



'''simple docstring'''



import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class 	lowerCamelCase_							(					unittest.TestCase			):







						@require_torch
						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			pipeline(
													    task='''zero-shot-audio-classification'''				,    model='''hf-internal-testing/tiny-clap-htsat-unfused'''				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			load_dataset('''ashraq/esc50'''				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			dataset['''train''']['''audio'''][-1]['''array''']
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			audio_classifier(lowerCAmelCase__				,    candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']				)
													self.assertEqual(
													    nested_simplify(lowerCAmelCase__				)				,    [{'''score''': 0.501, '''label''': '''Sound of a dog'''}, {'''score''': 0.499, '''label''': '''Sound of vaccum cleaner'''}]				,    )







						@unittest.skip('''No models are available in TF'''				)
						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													pass







						@slow
						@require_torch
						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			pipeline(
													    task='''zero-shot-audio-classification'''				,    model='''laion/clap-htsat-unfused'''				,    )
													# This is an audio of a dog
													SCREAMING_SNAKE_CASE					:      str     					=			load_dataset('''ashraq/esc50'''				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			dataset['''train''']['''audio'''][-1]['''array''']
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			audio_classifier(lowerCAmelCase__				,    candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']				)

													self.assertEqual(
													    nested_simplify(lowerCAmelCase__				)				,    [
													        {'''score''': 0.999, '''label''': '''Sound of a dog'''},
													        {'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
													    ]				,    )

													SCREAMING_SNAKE_CASE					:      Tuple     					=			audio_classifier([audio] * 5				,    candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']				)
													self.assertEqual(
													    nested_simplify(lowerCAmelCase__				)				,    [
													        [
													            {'''score''': 0.999, '''label''': '''Sound of a dog'''},
													            {'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
													        ],
													    ]
													    * 5				,    )
													SCREAMING_SNAKE_CASE					:      Any     					=			audio_classifier(
													    [audio] * 5				,    candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']				,    batch_size=5				)
													self.assertEqual(
													    nested_simplify(lowerCAmelCase__				)				,    [
													        [
													            {'''score''': 0.999, '''label''': '''Sound of a dog'''},
													            {'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
													        ],
													    ]
													    * 5				,    )







						@unittest.skip('''No models are available in TF'''				)
						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													pass









'''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 					UpperCAmelCase   (						A  :				Union[str, Any]					,		A  :				Optional[int]					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      List[str]     					=			1.5
							SCREAMING_SNAKE_CASE					:      str     					=			int(factor * num_class_images				)
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			ClipClient(
							    url='''https://knn.laion.ai/knn-service'''					,		indice_name='''laion_400m'''					,		num_images=A					,		aesthetic_weight=0.1				)

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

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

							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			0
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			0
							SCREAMING_SNAKE_CASE					:      Tuple     					=			tqdm(desc='''downloading real regularization images'''					,		total=A				)

							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:
																					SCREAMING_SNAKE_CASE					:      int     					=			class_images[count]
																					count += 1
																					try:
																												SCREAMING_SNAKE_CASE					:      int     					=			requests.get(images['''url''']				)
																												if img.status_code == 200:
																																			SCREAMING_SNAKE_CASE					:      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 					UpperCAmelCase   (						):
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			argparse.ArgumentParser(''''''					,		add_help=A				)
							parser.add_argument('''--class_prompt'''					,		help='''text prompt to retrieve images'''					,		required=A					,		type=A				)
							parser.add_argument('''--class_data_dir'''					,		help='''path to save images'''					,		required=A					,		type=A				)
							parser.add_argument('''--num_class_images'''					,		help='''number of images to download'''					,		default=200					,		type=A				)
							return parser.parse_args()


if __name__ == "__main__":
							lowerCAmelCase_     :       List[str]								=						parse_args()
							retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)




