

import pytest

from datasets import inspect_metric, list_metrics, load_metric

@pytest.fixture
def 			lowerCamelCase__			(       _a):
	monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"				,      set())

@pytest.fixture
def 			lowerCamelCase__			(       _a):






	class _UpperCamelCase				:
				'''simple docstring'''







				def __init__(      self				:			int   ,   a				:			Union[str, Any]						)				->							Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE :   Optional[int]					  =							metric_id






	class _UpperCamelCase				:
				'''simple docstring'''







				lowerCamelCase__							     =[MetricMock(__A ) for metric_id in ['accuracy', 'mse', 'precision', 'codeparrot/apps_metric']]
				def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							int:

					"""simple docstring"""

					return self._metrics

	monkeypatch.setattr("datasets.inspect.huggingface_hub"				,      HfhMock())


@pytest.mark.parametrize(
    "func, args"				,      [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))])
def 			lowerCamelCase__			(       _a				,      _a				,      _a				,      _a				,      _a):
	if "tmp_path" in args:
		SCREAMING_SNAKE_CASE :   int					  =							tuple(arg if arg != "tmp_path" else tmp_path for arg in args)
	with pytest.warns(_a				,      match="https://huggingface.co/docs/evaluate"):
		func(*_a)


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
			a_      =     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.',
			)

			a_      =     parser.parse_args()

			a_      =     UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

			a_      =     CLIPImageProcessor()
			a_      =     CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

			a_      =     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)