from typing import TYPE_CHECKING

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


a  :      List[Any]            =    {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      List[Any]            =    [
     """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """RobertaPreLayerNormForCausalLM""",
     """RobertaPreLayerNormForMaskedLM""",
     """RobertaPreLayerNormForMultipleChoice""",
     """RobertaPreLayerNormForQuestionAnswering""",
     """RobertaPreLayerNormForSequenceClassification""",
     """RobertaPreLayerNormForTokenClassification""",
     """RobertaPreLayerNormModel""",
     """RobertaPreLayerNormPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      Optional[int]            =    [
     """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFRobertaPreLayerNormForCausalLM""",
     """TFRobertaPreLayerNormForMaskedLM""",
     """TFRobertaPreLayerNormForMultipleChoice""",
     """TFRobertaPreLayerNormForQuestionAnswering""",
     """TFRobertaPreLayerNormForSequenceClassification""",
     """TFRobertaPreLayerNormForTokenClassification""",
     """TFRobertaPreLayerNormMainLayer""",
     """TFRobertaPreLayerNormModel""",
     """TFRobertaPreLayerNormPreTrainedModel""",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      Union[str, Any]            =    [
     """FlaxRobertaPreLayerNormForCausalLM""",
     """FlaxRobertaPreLayerNormForMaskedLM""",
     """FlaxRobertaPreLayerNormForMultipleChoice""",
     """FlaxRobertaPreLayerNormForQuestionAnswering""",
     """FlaxRobertaPreLayerNormForSequenceClassification""",
     """FlaxRobertaPreLayerNormForTokenClassification""",
     """FlaxRobertaPreLayerNormModel""",
     """FlaxRobertaPreLayerNormPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_roberta_prelayernorm import (
     ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
     RobertaPreLayerNormConfig,
     RobertaPreLayerNormOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roberta_prelayernorm import (
      ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
      RobertaPreLayerNormForCausalLM,
      RobertaPreLayerNormForMaskedLM,
      RobertaPreLayerNormForMultipleChoice,
      RobertaPreLayerNormForQuestionAnswering,
      RobertaPreLayerNormForSequenceClassification,
      RobertaPreLayerNormForTokenClassification,
      RobertaPreLayerNormModel,
      RobertaPreLayerNormPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roberta_prelayernorm import (
      TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRobertaPreLayerNormForCausalLM,
      TFRobertaPreLayerNormForMaskedLM,
      TFRobertaPreLayerNormForMultipleChoice,
      TFRobertaPreLayerNormForQuestionAnswering,
      TFRobertaPreLayerNormForSequenceClassification,
      TFRobertaPreLayerNormForTokenClassification,
      TFRobertaPreLayerNormMainLayer,
      TFRobertaPreLayerNormModel,
      TFRobertaPreLayerNormPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roberta_prelayernorm import (
      FlaxRobertaPreLayerNormForCausalLM,
      FlaxRobertaPreLayerNormForMaskedLM,
      FlaxRobertaPreLayerNormForMultipleChoice,
      FlaxRobertaPreLayerNormForQuestionAnswering,
      FlaxRobertaPreLayerNormForSequenceClassification,
      FlaxRobertaPreLayerNormForTokenClassification,
      FlaxRobertaPreLayerNormModel,
      FlaxRobertaPreLayerNormPreTrainedModel,
  )

else:
 import sys

 a  :      List[str]            =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block

@dataclass
class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				torch.FloatTensor





class 					_lowercase    (     UpperCAmelCase__ ,  UpperCAmelCase__			):






    '''simple docstring'''




    @register_to_config
    def __init__(    self       ,						lowerCamelCase__ = 65_536       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = 2       ,						lowerCamelCase__ = 2       ,						lowerCamelCase__ = 0       ,						lowerCamelCase__ = "fourier"       ,						lowerCamelCase__ = True       ,						lowerCamelCase__ = False       ,						lowerCamelCase__ = 0.0       ,						lowerCamelCase__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")       ,						lowerCamelCase__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")       ,						lowerCamelCase__ = "UNetMidBlock1D"       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = (32, 32, 64)       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = 8       ,						lowerCamelCase__ = 1       ,						lowerCamelCase__ = False       ,						):
       super().__init__()
       lowerCAmelCase_:							Optional[Any]       =	sample_size

       # time
       if time_embedding_type == "fourier":
          lowerCAmelCase_:							Dict       =	GaussianFourierProjection(
              embedding_size=8       ,						set_W_to_weight=lowerCamelCase__       ,						log=lowerCamelCase__       ,						flip_sin_to_cos=lowerCamelCase__ )
          lowerCAmelCase_:							List[str]       =	2 * block_out_channels[0]
       elif time_embedding_type == "positional":
          lowerCAmelCase_:							Tuple       =	Timesteps(
              block_out_channels[0]       ,						flip_sin_to_cos=lowerCamelCase__       ,						downscale_freq_shift=lowerCamelCase__ )
          lowerCAmelCase_:							Dict       =	block_out_channels[0]

       if use_timestep_embedding:
          lowerCAmelCase_:							Tuple       =	block_out_channels[0] * 4
          lowerCAmelCase_:							Any       =	TimestepEmbedding(
              in_channels=lowerCamelCase__       ,						time_embed_dim=lowerCamelCase__       ,						act_fn=lowerCamelCase__       ,						out_dim=block_out_channels[0]       ,						)

       lowerCAmelCase_:							str       =	nn.ModuleList([] )
       lowerCAmelCase_:							Dict       =	None
       lowerCAmelCase_:							Optional[Any]       =	nn.ModuleList([] )
       lowerCAmelCase_:							int       =	None

