


from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


__UpperCamelCase    		=       transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def        UpperCamelCase_(				_A   :Any  )->		Tuple:
			if isinstance(_A				,     torch.Tensor  ):
						return image
			elif isinstance(_A				,     PIL.Image.Image  ):
						UpperCamelCase__					=       [image]

			UpperCamelCase__					=       [trans(img.convert("RGB"  )  ) for img in image]
			UpperCamelCase__					=       torch.stack(_A  )
			return image







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						super().__init__()

						# make sure scheduler can always be converted to DDIM
						UpperCamelCase__					=       DDIMScheduler.from_config(scheduler.config     )

						self.register_modules(unet=snake_case      ,						scheduler=snake_case     )




			def 				snake_case__				(							self      ,						snake_case     ):

						'''simple docstring'''


						if strength < 0 or strength > 1:
									raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       min(int(num_inference_steps * strength     )      ,						snake_case     )

						UpperCamelCase__					=       max(num_inference_steps - init_timestep      ,						0     )
						UpperCamelCase__					=       self.scheduler.timesteps[t_start:]

						return timesteps, num_inference_steps - t_start




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case=None     ):

						'''simple docstring'''


						if not isinstance(snake_case      ,						(torch.Tensor, PIL.Image.Image, list)     ):
									raise ValueError(
									    F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(snake_case     )}'''     )

						UpperCamelCase__					=       image.to(device=snake_case      ,						dtype=snake_case     )

						if isinstance(snake_case      ,						snake_case     ) and len(snake_case     ) != batch_size:
									raise ValueError(
									    F'''You have passed a list of generators of length {len(snake_case     )}, but requested an effective batch'''
									    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''     )

						UpperCamelCase__					=       init_latents.shape
						UpperCamelCase__					=       randn_tensor(snake_case      ,						generator=snake_case      ,						device=snake_case      ,						dtype=snake_case     )

						# get latents
						print("add noise to latents at timestep"      ,						snake_case     )
						UpperCamelCase__					=       self.scheduler.add_noise(snake_case      ,						snake_case      ,						snake_case     )
						UpperCamelCase__					=       init_latents

						return latents







			@torch.no_grad()
			def __call__(							self      ,						snake_case = None      ,						snake_case = 0.8      ,						snake_case = 1      ,						snake_case = None      ,						snake_case = 0.0      ,						snake_case = 50      ,						snake_case = None      ,						snake_case = "pil"      ,						snake_case = True      ,						):

						'''simple docstring'''


						self.check_inputs(snake_case     )

						# 2. Preprocess image
						UpperCamelCase__					=       preprocess(snake_case     )

						# 3. set timesteps
						self.scheduler.set_timesteps(snake_case      ,						device=self.device     )
						UpperCamelCase__,      UpperCamelCase__					=       self.get_timesteps(snake_case      ,						snake_case      ,						self.device     )
						UpperCamelCase__					=       timesteps[:1].repeat(snake_case     )

						# 4. Prepare latent variables
						UpperCamelCase__					=       self.prepare_latents(snake_case      ,						snake_case      ,						snake_case      ,						self.unet.dtype      ,						self.device      ,						snake_case     )
						UpperCamelCase__					=       latents

						# 5. Denoising loop
						for t in self.progress_bar(snake_case     ):
									# 1. predict noise model_output
									UpperCamelCase__					=       self.unet(snake_case      ,						snake_case     ).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									UpperCamelCase__					=       self.scheduler.step(
									    snake_case      ,						snake_case      ,						snake_case      ,						eta=snake_case      ,						use_clipped_model_output=snake_case      ,						generator=snake_case      ,						).prev_sample

						UpperCamelCase__					=       (image / 2 + 0.5).clamp(0      ,						1     )
						UpperCamelCase__					=       image.cpu().permute(0      ,						2      ,						3      ,						1     ).numpy()
						if output_type == "pil":
									UpperCamelCase__					=       self.numpy_to_pil(snake_case     )

						if not return_dict:
									return (image, latent_timestep.item())

						return ImagePipelineOutput(images=snake_case     )






