




import math

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





def __UpperCamelCase			(       _A ,  _A ,  _A = 1 ,  _A = 1 ,  _A = 1.0E4 ,  _A = False ,  _A = 1.0 ,  ):
						assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
						assert embedding_dim % 2 == 0, f"Embedding dimension {embedding_dim} should be even"
						lowerCAmelCase_      			=  float(embedding_dim // 2  )
						lowerCAmelCase_      			=  math.log(max_timescale / min_timescale  ) / (num_timescales - freq_shift)
						lowerCAmelCase_      			=  min_timescale * jnp.exp(jnp.arange(_A ,  dtype=jnp.floataa  ) * -log_timescale_increment  )
						lowerCAmelCase_      			=  jnp.expand_dims(_A ,  1  ) * jnp.expand_dims(_A ,  0  )

						# scale embeddings
						lowerCAmelCase_      			=  scale * emb

						if flip_sin_to_cos:
												lowerCAmelCase_      			=  jnp.concatenate([jnp.cos(_A  ), jnp.sin(_A  )] ,  axis=1  )
						else:
												lowerCAmelCase_      			=  jnp.concatenate([jnp.sin(_A  ), jnp.cos(_A  )] ,  axis=1  )
						lowerCAmelCase_      			=  jnp.reshape(_A ,  [jnp.shape(_A  )[0], embedding_dim]  )
						return signal


class  A		(  nn.Module     ):
		__snake_case									=   32
		__snake_case									=   jnp.floataa



		@nn.compact
		def __call__(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  nn.Dense(self.time_embed_dim,	dtype=self.dtype,	name='''linear_1'''   )(UpperCamelCase__   )
								lowerCAmelCase_      			=  nn.silu(UpperCamelCase__   )
								lowerCAmelCase_      			=  nn.Dense(self.time_embed_dim,	dtype=self.dtype,	name='''linear_2'''   )(UpperCamelCase__   )
								return temb







class  A		(  nn.Module     ):
		__snake_case									=   32
		__snake_case									=   False
		__snake_case									=   1



		@nn.compact
		def __call__(     self,	UpperCamelCase__   ):







								"""simple docstring"""



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










import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
						import torch

						from transformers import AutoModelForImageClassification

if is_vision_available():
						from transformers import AutoImageProcessor


@require_torch
@require_vision
class  A		(  unittest.TestCase     ):



		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''   )
								lowerCAmelCase_      			=  AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''   )
								model.to(UpperCamelCase__   )

								from datasets import load_dataset

								lowerCAmelCase_      			=  load_dataset('''nielsr/rvlcdip-demo'''   )

								lowerCAmelCase_      			=  dataset['''train'''][0]['''image'''].convert('''RGB'''   )

								lowerCAmelCase_      			=  image_processor(UpperCamelCase__,	return_tensors='''pt'''   ).to(UpperCamelCase__   )

								# forward pass
								with torch.no_grad():
														lowerCAmelCase_      			=  model(**UpperCamelCase__   )
														lowerCAmelCase_      			=  outputs.logits

								lowerCAmelCase_      			=  torch.Size((1, 16)   )
								self.assertEqual(logits.shape,	UpperCamelCase__   )

								lowerCAmelCase_      			=  torch.tensor(
								    [-0.4_158, -0.4_092, -0.4_347],	device=UpperCamelCase__,	dtype=torch.float,	)
								self.assertTrue(torch.allclose(logits[0, :3],	UpperCamelCase__,	atol=1E-4   )   )