       # down
       lowerCAmelCase_:							List[str]       =	in_channels
       for i, down_block_type in enumerate(lowerCamelCase__ ):
          lowerCAmelCase_:							Optional[int]       =	output_channel
          lowerCAmelCase_:							Optional[Any]       =	block_out_channels[i]

          if i == 0:
             input_channel += extra_in_channels

          lowerCAmelCase_:							List[str]       =	i == len(lowerCamelCase__ ) - 1

          lowerCAmelCase_:							List[str]       =	get_down_block(
              lowerCamelCase__       ,						num_layers=lowerCamelCase__       ,						in_channels=lowerCamelCase__       ,						out_channels=lowerCamelCase__       ,						temb_channels=block_out_channels[0]       ,						add_downsample=not is_final_block or downsample_each_block       ,						)
          self.down_blocks.append(lowerCamelCase__ )

       # mid
       lowerCAmelCase_:							Optional[int]       =	get_mid_block(
           lowerCamelCase__       ,						in_channels=block_out_channels[-1]       ,						mid_channels=block_out_channels[-1]       ,						out_channels=block_out_channels[-1]       ,						embed_dim=block_out_channels[0]       ,						num_layers=lowerCamelCase__       ,						add_downsample=lowerCamelCase__       ,						)

       # up
       lowerCAmelCase_:							Dict       =	list(reversed(lowerCamelCase__ ) )
       lowerCAmelCase_:							Any       =	reversed_block_out_channels[0]
       if out_block_type is None:
          lowerCAmelCase_:							str       =	out_channels
       else:
          lowerCAmelCase_:							Any       =	block_out_channels[0]

       for i, up_block_type in enumerate(lowerCamelCase__ ):
          lowerCAmelCase_:							Dict       =	output_channel
          lowerCAmelCase_:							int       =	(
              reversed_block_out_channels[i + 1] if i < len(lowerCamelCase__ ) - 1 else final_upsample_channels
          )

          lowerCAmelCase_:							Optional[int]       =	i == len(lowerCamelCase__ ) - 1

          lowerCAmelCase_:							Union[str, Any]       =	get_up_block(
              lowerCamelCase__       ,						num_layers=lowerCamelCase__       ,						in_channels=lowerCamelCase__       ,						out_channels=lowerCamelCase__       ,						temb_channels=block_out_channels[0]       ,						add_upsample=not is_final_block       ,						)
          self.up_blocks.append(lowerCamelCase__ )
          lowerCAmelCase_:							str       =	output_channel

       # out
       lowerCAmelCase_:							List[str]       =	norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4       ,						32 )
       lowerCAmelCase_:							int       =	get_out_block(
           out_block_type=lowerCamelCase__       ,						num_groups_out=lowerCamelCase__       ,						embed_dim=block_out_channels[0]       ,						out_channels=lowerCamelCase__       ,						act_fn=lowerCamelCase__       ,						fc_dim=block_out_channels[-1] // 4       ,						)




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ = True       ,						):
       lowerCAmelCase_:							Any       =	timestep
       if not torch.is_tensor(lowerCamelCase__ ):
          lowerCAmelCase_:							Optional[Any]       =	torch.tensor([timesteps]       ,						dtype=torch.long       ,						device=sample.device )
       elif torch.is_tensor(lowerCamelCase__ ) and len(timesteps.shape ) == 0:
          lowerCAmelCase_:							List[Any]       =	timesteps[None].to(sample.device )

       lowerCAmelCase_:							Union[str, Any]       =	self.time_proj(lowerCamelCase__ )
       if self.config.use_timestep_embedding:
          lowerCAmelCase_:							Any       =	self.time_mlp(lowerCamelCase__ )
       else:
          lowerCAmelCase_:							Any       =	timestep_embed[..., None]
          lowerCAmelCase_:							str       =	timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
          lowerCAmelCase_:							Dict       =	timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )

       # 2. down
       lowerCAmelCase_:							Dict       =	()
       for downsample_block in self.down_blocks:
          lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[int]       =	downsample_block(hidden_states=lowerCamelCase__       ,						temb=lowerCamelCase__ )
          down_block_res_samples += res_samples

       # 3. mid
       if self.mid_block:
          lowerCAmelCase_:							int       =	self.mid_block(lowerCamelCase__       ,						lowerCamelCase__ )

       # 4. up
       for i, upsample_block in enumerate(self.up_blocks ):
          lowerCAmelCase_:							Any       =	down_block_res_samples[-1:]
          lowerCAmelCase_:							str       =	down_block_res_samples[:-1]
          lowerCAmelCase_:							List[str]       =	upsample_block(lowerCamelCase__       ,						res_hidden_states_tuple=lowerCamelCase__       ,						temb=lowerCamelCase__ )

       # 5. post-process
       if self.out_block:
          lowerCAmelCase_:							Any       =	self.out_block(lowerCamelCase__       ,						lowerCamelCase__ )

       if not return_dict:
          return (sample,)

       return UNetaDOutput(sample=lowerCamelCase__ )