




"""simple docstring"""

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor

class 			lowerCamelCase__    (  unittest.TestCase			):


			@property
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						int								=				1
					snake_case		:						Dict								=				3
					snake_case		:						int								=				8
					snake_case		:						int								=				8

					snake_case		:						List[str]								=				torch.rand((batch_size, num_channels, height, width)							)

					return sample


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if isinstance(image[0]				, PIL.Image.Image							):
							return np.stack([np.array(SCREAMING_SNAKE_CASE							) for i in image]				, axis=0							)
					elif isinstance(SCREAMING_SNAKE_CASE				, torch.Tensor							):
							return image.cpu().numpy().transpose(0				, 2				, 3				, 1							)
					return image


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[int]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						Any								=				self.dummy_sample
					snake_case		:						Optional[Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)

					for output_type in ["pt", "np", "pil"]:
							snake_case		:						Optional[Any]								=				image_processor.postprocess(
							    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE				, )
							snake_case		:						Optional[Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
							snake_case		:						Any								=				(input_np * 255).round() if output_type == "pil" else input_np
							assert (
							    np.abs(in_np - out_np							).max() < 1E-6
							), F'''decoded output does not match input for output_type {output_type}'''


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Dict								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)
					snake_case		:						Tuple								=				self.dummy_sample.cpu().numpy().transpose(0				, 2				, 3				, 1							)

					for output_type in ["pt", "np", "pil"]:
							snake_case		:						Dict								=				image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE							)

							snake_case		:						Union[str, Any]								=				self.to_np(SCREAMING_SNAKE_CASE							)
							snake_case		:						Tuple								=				(input_np * 255).round() if output_type == "pil" else input_np
							assert (
							    np.abs(in_np - out_np							).max() < 1E-6
							), F'''decoded output does not match input for output_type {output_type}'''


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				self.dummy_sample.cpu().numpy().transpose(0				, 2				, 3				, 1							)
					snake_case		:						int								=				image_processor.numpy_to_pil(SCREAMING_SNAKE_CASE							)

					for output_type in ["pt", "np", "pil"]:
							snake_case		:						Dict								=				image_processor.postprocess(image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type=SCREAMING_SNAKE_CASE							)
							for i, o in zip(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):
									snake_case		:						Dict								=				np.array(SCREAMING_SNAKE_CASE							)
									snake_case		:						Tuple								=				self.to_np(SCREAMING_SNAKE_CASE							) if output_type == "pil" else (self.to_np(SCREAMING_SNAKE_CASE							) * 255).round()
									assert (
									    np.abs(in_np - out_np							).max() < 1E-6
									), F'''decoded output does not match input for output_type {output_type}'''


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Tuple								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						List[str]								=				self.dummy_sample
					snake_case		:						Optional[int]								=				input_pt_ad.squeeze(0							)

					snake_case		:						Any								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Union[str, Any]								=				self.to_np(self.dummy_sample							)
					snake_case		:						Tuple								=				input_np_ad.squeeze(0							)

					snake_case		:						str								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )
					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					assert np.abs(out_pt_ad - out_pt_ad							).max() < 1E-6
					assert np.abs(out_np_ad - out_np_ad							).max() < 1E-6



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				VaeImageProcessor(do_resize=SCREAMING_SNAKE_CASE				, do_normalize=SCREAMING_SNAKE_CASE							)

					snake_case		:						Union[str, Any]								=				self.dummy_sample
					snake_case		:						List[str]								=				list(SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Any								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Dict								=				self.to_np(self.dummy_sample							)
					list(SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					snake_case		:						Union[str, Any]								=				image_processor.postprocess(
					    image_processor.preprocess(SCREAMING_SNAKE_CASE							)				, output_type="np"				, )

					assert np.abs(out_pt_ad - out_pt_list							).max() < 1E-6
					assert np.abs(out_np_ad - out_np_list							).max() < 1E-6







"""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   UpperCamelCase__     (		lowercase__  :     str   ):
		snake_case		:						List[str]								=				tmp_path / "text.txt"
		snake_case		:						Tuple								=				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(lowercase__       ,     "w"       ,     encoding="utf-8"   ) as f:
				f.write(lowercase__   )
		return str(lowercase__   )



@pytest.fixture
def   UpperCamelCase__     (		lowercase__  :     str       ,     lowercase__  :     Dict   ):
		snake_case		:						Any								=				tmp_path / "text_with_image.txt"
		with open(lowercase__       ,     "w"       ,     encoding="utf-8"   ) as f:
				f.write(lowercase__   )
		return str(lowercase__   )



@pytest.mark.parametrize("keep_linebreaks"       ,     [True, False]   )
def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]       ,     lowercase__  :     Tuple   ):
		with open(lowercase__       ,     encoding="utf-8"   ) as f:
				snake_case		:						List[Any]								=				f.read().splitlines(keepends=lowercase__   )
		snake_case		:						List[Any]								=				Text(keep_linebreaks=lowercase__       ,     encoding="utf-8"   )
		snake_case		:						Optional[int]								=				text._generate_tables([[text_file]]   )
		snake_case		:						int								=				pa.concat_tables([table for _, table in generator]   ).to_pydict()["text"]
		assert generated_content == expected_content



@require_pil
def   UpperCamelCase__     (		lowercase__  :     Optional[Any]   ):
		with open(lowercase__       ,     encoding="utf-8"   ) as f:
				snake_case		:						Union[str, Any]								=				f.read().splitlines()[0]
		snake_case		:						List[Any]								=				Text(encoding="utf-8"       ,     features=Features({"image": Image()}   )   )
		snake_case		:						Tuple								=				text._generate_tables([[text_file_with_image]]   )
		snake_case		:						List[str]								=				pa.concat_tables([table for _, table in generator]   )
		assert pa_table.schema.field("image"   ).type == Image()()
		snake_case		:						Optional[Any]								=				pa_table.to_pydict()["image"]
		assert generated_content == [{"path": image_file, "bytes": None}]



@pytest.mark.parametrize("sample_by"       ,     ["line", "paragraph", "document"]   )
def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]       ,     lowercase__  :     List[str]   ):
		with open(lowercase__       ,     encoding="utf-8"   ) as f:
				snake_case		:						Tuple								=				f.read()
		if sample_by == "line":
				snake_case		:						Optional[int]								=				expected_content.splitlines()
		elif sample_by == "paragraph":
				snake_case		:						str								=				expected_content.split("\n\n"   )
		elif sample_by == "document":
				snake_case		:						List[Any]								=				[expected_content]
		snake_case		:						str								=				Text(sample_by=lowercase__       ,     encoding="utf-8"       ,     chunksize=100   )
		snake_case		:						Optional[int]								=				text._generate_tables([[text_file]]   )
		snake_case		:						Optional[Any]								=				pa.concat_tables([table for _, table in generator]   ).to_pydict()["text"]
		assert generated_content == expected_content

