




import math

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


def 							__SCREAMING_SNAKE_CASE  (			snake_case_	,    snake_case_	,    snake_case_ = 1	,    snake_case_ = 1	,    snake_case_ = 1.0e4	,    snake_case_ = False	,    snake_case_ = 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"""
						_UpperCAmelCase													=     float(embedding_dim // 2						)
						_UpperCAmelCase													=     math.log(max_timescale / min_timescale						) / (num_timescales - freq_shift)
						_UpperCAmelCase													=     min_timescale * jnp.exp(jnp.arange(_UpperCamelCase	,    dtype=jnp.floataa						) * -log_timescale_increment						)
						_UpperCAmelCase													=     jnp.expand_dims(_UpperCamelCase	,    1						) * jnp.expand_dims(_UpperCamelCase	,    0						)

						# scale embeddings
						_UpperCAmelCase													=     scale * emb

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






class     __lowerCAmelCase (       nn.Module ):
			snake_case_      : List[Any]      =     32
			snake_case_      : List[Any]      =     jnp.floataa







			@nn.compact
			def __call__(       self    :			int    ,    snake_case__    :			Union[str, Any]      ):




									"""simple docstring"""


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






class     __lowerCAmelCase (       nn.Module ):
			snake_case_      : Union[str, Any]      =     32
			snake_case_      : Dict      =     False
			snake_case_      : str      =     1







			@nn.compact
			def __call__(       self    :			Tuple    ,    snake_case__    :			Dict      ):




									"""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      )







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase :      int					 =     {"configuration_mbart": ["MBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "MBartConfig", "MBartOnnxConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      List[Any]					 =     ["MBartTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Dict					 =     ["MBartTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      int					 =     [
         "MBART_PRETRAINED_MODEL_ARCHIVE_LIST",
         "MBartForCausalLM",
         "MBartForConditionalGeneration",
         "MBartForQuestionAnswering",
         "MBartForSequenceClassification",
         "MBartModel",
         "MBartPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "TFMBartForConditionalGeneration",
         "TFMBartModel",
         "TFMBartPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Optional[Any]					 =     [
         "FlaxMBartForConditionalGeneration",
         "FlaxMBartForQuestionAnswering",
         "FlaxMBartForSequenceClassification",
         "FlaxMBartModel",
         "FlaxMBartPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart import MBartTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbart_fast import MBartTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mbart import (
              MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
              MBartForCausalLM,
              MBartForConditionalGeneration,
              MBartForQuestionAnswering,
              MBartForSequenceClassification,
              MBartModel,
              MBartPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_mbart import (
              FlaxMBartForConditionalGeneration,
              FlaxMBartForQuestionAnswering,
              FlaxMBartForSequenceClassification,
              FlaxMBartModel,
              FlaxMBartPreTrainedModel,
          )

else:
     import sys

     lowerCamelCase :      List[Any]					 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
