'''simple docstring'''




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 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = 42
			UpperCamelCase__       = 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 					__magic_name__		(					_UpperCAmelCase):
									UpperCamelCase__       = 42
									UpperCamelCase__       = 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






'''simple docstring'''




import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm




def 							lowerCamelCase			(   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      Optional[Any]				)    ->   List[Any]:
							lowercase_      :    str       		= 1.5
							lowercase_      :    List[Any]       		= int(factor * num_class_images				)
							lowercase_      :    int       		= ClipClient(
							    url="""https://knn.laion.ai/knn-service"""   ,   indice_name="""laion_400m"""   ,   num_images=UpperCAmelCase__   ,   aesthetic_weight=0.1				)

							os.makedirs(F'''{class_data_dir}/images'''   ,   exist_ok=UpperCAmelCase__				)
							if len(list(Path(F'''{class_data_dir}/images'''				).iterdir()				)				) >= num_class_images:
														return

							while True:
														lowercase_      :    List[str]       		= client.query(text=UpperCAmelCase__				)
														if len(UpperCAmelCase__				) >= factor * num_class_images or num_images > 1e4:
																					break
														else:
																					lowercase_      :    List[str]       		= int(factor * num_images				)
																					lowercase_      :    List[str]       		= ClipClient(
																					    url="""https://knn.laion.ai/knn-service"""   ,   indice_name="""laion_400m"""   ,   num_images=UpperCAmelCase__   ,   aesthetic_weight=0.1   ,   )

							lowercase_      :    List[str]       		= 0
							lowercase_      :    Dict       		= 0
							lowercase_      :    Tuple       		= tqdm(desc="""downloading real regularization images"""   ,   total=UpperCAmelCase__				)

							with open(F'''{class_data_dir}/caption.txt'''   ,   """w"""				) as fa, open(F'''{class_data_dir}/urls.txt'''   ,   """w"""				) as fa, open(
							    F'''{class_data_dir}/images.txt'''   ,   """w"""				) as fa:
														while total < num_class_images:
																					lowercase_      :    str       		= class_images[count]
																					count += 1
																					try:
																												lowercase_      :    Union[str, Any]       		= requests.get(images["""url"""]				)
																												if img.status_code == 200:
																																			lowercase_      :    List[str]       		= Image.open(BytesIO(img.content				)				)
																																			with open(F'''{class_data_dir}/images/{total}.jpg'''   ,   """wb"""				) as f:
																																										f.write(img.content				)
																																			fa.write(images["""caption"""] + """\n"""				)
																																			fa.write(images["""url"""] + """\n"""				)
																																			fa.write(F'''{class_data_dir}/images/{total}.jpg''' + """\n"""				)
																																			total += 1
																																			pbar.update(1				)
																												else:
																																			continue
																					except Exception:
																												continue
							return




def 							lowerCamelCase			(   )    ->   Optional[Any]:
							lowercase_      :    Any       		= argparse.ArgumentParser(""""""   ,   add_help=UpperCAmelCase__				)
							parser.add_argument("""--class_prompt"""   ,   help="""text prompt to retrieve images"""   ,   required=UpperCAmelCase__   ,   type=UpperCAmelCase__				)
							parser.add_argument("""--class_data_dir"""   ,   help="""path to save images"""   ,   required=UpperCAmelCase__   ,   type=UpperCAmelCase__				)
							parser.add_argument("""--num_class_images"""   ,   help="""number of images to download"""   ,   default=200   ,   type=UpperCAmelCase__				)
							return parser.parse_args()


if __name__ == "__main__":
						_lowercase		:				Dict	       =		parse_args()
						retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)





