code: 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 UNetMidBlockaD, get_down_block, get_up_block
  
  @dataclass
  class 				_lowercase						(    snake_case_ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							Optional[int]        =						42
  
  
  
  
  
  class 				_lowercase						(    snake_case_   ,     snake_case_ ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      @register_to_config
      def __init__(						self					,	lowerCamelCase__ = None					,	lowerCamelCase__ = 3					,	lowerCamelCase__ = 3					,	lowerCamelCase__ = False					,	lowerCamelCase__ = "positional"					,	lowerCamelCase__ = 0					,	lowerCamelCase__ = True					,	lowerCamelCase__ = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")					,	lowerCamelCase__ = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")					,	lowerCamelCase__ = (224, 448, 672, 896)					,	lowerCamelCase__ = 2					,	lowerCamelCase__ = 1					,	lowerCamelCase__ = 1					,	lowerCamelCase__ = "conv"					,	lowerCamelCase__ = "conv"					,	lowerCamelCase__ = "silu"					,	lowerCamelCase__ = 8					,	lowerCamelCase__ = 32					,	lowerCamelCase__ = 1E-5					,	lowerCamelCase__ = "default"					,	lowerCamelCase__ = True					,	lowerCamelCase__ = None					,	lowerCamelCase__ = None					,	):
         super().__init__()
  
         lowerCAmelCase_:       Union[str, Any]         =  sample_size
         lowerCAmelCase_:       Dict         =  block_out_channels[0] * 4
  
         # Check inputs
         if len(lowerCamelCase__   ) != len(lowerCamelCase__   ):
            raise ValueError(
                F'''Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.'''   )
  
         if len(lowerCamelCase__   ) != len(lowerCamelCase__   ):
            raise ValueError(
                F'''Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.'''   )
  
         # input
         lowerCAmelCase_:       Union[str, Any]         =  nn.Convad(lowerCamelCase__					,	block_out_channels[0]					,	kernel_size=3					,	padding=(1, 1)   )
  
         # time
         if time_embedding_type == "fourier":
            lowerCAmelCase_:       Any         =  GaussianFourierProjection(embedding_size=block_out_channels[0]					,	scale=16   )
            lowerCAmelCase_:       Tuple         =  2 * block_out_channels[0]
         elif time_embedding_type == "positional":
            lowerCAmelCase_:       int         =  Timesteps(block_out_channels[0]					,	lowerCamelCase__					,	lowerCamelCase__   )
            lowerCAmelCase_:       Any         =  block_out_channels[0]
  
         lowerCAmelCase_:       Dict         =  TimestepEmbedding(lowerCamelCase__					,	lowerCamelCase__   )
  
         # class embedding
         if class_embed_type is None and num_class_embeds is not None:
            lowerCAmelCase_:       Dict         =  nn.Embedding(lowerCamelCase__					,	lowerCamelCase__   )
         elif class_embed_type == "timestep":
            lowerCAmelCase_:       int         =  TimestepEmbedding(lowerCamelCase__					,	lowerCamelCase__   )
         elif class_embed_type == "identity":
            lowerCAmelCase_:       Optional[int]         =  nn.Identity(lowerCamelCase__					,	lowerCamelCase__   )
         else:
            lowerCAmelCase_:       List[Any]         =  None
  
         lowerCAmelCase_:       Optional[int]         =  nn.ModuleList([]   )
         lowerCAmelCase_:       Optional[Any]         =  None
         lowerCAmelCase_:       List[Any]         =  nn.ModuleList([]   )
  
