





'''simple docstring'''






from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


__UpperCamelCase      :   Dict								=     logging.get_logger(__name__)  # pylint: disable=invalid-name



class  lowerCamelCase__				(						snake_case_						):


	"""simple docstring"""





	__magic_name__							   =		["""safety_checker""", """feature_extractor"""]




	def __init__(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__ = True   ,     )       ->				str:
					super().__init__()
					if hasattr(scheduler.config   ,     '''steps_offset'''			) and scheduler.config.steps_offset != 1:
									_A :				Dict				=  (
									    F"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
									    F""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
									    '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
									    ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
									    ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
									    ''' file'''
									)
									deprecate('''steps_offset!=1'''   ,     '''1.0.0'''   ,     UpperCAmelCase__   ,     standard_warn=UpperCAmelCase__			)
									_A :				Dict				=  dict(scheduler.config			)
									_A :				List[str]				=  1
									_A :				Optional[Any]				=  FrozenDict(UpperCAmelCase__			)

					self.register_modules(
					    vae=UpperCAmelCase__   ,     text_encoder=UpperCAmelCase__   ,     tokenizer=UpperCAmelCase__   ,     unet=UpperCAmelCase__   ,     scheduler=UpperCAmelCase__   ,     safety_checker=UpperCAmelCase__   ,     feature_extractor=UpperCAmelCase__   ,     )
					self.register_to_config(requires_safety_checker=UpperCAmelCase__			)




	@property
	def 				_lowerCamelCase				(					self			)       ->				Dict[str, Any]:
					return {k: getattr(self   ,     UpperCAmelCase__			) for k in self.config.keys() if not k.startswith('''_'''			)}




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__ = "auto"			)       ->				str:
					if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									_A :				Optional[int]				=  self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(UpperCAmelCase__			)




	def 				_lowerCamelCase				(					self			)       ->				str:
					self.enable_attention_slicing(UpperCAmelCase__			)




	@torch.no_grad()
	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__ = 0.8   ,     UpperCAmelCase__ = 5_0   ,     UpperCAmelCase__ = 7.5   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = 1   ,     UpperCAmelCase__ = 0.0   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = "pil"   ,     UpperCAmelCase__ = True   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = 1   ,     )       ->				str:
					# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
					return StableDiffusionInpaintPipelineLegacy(**self.components			)(
					    prompt=UpperCAmelCase__   ,     image=UpperCAmelCase__   ,     mask_image=UpperCAmelCase__   ,     strength=UpperCAmelCase__   ,     num_inference_steps=UpperCAmelCase__   ,     guidance_scale=UpperCAmelCase__   ,     negative_prompt=UpperCAmelCase__   ,     num_images_per_prompt=UpperCAmelCase__   ,     eta=UpperCAmelCase__   ,     generator=UpperCAmelCase__   ,     output_type=UpperCAmelCase__   ,     return_dict=UpperCAmelCase__   ,     callback=UpperCAmelCase__   ,     )




	@torch.no_grad()
	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__ = 0.8   ,     UpperCAmelCase__ = 5_0   ,     UpperCAmelCase__ = 7.5   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = 1   ,     UpperCAmelCase__ = 0.0   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = "pil"   ,     UpperCAmelCase__ = True   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = 1   ,     **UpperCAmelCase__   ,     )       ->				Dict:
					# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
					return StableDiffusionImgaImgPipeline(**self.components			)(
					    prompt=UpperCAmelCase__   ,     image=UpperCAmelCase__   ,     strength=UpperCAmelCase__   ,     num_inference_steps=UpperCAmelCase__   ,     guidance_scale=UpperCAmelCase__   ,     negative_prompt=UpperCAmelCase__   ,     num_images_per_prompt=UpperCAmelCase__   ,     eta=UpperCAmelCase__   ,     generator=UpperCAmelCase__   ,     output_type=UpperCAmelCase__   ,     return_dict=UpperCAmelCase__   ,     callback=UpperCAmelCase__   ,     callback_steps=UpperCAmelCase__   ,     )




	@torch.no_grad()
	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__ = 5_1_2   ,     UpperCAmelCase__ = 5_1_2   ,     UpperCAmelCase__ = 5_0   ,     UpperCAmelCase__ = 7.5   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = 1   ,     UpperCAmelCase__ = 0.0   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = "pil"   ,     UpperCAmelCase__ = True   ,     UpperCAmelCase__ = None   ,     UpperCAmelCase__ = 1   ,     )       ->				Optional[Any]:
					# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
					return StableDiffusionPipeline(**self.components			)(
					    prompt=UpperCAmelCase__   ,     height=UpperCAmelCase__   ,     width=UpperCAmelCase__   ,     num_inference_steps=UpperCAmelCase__   ,     guidance_scale=UpperCAmelCase__   ,     negative_prompt=UpperCAmelCase__   ,     num_images_per_prompt=UpperCAmelCase__   ,     eta=UpperCAmelCase__   ,     generator=UpperCAmelCase__   ,     latents=UpperCAmelCase__   ,     output_type=UpperCAmelCase__   ,     return_dict=UpperCAmelCase__   ,     callback=UpperCAmelCase__   ,     callback_steps=UpperCAmelCase__   ,     )







'''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      lowercase  (  lowerCAmelCase :						Union[str, Any]):



				"""simple docstring"""


				_A :				int				=  tmp_path / '''text.txt'''
				_A :				int				=  textwrap.dedent(
				    '''\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        ''')
				with open(lowerCAmelCase   , '''w'''   , encoding='''utf-8''') as f:
								f.write(lowerCAmelCase)
				return str(lowerCAmelCase)





@pytest.fixture
def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Tuple):



				"""simple docstring"""


				_A :				str				=  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      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Union[str, Any]):



				"""simple docstring"""


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





@require_pil
def      lowercase  (  lowerCAmelCase :						Dict):



				"""simple docstring"""


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





@pytest.mark.parametrize('''sample_by'''   , ['''line''', '''paragraph''', '''document'''])
def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						Optional[int]):



				"""simple docstring"""


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