





import math

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



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase = 1							,       _lowerCAmelCase = 1							,       _lowerCAmelCase = 1.0e4							,       _lowerCAmelCase = False							,       _lowerCAmelCase = 1.0							,       )						->			jnp.ndarray:
 assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
 assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
 UpperCamelCase     : Dict	     =       float(embedding_dim // 2					)
 UpperCamelCase     : Optional[int]	     =       math.log(max_timescale / min_timescale					) / (num_timescales - freq_shift)
 UpperCamelCase     : Tuple	     =       min_timescale * jnp.exp(jnp.arange(_lowerCAmelCase							,       dtype=jnp.floataa					) * -log_timescale_increment					)
 UpperCamelCase     : Any	     =       jnp.expand_dims(_lowerCAmelCase							,       1					) * jnp.expand_dims(_lowerCAmelCase							,       0					)

 # scale embeddings
 UpperCamelCase     : str	     =       scale * emb

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



class  A__       (	nn.Module      ):
      _UpperCAmelCase     :int						   =       3_2
      _UpperCAmelCase     :jnp.dtype						   =       jnp.floataa





      @nn.compact
      def __call__(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Tuple	     =       nn.Dense(self.time_embed_dim	,							dtype=self.dtype	,							name="linear_1"       )(A_       )
       UpperCamelCase     : Dict	     =       nn.silu(A_       )
       UpperCamelCase     : List[Any]	     =       nn.Dense(self.time_embed_dim	,							dtype=self.dtype	,							name="linear_2"       )(A_       )
       return temb



class  A__       (	nn.Module      ):
      _UpperCAmelCase     :int						   =       3_2
      _UpperCAmelCase     :bool						   =       False
      _UpperCAmelCase     :float						   =       1





      @nn.compact
      def __call__(			self	,							A_       ):
       '''simple docstring'''




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







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor



class  A__       (	unittest.TestCase      ):





      def __init__(			self	,							A_	,							A_=13	,							A_=3	,							A_=224	,							A_=30	,							A_=400	,							A_=True	,							A_=None	,							A_=True	,							A_=[0.5, 0.5, 0.5]	,							A_=[0.5, 0.5, 0.5]	,							):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       size if size is not None else {"height": 18, "width": 18}
       UpperCamelCase     : Tuple	     =       parent
       UpperCamelCase     : Tuple	     =       batch_size
       UpperCamelCase     : Tuple	     =       num_channels
       UpperCamelCase     : str	     =       image_size
       UpperCamelCase     : Optional[int]	     =       min_resolution
       UpperCamelCase     : List[Any]	     =       max_resolution
       UpperCamelCase     : Union[str, Any]	     =       do_resize
       UpperCamelCase     : str	     =       size
       UpperCamelCase     : List[str]	     =       do_normalize
       UpperCamelCase     : Any	     =       image_mean
       UpperCamelCase     : int	     =       image_std





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }



@require_torch
@require_vision
class  A__       (	__snake_case	,       unittest.TestCase      ):
      _UpperCAmelCase     :Tuple						   =       ViTImageProcessor if is_vision_available() else None





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : int	     =       EfficientFormerImageProcessorTester(self       )





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self.image_proc_tester.prepare_image_processor_dict()





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       self.image_processing_class(**self.image_processor_dict       )
       self.assertTrue(hasattr(A_	,							"image_mean"       )       )
       self.assertTrue(hasattr(A_	,							"image_std"       )       )
       self.assertTrue(hasattr(A_	,							"do_normalize"       )       )
       self.assertTrue(hasattr(A_	,							"do_resize"       )       )
       self.assertTrue(hasattr(A_	,							"size"       )       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       pass





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random PIL images
       UpperCamelCase     : Union[str, Any]	     =       prepare_image_inputs(self.image_proc_tester	,							equal_resolution=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							Image.Image       )

       # Test not batched input
       UpperCamelCase     : Optional[Any]	     =       image_processor(image_inputs[0]	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)

       # Test batched
       UpperCamelCase     : Tuple	     =       image_processor(A_	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Optional[int]	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random numpy tensors
       UpperCamelCase     : List[Any]	     =       prepare_image_inputs(self.image_proc_tester	,							equal_resolution=A_	,							numpify=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							np.ndarray       )

       # Test not batched input
       UpperCamelCase     : Dict	     =       image_processor(image_inputs[0]	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)

       # Test batched
       UpperCamelCase     : Optional[Any]	     =       image_processor(A_	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       self.image_processing_class(**self.image_processor_dict       )
       # create random PyTorch tensors
       UpperCamelCase     : Any	     =       prepare_image_inputs(self.image_proc_tester	,							equal_resolution=A_	,							torchify=A_       )
       for image in image_inputs:
        self.assertIsInstance(A_	,							torch.Tensor       )

       # Test not batched input
       UpperCamelCase     : Any	     =       image_processor(image_inputs[0]	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)

       # Test batched
       UpperCamelCase     : Any	     =       image_processor(A_	,							return_tensors="pt"       ).pixel_values
       self.assertEqual(
           encoded_images.shape	,							(
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )	,							)