import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil
@pytest.fixture
def        UpperCamelCase_(				_A   :Union[str, Any]  )->		int:
			UpperCamelCase__					=       tmp_path / "file.csv"
			UpperCamelCase__					=       textwrap.dedent(
			    "\\n        header1,header2\n        1,2\n        10,20\n        "  )
			with open(_A				,     "w"  ) as f:
						f.write(_A  )
			return str(_A  )
@pytest.fixture
def        UpperCamelCase_(				_A   :int  )->		Union[str, Any]:
			UpperCamelCase__					=       tmp_path / "malformed_file.csv"
			UpperCamelCase__					=       textwrap.dedent(
			    "\\n        header1,header2\n        1,2\n        10,20,\n        "  )
			with open(_A				,     "w"  ) as f:
						f.write(_A  )
			return str(_A  )
@pytest.fixture
def        UpperCamelCase_(				_A   :List[Any]				,     _A   :Dict  )->		Optional[Any]:
			UpperCamelCase__					=       tmp_path / "csv_with_image.csv"
			UpperCamelCase__					=       textwrap.dedent(
			    F'''\
        image
        {image_file}
        '''  )
			with open(_A				,     "w"  ) as f:
						f.write(_A  )
			return str(_A  )
@pytest.fixture
def        UpperCamelCase_(				_A   :List[Any]  )->		Any:
			UpperCamelCase__					=       tmp_path / "csv_with_label.csv"
			UpperCamelCase__					=       textwrap.dedent(
			    "\\n        label\n        good\n        bad\n        good\n        "  )
			with open(_A				,     "w"  ) as f:
						f.write(_A  )
			return str(_A  )
@pytest.fixture
def        UpperCamelCase_(				_A   :Any  )->		int:
			UpperCamelCase__					=       tmp_path / "csv_with_int_list.csv"
			UpperCamelCase__					=       textwrap.dedent(
			    "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        "  )
			with open(_A				,     "w"  ) as f:
						f.write(_A  )
			return str(_A  )
def        UpperCamelCase_(				_A   :Optional[Any]				,     _A   :Optional[Any]				,     _A   :List[str]  )->		Union[str, Any]:
			UpperCamelCase__					=       Csv()
			UpperCamelCase__					=       csv._generate_tables([[csv_file, malformed_csv_file]]  )
			with pytest.raises(_A				,     match="Error tokenizing data"  ):
						for _ in generator:
									pass
			assert any(
			    record.levelname == "ERROR"
			    and "Failed to read file" in record.message
			    and os.path.basename(_A  ) in record.message
			    for record in caplog.records  )
@require_pil
def        UpperCamelCase_(				_A   :Any  )->		int:
			with open(_A				,     encoding="utf-8"  ) as f:
						UpperCamelCase__					=       f.read().splitlines()[1]
			UpperCamelCase__					=       Csv(encoding="utf-8"				,     features=Features({"image": Image()}  )  )
			UpperCamelCase__					=       csv._generate_tables([[csv_file_with_image]]  )
			UpperCamelCase__					=       pa.concat_tables([table for _, table in generator]  )
			assert pa_table.schema.field("image"  ).type == Image()()
			UpperCamelCase__					=       pa_table.to_pydict()["image"]
			assert generated_content == [{"path": image_file, "bytes": None}]
def        UpperCamelCase_(				_A   :Any  )->		Dict:
			with open(_A				,     encoding="utf-8"  ) as f:
						UpperCamelCase__					=       f.read().splitlines()[1:]
			UpperCamelCase__					=       Csv(encoding="utf-8"				,     features=Features({"label": ClassLabel(names=["good", "bad"]  )}  )  )
			UpperCamelCase__					=       csv._generate_tables([[csv_file_with_label]]  )
			UpperCamelCase__					=       pa.concat_tables([table for _, table in generator]  )
			assert pa_table.schema.field("label"  ).type == ClassLabel(names=["good", "bad"]  )()
			UpperCamelCase__					=       pa_table.to_pydict()["label"]
			assert generated_content == [ClassLabel(names=["good", "bad"]  ).straint(_A  ) for label in labels]







def        UpperCamelCase_(				_A   :Dict  )->		Optional[Any]:
			UpperCamelCase__					=       Csv(encoding="utf-8"				,     sep=","				,     converters={"int_list": lambda _A  : [int(_A  ) for i in x.split()]}  )
			UpperCamelCase__					=       csv._generate_tables([[csv_file_with_int_list]]  )
			UpperCamelCase__					=       pa.concat_tables([table for _, table in generator]  )
			assert pa.types.is_list(pa_table.schema.field("int_list"  ).type  )
			UpperCamelCase__					=       pa_table.to_pydict()["int_list"]
			assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]


