from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)



@dataclass
class __A     (      __lowercase						):

							'''simple docstring'''

							a_     =					42
							a_     =					42


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
						from .pipeline_cycle_diffusion import CycleDiffusionPipeline
						from .pipeline_stable_diffusion import StableDiffusionPipeline
						from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
						from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
						from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
						from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
						from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
						from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
						from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
						from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
						from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
						from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
						from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
						from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
						from .pipeline_stable_unclip import StableUnCLIPPipeline
						from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
						from .safety_checker import StableDiffusionSafetyChecker
						from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
						from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.26.0")):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import (
						    StableDiffusionDepthaImgPipeline,
						    StableDiffusionDiffEditPipeline,
						    StableDiffusionPixaPixZeroPipeline,
						)
else:
						from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
						from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
						from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
						if not (
						    is_torch_available()
						    and is_transformers_available()
						    and is_k_diffusion_available()
						    and is_k_diffusion_version(">=", "0.0.12")
						):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
						from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
						if not (is_transformers_available() and is_onnx_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_onnx_objects import *  # noqa F403
else:
						from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
						from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
						from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
						from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
						from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
						import flax



						@flax.struct.dataclass
						class __A     (      __lowercase						):

													'''simple docstring'''

													a_     =					42
													a_     =					42
						from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
						from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
						from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
						from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
						from .safety_checker_flax import FlaxStableDiffusionSafetyChecker








import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader


def 				__A(lowerCAmelCase			)		->					Optional[int]:

		"""simple docstring"""






		_UpperCamelCase         =						np.inf

		def set_batch_size(lowerCAmelCase			) -> None:
				nonlocal batch_size
				if isinstance(lowerCAmelCase    ,	lowerCAmelCase			):
						_UpperCamelCase         =						min(lowerCAmelCase    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS			)
				elif isinstance(lowerCAmelCase    ,	lowerCAmelCase			):
						_UpperCamelCase         =						min(lowerCAmelCase    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS			)
				elif isinstance(lowerCAmelCase    ,	lowerCAmelCase			) and feature.dtype == "binary":
						_UpperCamelCase         =						min(lowerCAmelCase    ,	config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS			)

		_visit(lowerCAmelCase    ,	lowerCAmelCase			)

		return None if batch_size is np.inf else batch_size







class 							lowerCAmelCase__   ( __lowercase						):

						def __init__(      self							,				a							,				a = None							,				a = None							,				a = None							,				a = False							,				a = False							,				a = None							,				**a							,				)       ->   Tuple:


								'''simple docstring'''

								super().__init__(
								    a							,				split=a							,				features=a							,				cache_dir=a							,				keep_in_memory=a							,				streaming=a							,				num_proc=a							,				**a							,				)
								_UpperCamelCase         =						path_or_paths if isinstance(a							,				a					) else {self.split: path_or_paths}
								_UpperCamelCase         =						_PACKAGED_DATASETS_MODULES["""parquet"""][1]
								_UpperCamelCase         =						Parquet(
								    cache_dir=a							,				data_files=a							,				features=a							,				hash=a							,				**a							,				)







						def 			A_					(      self					)       ->   Optional[int]:


								'''simple docstring'''

								if self.streaming:
										_UpperCamelCase         =						self.builder.as_streaming_dataset(split=self.split					)
								# Build regular (map-style) dataset
								else:
										_UpperCamelCase         =						None
										_UpperCamelCase         =						None
										_UpperCamelCase         =						None
										_UpperCamelCase         =						None

										self.builder.download_and_prepare(
										    download_config=a							,				download_mode=a							,				verification_mode=a							,				base_path=a							,				num_proc=self.num_proc							,				)
										_UpperCamelCase         =						self.builder.as_dataset(
										    split=self.split							,				verification_mode=a							,				in_memory=self.keep_in_memory					)
								return dataset







class 							lowerCAmelCase__   :

						def __init__(      self							,				a							,				a							,				a = None							,				**a							,				)       ->   str:


								'''simple docstring'''

								_UpperCamelCase         =						dataset
								_UpperCamelCase         =						path_or_buf
								_UpperCamelCase         =						batch_size or get_writer_batch_size(dataset.features					)
								_UpperCamelCase         =						parquet_writer_kwargs

						def 			A_					(      self					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

								if isinstance(self.path_or_buf							,				(str, bytes, os.PathLike)					):
										with open(self.path_or_buf							,				"""wb+"""					) as buffer:
												_UpperCamelCase         =						self._write(file_obj=a							,				batch_size=a							,				**self.parquet_writer_kwargs					)
								else:
										_UpperCamelCase         =						self._write(file_obj=self.path_or_buf							,				batch_size=a							,				**self.parquet_writer_kwargs					)
								return written







						def 			A_					(      self							,				a							,				a							,				**a					)       ->   int:


								'''simple docstring'''

								_UpperCamelCase         =						0
								_UpperCamelCase         =						parquet_writer_kwargs.pop("""path_or_buf"""							,				a					)
								_UpperCamelCase         =						self.dataset.features.arrow_schema

								_UpperCamelCase         =						pq.ParquetWriter(a							,				schema=a							,				**a					)

								for offset in logging.tqdm(
								    range(0							,				len(self.dataset					)							,				a					)							,				unit="""ba"""							,				disable=not logging.is_progress_bar_enabled()							,				desc="""Creating parquet from Arrow format"""							,				):
										_UpperCamelCase         =						query_table(
										    table=self.dataset._data							,				key=slice(a							,				offset + batch_size					)							,				indices=self.dataset._indices if self.dataset._indices is not None else None							,				)
										writer.write_table(a					)
										written += batch.nbytes
								writer.close()
								return written



