


'''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 (      __lowerCamelCase		:			List[str]					)   ->  List[str]:







     '''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(__lowerCamelCase   ,	"w"   ,	encoding="utf-8"					) as f:
          f.write(__lowerCamelCase					)
     return str(__lowerCamelCase					)






@pytest.fixture
def __UpperCamelCase (      __lowerCamelCase		:			List[Any]   ,	__lowerCamelCase		:			List[str]					)   ->  Union[str, Any]:







     '''simple docstring'''






     _a            =		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 (      __lowerCamelCase		:			Tuple   ,	__lowerCamelCase		:			Any					)   ->  str:







     '''simple docstring'''






     with open(__lowerCamelCase   ,	encoding="utf-8"					) as f:
          _a            =		f.read().splitlines(keepends=__lowerCamelCase					)
     _a            =		Text(keep_linebreaks=__lowerCamelCase   ,	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 __UpperCamelCase (      __lowerCamelCase		:			Tuple					)   ->  Optional[int]:







     '''simple docstring'''






     with open(__lowerCamelCase   ,	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 __UpperCamelCase (      __lowerCamelCase		:			str   ,	__lowerCamelCase		:			Optional[Any]					)   ->  Union[str, Any]:







     '''simple docstring'''






     with open(__lowerCamelCase   ,	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=__lowerCamelCase   ,	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





'''simple docstring'''

import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			,      lowerCamelCase__			,      unittest.TestCase			):
     UpperCAmelCase			       =					IFImgaImgPipeline
     UpperCAmelCase			       =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
     UpperCAmelCase			       =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     UpperCAmelCase			       =					PipelineTesterMixin.required_optional_params - {'''latents'''}






     def 			a_       (      self		)							->     Optional[Any]:
          return self._get_dummy_components()






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=0		)							->     Any:
          if str(__UpperCamelCase		).startswith("mps"		):
               _a            =		torch.manual_seed(__UpperCamelCase		)
          else:
               _a            =		torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)

          _a            =		floats_tensor((1, 3, 32, 32)  ,				rng=random.Random(__UpperCamelCase		)		).to(__UpperCamelCase		)

          _a            =		{
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }

          return inputs






     def 			a_       (      self		)							->     Any:
          self._test_save_load_optional_components()






     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()  ,				reason="XFormers attention is only available with CUDA and `xformers` installed"  ,				)
     def 			a_       (      self		)							->     Dict:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)






     @unittest.skipIf(torch_device != "cuda"  ,				reason="float16 requires CUDA"		)
     def 			a_       (      self		)							->     List[Any]:
          # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
          super().test_save_load_floataa(expected_max_diff=1e-1		)






     @unittest.skipIf(torch_device != "cuda"  ,				reason="float16 requires CUDA"		)
     def 			a_       (      self		)							->     int:
          super().test_floataa_inference(expected_max_diff=1e-1		)






     def 			a_       (      self		)							->     Dict:
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)






     def 			a_       (      self		)							->     Optional[Any]:
          self._test_save_load_local()






     def 			a_       (      self		)							->     Union[str, Any]:
          self._test_inference_batch_single_identical(
              expected_max_diff=1e-2  ,				)

