






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   SCREAMING_SNAKE_CASE (       lowercase_    ):





							"""simple docstring"""

							A_       =    42





class   SCREAMING_SNAKE_CASE (       lowercase_     ,		lowercase_    ):





							"""simple docstring"""

							@register_to_config
							def __init__(     self:      Tuple						,   __A:      Optional[Union[int, Tuple[int, int]]] = None						,   __A:      int = 3						,   __A:      int = 3						,   __A:      bool = False						,   __A:      str = "positional"						,   __A:      int = 0						,   __A:      bool = True						,   __A:      Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")						,   __A:      Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")						,   __A:      Tuple[int] = (2_24, 4_48, 6_72, 8_96)						,   __A:      int = 2						,   __A:      float = 1						,   __A:      int = 1						,   __A:      str = "conv"						,   __A:      str = "conv"						,   __A:      str = "silu"						,   __A:      Optional[int] = 8						,   __A:      int = 32						,   __A:      float = 1e-5						,   __A:      str = "default"						,   __A:      bool = True						,   __A:      Optional[str] = None						,   __A:      Optional[int] = None						,   )			-> Optional[Any]:
												super().__init__()

												_A									=       sample_size
												_A									=       block_out_channels[0] * 4

												# Check inputs
												if len(_lowercase      ) != len(_lowercase      ):
																	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(_lowercase      ) != len(_lowercase      ):
																	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(_lowercase						,   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]						,   _lowercase						,   _lowercase      )
																	_A									=       block_out_channels[0]

												_A									=       TimestepEmbedding(_lowercase						,   _lowercase      )

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

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

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

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

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

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

																	_A									=       i == len(_lowercase      ) - 1

																	_A									=       get_up_block(
																	    _lowercase						,   num_layers=layers_per_block + 1						,   in_channels=_lowercase						,   out_channels=_lowercase						,   prev_output_channel=_lowercase						,   temb_channels=_lowercase						,   add_upsample=not is_final_block						,   resnet_eps=_lowercase						,   resnet_act_fn=_lowercase						,   resnet_groups=_lowercase						,   attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel						,   resnet_time_scale_shift=_lowercase						,   upsample_type=_lowercase						,   )
																	self.up_blocks.append(_lowercase      )
																	_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=_lowercase						,   eps=_lowercase      )
												_A									=       nn.SiLU()
												_A									=       nn.Convad(block_out_channels[0]						,   _lowercase						,   kernel_size=3						,   padding=1      )



							def 					__A       (     self:      Optional[int]						,   __A:      torch.FloatTensor						,   __A:      Union[torch.Tensor, float, int]						,   __A:      Optional[torch.Tensor] = None						,   __A:      bool = True						,   )			-> Optional[int]:





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

												# 1. time
												_A									=       timestep
												if not torch.is_tensor(_lowercase      ):
																	_A									=       torch.tensor([timesteps]						,   dtype=torch.long						,   device=sample.device      )
												elif torch.is_tensor(_lowercase      ) 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(_lowercase      )

												# 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(_lowercase      )

												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(_lowercase      )

																	_A									=       self.class_embedding(_lowercase      ).to(dtype=self.dtype      )
																	_A									=       emb + class_emb

												# 2. pre-process
												_A									=       sample
												_A									=       self.conv_in(_lowercase      )

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

																	down_block_res_samples += res_samples

												# 4. mid
												_A									=       self.mid_block(_lowercase						,   _lowercase      )

												# 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(_lowercase						,   '''skip_conv'''      ):
																						_A							,_A									=       upsample_block(_lowercase						,   _lowercase						,   _lowercase						,   _lowercase      )
																	else:
																						_A									=       upsample_block(_lowercase						,   _lowercase						,   _lowercase      )

																	# 6. post-process
												_A									=       self.conv_norm_out(_lowercase      )
												_A									=       self.conv_act(_lowercase      )
												_A									=       self.conv_out(_lowercase      )

												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=_lowercase      )










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_snake_case				= {}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= ["""MBart50Tokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= ["""MBart50TokenizerFast"""]


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa import MBartaaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
  import sys

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






