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






			def __call__(      self  ):
									return self.pa_type






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

									if isinstance(__UpperCamelCase				,			__UpperCamelCase  ):
															_lowercase					       =					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    UpperCamelCase_				(      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:
															_lowercase					       =					{}

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






			def    UpperCamelCase_				(      self  ):
									from .features import Value

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






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									if pa.types.is_string(storage.type  ):
															_lowercase					       =					pa.array([None] * len(__UpperCamelCase  )				,			type=pa.binary()  )
															_lowercase					       =					pa.StructArray.from_arrays([bytes_array, storage]				,			["""bytes""", """path"""]				,			mask=storage.is_null()  )
									elif pa.types.is_binary(storage.type  ):
															_lowercase					       =					pa.array([None] * len(__UpperCamelCase  )				,			type=pa.string()  )
															_lowercase					       =					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:
																					_lowercase					       =					storage.field("""bytes"""  )
															else:
																					_lowercase					       =					pa.array([None] * len(__UpperCamelCase  )				,			type=pa.binary()  )
															if storage.type.get_field_index("""path"""  ) >= 0:
																					_lowercase					       =					storage.field("""path"""  )
															else:
																					_lowercase					       =					pa.array([None] * len(__UpperCamelCase  )				,			type=pa.string()  )
															_lowercase					       =					pa.StructArray.from_arrays([bytes_array, path_array]				,			["""bytes""", """path"""]				,			mask=storage.is_null()  )
									elif pa.types.is_list(storage.type  ):
															_lowercase					       =					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()				,			)
															_lowercase					       =					pa.array([None] * len(__UpperCamelCase  )				,			type=pa.string()  )
															_lowercase					       =					pa.StructArray.from_arrays(
															    [bytes_array, path_array]				,			["""bytes""", """path"""]				,			mask=bytes_array.is_null()  )
									return array_cast(__UpperCamelCase				,			self.pa_type  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):

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

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







def 	UpperCamelCase__  (     )  ->     List[str]:
						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()
												_lowercase					       =					list(set(PIL.Image.OPEN.keys()       ) & set(PIL.Image.SAVE.keys()       )       )
						return _IMAGE_COMPRESSION_FORMATS




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	"PIL.Image.Image"       )  ->     bytes:
						_lowercase					       =					BytesIO()
						if image.format in list_image_compression_formats():
												_lowercase					       =					image.format
						else:
												_lowercase					       =					"""PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
						image.save(SCREAMING_SNAKE_CASE_		,   format=SCREAMING_SNAKE_CASE_       )
						return buffer.getvalue()




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	"PIL.Image.Image"       )  ->     dict:
						if hasattr(SCREAMING_SNAKE_CASE_		,   """filename"""       ) and image.filename != "":
												return {"path": image.filename, "bytes": None}
						else:
												return {"path": None, "bytes": image_to_bytes(SCREAMING_SNAKE_CASE_       )}




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	np.ndarray       )  ->     dict:
						if config.PIL_AVAILABLE:
												import PIL.Image
						else:
												raise ImportError("""To support encoding images, please install 'Pillow'."""       )

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

						_lowercase					       =					None

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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]       )  ->     List[dict]:
						if config.PIL_AVAILABLE:
												import PIL.Image
						else:
												raise ImportError("""To support encoding images, please install 'Pillow'."""       )

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


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import VivitImageProcessor



class    a_ (			unittest.TestCase    ):






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase=7				,			__UpperCamelCase=3				,			__UpperCamelCase=10				,			__UpperCamelCase=18				,			__UpperCamelCase=30				,			__UpperCamelCase=400				,			__UpperCamelCase=True				,			__UpperCamelCase=None				,			__UpperCamelCase=True				,			__UpperCamelCase=[0.5, 0.5, 0.5]				,			__UpperCamelCase=[0.5, 0.5, 0.5]				,			__UpperCamelCase=None				,			):
									_lowercase					       =					size if size is not None else {"""shortest_edge""": 18}
									_lowercase					       =					crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

									_lowercase					       =					parent
									_lowercase					       =					batch_size
									_lowercase					       =					num_channels
									_lowercase					       =					num_frames
									_lowercase					       =					image_size
									_lowercase					       =					min_resolution
									_lowercase					       =					max_resolution
									_lowercase					       =					do_resize
									_lowercase					       =					size
									_lowercase					       =					do_normalize
									_lowercase					       =					image_mean
									_lowercase					       =					image_std
									_lowercase					       =					crop_size






			def    UpperCamelCase_				(      self  ):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "crop_size": self.crop_size,
									}







@require_torch
@require_vision
class    a_ (			_a					,			unittest.TestCase    ):
			a	:			str					   =							VivitImageProcessor if is_vision_available() else None






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					VivitImageProcessingTester(self  )






			@property
			def    UpperCamelCase_				(      self  ):
									return self.image_processor_tester.prepare_image_processor_dict()






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""image_mean"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""image_std"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""do_normalize"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""do_resize"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""do_center_crop"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""size"""  )  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.image_processing_class.from_dict(self.image_processor_dict  )
									self.assertEqual(image_processor.size				,			{"""shortest_edge""": 18}  )
									self.assertEqual(image_processor.crop_size				,			{"""height""": 18, """width""": 18}  )

									_lowercase					       =					self.image_processing_class.from_dict(self.image_processor_dict				,			size=42				,			crop_size=84  )
									self.assertEqual(image_processor.size				,			{"""shortest_edge""": 42}  )
									self.assertEqual(image_processor.crop_size				,			{"""height""": 84, """width""": 84}  )






			def    UpperCamelCase_				(      self  ):
									# Initialize image_processing
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									# create random PIL videos
									_lowercase					       =					prepare_video_inputs(self.image_processor_tester				,			equal_resolution=__UpperCamelCase  )
									for video in video_inputs:
															self.assertIsInstance(__UpperCamelCase				,			__UpperCamelCase  )
															self.assertIsInstance(video[0]				,			Image.Image  )

									# Test not batched input
									_lowercase					       =					image_processing(video_inputs[0]				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)

									# Test batched
									_lowercase					       =					image_processing(__UpperCamelCase				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)






			def    UpperCamelCase_				(      self  ):
									# Initialize image_processing
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									# create random numpy tensors
									_lowercase					       =					prepare_video_inputs(self.image_processor_tester				,			equal_resolution=__UpperCamelCase				,			numpify=__UpperCamelCase  )
									for video in video_inputs:
															self.assertIsInstance(__UpperCamelCase				,			__UpperCamelCase  )
															self.assertIsInstance(video[0]				,			np.ndarray  )

									# Test not batched input
									_lowercase					       =					image_processing(video_inputs[0]				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)

									# Test batched
									_lowercase					       =					image_processing(__UpperCamelCase				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)






			def    UpperCamelCase_				(      self  ):
									# Initialize image_processing
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									# create random PyTorch tensors
									_lowercase					       =					prepare_video_inputs(self.image_processor_tester				,			equal_resolution=__UpperCamelCase				,			torchify=__UpperCamelCase  )
									for video in video_inputs:
															self.assertIsInstance(__UpperCamelCase				,			__UpperCamelCase  )
															self.assertIsInstance(video[0]				,			torch.Tensor  )

									# Test not batched input
									_lowercase					       =					image_processing(video_inputs[0]				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)

									# Test batched
									_lowercase					       =					image_processing(__UpperCamelCase				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)