         # down
         lowerCAmelCase_:       Tuple         =  block_out_channels[0]
         for i, down_block_type in enumerate(lowerCamelCase__   ):
            lowerCAmelCase_:       List[str]         =  output_channel
            lowerCAmelCase_:       Union[str, Any]         =  block_out_channels[i]
            lowerCAmelCase_:       Union[str, Any]         =  i == len(lowerCamelCase__   ) - 1
  
            lowerCAmelCase_:       Dict         =  get_down_block(
                lowerCamelCase__					,	num_layers=lowerCamelCase__					,	in_channels=lowerCamelCase__					,	out_channels=lowerCamelCase__					,	temb_channels=lowerCamelCase__					,	add_downsample=not is_final_block					,	resnet_eps=lowerCamelCase__					,	resnet_act_fn=lowerCamelCase__					,	resnet_groups=lowerCamelCase__					,	attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel					,	downsample_padding=lowerCamelCase__					,	resnet_time_scale_shift=lowerCamelCase__					,	downsample_type=lowerCamelCase__					,	)
            self.down_blocks.append(lowerCamelCase__   )
  
         # mid
         lowerCAmelCase_:       Optional[int]         =  UNetMidBlockaD(
             in_channels=block_out_channels[-1]					,	temb_channels=lowerCamelCase__					,	resnet_eps=lowerCamelCase__					,	resnet_act_fn=lowerCamelCase__					,	output_scale_factor=lowerCamelCase__					,	resnet_time_scale_shift=lowerCamelCase__					,	attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]					,	resnet_groups=lowerCamelCase__					,	add_attention=lowerCamelCase__					,	)
  
         # up
         lowerCAmelCase_:       str         =  list(reversed(lowerCamelCase__   )   )
         lowerCAmelCase_:       List[Any]         =  reversed_block_out_channels[0]
         for i, up_block_type in enumerate(lowerCamelCase__   ):
            lowerCAmelCase_:       Optional[Any]         =  output_channel
            lowerCAmelCase_:       Optional[int]         =  reversed_block_out_channels[i]
            lowerCAmelCase_:       Any         =  reversed_block_out_channels[min(i + 1					,	len(lowerCamelCase__   ) - 1   )]
  
            lowerCAmelCase_:       List[str]         =  i == len(lowerCamelCase__   ) - 1
  
            lowerCAmelCase_:       Optional[Any]         =  get_up_block(
                lowerCamelCase__					,	num_layers=layers_per_block + 1					,	in_channels=lowerCamelCase__					,	out_channels=lowerCamelCase__					,	prev_output_channel=lowerCamelCase__					,	temb_channels=lowerCamelCase__					,	add_upsample=not is_final_block					,	resnet_eps=lowerCamelCase__					,	resnet_act_fn=lowerCamelCase__					,	resnet_groups=lowerCamelCase__					,	attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel					,	resnet_time_scale_shift=lowerCamelCase__					,	upsample_type=lowerCamelCase__					,	)
            self.up_blocks.append(lowerCamelCase__   )
            lowerCAmelCase_:       int         =  output_channel
  
         # out
         lowerCAmelCase_:       List[Any]         =  norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4					,	32   )
         lowerCAmelCase_:       List[str]         =  nn.GroupNorm(num_channels=block_out_channels[0]					,	num_groups=lowerCamelCase__					,	eps=lowerCamelCase__   )
         lowerCAmelCase_:       int         =  nn.SiLU()
         lowerCAmelCase_:       Union[str, Any]         =  nn.Convad(block_out_channels[0]					,	lowerCamelCase__					,	kernel_size=3					,	padding=1   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__ = None					,	lowerCamelCase__ = True					,	):
         # 0. center input if necessary
         if self.config.center_input_sample:
            lowerCAmelCase_:       Optional[Any]         =  2 * sample - 1.0
  
         # 1. time
         lowerCAmelCase_:       List[str]         =  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_:       Union[str, Any]         =  timesteps[None].to(sample.device   )
  
         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         lowerCAmelCase_:       Optional[Any]         =  timesteps * torch.ones(sample.shape[0]					,	dtype=timesteps.dtype					,	device=timesteps.device   )
  
         lowerCAmelCase_:       Any         =  self.time_proj(lowerCamelCase__   )
  
