
import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


snake_case__  							=							10_00_00_00_00_00
snake_case__  							=							1_00_00

snake_case__							,		snake_case__  							=							os.path.split(__file__)
snake_case__  							=							os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))

def        lowerCamelCase_      (			UpperCAmelCase_							:				int							,			UpperCAmelCase_							:				int       ):
						lowercase       :    int											=  pa.Table.from_pydict({'''col''': [0] * chunk_size}       )
						lowercase       :    Tuple											=  pa.concat_tables([table] * (num_examples // chunk_size)       )
						return datasets.Dataset(UpperCAmelCase_							,			fingerprint='''table_100B'''       )







@dataclass
class 							UpperCAmelCase	:
					a__: int
					a__: int
					a__: int
					a__: int

					def        _lowerCAmelCase	(			self      :					Optional[Any]					):
											lowercase       :    Any											=  np.random.default_rng(self.seed					)
											lowercase       :    Tuple											=  rng.integers(low=self.low ,      high=self.high ,      size=self.size					).tolist()

					def __iter__(			self      :					Dict					):
											return iter(self._sampled_values					)




					def __len__(			self      :					Tuple					):
											return self.size





@get_duration
def        lowerCamelCase_      (			UpperCAmelCase_							:				datasets.Dataset       ):
						lowercase       :    int											=  dataset[0]

@get_duration
def        lowerCamelCase_      (			UpperCAmelCase_							:				datasets.Dataset       ):
						lowercase       :    Any											=  dataset[-1]

@get_duration
def        lowerCamelCase_      (			UpperCAmelCase_							:				datasets.Dataset       ):
						lowercase       :    Tuple											=  dataset[range(len(UpperCAmelCase_       ) // 2							,			len(UpperCAmelCase_       ) // 2 + 10_24       )]

@get_duration
def        lowerCamelCase_      (			UpperCAmelCase_							:				datasets.Dataset       ):
						lowercase       :    Dict											=  dataset[RandIter(0							,			len(UpperCAmelCase_       )							,			10_24							,			seed=42       )]

def        lowerCamelCase_      (			):
						lowercase       :    Dict											=  {'''num examples''': SPEED_TEST_N_EXAMPLES}
						lowercase       :    Optional[Any]											=  (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
						print('''generating dataset'''       )
						lowercase       :    List[str]											=  generate_100B_dataset(num_examples=UpperCAmelCase_							,			chunk_size=UpperCAmelCase_       )
						print('''Functions'''       )
						for func in functions:
												print(func.__name__       )
												lowercase       :    Tuple											=  func(UpperCAmelCase_       )

						with open(UpperCAmelCase_							,			'''wb'''       ) as f:
												f.write(json.dumps(UpperCAmelCase_       ).encode('''utf-8'''       )       )


if __name__ == "__main__":  # useful to run the profiler
				benchmark_table_100B()




from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_pt_objects import *  # noqa F403
else:
				from .consistency_models import ConsistencyModelPipeline
				from .dance_diffusion import DanceDiffusionPipeline
				from .ddim import DDIMPipeline
				from .ddpm import DDPMPipeline
				from .dit import DiTPipeline
				from .latent_diffusion import LDMSuperResolutionPipeline
				from .latent_diffusion_uncond import LDMPipeline
				from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
				from .pndm import PNDMPipeline
				from .repaint import RePaintPipeline
				from .score_sde_ve import ScoreSdeVePipeline
				from .stochastic_karras_ve import KarrasVePipeline

try:
				if not (is_torch_available() and is_librosa_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
				from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
				if not (is_torch_available() and is_transformers_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
				from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
				from .audioldm import AudioLDMPipeline
				from .controlnet import (
				    StableDiffusionControlNetImgaImgPipeline,
				    StableDiffusionControlNetInpaintPipeline,
				    StableDiffusionControlNetPipeline,
				)
				from .deepfloyd_if import (
				    IFImgaImgPipeline,
				    IFImgaImgSuperResolutionPipeline,
				    IFInpaintingPipeline,
				    IFInpaintingSuperResolutionPipeline,
				    IFPipeline,
				    IFSuperResolutionPipeline,
				)
				from .kandinsky import (
				    KandinskyImgaImgPipeline,
				    KandinskyInpaintPipeline,
				    KandinskyPipeline,
				    KandinskyPriorPipeline,
				)
				from .kandinskya_a import (
				    KandinskyVaaControlnetImgaImgPipeline,
				    KandinskyVaaControlnetPipeline,
				    KandinskyVaaImgaImgPipeline,
				    KandinskyVaaInpaintPipeline,
				    KandinskyVaaPipeline,
				    KandinskyVaaPriorEmbaEmbPipeline,
				    KandinskyVaaPriorPipeline,
				)
				from .latent_diffusion import LDMTextToImagePipeline
				from .paint_by_example import PaintByExamplePipeline
				from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
				from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
				from .stable_diffusion import (
				    CycleDiffusionPipeline,
				    StableDiffusionAttendAndExcitePipeline,
				    StableDiffusionDepthaImgPipeline,
				    StableDiffusionDiffEditPipeline,
				    StableDiffusionImageVariationPipeline,
				    StableDiffusionImgaImgPipeline,
				    StableDiffusionInpaintPipeline,
				    StableDiffusionInpaintPipelineLegacy,
				    StableDiffusionInstructPixaPixPipeline,
				    StableDiffusionLatentUpscalePipeline,
				    StableDiffusionLDMaDPipeline,
				    StableDiffusionModelEditingPipeline,
				    StableDiffusionPanoramaPipeline,
				    StableDiffusionParadigmsPipeline,
				    StableDiffusionPipeline,
				    StableDiffusionPixaPixZeroPipeline,
				    StableDiffusionSAGPipeline,
				    StableDiffusionUpscalePipeline,
				    StableUnCLIPImgaImgPipeline,
				    StableUnCLIPPipeline,
				)
				from .stable_diffusion_safe import StableDiffusionPipelineSafe
				from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
				from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
				from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
				from .versatile_diffusion import (
				    VersatileDiffusionDualGuidedPipeline,
				    VersatileDiffusionImageVariationPipeline,
				    VersatileDiffusionPipeline,
				    VersatileDiffusionTextToImagePipeline,
				)
				from .vq_diffusion import VQDiffusionPipeline


try:
				if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
				from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
				if not is_onnx_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_onnx_objects import *  # noqa F403
else:
				from .onnx_utils import OnnxRuntimeModel

try:
				if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
				from .stable_diffusion import (
				    OnnxStableDiffusionImgaImgPipeline,
				    OnnxStableDiffusionInpaintPipeline,
				    OnnxStableDiffusionInpaintPipelineLegacy,
				    OnnxStableDiffusionPipeline,
				    OnnxStableDiffusionUpscalePipeline,
				    StableDiffusionOnnxPipeline,
				)

try:
				if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
				from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_flax_objects import *  # noqa F403
else:
				from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
				if not (is_flax_available() and is_transformers_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
				from .controlnet import FlaxStableDiffusionControlNetPipeline
				from .stable_diffusion import (
				    FlaxStableDiffusionImgaImgPipeline,
				    FlaxStableDiffusionInpaintPipeline,
				    FlaxStableDiffusionPipeline,
				)
try:
				if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
				from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline


