




'''simple docstring'''


import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
						import jax
						import jax.numpy as jnp



@slow
@require_flax
class 					lowercase						(  unittest.TestCase ):


							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case)							->				Optional[int]:
								return F"""gaussian_noise_s={seed}_shape={"_".join([str(_snake_case) for s in shape])}.npy"""


							def 						_snake_case							(			self)							->				Any:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()


							def 						_snake_case							(			self    ,			_snake_case=0    ,			_snake_case=(4, 4, 64, 64)    ,			_snake_case=False)							->				Any:
								UpperCAmelCase_		:      str       	=					jnp.bfloataa if fpaa else jnp.floataa
								UpperCAmelCase_		:      Any       	=					jnp.array(load_hf_numpy(self.get_file_format(_snake_case    ,			_snake_case))    ,			dtype=_snake_case)
								return image


							def 						_snake_case							(			self    ,			_snake_case=False    ,			_snake_case="CompVis/stable-diffusion-v1-4")							->				Any:
								UpperCAmelCase_		:      Any       	=					jnp.bfloataa if fpaa else jnp.floataa
								UpperCAmelCase_		:      Union[str, Any]       	=					'bf16' if fpaa else None

								UpperCAmelCase_		:      int       	=					FlaxUNetaDConditionModel.from_pretrained(
								    _snake_case    ,			subfolder='unet'    ,			dtype=_snake_case    ,			revision=_snake_case)
								return model, params


							def 						_snake_case							(			self    ,			_snake_case=0    ,			_snake_case=(4, 77, 768)    ,			_snake_case=False)							->				Any:
								UpperCAmelCase_		:      List[str]       	=					jnp.bfloataa if fpaa else jnp.floataa
								UpperCAmelCase_		:      int       	=					jnp.array(load_hf_numpy(self.get_file_format(_snake_case    ,			_snake_case))    ,			dtype=_snake_case)
								return hidden_states


							@parameterized.expand(
							    [
							        # fmt: off
							        [83, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
							        [17, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
							        [8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
							        [3, 1000, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
							        # fmt: on
							    ])
							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				Dict:
								UpperCAmelCase_		:      Any       	=					self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'    ,			fpaa=_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					self.get_latents(_snake_case    ,			fpaa=_snake_case)
								UpperCAmelCase_		:      Optional[int]       	=					self.get_encoder_hidden_states(_snake_case    ,			fpaa=_snake_case)

								UpperCAmelCase_		:      int       	=					model.apply(
								    {'params': params}    ,			_snake_case    ,			jnp.array(_snake_case    ,			dtype=jnp.intaa)    ,			encoder_hidden_states=_snake_case    ,			).sample

								assert sample.shape == latents.shape

								UpperCAmelCase_		:      str       	=					jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()))    ,			dtype=jnp.floataa)
								UpperCAmelCase_		:      Dict       	=					jnp.array(_snake_case    ,			dtype=jnp.floataa)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
								assert jnp.allclose(_snake_case    ,			_snake_case    ,			atol=1e-2)





							@parameterized.expand(
							    [
							        # fmt: off
							        [83, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
							        [17, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
							        [8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
							        [3, 1000, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
							        # fmt: on
							    ])
							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				Optional[int]:
								UpperCAmelCase_		:      Optional[int]       	=					self.get_unet_model(model_id='stabilityai/stable-diffusion-2'    ,			fpaa=_snake_case)
								UpperCAmelCase_		:      Optional[int]       	=					self.get_latents(_snake_case    ,			shape=(4, 4, 96, 96)    ,			fpaa=_snake_case)
								UpperCAmelCase_		:      Any       	=					self.get_encoder_hidden_states(_snake_case    ,			shape=(4, 77, 1024)    ,			fpaa=_snake_case)

								UpperCAmelCase_		:      List[Any]       	=					model.apply(
								    {'params': params}    ,			_snake_case    ,			jnp.array(_snake_case    ,			dtype=jnp.intaa)    ,			encoder_hidden_states=_snake_case    ,			).sample

								assert sample.shape == latents.shape

								UpperCAmelCase_		:      Dict       	=					jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()))    ,			dtype=jnp.floataa)
								UpperCAmelCase_		:      Tuple       	=					jnp.array(_snake_case    ,			dtype=jnp.floataa)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
								assert jnp.allclose(_snake_case    ,			_snake_case    ,			atol=1e-2)









'''simple docstring'''


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)






