




"""simple docstring"""
import math

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

def        snake_case   (     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"""
			UpperCAmelCase_  :				Any      =      float(embedding_dim // 2		)
			UpperCAmelCase_  :				Optional[int]      =      math.log(max_timescale / min_timescale		) / (num_timescales - freq_shift)
			UpperCAmelCase_  :				Any      =      min_timescale * jnp.exp(jnp.arange(A__			,dtype=jnp.floataa		) * -log_timescale_increment		)
			UpperCAmelCase_  :				Dict      =      jnp.expand_dims(A__			,1		) * jnp.expand_dims(A__			,0		)

			# scale embeddings
			UpperCAmelCase_  :				str      =      scale * emb

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







class     UpperCamelCase_ (nn.Module  ):
	__magic_name__			   =       32
	__magic_name__			   =       jnp.floataa





	@nn.compact
	def __call__(   self      :				int			,			lowerCAmelCase_      :				str		)		->    Any:
				UpperCAmelCase_  :				Dict      =      nn.Dense(self.time_embed_dim			,			dtype=self.dtype			,			name="linear_1"		)(lowerCAmelCase_		)
				UpperCAmelCase_  :				List[Any]      =      nn.silu(lowerCAmelCase_		)
				UpperCAmelCase_  :				List[Any]      =      nn.Dense(self.time_embed_dim			,			dtype=self.dtype			,			name="linear_2"		)(lowerCAmelCase_		)
				return temb







class     UpperCamelCase_ (nn.Module  ):
	__magic_name__			   =       32
	__magic_name__			   =       False
	__magic_name__			   =       1





	@nn.compact
	def __call__(   self      :				Optional[Any]			,			lowerCAmelCase_      :				Dict		)		->    Optional[Any]:
				return get_sinusoidal_embeddings(
				    lowerCAmelCase_			,			embedding_dim=self.dim			,			flip_sin_to_cos=self.flip_sin_to_cos			,			freq_shift=self.freq_shift		)











"""simple docstring"""
import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format







class     UpperCamelCase_ :
	__magic_name__			   =       None





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    List[Any]:
				UpperCAmelCase_  :				Tuple      =      self.feature_extraction_class(**self.feat_extract_dict		)
				UpperCAmelCase_  :				Union[str, Any]      =      json.loads(feat_extract.to_json_string()		)
				for key, value in self.feat_extract_dict.items():
							self.assertEqual(obj[key]			,			lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    int:
				UpperCAmelCase_  :				Dict      =      self.feature_extraction_class(**self.feat_extract_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase_  :				List[Any]      =      os.path.join(lowerCAmelCase_			,			"feat_extract.json"		)
							feat_extract_first.to_json_file(lowerCAmelCase_		)
							UpperCAmelCase_  :				Any      =      self.feature_extraction_class.from_json_file(lowerCAmelCase_		)

				self.assertEqual(feat_extract_second.to_dict()			,			feat_extract_first.to_dict()		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    Dict:
				UpperCAmelCase_  :				Optional[Any]      =      self.feature_extraction_class(**self.feat_extract_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase_  :				Optional[Any]      =      feat_extract_first.save_pretrained(lowerCAmelCase_		)[0]
							check_json_file_has_correct_format(lowerCAmelCase_		)
							UpperCAmelCase_  :				Any      =      self.feature_extraction_class.from_pretrained(lowerCAmelCase_		)

				self.assertEqual(feat_extract_second.to_dict()			,			feat_extract_first.to_dict()		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    Optional[Any]:
				UpperCAmelCase_  :				Optional[int]      =      self.feature_extraction_class()
				self.assertIsNotNone(lowerCAmelCase_		)



