





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase_					:    Any      	=			{
    'configuration_xlm_roberta_xl': [
        'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaXLConfig',
        'XLMRobertaXLOnnxConfig',
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_					:    Tuple      	=			[
					    'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'XLMRobertaXLForCausalLM',
					    'XLMRobertaXLForMaskedLM',
					    'XLMRobertaXLForMultipleChoice',
					    'XLMRobertaXLForQuestionAnswering',
					    'XLMRobertaXLForSequenceClassification',
					    'XLMRobertaXLForTokenClassification',
					    'XLMRobertaXLModel',
					    'XLMRobertaXLPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_xlm_roberta_xl import (
					    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    XLMRobertaXLConfig,
					    XLMRobertaXLOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm_roberta_xl import (
										    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMRobertaXLForCausalLM,
										    XLMRobertaXLForMaskedLM,
										    XLMRobertaXLForMultipleChoice,
										    XLMRobertaXLForQuestionAnswering,
										    XLMRobertaXLForSequenceClassification,
										    XLMRobertaXLForTokenClassification,
										    XLMRobertaXLModel,
										    XLMRobertaXLPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase_					:    Optional[int]      	=			_LazyModule(__name__, globals()['__file__'], _import_structure)

'''simple docstring'''




import math

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






def 				snake_case__							(			_A:					jnp.ndarray      ,   _A:					int      ,   _A:					float = 1      ,   _A:					float = 1      ,   _A:					float = 1.0e4      ,   _A:					bool = False      ,   _A:					float = 1.0      ,   )    ->	jnp.ndarray:

		'''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"
		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	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       3_2
			UpperCAmelCase_			:     jnp.dtype      =       jnp.floataa



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




					"""simple docstring"""


					lowerCAmelCase   					=  nn.Dense(self.time_embed_dim       ,    dtype=self.dtype       ,    name="""linear_1""")(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.silu(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.Dense(self.time_embed_dim       ,    dtype=self.dtype       ,    name="""linear_2""")(__lowerCAmelCase)
					return temb







class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       3_2
			UpperCAmelCase_			:     bool      =       False
			UpperCAmelCase_			:     float      =       1



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




					"""simple docstring"""


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




