

'''simple docstring'''


import math

import flax.linen as nn
import jax.numpy as jnp




def 	_a       (				__lowerCAmelCase		:      jnp.ndarray   ,							__lowerCAmelCase		:      int   ,							__lowerCAmelCase		:      float = 1   ,							__lowerCAmelCase		:      float = 1   ,							__lowerCAmelCase		:      float = 1.0e4   ,							__lowerCAmelCase		:      bool = False   ,							__lowerCAmelCase		:      float = 1.0   ,							):

		"""simple docstring"""

		assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
		assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
		snake_case__						:       str       						=  float(embedding_dim // 2     )
		snake_case__						:       List[Any]       						=  math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
		snake_case__						:       Optional[Any]       						=  min_timescale * jnp.exp(jnp.arange(__lowerCAmelCase   ,							dtype=jnp.floataa     ) * -log_timescale_increment     )
		snake_case__						:       Union[str, Any]       						=  jnp.expand_dims(__lowerCAmelCase   ,							1     ) * jnp.expand_dims(__lowerCAmelCase   ,							0     )

		# scale embeddings
		snake_case__						:       Optional[Any]       						=  scale * emb

		if flip_sin_to_cos:
				snake_case__						:       int       						=  jnp.concatenate([jnp.cos(__lowerCAmelCase     ), jnp.sin(__lowerCAmelCase     )]   ,							axis=1     )
		else:
				snake_case__						:       Any       						=  jnp.concatenate([jnp.sin(__lowerCAmelCase     ), jnp.cos(__lowerCAmelCase     )]   ,							axis=1     )
		snake_case__						:       str       						=  jnp.reshape(__lowerCAmelCase   ,							[jnp.shape(__lowerCAmelCase     )[0], embedding_dim]     )
		return signal




class     a			(				nn.Module       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    32
						__UpperCAmelCase			     =    jnp.floataa







						@nn.compact
						def __call__(							self					:      List[Any]			,			snake_case_					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  nn.Dense(self.time_embed_dim			,			dtype=self.dtype			,			name='''linear_1'''  )(snake_case_  )
								snake_case__						:       Optional[Any]       						=  nn.silu(snake_case_  )
								snake_case__						:       Union[str, Any]       						=  nn.Dense(self.time_embed_dim			,			dtype=self.dtype			,			name='''linear_2'''  )(snake_case_  )
								return temb





class     a			(				nn.Module       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    32
						__UpperCAmelCase			     =    False
						__UpperCAmelCase			     =    1







						@nn.compact
						def __call__(							self					:      str			,			snake_case_					:      Optional[int]  ):



								'''simple docstring'''



								return get_sinusoidal_embeddings(
								    snake_case_			,			embedding_dim=self.dim			,			flip_sin_to_cos=self.flip_sin_to_cos			,			freq_shift=self.freq_shift  )






'''simple docstring'''


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
					lowerCAmelCase__						:       Dict										=						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__						:       List[Any]										=						parser.parse_args()

					lowerCAmelCase__						:       Optional[Any]										=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

					lowerCAmelCase__						:       Optional[int]										=						CLIPImageProcessor()
					lowerCAmelCase__						:       str										=						CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

					lowerCAmelCase__						:       Dict										=						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)



