'''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 (							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							Optional[int]  					=       tmp_path / 'text.txt'
     lowercase_       :							int  					=       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(__lowerCamelCase	,							'w'	,							encoding='utf-8'				) as f:
          f.write(__lowerCamelCase				)
     return str(__lowerCamelCase				)

@pytest.fixture
def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							Union[str, Any]  					=       tmp_path / 'text_with_image.txt'
     with open(__lowerCamelCase	,							'w'	,							encoding='utf-8'				) as f:
          f.write(__lowerCamelCase				)
     return str(__lowerCamelCase				)

@pytest.mark.parametrize('keep_linebreaks'	,							[True, False]				)
def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):






     with open(__lowerCamelCase	,							encoding='utf-8'				) as f:
          lowercase_       :							Dict  					=       f.read().splitlines(keepends=__lowerCamelCase				)
     lowercase_       :							List[str]  					=       Text(keep_linebreaks=__lowerCamelCase	,							encoding='utf-8'				)
     lowercase_       :							List[Any]  					=       text._generate_tables([[text_file]]				)
     lowercase_       :							Optional[int]  					=       pa.concat_tables([table for _, table in generator]				).to_pydict()['text']
     assert generated_content == expected_content

@require_pil
def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):






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






@pytest.mark.parametrize('sample_by'	,							['line', 'paragraph', 'document']				)
def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):






     with open(__lowerCamelCase	,							encoding='utf-8'				) as f:
          lowercase_       :							List[Any]  					=       f.read()
     if sample_by == "line":
          lowercase_       :							List[Any]  					=       expected_content.splitlines()
     elif sample_by == "paragraph":
          lowercase_       :							Dict  					=       expected_content.split('\n\n'				)
     elif sample_by == "document":
          lowercase_       :							int  					=       [expected_content]
     lowercase_       :							List[str]  					=       Text(sample_by=__lowerCamelCase	,							encoding='utf-8'	,							chunksize=100				)
     lowercase_       :							Any  					=       text._generate_tables([[text_file]]				)
     lowercase_       :							str  					=       pa.concat_tables([table for _, table in generator]				).to_pydict()['text']
     assert generated_content == expected_content








'''simple docstring'''

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowercase__					  =		False





@nightly
@require_torch_gpu
class     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






     def 			a_       (      self		)							->     Union[str, Any]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def 			a_       (      self		)							->     Dict:
          _a            =		VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)

          _a            =		torch.manual_seed(0		)
          _a            =		pipe(
              prompt="first prompt"  ,				image=__UpperCamelCase  ,				text_to_image_strength=0.75  ,				generator=__UpperCamelCase  ,				guidance_scale=7.5  ,				num_inference_steps=2  ,				output_type="numpy"  ,				).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCamelCase		)
               _a            =		VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCamelCase		)

          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		generator.manual_seed(0		)
          _a            =		pipe(
              prompt="first prompt"  ,				image=__UpperCamelCase  ,				text_to_image_strength=0.75  ,				generator=__UpperCamelCase  ,				guidance_scale=7.5  ,				num_inference_steps=2  ,				output_type="numpy"  ,				).images

          assert np.abs(image - new_image		).sum() < 1e-5, "Models don't have the same forward pass"






     def 			a_       (      self		)							->     int:
          _a            =		VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		"cyberpunk 2077"
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)
          _a            =		torch.manual_seed(0		)
          _a            =		pipe(
              prompt=__UpperCamelCase  ,				image=__UpperCamelCase  ,				text_to_image_strength=0.75  ,				generator=__UpperCamelCase  ,				guidance_scale=7.5  ,				num_inference_steps=50  ,				output_type="numpy"  ,				).images

          _a            =		image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          _a            =		np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]		)

          assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2

