

"""simple docstring"""




import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil
@pytest.fixture
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   tmp_path / 'text.txt'
					A     =   textwrap.dedent(
					    "\\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n\n        Second paragraph:\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n        " )
					with open(__a     ,				"w"     ,				encoding="utf-8" ) as f:
										f.write(__a )
					return str(__a )
@pytest.fixture
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   tmp_path / 'text_with_image.txt'
					with open(__a     ,				"w"     ,				encoding="utf-8" ) as f:
										f.write(__a )
					return str(__a )
@pytest.mark.parametrize("keep_linebreaks"     ,				[True, False] )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					with open(__a     ,				encoding="utf-8" ) as f:
										A     =   f.read().splitlines(keepends=__a )
					A     =   Text(keep_linebreaks=__a     ,				encoding="utf-8" )
					A     =   text._generate_tables([[text_file]] )
					A     =   pa.concat_tables([table for _, table in generator] ).to_pydict()['text']
					assert generated_content == expected_content
@require_pil
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					with open(__a     ,				encoding="utf-8" ) as f:
										A     =   f.read().splitlines()[0]
					A     =   Text(encoding="utf-8"     ,				features=Features({"image": Image()} ) )
					A     =   text._generate_tables([[text_file_with_image]] )
					A     =   pa.concat_tables([table for _, table in generator] )
					assert pa_table.schema.field("image" ).type == Image()()
					A     =   pa_table.to_pydict()['image']
					assert generated_content == [{"path": image_file, "bytes": None}]
@pytest.mark.parametrize("sample_by"     ,				["line", "paragraph", "document"] )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					with open(__a     ,				encoding="utf-8" ) as f:
										A     =   f.read()
					if sample_by == "line":
										A     =   expected_content.splitlines()
					elif sample_by == "paragraph":
										A     =   expected_content.split("\n\n" )
					elif sample_by == "document":
										A     =   [expected_content]
					A     =   Text(sample_by=__a     ,				encoding="utf-8"     ,				chunksize=100 )
					A     =   text._generate_tables([[text_file]] )
					A     =   pa.concat_tables([table for _, table in generator] ).to_pydict()['text']
					assert generated_content == expected_content












import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Tuple			   =							CONFIG_NAME






						@register_to_config
						def __init__(   self   :   Any		, _A   :   bool = True		, _A   :   int = 8		, _A   :   str = "lanczos"		, _A   :   bool = True		, _A   :   bool = False		, )				->     int:







							"""simple docstring"""


							super().__init__()






						@staticmethod
						def UpperCAmelCase_					(   _A   :   np.ndarray						)				->     PIL.Image.Image:







							"""simple docstring"""


							if images.ndim == 3:
								snake_case_		:      Optional[int]           =						images[None, ...]
							snake_case_		:      Any           =						(images * 255).round().astype('uint8'						)
							if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								snake_case_		:      List[str]           =						[Image.fromarray(image.squeeze()		, mode='L'						) for image in images]
							else:
								snake_case_		:      Any           =						[Image.fromarray(_A						) for image in images]

							return pil_images






						@staticmethod
						def UpperCAmelCase_					(   _A   :   Union[List[PIL.Image.Image], PIL.Image.Image]						)				->     np.ndarray:







							"""simple docstring"""


							if not isinstance(_A		, _A						):
								snake_case_		:      Tuple           =						[images]
							snake_case_		:      int           =						[np.array(_A						).astype(np.floataa						) / 2_5_5.0 for image in images]
							snake_case_		:      Dict           =						np.stack(_A		, axis=0						)

							return images






						@staticmethod
						def UpperCAmelCase_					(   _A   :   np.ndarray						)				->     torch.FloatTensor:







							"""simple docstring"""


							if images.ndim == 3:
								snake_case_		:      Union[str, Any]           =						images[..., None]

							snake_case_		:      Any           =						torch.from_numpy(images.transpose(0		, 3		, 1		, 2						)						)
							return images






						@staticmethod
						def UpperCAmelCase_					(   _A   :   torch.FloatTensor						)				->     np.ndarray:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						images.cpu().permute(0		, 2		, 3		, 1						).float().numpy()
							return images






						@staticmethod
						def UpperCAmelCase_					(   _A   :   Optional[int]						)				->     Optional[Any]:







							"""simple docstring"""


							return 2.0 * images - 1.0






						@staticmethod
						def UpperCAmelCase_					(   _A   :   Optional[int]						)				->     Tuple:







