.HUB_DATASETS_URL						)['repo_id']
                               lowerCamelCase_	  =					token_per_repo_id.get(A_						)
                         except ValueError:
                               lowerCamelCase_	  =					None
                         with xopen(A_		,							'rb'		,							use_auth_token=A_						) 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        a__				(       self      :						int						)     ->			Union["FeatureType", Dict[str, "FeatureType"]]:

       """simple docstring"""






       from .features import Value

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

 def        a__				(       self      :						Optional[int]		,							A_      :						Union[pa.StringArray, pa.StructArray, pa.ListArray]						)     ->			pa.StructArray:

       """simple docstring"""






       if pa.types.is_string(storage.type						):
             lowerCamelCase_	  =					pa.array([None] * len(A_						)		,							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(A_						)		,							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(A_						)		,							type=pa.binary()						)
             if storage.type.get_field_index('path'						) >= 0:
                   lowerCamelCase_	  =					storage.field('path'						)
             else:
                   lowerCamelCase_	  =					pa.array([None] * len(A_						)		,							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(A_						)						)['bytes'] if arr is not None else None for arr in storage.to_pylist()]		,							type=pa.binary()		,							)
             lowerCamelCase_	  =					pa.array([None] * len(A_						)		,							type=pa.string()						)
             lowerCamelCase_	  =					pa.StructArray.from_arrays(
                 [bytes_array, path_array]		,							['bytes', 'path']		,							mask=bytes_array.is_null()						)
       return array_cast(A_		,							self.pa_type						)





 def        a__				(       self      :						str		,							A_      :						pa.StructArray						)     ->			pa.StructArray:

       """simple docstring"""






       @no_op_if_value_is_null
       def path_to_bytes(A_      :						List[Any]						):
             with xopen(A_		,							'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(A_						) 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(A_		,							self.pa_type						)

def     _SCREAMING_SNAKE_CASE    (							):


      '''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     _SCREAMING_SNAKE_CASE    (							lowercase			: "PIL.Image.Image"  ):


      '''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(lowercase			,							format=lowercase  )
      return buffer.getvalue()

def     _SCREAMING_SNAKE_CASE    (							lowercase			: "PIL.Image.Image"  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: np.ndarray  ):


      '''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(lowercase  )
                  lowerCamelCase_	  =					np.dtype(lowercase  )
                  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(lowercase  )  )
      return {"path": None, "bytes": image_to_bytes(lowercase  )}






def     _SCREAMING_SNAKE_CASE    (							lowercase			: Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]  ):


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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase       :	Dict									=     {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Optional[int]									=     ["ViTFeatureExtractor"]
 lowerCamelCase       :	Dict									=     ["ViTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	int									=     [
     "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTForImageClassification",
     "ViTForMaskedImageModeling",
     "ViTModel",
     "ViTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Tuple									=     [
     "TFViTForImageClassification",
     "TFViTModel",
     "TFViTPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Dict									=     [
     "FlaxViTForImageClassification",
     "FlaxViTModel",
     "FlaxViTPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_vit import ViTFeatureExtractor
  from .image_processing_vit import ViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit import (
      VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTForImageClassification,
      ViTForMaskedImageModeling,
      ViTModel,
      ViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
 import sys

 lowerCamelCase       :	Any									=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



