"""simple docstring"""




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      UpperCamelCase__(      lowercase_			):
						lowerCAmelCase__	:    Tuple    				=		42






class      UpperCamelCase__(      lowercase_		,    lowercase_			):





						@register_to_config
						def __init__(							self		,__UpperCAmelCase = None		,__UpperCAmelCase = 3		,__UpperCAmelCase = 3		,__UpperCAmelCase = False		,__UpperCAmelCase = "positional"		,__UpperCAmelCase = 0		,__UpperCAmelCase = True		,__UpperCAmelCase = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")		,__UpperCAmelCase = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")		,__UpperCAmelCase = (2_24, 4_48, 6_72, 8_96)		,__UpperCAmelCase = 2		,__UpperCAmelCase = 1		,__UpperCAmelCase = 1		,__UpperCAmelCase = "conv"		,__UpperCAmelCase = "conv"		,__UpperCAmelCase = "silu"		,__UpperCAmelCase = 8		,__UpperCAmelCase = 32		,__UpperCAmelCase = 1e-5		,__UpperCAmelCase = "default"		,__UpperCAmelCase = True		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,)       ->					int:
													super().__init__()

													A__						  =				sample_size
													A__						  =				block_out_channels[0] * 4

													# Check inputs
													if len(UpperCAmelCase_  ) != len(UpperCAmelCase_  ):
																				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(UpperCAmelCase_  ) != len(UpperCAmelCase_  ):
																				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
													A__						  =				nn.Convad(UpperCAmelCase_		,block_out_channels[0]		,kernel_size=3		,padding=(1, 1)  )

													# time
													if time_embedding_type == "fourier":
																				A__						  =				GaussianFourierProjection(embedding_size=block_out_channels[0]		,scale=16  )
																				A__						  =				2 * block_out_channels[0]
													elif time_embedding_type == "positional":
																				A__						  =				Timesteps(block_out_channels[0]		,UpperCAmelCase_		,UpperCAmelCase_  )
																				A__						  =				block_out_channels[0]

													A__						  =				TimestepEmbedding(UpperCAmelCase_		,UpperCAmelCase_  )

													# class embedding
													if class_embed_type is None and num_class_embeds is not None:
																				A__						  =				nn.Embedding(UpperCAmelCase_		,UpperCAmelCase_  )
													elif class_embed_type == "timestep":
																				A__						  =				TimestepEmbedding(UpperCAmelCase_		,UpperCAmelCase_  )
													elif class_embed_type == "identity":
																				A__						  =				nn.Identity(UpperCAmelCase_		,UpperCAmelCase_  )
													else:
																				A__						  =				None

													A__						  =				nn.ModuleList([]  )
													A__						  =				None
													A__						  =				nn.ModuleList([]  )

													# down
													A__						  =				block_out_channels[0]
													for i, down_block_type in enumerate(UpperCAmelCase_  ):
																				A__						  =				output_channel
																				A__						  =				block_out_channels[i]
																				A__						  =				i == len(UpperCAmelCase_  ) - 1

																				A__						  =				get_down_block(
																				    UpperCAmelCase_		,num_layers=UpperCAmelCase_		,in_channels=UpperCAmelCase_		,out_channels=UpperCAmelCase_		,temb_channels=UpperCAmelCase_		,add_downsample=not is_final_block		,resnet_eps=UpperCAmelCase_		,resnet_act_fn=UpperCAmelCase_		,resnet_groups=UpperCAmelCase_		,attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel		,downsample_padding=UpperCAmelCase_		,resnet_time_scale_shift=UpperCAmelCase_		,downsample_type=UpperCAmelCase_		,)
																				self.down_blocks.append(UpperCAmelCase_  )

													# mid
													A__						  =				UNetMidBlockaD(
													    in_channels=block_out_channels[-1]		,temb_channels=UpperCAmelCase_		,resnet_eps=UpperCAmelCase_		,resnet_act_fn=UpperCAmelCase_		,output_scale_factor=UpperCAmelCase_		,resnet_time_scale_shift=UpperCAmelCase_		,attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]		,resnet_groups=UpperCAmelCase_		,add_attention=UpperCAmelCase_		,)

													# up
													A__						  =				list(reversed(UpperCAmelCase_  )  )
													A__						  =				reversed_block_out_channels[0]
													for i, up_block_type in enumerate(UpperCAmelCase_  ):
																				A__						  =				output_channel
																				A__						  =				reversed_block_out_channels[i]
																				A__						  =				reversed_block_out_channels[min(i + 1		,len(UpperCAmelCase_  ) - 1  )]

