'''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       :     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
__SCREAMING_SNAKE_CASE       :     Dict     =  [
    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 	lowerCamelCase_	:
      '''simple docstring'''

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




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




      def      _A	(       self						:				Tuple						,					A						:				Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]       ):
             if config.PIL_AVAILABLE:
                    import PIL.Image
             else:
                    raise ImportError("To support encoding images, please install 'Pillow'."       )

             if isinstance(A						,					A       ):
                    _UpperCAmelCase     :  Optional[int]							      =    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						:				List[str]						,					A						:				dict						,					A						:				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:
                    _UpperCAmelCase     :  List[Any]							      =    {}

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




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

             @no_op_if_value_is_null
             def path_to_bytes(A						:				Dict       ):
                    with xopen(A						,					"rb"       ) as f:
                           _UpperCAmelCase     :  List[str]							      =    f.read()
                    return bytes_

             _UpperCAmelCase     :  str							      =    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(A       ) if path is not None else None for path in storage.field("path"       ).to_pylist()]						,					type=pa.string()						,					)
             _UpperCAmelCase     :  Union[str, Any]							      =    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     :  Any							      =    list(set(PIL.Image.OPEN.keys()    ) & set(PIL.Image.SAVE.keys()    )    )
       return _IMAGE_COMPRESSION_FORMATS

def 	UpperCamelCase_							(					_UpperCAmelCase  :      "PIL.Image.Image"    )    ->    bytes:


       """simple docstring"""







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

def 	UpperCamelCase_							(					_UpperCAmelCase  :      "PIL.Image.Image"    )    ->    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 	UpperCamelCase_							(					_UpperCAmelCase  :      np.ndarray    )    ->    dict:


       """simple docstring"""







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

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

       _UpperCAmelCase     :  Any							      =    None

       # Multi-channel array case (only np.dtype("|u1") is allowed)
       if array.shape[2:]:
              _UpperCAmelCase     :  Dict							      =    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     :  List[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     :  Optional[Any]							      =    PIL.Image.fromarray(array.astype(_UpperCAmelCase    )    )
       return {"path": None, "bytes": image_to_bytes(_UpperCAmelCase    )}


def 	UpperCamelCase_							(					_UpperCAmelCase  :      Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]    )    ->    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     :  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    ):
                     _UpperCAmelCase     :  Dict							      =    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






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import PoolFormerImageProcessor




class 					snake_case__						(       unittest.TestCase     ):






     """simple docstring"""


     def __init__(       self  :  Optional[int], _snake_case  :  List[Any], _snake_case  :  str=7, _snake_case  :  Tuple=3, _snake_case  :  List[str]=3_0, _snake_case  :  Tuple=4_0_0, _snake_case  :  Any=True, _snake_case  :  List[Any]=None, _snake_case  :  int=0.9, _snake_case  :  Optional[Any]=None, _snake_case  :  str=True, _snake_case  :  Union[str, Any]=[0.5, 0.5, 0.5], _snake_case  :  Union[str, Any]=[0.5, 0.5, 0.5], )   ->List[Any]:
            snake_case__							:	int   	=	size if size is not None else {'shortest_edge': 3_0}
            snake_case__							:	Tuple   	=	crop_size if crop_size is not None else {'height': 3_0, 'width': 3_0}
            snake_case__							:	Union[str, Any]   	=	parent
            snake_case__							:	Dict   	=	batch_size
            snake_case__							:	int   	=	num_channels
            snake_case__							:	Tuple   	=	min_resolution
            snake_case__							:	Any   	=	max_resolution
            snake_case__							:	List[Any]   	=	do_resize_and_center_crop
            snake_case__							:	str   	=	size
            snake_case__							:	str   	=	crop_pct
            snake_case__							:	List[str]   	=	crop_size
            snake_case__							:	Optional[int]   	=	do_normalize
            snake_case__							:	Tuple   	=	image_mean
            snake_case__							:	Tuple   	=	image_std






     def    lowercase_   (       self  :  Optional[int]					)   ->int:
            return {
                "size": self.size,
                "do_resize_and_center_crop": self.do_resize_and_center_crop,
                "crop_pct": self.crop_pct,
                "crop_size": self.crop_size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
            }




