



"""simple docstring"""







from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


UpperCAmelCase:   Any       			=							logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase:   List[str]       			=							"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")

        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""


def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase					,							__UpperCAmelCase					,							__UpperCAmelCase=8	):
		_lowercase							:      Dict			=					height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		_lowercase							:      Optional[int]			=					width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor


def       __SCREAMING_SNAKE_CASE      (   __UpperCAmelCase					,							__UpperCAmelCase=512					,							__UpperCAmelCase=512	):
		_lowercase							:      int			=					pil_image.resize((w, h)					,							resample=Image.BICUBIC					,							reducing_gap=1	)
		_lowercase							:      Optional[Any]			=					np.array(pil_image.convert("""RGB"""	)	)
		_lowercase							:      Dict			=					arr.astype(np.floataa	) / 1_2_7.5 - 1
		_lowercase							:      List[str]			=					np.transpose(__UpperCAmelCase					,							[2, 0, 1]	)
		_lowercase							:      Union[str, Any]			=					torch.from_numpy(__UpperCAmelCase	).unsqueeze(0	)
		return image





class    UpperCamelCase     (			snake_case    ):




	"""simple docstring"""






	def __init__(			self							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,):
			super().__init__()

			self.register_modules(
			    unet=UpperCAmelCase_							,scheduler=UpperCAmelCase_							,movq=UpperCAmelCase_							,)
			_lowercase							:      Tuple			=					2 ** (len(self.movq.config.block_out_channels					) - 1)






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					):
			# get the original timestep using init_timestep
			_lowercase							:      Any			=					min(int(num_inference_steps * strength					)							,UpperCAmelCase_					)

			_lowercase							:      Union[str, Any]			=					max(num_inference_steps - init_timestep							,0					)
			_lowercase							:      Dict			=					self.scheduler.timesteps[t_start:]

			return timesteps, num_inference_steps - t_start






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_=None					):
			if not isinstance(UpperCAmelCase_							,(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(UpperCAmelCase_					)}"""					)

			_lowercase							:      int			=					image.to(device=UpperCAmelCase_							,dtype=UpperCAmelCase_					)

			_lowercase							:      str			=					batch_size * num_images_per_prompt

			if image.shape[1] == 4:
					_lowercase							:      Union[str, Any]			=					image

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

					elif isinstance(UpperCAmelCase_							,UpperCAmelCase_					):
							_lowercase							:      List[str]			=					[
							    self.movq.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(UpperCAmelCase_					)
							]
							_lowercase							:      Optional[Any]			=					torch.cat(UpperCAmelCase_							,dim=0					)
					else:
							_lowercase							:      Optional[int]			=					self.movq.encode(UpperCAmelCase_					).latent_dist.sample(UpperCAmelCase_					)

					_lowercase							:      Optional[Any]			=					self.movq.config.scaling_factor * init_latents

			_lowercase							:      int			=					torch.cat([init_latents]							,dim=0					)

			_lowercase							:      Tuple			=					init_latents.shape
			_lowercase							:      int			=					randn_tensor(UpperCAmelCase_							,generator=UpperCAmelCase_							,device=UpperCAmelCase_							,dtype=UpperCAmelCase_					)

			# get latents
			_lowercase							:      str			=					self.scheduler.add_noise(UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					)

			_lowercase							:      Any			=					init_latents

			return latents






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_=0					):
			if is_accelerate_available():
					from accelerate import cpu_offload
			else:
					raise ImportError("""Please install accelerate via `pip install accelerate`"""					)

			_lowercase							:      Any			=					torch.device(f"""cuda:{gpu_id}"""					)

			_lowercase							:      Any			=					[
			    self.unet,
			    self.movq,
			]
			for cpu_offloaded_model in models:
					if cpu_offloaded_model is not None:
							cpu_offload(UpperCAmelCase_							,UpperCAmelCase_					)






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_=0					):
			if is_accelerate_available() and is_accelerate_version(""">="""							,"""0.17.0.dev0"""					):
					from accelerate import cpu_offload_with_hook
			else:
					raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""					)

			_lowercase							:      Optional[Any]			=					torch.device(f"""cuda:{gpu_id}"""					)

			if self.device.type != "cpu":
					self.to("""cpu"""							,silence_dtype_warnings=UpperCAmelCase_					)
					torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

			_lowercase							:      List[Any]			=					None
			for cpu_offloaded_model in [self.unet, self.movq]:
					_lowercase			,   _lowercase							:      Optional[int]			=					cpu_offload_with_hook(UpperCAmelCase_							,UpperCAmelCase_							,prev_module_hook=UpperCAmelCase_					)