							"""simple docstring"""


							return (images / 2 + 0.5).clamp(0		, 1						)






						@staticmethod
						def UpperCAmelCase_					(   _A   :   PIL.Image.Image						)				->     PIL.Image.Image:







							"""simple docstring"""


							snake_case_		:      Optional[int]           =						image.convert('RGB'						)
							return image






						def UpperCAmelCase_					(   self   :   List[str]		, _A   :   PIL.Image.Image		, _A   :   Optional[int] = None		, _A   :   Optional[int] = None		, )				->     PIL.Image.Image:







							"""simple docstring"""


							if height is None:
								snake_case_		:      Union[str, Any]           =						image.height
							if width is None:
								snake_case_		:      int           =						image.width

							snake_case_    ,snake_case_		:      str           =						(
							    x - x % self.config.vae_scale_factor for x in (width, height)
							)  # resize to integer multiple of vae_scale_factor
							snake_case_		:      Optional[Any]           =						image.resize((width, height)		, resample=PIL_INTERPOLATION[self.config.resample]						)
							return image






						def UpperCAmelCase_					(   self   :   Dict		, _A   :   Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]		, _A   :   Optional[int] = None		, _A   :   Optional[int] = None		, )				->     torch.Tensor:







							"""simple docstring"""


							snake_case_		:      List[Any]           =						(PIL.Image.Image, np.ndarray, torch.Tensor)
							if isinstance(_A		, _A						):
								snake_case_		:      Optional[int]           =						[image]
							elif not (isinstance(_A		, _A						) and all(isinstance(_A		, _A						) for i in image						)):
								raise ValueError(
								    F"""Input is in incorrect format: {[type(_A						) for i in image]}. Currently, we only support {', '.join(_A						)}"""						)

							if isinstance(image[0]		, PIL.Image.Image						):
								if self.config.do_convert_rgb:
									snake_case_		:      str           =						[self.convert_to_rgb(_A						) for i in image]
								if self.config.do_resize:
									snake_case_		:      Union[str, Any]           =						[self.resize(_A		, _A		, _A						) for i in image]
								snake_case_		:      int           =						self.pil_to_numpy(_A						)  # to np
								snake_case_		:      str           =						self.numpy_to_pt(_A						)  # to pt

							elif isinstance(image[0]		, np.ndarray						):
								snake_case_		:      List[Any]           =						np.concatenate(_A		, axis=0						) if image[0].ndim == 4 else np.stack(_A		, axis=0						)
								snake_case_		:      Any           =						self.numpy_to_pt(_A						)
								snake_case_    ,snake_case_    ,snake_case_    ,snake_case_		:      Tuple           =						image.shape
								if self.config.do_resize and (
								    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
								):
									raise ValueError(
									    F"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
									    F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""						)

							elif isinstance(image[0]		, torch.Tensor						):
								snake_case_		:      List[Any]           =						torch.cat(_A		, axis=0						) if image[0].ndim == 4 else torch.stack(_A		, axis=0						)
								snake_case_    ,snake_case_    ,snake_case_    ,snake_case_		:      Any           =						image.shape

								# don't need any preprocess if the image is latents
								if channel == 4:
									return image

								if self.config.do_resize and (
								    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
								):
									raise ValueError(
									    F"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
									    F"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""						)

        # expected range [0,1], normalize to [-1,1]
							snake_case_		:      Tuple           =						self.config.do_normalize
							if image.min() < 0:
								warnings.warn(
								    'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
								    F"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""		, _A		, )
								snake_case_		:      Optional[Any]           =						False

							if do_normalize:
								snake_case_		:      Dict           =						self.normalize(_A						)

							return image






						def UpperCAmelCase_					(   self   :   Any		, _A   :   torch.FloatTensor		, _A   :   str = "pil"		, _A   :   Optional[List[bool]] = None		, )				->     int:







