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


class   lowerCamelCase					(nn.Module					):
  """simple docstring"""







  lowerCamelCase__     =						42
  lowerCamelCase__     =						jnp.floataa







  def    __A     (							self :       List[str]   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  nn.Conv(
            self.out_channels  ,     kernel_size=(3, 3)  ,     strides=(1, 1)  ,     padding=((1, 1), (1, 1))  ,     dtype=self.dtype  ,     )







  def __call__(							self :       Optional[int]  ,     __magic_name__ :       List[str]   )  ->	Any:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  hidden_states.shape
        SCREAMING_SNAKE_CASE_          =  jax.image.resize(
            __magic_name__  ,     shape=(batch, height * 2, width * 2, channels)  ,     method="nearest"  ,     )
        SCREAMING_SNAKE_CASE_          =  self.conv(__magic_name__   )
        return hidden_states



class   lowerCamelCase					(nn.Module					):
  """simple docstring"""







  lowerCamelCase__     =						42
  lowerCamelCase__     =						jnp.floataa







  def    __A     (							self :       Tuple   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  nn.Conv(
            self.out_channels  ,     kernel_size=(3, 3)  ,     strides=(2, 2)  ,     padding=((1, 1), (1, 1))  ,     dtype=self.dtype  ,     )







  def __call__(							self :       Union[str, Any]  ,     __magic_name__ :       Optional[int]   )  ->	List[str]:
        # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
        # hidden_states = jnp.pad(hidden_states, pad_width=pad)
        SCREAMING_SNAKE_CASE_          =  self.conv(__magic_name__   )
        return hidden_states



class   lowerCamelCase					(nn.Module					):
  """simple docstring"""







  lowerCamelCase__     =						42
  lowerCamelCase__     =						None
  lowerCamelCase__     =						0.0
  lowerCamelCase__     =						None
  lowerCamelCase__     =						jnp.floataa







  def    __A     (							self :       int   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  self.in_channels if self.out_channels is None else self.out_channels

        SCREAMING_SNAKE_CASE_          =  nn.GroupNorm(num_groups=32  ,     epsilon=1e-5   )
        SCREAMING_SNAKE_CASE_          =  nn.Conv(
            __magic_name__  ,     kernel_size=(3, 3)  ,     strides=(1, 1)  ,     padding=((1, 1), (1, 1))  ,     dtype=self.dtype  ,     )

        SCREAMING_SNAKE_CASE_          =  nn.Dense(__magic_name__  ,     dtype=self.dtype   )

        SCREAMING_SNAKE_CASE_          =  nn.GroupNorm(num_groups=32  ,     epsilon=1e-5   )
        SCREAMING_SNAKE_CASE_          =  nn.Dropout(self.dropout_prob   )
        SCREAMING_SNAKE_CASE_          =  nn.Conv(
            __magic_name__  ,     kernel_size=(3, 3)  ,     strides=(1, 1)  ,     padding=((1, 1), (1, 1))  ,     dtype=self.dtype  ,     )

        SCREAMING_SNAKE_CASE_          =  self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

        SCREAMING_SNAKE_CASE_          =  None
        if use_nin_shortcut:
              SCREAMING_SNAKE_CASE_          =  nn.Conv(
                  __magic_name__  ,     kernel_size=(1, 1)  ,     strides=(1, 1)  ,     padding="VALID"  ,     dtype=self.dtype  ,     )







  def __call__(							self :       Union[str, Any]  ,     __magic_name__ :       Dict  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Optional[Any]=True   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  hidden_states
        SCREAMING_SNAKE_CASE_          =  self.norma(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  nn.swish(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.conva(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  self.time_emb_proj(nn.swish(__magic_name__   )   )
        SCREAMING_SNAKE_CASE_          =  jnp.expand_dims(jnp.expand_dims(__magic_name__  ,     1   )  ,     1   )
        SCREAMING_SNAKE_CASE_          =  hidden_states + temb

        SCREAMING_SNAKE_CASE_          =  self.norma(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  nn.swish(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.dropout(__magic_name__  ,     __magic_name__   )
        SCREAMING_SNAKE_CASE_          =  self.conva(__magic_name__   )

        if self.conv_shortcut is not None:
              SCREAMING_SNAKE_CASE_          =  self.conv_shortcut(__magic_name__   )

        return hidden_states + residual



from typing import TYPE_CHECKING

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


A     :  int					     =       {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  str					     =       ["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Any					     =       [
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  int					     =       [
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      A     :  Tuple					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