			# We'll offload the last model manually.
			_lowercase							:      List[str]			=					hook






	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 					lowerCamelCase__ (			self					):
			if not hasattr(self.unet							,"""_hf_hook"""					):
					return self.device
			for module in self.unet.modules():
					if (
					    hasattr(UpperCAmelCase_							,"""_hf_hook"""					)
					    and hasattr(module._hf_hook							,"""execution_device"""					)
					    and module._hf_hook.execution_device is not None
					):
							return torch.device(module._hf_hook.execution_device					)
			return self.device






	@torch.no_grad()
	@replace_example_docstring(UpperCAmelCase_					)
	def __call__(			self							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_ = 5_12							,UpperCAmelCase_ = 5_12							,UpperCAmelCase_ = 1_00							,UpperCAmelCase_ = 4.0							,UpperCAmelCase_ = 0.3							,UpperCAmelCase_ = 1							,UpperCAmelCase_ = None							,UpperCAmelCase_ = "pil"							,UpperCAmelCase_ = True							,):
			_lowercase							:      List[str]			=					self._execution_device

			_lowercase							:      Union[str, Any]			=					guidance_scale > 1.0

			if isinstance(UpperCAmelCase_							,UpperCAmelCase_					):
					_lowercase							:      Optional[int]			=					torch.cat(UpperCAmelCase_							,dim=0					)
			if isinstance(UpperCAmelCase_							,UpperCAmelCase_					):
					_lowercase							:      Optional[Any]			=					torch.cat(UpperCAmelCase_							,dim=0					)
			if isinstance(UpperCAmelCase_							,UpperCAmelCase_					):
					_lowercase							:      Dict			=					torch.cat(UpperCAmelCase_							,dim=0					)

			_lowercase							:      Dict			=					image_embeds.shape[0]

			if do_classifier_free_guidance:
					_lowercase							:      Optional[Any]			=					image_embeds.repeat_interleave(UpperCAmelCase_							,dim=0					)
					_lowercase							:      Union[str, Any]			=					negative_image_embeds.repeat_interleave(UpperCAmelCase_							,dim=0					)
					_lowercase							:      Optional[int]			=					hint.repeat_interleave(UpperCAmelCase_							,dim=0					)

			_lowercase							:      List[Any]			=					torch.cat([negative_image_embeds, image_embeds]							,dim=0					).to(dtype=self.unet.dtype							,device=UpperCAmelCase_					)
			_lowercase							:      Tuple			=					torch.cat([hint, hint]							,dim=0					).to(dtype=self.unet.dtype							,device=UpperCAmelCase_					)

			if not isinstance(UpperCAmelCase_							,UpperCAmelCase_					):
					_lowercase							:      Tuple			=					[image]
			if not all(isinstance(UpperCAmelCase_							,(PIL.Image.Image, torch.Tensor)					) for i in image					):
					raise ValueError(
					    f"""Input is in incorrect format: {[type(UpperCAmelCase_					) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""					)

			_lowercase							:      Dict			=					torch.cat([prepare_image(UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					) for i in image]							,dim=0					)
			_lowercase							:      Dict			=					image.to(dtype=image_embeds.dtype							,device=UpperCAmelCase_					)

			_lowercase							:      Any			=					self.movq.encode(UpperCAmelCase_					)["""latents"""]
			_lowercase							:      str			=					latents.repeat_interleave(UpperCAmelCase_							,dim=0					)
			self.scheduler.set_timesteps(UpperCAmelCase_							,device=UpperCAmelCase_					)
			_lowercase			,   _lowercase							:      Union[str, Any]			=					self.get_timesteps(UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					)
			_lowercase							:      Tuple			=					timesteps[:1].repeat(batch_size * num_images_per_prompt					)
			_lowercase			,   _lowercase							:      Optional[Any]			=					downscale_height_and_width(UpperCAmelCase_							,UpperCAmelCase_							,self.movq_scale_factor					)
			_lowercase							:      Any			=					self.prepare_latents(
			    UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,image_embeds.dtype							,UpperCAmelCase_							,UpperCAmelCase_					)
			for i, t in enumerate(self.progress_bar(UpperCAmelCase_					)					):
					# expand the latents if we are doing classifier free guidance
					_lowercase							:      Union[str, Any]			=					torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

					_lowercase							:      str			=					{"""image_embeds""": image_embeds, """hint""": hint}
					_lowercase							:      Dict			=					self.unet(
					    sample=UpperCAmelCase_							,timestep=UpperCAmelCase_							,encoder_hidden_states=UpperCAmelCase_							,added_cond_kwargs=UpperCAmelCase_							,return_dict=UpperCAmelCase_							,)[0]

					if do_classifier_free_guidance:
							_lowercase			,   _lowercase							:      Any			=					noise_pred.split(latents.shape[1]							,dim=1					)
							_lowercase			,   _lowercase							:      Union[str, Any]			=					noise_pred.chunk(2					)
							_lowercase			,   _lowercase							:      List[Any]			=					variance_pred.chunk(2					)
							_lowercase							:      Dict			=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
							_lowercase							:      Optional[int]			=					torch.cat([noise_pred, variance_pred_text]							,dim=1					)

					if not (
					    hasattr(self.scheduler.config							,"""variance_type"""					)
					    and self.scheduler.config.variance_type in ["learned", "learned_range"]
					):
							_lowercase			,   _lowercase							:      List[Any]			=					noise_pred.split(latents.shape[1]							,dim=1					)