         # timesteps does not contain any weights and will always return f32 tensors
         # but time_embedding might actually be running in fp16. so we need to cast here.
         # there might be better ways to encapsulate this.
         lowerCAmelCase_:       Any         =  t_emb.to(dtype=self.dtype   )
         lowerCAmelCase_:       Tuple         =  self.time_embedding(lowerCamelCase__   )
  
         if self.class_embedding is not None:
            if class_labels is None:
               raise ValueError("class_labels should be provided when doing class conditioning"   )
  
            if self.config.class_embed_type == "timestep":
               lowerCAmelCase_:       Union[str, Any]         =  self.time_proj(lowerCamelCase__   )
  
            lowerCAmelCase_:       Optional[Any]         =  self.class_embedding(lowerCamelCase__   ).to(dtype=self.dtype   )
            lowerCAmelCase_:       List[str]         =  emb + class_emb
  
         # 2. pre-process
         lowerCAmelCase_:       Any         =  sample
         lowerCAmelCase_:       List[str]         =  self.conv_in(lowerCamelCase__   )
  
         # 3. down
         lowerCAmelCase_:       int         =  (sample,)
         for downsample_block in self.down_blocks:
            if hasattr(lowerCamelCase__					,	"skip_conv"   ):
               lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       int         =  downsample_block(
                   hidden_states=lowerCamelCase__					,	temb=lowerCamelCase__					,	skip_sample=lowerCamelCase__   )
            else:
               lowerCAmelCase_     ,  lowerCAmelCase_:       Optional[int]         =  downsample_block(hidden_states=lowerCamelCase__					,	temb=lowerCamelCase__   )
  
            down_block_res_samples += res_samples
  
         # 4. mid
         lowerCAmelCase_:       Dict         =  self.mid_block(lowerCamelCase__					,	lowerCamelCase__   )
  
         # 5. up
         lowerCAmelCase_:       Any         =  None
         for upsample_block in self.up_blocks:
            lowerCAmelCase_:       Tuple         =  down_block_res_samples[-len(upsample_block.resnets   ) :]
            lowerCAmelCase_:       List[Any]         =  down_block_res_samples[: -len(upsample_block.resnets   )]
  
            if hasattr(lowerCamelCase__					,	"skip_conv"   ):
               lowerCAmelCase_     ,  lowerCAmelCase_:       Dict         =  upsample_block(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )
            else:
               lowerCAmelCase_:       List[str]         =  upsample_block(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )
  
          # 6. post-process
         lowerCAmelCase_:       Optional[Any]         =  self.conv_norm_out(lowerCamelCase__   )
         lowerCAmelCase_:       Dict         =  self.conv_act(lowerCamelCase__   )
         lowerCAmelCase_:       Tuple         =  self.conv_out(lowerCamelCase__   )
  
         if skip_sample is not None:
            sample += skip_sample
  
         if self.config.time_embedding_type == "fourier":
            lowerCAmelCase_:       Optional[int]         =  timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]   )))   )
            lowerCAmelCase_:       Optional[Any]         =  sample / timesteps
  
         if not return_dict:
            return (sample,)
  
         return UNetaDOutput(sample=lowerCamelCase__   )
style_context: from typing import TYPE_CHECKING
  