																				A__						  =				i == len(UpperCAmelCase_  ) - 1

																				A__						  =				get_up_block(
																				    UpperCAmelCase_		,num_layers=layers_per_block + 1		,in_channels=UpperCAmelCase_		,out_channels=UpperCAmelCase_		,prev_output_channel=UpperCAmelCase_		,temb_channels=UpperCAmelCase_		,add_upsample=not is_final_block		,resnet_eps=UpperCAmelCase_		,resnet_act_fn=UpperCAmelCase_		,resnet_groups=UpperCAmelCase_		,attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel		,resnet_time_scale_shift=UpperCAmelCase_		,upsample_type=UpperCAmelCase_		,)
																				self.up_blocks.append(UpperCAmelCase_  )
																				A__						  =				output_channel

													# out
													A__						  =				norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,32  )
													A__						  =				nn.GroupNorm(num_channels=block_out_channels[0]		,num_groups=UpperCAmelCase_		,eps=UpperCAmelCase_  )
													A__						  =				nn.SiLU()
													A__						  =				nn.Convad(block_out_channels[0]		,UpperCAmelCase_		,kernel_size=3		,padding=1  )





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = None		,__UpperCAmelCase = True		,)       ->					Union[UNetaDOutput, Tuple]:




													if self.config.center_input_sample:
																				A__						  =				2 * sample - 1.0

													# 1. time
													A__						  =				timestep
													if not torch.is_tensor(UpperCAmelCase_  ):
																				A__						  =				torch.tensor([timesteps]		,dtype=torch.long		,device=sample.device  )
													elif torch.is_tensor(UpperCAmelCase_  ) and len(timesteps.shape  ) == 0:
																				A__						  =				timesteps[None].to(sample.device  )

													# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
													A__						  =				timesteps * torch.ones(sample.shape[0]		,dtype=timesteps.dtype		,device=timesteps.device  )

													A__						  =				self.time_proj(UpperCAmelCase_  )

													# 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.
													A__						  =				t_emb.to(dtype=self.dtype  )
													A__						  =				self.time_embedding(UpperCAmelCase_  )

													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":
																											A__						  =				self.time_proj(UpperCAmelCase_  )

																				A__						  =				self.class_embedding(UpperCAmelCase_  ).to(dtype=self.dtype  )
																				A__						  =				emb + class_emb

													# 2. pre-process
													A__						  =				sample
													A__						  =				self.conv_in(UpperCAmelCase_  )

													# 3. down
													A__						  =				(sample,)
													for downsample_block in self.down_blocks:
																				if hasattr(UpperCAmelCase_		,'skip_conv'  ):
																											A__						  =				downsample_block(
																											    hidden_states=UpperCAmelCase_		,temb=UpperCAmelCase_		,skip_sample=UpperCAmelCase_  )
																				else:
																											A__						  =				downsample_block(hidden_states=UpperCAmelCase_		,temb=UpperCAmelCase_  )

																				down_block_res_samples += res_samples

													# 4. mid
													A__						  =				self.mid_block(UpperCAmelCase_		,UpperCAmelCase_  )

													# 5. up
													A__						  =				None
													for upsample_block in self.up_blocks:
																				A__						  =				down_block_res_samples[-len(upsample_block.resnets  ) :]
																				A__						  =				down_block_res_samples[: -len(upsample_block.resnets  )]

																				if hasattr(UpperCAmelCase_		,'skip_conv'  ):
																											A__						  =				upsample_block(UpperCAmelCase_		,UpperCAmelCase_		,UpperCAmelCase_		,UpperCAmelCase_  )
																				else:
																											A__						  =				upsample_block(UpperCAmelCase_		,UpperCAmelCase_		,UpperCAmelCase_  )

        # 6. post-process
													A__						  =				self.conv_norm_out(UpperCAmelCase_  )
													A__						  =				self.conv_act(UpperCAmelCase_  )
													A__						  =				self.conv_out(UpperCAmelCase_  )

													if skip_sample is not None:
																				sample += skip_sample

													if self.config.time_embedding_type == "fourier":
																				A__						  =				timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]  )))  )
																				A__						  =				sample / timesteps

													if not return_dict:
																				return (sample,)

													return UNetaDOutput(sample=UpperCAmelCase_  )













"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase					=     {
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     [
      """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """LEDForConditionalGeneration""",
      """LEDForQuestionAnswering""",
      """LEDForSequenceClassification""",
      """LEDModel""",
      """LEDPreTrainedModel""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     ["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
  from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
  from .tokenization_led import LEDTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_led_fast import LEDTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_led import (
        LED_PRETRAINED_MODEL_ARCHIVE_LIST,
        LEDForConditionalGeneration,
        LEDForQuestionAnswering,
        LEDForSequenceClassification,
        LEDModel,
        LEDPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
  import sys

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