					# compute the previous noisy sample x_t -> x_t-1

					_lowercase							:      Tuple			=					self.scheduler.step(
					    UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,generator=UpperCAmelCase_							,)[0]

			# post-processing
			_lowercase							:      str			=					self.movq.decode(UpperCAmelCase_							,force_not_quantize=UpperCAmelCase_					)["""sample"""]

			if output_type not in ["pt", "np", "pil"]:
					raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""					)

			if output_type in ["np", "pil"]:
					_lowercase							:      Tuple			=					image * 0.5 + 0.5
					_lowercase							:      List[str]			=					image.clamp(0							,1					)
					_lowercase							:      Optional[int]			=					image.cpu().permute(0							,2							,3							,1					).float().numpy()

			if output_type == "pil":
					_lowercase							:      Dict			=					self.numpy_to_pil(UpperCAmelCase_					)

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=UpperCAmelCase_					)











import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  assert isinstance(lowercase  ,   lowercase )
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("keep_in_memory"  ,   [False, True] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Dict		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase  ,   keep_in_memory=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )


@pytest.mark.parametrize(
    "features"  ,   [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]  ,   )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Any		  =			tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Any		  =			features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			(
      Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
  )
  SCREAMING_SNAKE_CASE				:					Tuple		  =			CsvDatasetReader(lowercase  ,   features=lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )


@pytest.mark.parametrize("split"  ,   [None, NamedSplit("train" ), "train", "test"] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Dict		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Dict		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase  ,   split=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )
  assert dataset.split == split if split else "train"


@pytest.mark.parametrize("path_type"  ,   [str, list] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  if issubclass(lowercase  ,   lowercase ):
    SCREAMING_SNAKE_CASE				:					Tuple		  =			csv_path
  elif issubclass(lowercase  ,   lowercase ):
    SCREAMING_SNAKE_CASE				:					int		  =			[csv_path]
  SCREAMING_SNAKE_CASE				:					Any		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Tuple		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase=("train",) ):

  """simple docstring"""






  assert isinstance(lowercase  ,   lowercase )
  for split in splits:
    SCREAMING_SNAKE_CASE				:					str		  =			dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("keep_in_memory"  ,   [False, True] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase  ,   keep_in_memory=lowercase ).read()
  _check_csv_datasetdict(lowercase  ,   lowercase )


@pytest.mark.parametrize(
    "features"  ,   [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]  ,   )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					int		  =			tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  SCREAMING_SNAKE_CASE				:					Any		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			(
      Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
  )
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CsvDatasetReader({"train": csv_path}  ,   features=lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_datasetdict(lowercase  ,   lowercase )


@pytest.mark.parametrize("split"  ,   [None, NamedSplit("train" ), "train", "test"] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  if split:
    SCREAMING_SNAKE_CASE				:					Dict		  =			{split: csv_path}
  else:
    SCREAMING_SNAKE_CASE				:					str		  =			{"train": csv_path, "test": csv_path}
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Dict		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Tuple		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_datasetdict(lowercase  ,   lowercase  ,   splits=list(path.keys() ) )
  assert all(dataset[split].split == split for split in path.keys() )


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  with open(lowercase  ,   encoding="utf-8" ) as csvfile:
    yield from csv.reader(lowercase )


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					List[Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			os.path.join(lowercase  ,   "tmp.csv" )
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase ).read()
  CsvDatasetWriter(dataset["train"]  ,   lowercase  ,   num_proc=1 ).write()

  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			iter_csv_file(lowercase )
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			iter_csv_file(lowercase )

  for rowa, rowa in zip(lowercase  ,   lowercase ):
    assert rowa == rowa


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			os.path.join(lowercase  ,   "tmp.csv" )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase ).read()
  CsvDatasetWriter(dataset["train"]  ,   lowercase  ,   num_proc=2 ).write()

  SCREAMING_SNAKE_CASE				:					Dict		  =			iter_csv_file(lowercase )
  SCREAMING_SNAKE_CASE				:					Tuple		  =			iter_csv_file(lowercase )

  for rowa, rowa in zip(lowercase  ,   lowercase ):
    assert rowa == rowa


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			os.path.join(lowercase  ,   "tmp.csv" )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase ).read()
  with pytest.raises(lowercase ):
    CsvDatasetWriter(dataset["train"]  ,   lowercase  ,   num_proc=0 )






