


'''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


__lowercase      :		Tuple			=      None
__lowercase      :		List[str]			=      '<' 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
__lowercase      :		Union[str, 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 				__UpperCamelCase							:
	A_           =       True
	A_           =       None
	# Automatically constructed
	A_           =       "PIL.Image.Image"
	A_           =       pa.struct({"bytes": pa.binary(), "path": pa.string()}   )
	A_           =       field(default="Image"						, init=_a						, repr=_a   )




	def __call__(  self				):

						'''simple docstring'''





						return self.pa_type




	def  __UpperCAmelCase				(  self		,							__a				):

						'''simple docstring'''





						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				):
											__a     :			List[str]          =    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  __UpperCAmelCase				(  self		,							__a		,							__a=None				):

						'''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:
											__a     :			Dict          =    {}

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




	def  __UpperCAmelCase				(  self				):

						'''simple docstring'''





						from .features import Value

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




	def  __UpperCAmelCase				(  self		,							__a				):

						'''simple docstring'''





						if pa.types.is_string(storage.type				):
											__a     :			Dict          =    pa.array([None] * len(_SCREAMING_SNAKE_CASE				)		,							type=pa.binary()				)
											__a     :			Dict          =    pa.StructArray.from_arrays([bytes_array, storage]		,							['bytes', 'path']		,							mask=storage.is_null()				)
						elif pa.types.is_binary(storage.type				):
											__a     :			List[str]          =    pa.array([None] * len(_SCREAMING_SNAKE_CASE				)		,							type=pa.string()				)
											__a     :			Tuple          =    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:
																__a     :			Dict          =    storage.field('bytes'				)
											else:
																__a     :			Any          =    pa.array([None] * len(_SCREAMING_SNAKE_CASE				)		,							type=pa.binary()				)
											if storage.type.get_field_index('path'				) >= 0:
																__a     :			int          =    storage.field('path'				)
											else:
																__a     :			List[str]          =    pa.array([None] * len(_SCREAMING_SNAKE_CASE				)		,							type=pa.string()				)
											__a     :			Optional[Any]          =    pa.StructArray.from_arrays([bytes_array, path_array]		,							['bytes', 'path']		,							mask=storage.is_null()				)
						elif pa.types.is_list(storage.type				):
											__a     :			Optional[Any]          =    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()		,							)
											__a     :			Any          =    pa.array([None] * len(_SCREAMING_SNAKE_CASE				)		,							type=pa.string()				)
											__a     :			Dict          =    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  __UpperCAmelCase				(  self		,							__a				):

						'''simple docstring'''





						@no_op_if_value_is_null
						def path_to_bytes(__a				):
											with xopen(_SCREAMING_SNAKE_CASE		,							'rb'				) as f:
																__a     :			Any          =    f.read()
											return bytes_

						__a     :			Union[str, 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()		,							)
						__a     :			List[str]          =    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()		,							)
						__a     :			Union[str, 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				():


					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()
										__a     :			Optional[int]          =    list(set(PIL.Image.OPEN.keys()  ) & set(PIL.Image.SAVE.keys()  )  )
					return _IMAGE_COMPRESSION_FORMATS






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  str  ):
					__a     :			Optional[int]          =    BytesIO()
					if image.format in list_image_compression_formats():
										__a     :			int          =    image.format
					else:
										__a     :			List[Any]          =    '''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
					image.save(_lowercase		, format=_lowercase  )
					return buffer.getvalue()






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Union[str, Any]  ):


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






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Any  ):


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

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

					__a     :			Optional[Any]          =    None

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






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  List[Any]  ):


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

					if objs:
										__a     :			Tuple          =    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  ):
															__a     :			Any          =    no_op_if_value_is_null(_lowercase  )
															return [obj_to_image_dict_func(_lowercase  ) for obj in objs]
										elif isinstance(_lowercase		, PIL.Image.Image  ):
															__a     :			Union[str, Any]          =    no_op_if_value_is_null(_lowercase  )
															return [obj_to_image_dict_func(_lowercase  ) for obj in objs]
										else:
															return objs
					else:
										return objs









import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
     import torch

     from transformers import AutoModelForImageClassification

if is_vision_available():
     from transformers import AutoImageProcessor

@require_torch
@require_vision
class     __a(	unittest.TestCase  ):






    """simple docstring"""





    @slow
    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				Tuple  =     AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''    )
         UpperCAmelCase_     :				Union[str, Any]  =     AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''    )
         model.to(_SCREAMING_SNAKE_CASE    )

         from datasets import load_dataset

         UpperCAmelCase_     :				Optional[int]  =     load_dataset('''nielsr/rvlcdip-demo'''    )

         UpperCAmelCase_     :				Optional[Any]  =     dataset['''train'''][0]['''image'''].convert('''RGB'''    )

         UpperCAmelCase_     :				str  =     image_processor(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).to(_SCREAMING_SNAKE_CASE    )

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_     :				Optional[int]  =     model(**_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				List[Any]  =     outputs.logits

         UpperCAmelCase_     :				Tuple  =     torch.Size((1, 16)    )
         self.assertEqual(logits.shape  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				int  =     torch.tensor(
             [-0.41_58, -0.40_92, -0.43_47]  ,device=_SCREAMING_SNAKE_CASE  ,dtype=torch.float  ,)
         self.assertTrue(torch.allclose(logits[0, :3]  ,_SCREAMING_SNAKE_CASE  ,atol=1e-4    )    )