


'''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[List[str]]        =       None
a__  :					Dict        =       '<' 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 					lowercase_     :
    __UpperCAmelCase   			=	True
    __UpperCAmelCase   			=	None
    # Automatically constructed
    __UpperCAmelCase   			=	"PIL.Image.Image"
    __UpperCAmelCase   			=	pa.struct({'bytes': pa.binary(), 'path': pa.string()}   )
    __UpperCAmelCase   			=	field(default='Image'  ,						init=a__  ,						repr=a__   )


    def __call__(     self    ):
          return self.pa_type


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

          if isinstance(a  ,					a    ):
                UpperCamelCase__										=				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  ,					a  ,					a=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__										=				{}

          UpperCamelCase__					,	UpperCamelCase__										=				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    ):
                            UpperCamelCase__										=				PIL.Image.open(a    )
                      else:
                            UpperCamelCase__										=				path.split("::"    )[-1]
                            try:
                                  UpperCamelCase__										=				string_to_dict(a  ,					config.HUB_DATASETS_URL    )["repo_id"]
                                  UpperCamelCase__										=				token_per_repo_id.get(a    )
                            except ValueError:
                                  UpperCamelCase__										=				None
                            with xopen(a  ,					"rb"  ,					use_auth_token=a    ) as f:
                                  UpperCamelCase__										=				BytesIO(f.read()    )
                            UpperCamelCase__										=				PIL.Image.open(bytes_    )
          else:
                UpperCamelCase__										=				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  ,					a    ):
          if pa.types.is_string(storage.type    ):
                UpperCamelCase__										=				pa.array([None] * len(a    )  ,					type=pa.binary()    )
                UpperCamelCase__										=				pa.StructArray.from_arrays([bytes_array, storage]  ,					["bytes", "path"]  ,					mask=storage.is_null()    )
          elif pa.types.is_binary(storage.type    ):
                UpperCamelCase__										=				pa.array([None] * len(a    )  ,					type=pa.string()    )
                UpperCamelCase__										=				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__										=				storage.field("bytes"    )
                else:
                      UpperCamelCase__										=				pa.array([None] * len(a    )  ,					type=pa.binary()    )
                if storage.type.get_field_index("path"    ) >= 0:
                      UpperCamelCase__										=				storage.field("path"    )
                else:
                      UpperCamelCase__										=				pa.array([None] * len(a    )  ,					type=pa.string()    )
                UpperCamelCase__										=				pa.StructArray.from_arrays([bytes_array, path_array]  ,					["bytes", "path"]  ,					mask=storage.is_null()    )
          elif pa.types.is_list(storage.type    ):
                UpperCamelCase__										=				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()  ,					)
                UpperCamelCase__										=				pa.array([None] * len(a    )  ,					type=pa.string()    )
                UpperCamelCase__										=				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  ,					a    ):

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

          UpperCamelCase__										=				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__										=				pa.array(
              [os.path.basename(a    ) if path is not None else None for path in storage.field("path"    ).to_pylist()]  ,					type=pa.string()  ,					)
          UpperCamelCase__										=				pa.StructArray.from_arrays([bytes_array, path_array]  ,					["bytes", "path"]  ,					mask=bytes_array.is_null()    )
          return array_cast(a  ,					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()
            UpperCamelCase__										=				list(set(PIL.Image.OPEN.keys()    ) & set(PIL.Image.SAVE.keys()    )    )
      return _IMAGE_COMPRESSION_FORMATS







def _UpperCamelCase       (     __A    )							->		bytes:


      '''simple docstring'''



      UpperCamelCase__										=				BytesIO()
      if image.format in list_image_compression_formats():
            UpperCamelCase__										=				image.format
      else:
            UpperCamelCase__										=				"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'."    )

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

      UpperCamelCase__										=				None

      # Multi-channel array case (only np.dtype("|u1") is allowed)
      if array.shape[2:]:
            UpperCamelCase__										=				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__										=				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__										=				dtype_byteorder + dtype_kind + str(__A    )
                  UpperCamelCase__										=				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}'''    )
      UpperCamelCase__										=				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:
            UpperCamelCase__					,	UpperCamelCase__										=				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    ):
                  UpperCamelCase__										=				no_op_if_value_is_null(__A    )
                  return [obj_to_image_dict_func(__A    ) for obj in objs]
            elif isinstance(__A      ,			PIL.Image.Image    ):
                  UpperCamelCase__										=				no_op_if_value_is_null(__A    )
                  return [obj_to_image_dict_func(__A    ) for obj in objs]
            else:
                  return objs
      else:
            return objs








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase_									=   {
    'configuration_pix2struct': [
        'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Pix2StructConfig',
        'Pix2StructTextConfig',
        'Pix2StructVisionConfig',
    ],
    'processing_pix2struct': ['Pix2StructProcessor'],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   ['Pix2StructImageProcessor']


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'Pix2StructPreTrainedModel',
      'Pix2StructForConditionalGeneration',
      'Pix2StructVisionModel',
      'Pix2StructTextModel',
  ]

if TYPE_CHECKING:
  from .configuration_pixastruct import (
      PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      PixaStructConfig,
      PixaStructTextConfig,
      PixaStructVisionConfig,
  )
  from .processing_pixastruct import PixaStructProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_pixastruct import PixaStructImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_pixastruct import (
        PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
        PixaStructForConditionalGeneration,
        PixaStructPreTrainedModel,
        PixaStructTextModel,
        PixaStructVisionModel,
    )

else:
  import sys

  lowercase_									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