  from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available
  
  
  a     :			Optional[Any]					    =						{
      """configuration_utils""": ["""GenerationConfig"""],
      """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Union[str, Any]					    =						[
       """Constraint""",
       """ConstraintListState""",
       """DisjunctiveConstraint""",
       """PhrasalConstraint""",
   ]
   a     :			Optional[int]					    =						[
       """BeamHypotheses""",
       """BeamScorer""",
       """BeamSearchScorer""",
       """ConstrainedBeamSearchScorer""",
   ]
   a     :			Any					    =						[
       """EpsilonLogitsWarper""",
       """EtaLogitsWarper""",
       """ForcedBOSTokenLogitsProcessor""",
       """ForcedEOSTokenLogitsProcessor""",
       """HammingDiversityLogitsProcessor""",
       """InfNanRemoveLogitsProcessor""",
       """LogitsProcessor""",
       """LogitsProcessorList""",
       """LogitsWarper""",
       """MinLengthLogitsProcessor""",
       """MinNewTokensLengthLogitsProcessor""",
       """NoBadWordsLogitsProcessor""",
       """NoRepeatNGramLogitsProcessor""",
       """PrefixConstrainedLogitsProcessor""",
       """RepetitionPenaltyLogitsProcessor""",
       """SequenceBiasLogitsProcessor""",
       """EncoderRepetitionPenaltyLogitsProcessor""",
       """TemperatureLogitsWarper""",
       """TopKLogitsWarper""",
       """TopPLogitsWarper""",
       """TypicalLogitsWarper""",
       """EncoderNoRepeatNGramLogitsProcessor""",
       """ExponentialDecayLengthPenalty""",
       """LogitNormalization""",
   ]
   a     :			int					    =						[
       """MaxNewTokensCriteria""",
       """MaxLengthCriteria""",
       """MaxTimeCriteria""",
       """StoppingCriteria""",
       """StoppingCriteriaList""",
       """validate_stopping_criteria""",
   ]
   a     :			Dict					    =						[
       """GenerationMixin""",
       """top_k_top_p_filtering""",
       """GreedySearchEncoderDecoderOutput""",
       """GreedySearchDecoderOnlyOutput""",
       """SampleEncoderDecoderOutput""",
       """SampleDecoderOnlyOutput""",
       """BeamSearchEncoderDecoderOutput""",
       """BeamSearchDecoderOnlyOutput""",
       """BeamSampleEncoderDecoderOutput""",
       """BeamSampleDecoderOnlyOutput""",
       """ContrastiveSearchEncoderDecoderOutput""",
       """ContrastiveSearchDecoderOnlyOutput""",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Optional[Any]					    =						[
       """TFForcedBOSTokenLogitsProcessor""",
       """TFForcedEOSTokenLogitsProcessor""",
       """TFLogitsProcessor""",
       """TFLogitsProcessorList""",
       """TFLogitsWarper""",
       """TFMinLengthLogitsProcessor""",
       """TFNoBadWordsLogitsProcessor""",
       """TFNoRepeatNGramLogitsProcessor""",
       """TFRepetitionPenaltyLogitsProcessor""",
       """TFTemperatureLogitsWarper""",
       """TFTopKLogitsWarper""",
       """TFTopPLogitsWarper""",
       """TFForceTokensLogitsProcessor""",
       """TFSuppressTokensAtBeginLogitsProcessor""",
       """TFSuppressTokensLogitsProcessor""",
   ]
   a     :			Any					    =						[
       """TFGenerationMixin""",
       """tf_top_k_top_p_filtering""",
       """TFGreedySearchDecoderOnlyOutput""",
       """TFGreedySearchEncoderDecoderOutput""",
       """TFSampleEncoderDecoderOutput""",
       """TFSampleDecoderOnlyOutput""",
       """TFBeamSearchEncoderDecoderOutput""",
       """TFBeamSearchDecoderOnlyOutput""",
       """TFBeamSampleEncoderDecoderOutput""",
       """TFBeamSampleDecoderOnlyOutput""",
       """TFContrastiveSearchEncoderDecoderOutput""",
       """TFContrastiveSearchDecoderOnlyOutput""",
   ]
  
  try:
   if not is_flax_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Union[str, Any]					    =						[
       """FlaxForcedBOSTokenLogitsProcessor""",
       """FlaxForcedEOSTokenLogitsProcessor""",
       """FlaxLogitsProcessor""",
       """FlaxLogitsProcessorList""",
       """FlaxLogitsWarper""",
       """FlaxMinLengthLogitsProcessor""",
       """FlaxTemperatureLogitsWarper""",
       """FlaxTopKLogitsWarper""",
       """FlaxTopPLogitsWarper""",
   ]
   a     :			Dict					    =						[
       """FlaxGenerationMixin""",
       """FlaxGreedySearchOutput""",
       """FlaxSampleOutput""",
       """FlaxBeamSearchOutput""",
   ]
  