							"""simple docstring"""


							if not isinstance(_A		, torch.Tensor						):
								raise ValueError(
								    F"""Input for postprocessing is in incorrect format: {type(_A						)}. We only support pytorch tensor"""						)
							if output_type not in ["latent", "pt", "np", "pil"]:
								snake_case_		:      List[Any]           =						(
								    F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
								    '`pil`, `np`, `pt`, `latent`'
								)
								deprecate('Unsupported output_type'		, '1.0.0'		, _A		, standard_warn=_A						)
								snake_case_		:      Optional[int]           =						'np'

							if output_type == "latent":
								return image

							if do_denormalize is None:
								snake_case_		:      Optional[Any]           =						[self.config.do_normalize] * image.shape[0]

							snake_case_		:      Dict           =						torch.stack(
							    [self.denormalize(image[i]						) if do_denormalize[i] else image[i] for i in range(image.shape[0]						)]						)

							if output_type == "pt":
								return image

							snake_case_		:      Dict           =						self.pt_to_numpy(_A						)

							if output_type == "np":
								return image

							if output_type == "pil":
								return self.numpy_to_pil(_A						)
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Union[str, Any]			   =							CONFIG_NAME






						@register_to_config
						def __init__(   self   :   List[str]		, _A   :   bool = True		, _A   :   int = 8		, _A   :   str = "lanczos"		, _A   :   bool = True		, )				->     Optional[Any]:







							"""simple docstring"""


							super().__init__()






						@staticmethod
						def UpperCAmelCase_					(   _A   :   int						)				->     Dict:







							"""simple docstring"""


							if images.ndim == 3:
								snake_case_		:      Dict           =						images[None, ...]
							snake_case_		:      Optional[int]           =						(images * 255).round().astype('uint8'						)
							if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								snake_case_		:      Union[str, Any]           =						[Image.fromarray(image.squeeze()		, mode='L'						) for image in images]
							else:
								snake_case_		:      Any           =						[Image.fromarray(image[:, :, :3]						) for image in images]

							return pil_images






						@staticmethod
						def UpperCAmelCase_					(   _A   :   Optional[Any]						)				->     Optional[int]:







							"""simple docstring"""


							return image[:, :, 1] * 2**8 + image[:, :, 2]






						def UpperCAmelCase_					(   self   :   List[Any]		, _A   :   Optional[int]						)				->     Any:







							"""simple docstring"""


							if images.ndim == 3:
								snake_case_		:      int           =						images[None, ...]
							snake_case_		:      Any           =						images[:, :, :, 3:]
							if images.shape[-1] == 6:
								snake_case_		:      Optional[int]           =						(images_depth * 255).round().astype('uint8'						)
								snake_case_		:      List[str]           =						[
								    Image.fromarray(self.rgblike_to_depthmap(_A						)		, mode='I;16'						) for image_depth in images_depth
								]
							elif images.shape[-1] == 4:
								snake_case_		:      Dict           =						(images_depth * 6_5_5_3_5.0).astype(np.uintaa						)
								snake_case_		:      Union[str, Any]           =						[Image.fromarray(_A		, mode='I;16'						) for image_depth in images_depth]
							else:
								raise Exception('Not supported'						)

							return pil_images






						def UpperCAmelCase_					(   self   :   Any		, _A   :   torch.FloatTensor		, _A   :   str = "pil"		, _A   :   Optional[List[bool]] = None		, )				->     Dict:







							"""simple docstring"""


							if not isinstance(_A		, torch.Tensor						):
								raise ValueError(
								    F"""Input for postprocessing is in incorrect format: {type(_A						)}. We only support pytorch tensor"""						)
							if output_type not in ["latent", "pt", "np", "pil"]:
								snake_case_		:      int           =						(
								    F"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
								    '`pil`, `np`, `pt`, `latent`'
								)
								deprecate('Unsupported output_type'		, '1.0.0'		, _A		, standard_warn=_A						)
								snake_case_		:      str           =						'np'

							if do_denormalize is None:
								snake_case_		:      Union[str, Any]           =						[self.config.do_normalize] * image.shape[0]

							snake_case_		:      List[Any]           =						torch.stack(
							    [self.denormalize(image[i]						) if do_denormalize[i] else image[i] for i in range(image.shape[0]						)]						)

							snake_case_		:      int           =						self.pt_to_numpy(_A						)

							if output_type == "np":
								if image.shape[-1] == 6:
									snake_case_		:      Tuple           =						np.stack([self.rgblike_to_depthmap(im[:, :, 3:]						) for im in image]		, axis=0						)
								else:
									snake_case_		:      Any           =						image[:, :, :, 3:]
								return image[:, :, :, :3], image_depth

							if output_type == "pil":
								return self.numpy_to_pil(_A						), self.numpy_to_depth(_A						)
							else:
								raise Exception(F"""This type {output_type} is not supported"""						)

