


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
				lowerCAmelCase							=						argparse.ArgumentParser()

				parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

				parser.add_argument(
				    """--txt2img_unclip""",
				    default="""kakaobrain/karlo-v1-alpha""",
				    type=str,
				    required=False,
				    help="""The pretrained txt2img unclip.""",
				)

				lowerCAmelCase							=						parser.parse_args()

				lowerCAmelCase							=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

				lowerCAmelCase							=						CLIPImageProcessor()
				lowerCAmelCase							=						CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

				lowerCAmelCase							=						UnCLIPImageVariationPipeline(
				    decoder=txtaimg.decoder,
				    text_encoder=txtaimg.text_encoder,
				    tokenizer=txtaimg.tokenizer,
				    text_proj=txtaimg.text_proj,
				    feature_extractor=feature_extractor,
				    image_encoder=image_encoder,
				    super_res_first=txtaimg.super_res_first,
				    super_res_last=txtaimg.super_res_last,
				    decoder_scheduler=txtaimg.decoder_scheduler,
				    super_res_scheduler=txtaimg.super_res_scheduler,
				)

				imgaimg.save_pretrained(args.dump_path)





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    A(						self):
							__UpperCAmelCase :       str  						=  pipeline(
							    task='''zero-shot-audio-classification'''							,    model='''hf-internal-testing/tiny-clap-htsat-unfused''')
							__UpperCAmelCase :       Optional[int]  						=  load_dataset('''ashraq/esc50''')
							__UpperCAmelCase :       Dict  						=  dataset['''train''']['''audio'''][-1]['''array''']
							__UpperCAmelCase :       Union[str, Any]  						=  audio_classifier(lowercase__							,    candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''])
							self.assertEqual(
							    nested_simplify(lowercase__)							,    [{'''score''': 0.5_0_1, '''label''': '''Sound of a dog'''}, {'''score''': 0.4_9_9, '''label''': '''Sound of vaccum cleaner'''}]							,    )


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


	@slow
	@require_torch
	def    A(						self):
							__UpperCAmelCase :       int  						=  pipeline(
							    task='''zero-shot-audio-classification'''							,    model='''laion/clap-htsat-unfused'''							,    )
							# This is an audio of a dog
							__UpperCAmelCase :       Optional[Any]  						=  load_dataset('''ashraq/esc50''')
							__UpperCAmelCase :       Union[str, Any]  						=  dataset['''train''']['''audio'''][-1]['''array''']
							__UpperCAmelCase :       Union[str, Any]  						=  audio_classifier(lowercase__							,    candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''])

							self.assertEqual(
							    nested_simplify(lowercase__)							,    [
							        {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
							        {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
							    ]							,    )

							__UpperCAmelCase :       Optional[Any]  						=  audio_classifier([audio] * 5							,    candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''])
							self.assertEqual(
							    nested_simplify(lowercase__)							,    [
							        [
							            {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
							            {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
							        ],
							    ]
							    * 5							,    )
							__UpperCAmelCase :       Optional[Any]  						=  audio_classifier(
							    [audio] * 5							,    candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']							,    batch_size=5)
							self.assertEqual(
							    nested_simplify(lowercase__)							,    [
							        [
							            {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
							            {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
							        ],
							    ]
							    * 5							,    )


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