  if TYPE_CHECKING:
   from .configuration_utils import GenerationConfig
   from .streamers import TextIteratorStreamer, TextStreamer
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
    from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
    from .logits_process import (
        EncoderNoRepeatNGramLogitsProcessor,
        EncoderRepetitionPenaltyLogitsProcessor,
        EpsilonLogitsWarper,
        EtaLogitsWarper,
        ExponentialDecayLengthPenalty,
        ForcedBOSTokenLogitsProcessor,
        ForcedEOSTokenLogitsProcessor,
        HammingDiversityLogitsProcessor,
        InfNanRemoveLogitsProcessor,
        LogitNormalization,
        LogitsProcessor,
        LogitsProcessorList,
        LogitsWarper,
        MinLengthLogitsProcessor,
        MinNewTokensLengthLogitsProcessor,
        NoBadWordsLogitsProcessor,
        NoRepeatNGramLogitsProcessor,
        PrefixConstrainedLogitsProcessor,
        RepetitionPenaltyLogitsProcessor,
        SequenceBiasLogitsProcessor,
        TemperatureLogitsWarper,
        TopKLogitsWarper,
        TopPLogitsWarper,
        TypicalLogitsWarper,
    )
    from .stopping_criteria import (
        MaxLengthCriteria,
        MaxNewTokensCriteria,
        MaxTimeCriteria,
        StoppingCriteria,
        StoppingCriteriaList,
        validate_stopping_criteria,
    )
    from .utils import (
        BeamSampleDecoderOnlyOutput,
        BeamSampleEncoderDecoderOutput,
        BeamSearchDecoderOnlyOutput,
        BeamSearchEncoderDecoderOutput,
        ContrastiveSearchDecoderOnlyOutput,
        ContrastiveSearchEncoderDecoderOutput,
        GenerationMixin,
        GreedySearchDecoderOnlyOutput,
        GreedySearchEncoderDecoderOutput,
        SampleDecoderOnlyOutput,
        SampleEncoderDecoderOutput,
        top_k_top_p_filtering,
    )
  
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tf_logits_process import (
        TFForcedBOSTokenLogitsProcessor,
        TFForcedEOSTokenLogitsProcessor,
        TFForceTokensLogitsProcessor,
        TFLogitsProcessor,
        TFLogitsProcessorList,
        TFLogitsWarper,
        TFMinLengthLogitsProcessor,
        TFNoBadWordsLogitsProcessor,
        TFNoRepeatNGramLogitsProcessor,
        TFRepetitionPenaltyLogitsProcessor,
        TFSuppressTokensAtBeginLogitsProcessor,
        TFSuppressTokensLogitsProcessor,
        TFTemperatureLogitsWarper,
        TFTopKLogitsWarper,
        TFTopPLogitsWarper,
    )
    from .tf_utils import (
        TFBeamSampleDecoderOnlyOutput,
        TFBeamSampleEncoderDecoderOutput,
        TFBeamSearchDecoderOnlyOutput,
        TFBeamSearchEncoderDecoderOutput,
        TFContrastiveSearchDecoderOnlyOutput,
        TFContrastiveSearchEncoderDecoderOutput,
        TFGenerationMixin,
        TFGreedySearchDecoderOnlyOutput,
        TFGreedySearchEncoderDecoderOutput,
        TFSampleDecoderOnlyOutput,
        TFSampleEncoderDecoderOutput,
        tf_top_k_top_p_filtering,
    )
  
   try:
    if not is_flax_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .flax_logits_process import (
        FlaxForcedBOSTokenLogitsProcessor,
        FlaxForcedEOSTokenLogitsProcessor,
        FlaxLogitsProcessor,
        FlaxLogitsProcessorList,
        FlaxLogitsWarper,
        FlaxMinLengthLogitsProcessor,
        FlaxTemperatureLogitsWarper,
        FlaxTopKLogitsWarper,
        FlaxTopPLogitsWarper,
    )
    from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
  else:
   import sys
  
   a     :			str					    =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)