@require_torch
@require_vision
class 					snake_case__						(       lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	PoolFormerImageProcessor if is_vision_available() else None






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Dict:
            snake_case__							:	Union[str, Any]   	=	PoolFormerImageProcessingTester(self					)






     @property
     def    lowercase_   (       self  :  int					)   ->Dict:
            return self.image_processor_tester.prepare_image_processor_dict()






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Optional[int]:
            snake_case__							:	List[str]   	=	self.image_processing_class(**self.image_processor_dict					)
            self.assertTrue(hasattr(_snake_case, 'do_resize_and_center_crop'					)					)
            self.assertTrue(hasattr(_snake_case, 'size'					)					)
            self.assertTrue(hasattr(_snake_case, 'crop_pct'					)					)
            self.assertTrue(hasattr(_snake_case, 'do_normalize'					)					)
            self.assertTrue(hasattr(_snake_case, 'image_mean'					)					)
            self.assertTrue(hasattr(_snake_case, 'image_std'					)					)






     def    lowercase_   (       self  :  List[str]					)   ->List[str]:
            snake_case__							:	Union[str, Any]   	=	self.image_processing_class.from_dict(self.image_processor_dict					)
            self.assertEqual(image_processor.size, {'shortest_edge': 3_0}					)
            self.assertEqual(image_processor.crop_size, {'height': 3_0, 'width': 3_0}					)

            snake_case__							:	int   	=	self.image_processing_class.from_dict(self.image_processor_dict, size=4_2, crop_size=8_4					)
            self.assertEqual(image_processor.size, {'shortest_edge': 4_2}					)
            self.assertEqual(image_processor.crop_size, {'height': 8_4, 'width': 8_4}					)






     def    lowercase_   (       self  :  List[Any]					)   ->List[Any]:
            pass






     def    lowercase_   (       self  :  List[str]					)   ->str:
            # Initialize image_processing
            snake_case__							:	Union[str, Any]   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random PIL images
            snake_case__							:	List[str]   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case					)
            for image in image_inputs:
                   self.assertIsInstance(_snake_case, Image.Image					)

            # Test not batched input
            snake_case__							:	Optional[int]   	=	image_processing(image_inputs[0], return_tensors='pt'					).pixel_values

            self.assertEqual(
                encoded_images.shape, (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )

            # Test batched
            snake_case__							:	str   	=	image_processing(_snake_case, return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )






     def    lowercase_   (       self  :  int					)   ->List[Any]:
            # Initialize image_processing
            snake_case__							:	Optional[int]   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random numpy tensors
            snake_case__							:	Dict   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case, numpify=_snake_case					)
            for image in image_inputs:
                   self.assertIsInstance(_snake_case, np.ndarray					)

            # Test not batched input
            snake_case__							:	Dict   	=	image_processing(image_inputs[0], return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )

            # Test batched
            snake_case__							:	List[Any]   	=	image_processing(_snake_case, return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )






     def    lowercase_   (       self  :  List[str]					)   ->List[str]:
            # Initialize image_processing
            snake_case__							:	Tuple   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random PyTorch tensors
            snake_case__							:	List[str]   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=_snake_case, torchify=_snake_case					)
            for image in image_inputs:
                   self.assertIsInstance(_snake_case, torch.Tensor					)

            # Test not batched input
            snake_case__							:	Tuple   	=	image_processing(image_inputs[0], return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )

            # Test batched
            snake_case__							:	Optional[Any]   	=	image_processing(_snake_case, return_tensors='pt'					).pixel_values
            self.assertEqual(
                encoded_images.shape, (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['height'],
                    self.image_processor_tester.crop_size['width'],
                ), )






