
import math

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






def 							A		(       snake_case   :str       ,      snake_case   :Optional[Any]       ,      snake_case   :Optional[int] = 1       ,      snake_case   :Union[str, Any] = 1       ,      snake_case   :Union[str, Any] = 1.0e4       ,      snake_case   :List[str] = False       ,      snake_case   :int = 1.0       ,      )   ->			Tuple:
 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(A_       ,      dtype=jnp.floataa	) * -log_timescale_increment	)
 __UpperCamelCase       						= jnp.expand_dims(A_       ,      1	) * jnp.expand_dims(A_       ,      0	)

 # scale embeddings
 __UpperCamelCase       						= scale * emb

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

class __lowerCAmelCase	(   nn.Module    ):
     lowercase		     =		32
     lowercase		     =		jnp.floataa





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

      '''simple docstring'''




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


class __lowerCAmelCase	(   nn.Module    ):
     lowercase		     =		32
     lowercase		     =		False
     lowercase		     =		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   )





"""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,
)


UpperCamelCase			:       List[Any]									=				{
    "configuration_xlm_roberta": [
        "XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XLMRobertaConfig",
        "XLMRobertaOnnxConfig",
    ],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Optional[Any]									=				["XLMRobertaTokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       int									=				["XLMRobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Optional[int]									=				[
          "XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "XLMRobertaForCausalLM",
          "XLMRobertaForMaskedLM",
          "XLMRobertaForMultipleChoice",
          "XLMRobertaForQuestionAnswering",
          "XLMRobertaForSequenceClassification",
          "XLMRobertaForTokenClassification",
          "XLMRobertaModel",
          "XLMRobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       List[str]									=				[
          "TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFXLMRobertaForCausalLM",
          "TFXLMRobertaForMaskedLM",
          "TFXLMRobertaForMultipleChoice",
          "TFXLMRobertaForQuestionAnswering",
          "TFXLMRobertaForSequenceClassification",
          "TFXLMRobertaForTokenClassification",
          "TFXLMRobertaModel",
          "TFXLMRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       List[str]									=				[
          "FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "FlaxXLMRobertaForMaskedLM",
          "FlaxXLMRobertaForCausalLM",
          "FlaxXLMRobertaForMultipleChoice",
          "FlaxXLMRobertaForQuestionAnswering",
          "FlaxXLMRobertaForSequenceClassification",
          "FlaxXLMRobertaForTokenClassification",
          "FlaxXLMRobertaModel",
          "FlaxXLMRobertaPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_xlm_roberta import (
          XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
          XLMRobertaConfig,
          XLMRobertaOnnxConfig,
      )

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_roberta import XLMRobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xlm_roberta import (
                XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                XLMRobertaForCausalLM,
                XLMRobertaForMaskedLM,
                XLMRobertaForMultipleChoice,
                XLMRobertaForQuestionAnswering,
                XLMRobertaForSequenceClassification,
                XLMRobertaForTokenClassification,
                XLMRobertaModel,
                XLMRobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_xlm_roberta import (
                TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFXLMRobertaForCausalLM,
                TFXLMRobertaForMaskedLM,
                TFXLMRobertaForMultipleChoice,
                TFXLMRobertaForQuestionAnswering,
                TFXLMRobertaForSequenceClassification,
                TFXLMRobertaForTokenClassification,
                TFXLMRobertaModel,
                TFXLMRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_xlm_roberta import (
                FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxXLMRobertaForCausalLM,
                FlaxXLMRobertaForMaskedLM,
                FlaxXLMRobertaForMultipleChoice,
                FlaxXLMRobertaForQuestionAnswering,
                FlaxXLMRobertaForSequenceClassification,
                FlaxXLMRobertaForTokenClassification,
                FlaxXLMRobertaModel,
                FlaxXLMRobertaPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase			:       Dict									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



