





"""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 get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		torch.FloatTensor






class     SCREAMING_SNAKE_CASE       (UpperCAmelCase      ,     UpperCAmelCase				):


   @register_to_config
   def __init__(   self      :    List[Any]      ,					a      :    int = 65_536      ,					a      :    Optional[int] = None      ,					a      :    int = 2      ,					a      :    int = 2      ,					a      :    int = 0      ,					a      :    str = "fourier"      ,					a      :    bool = True      ,					a      :    bool = False      ,					a      :    float = 0.0      ,					a      :    Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")      ,					a      :    Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")      ,					a      :    Tuple[str] = "UNetMidBlock1D"      ,					a      :    str = None      ,					a      :    Tuple[int] = (32, 32, 64)      ,					a      :    str = None      ,					a      :    int = 8      ,					a      :    int = 1      ,					a      :    bool = False      ,					)->       Optional[Any]:





     """simple docstring"""







     super().__init__()
     lowercase__           =  sample_size

     # time
     if time_embedding_type == "fourier":
       lowercase__           =  GaussianFourierProjection(
           embedding_size=8      ,					set_W_to_weight=a      ,					log=a      ,					flip_sin_to_cos=a	)
       lowercase__           =  2 * block_out_channels[0]
     elif time_embedding_type == "positional":
       lowercase__           =  Timesteps(
           block_out_channels[0]      ,					flip_sin_to_cos=a      ,					downscale_freq_shift=a	)
       lowercase__           =  block_out_channels[0]

     if use_timestep_embedding:
       lowercase__           =  block_out_channels[0] * 4
       lowercase__           =  TimestepEmbedding(
           in_channels=a      ,					time_embed_dim=a      ,					act_fn=a      ,					out_dim=block_out_channels[0]      ,					)

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

     # down
     lowercase__           =  in_channels
     for i, down_block_type in enumerate(a	):
       lowercase__           =  output_channel
       lowercase__           =  block_out_channels[i]

       if i == 0:
         input_channel += extra_in_channels

       lowercase__           =  i == len(a	) - 1

       lowercase__           =  get_down_block(
           a      ,					num_layers=a      ,					in_channels=a      ,					out_channels=a      ,					temb_channels=block_out_channels[0]      ,					add_downsample=not is_final_block or downsample_each_block      ,					)
       self.down_blocks.append(a	)

     # mid
     lowercase__           =  get_mid_block(
         a      ,					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=a      ,					add_downsample=a      ,					)

     # up
     lowercase__           =  list(reversed(a	)	)
     lowercase__           =  reversed_block_out_channels[0]
     if out_block_type is None:
       lowercase__           =  out_channels
     else:
       lowercase__           =  block_out_channels[0]

     for i, up_block_type in enumerate(a	):
       lowercase__           =  output_channel
       lowercase__           =  (
           reversed_block_out_channels[i + 1] if i < len(a	) - 1 else final_upsample_channels
       )

       lowercase__           =  i == len(a	) - 1

       lowercase__           =  get_up_block(
           a      ,					num_layers=a      ,					in_channels=a      ,					out_channels=a      ,					temb_channels=block_out_channels[0]      ,					add_upsample=not is_final_block      ,					)
       self.up_blocks.append(a	)
       lowercase__           =  output_channel

     # out
     lowercase__           =  norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4      ,					32	)
     lowercase__           =  get_out_block(
         out_block_type=a      ,					num_groups_out=a      ,					embed_dim=block_out_channels[0]      ,					out_channels=a      ,					act_fn=a      ,					fc_dim=block_out_channels[-1] // 4      ,					)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    torch.FloatTensor      ,					a      :    Union[torch.Tensor, float, int]      ,					a      :    bool = True      ,					)->       Union[UNetaDOutput, Tuple]:





     """simple docstring"""







     lowercase__           =  timestep
     if not torch.is_tensor(a	):
       lowercase__           =  torch.tensor([timesteps]      ,					dtype=torch.long      ,					device=sample.device	)
     elif torch.is_tensor(a	) and len(timesteps.shape	) == 0:
       lowercase__           =  timesteps[None].to(sample.device	)

     lowercase__           =  self.time_proj(a	)
     if self.config.use_timestep_embedding:
       lowercase__           =  self.time_mlp(a	)
     else:
       lowercase__           =  timestep_embed[..., None]
       lowercase__           =  timestep_embed.repeat([1, 1, sample.shape[2]]	).to(sample.dtype	)
       lowercase__           =  timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])	)

     # 2. down
     lowercase__           =  ()
     for downsample_block in self.down_blocks:
       lowercase__      ,   lowercase__           =  downsample_block(hidden_states=a      ,					temb=a	)
       down_block_res_samples += res_samples

     # 3. mid
     if self.mid_block:
       lowercase__           =  self.mid_block(a      ,					a	)

     # 4. up
     for i, upsample_block in enumerate(self.up_blocks	):
       lowercase__           =  down_block_res_samples[-1:]
       lowercase__           =  down_block_res_samples[:-1]
       lowercase__           =  upsample_block(a      ,					res_hidden_states_tuple=a      ,					temb=a	)

     # 5. post-process
     if self.out_block:
       lowercase__           =  self.out_block(a      ,					a	)

     if not return_dict:
       return (sample,)

     return UNetaDOutput(sample=a	)









import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_          =		logging.get_logger(__name__)

lowercase_          =		{
    """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
    """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/config.json""",
}
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		str			 =	'xlnet'
   _UpperCamelCase   :		Optional[Any]			 =	['mems']
   _UpperCamelCase   :		Union[str, Any]			 =	{
       'n_token': 'vocab_size',  # Backward compatibility
       'hidden_size': 'd_model',
       'num_attention_heads': 'n_head',
       'num_hidden_layers': 'n_layer',
   }


   def __init__(   self      :    str      ,					a      :    int=32_000      ,					a      :    Any=1_024      ,					a      :    Optional[Any]=24      ,					a      :    str=16      ,					a      :    int=4_096      ,					a      :    List[str]="gelu"      ,					a      :    Any=True      ,					a      :    Dict="bi"      ,					a      :    str=0.02      ,					a      :    List[str]=1E-1_2      ,					a      :    Tuple=0.1      ,					a      :    Optional[Any]=512      ,					a      :    Tuple=None      ,					a      :    Union[str, Any]=True      ,					a      :    int=False      ,					a      :    int=False      ,					a      :    Tuple=-1      ,					a      :    Any=False      ,					a      :    Tuple="last"      ,					a      :    List[str]=True      ,					a      :    Optional[Any]="tanh"      ,					a      :    List[Any]=0.1      ,					a      :    int=5      ,					a      :    List[Any]=5      ,					a      :    Optional[int]=5      ,					a      :    Dict=1      ,					a      :    Optional[Any]=2      ,					**a      :    List[Any]      ,					)->       List[str]:





     """simple docstring"""







     lowercase__           =  vocab_size
     lowercase__           =  d_model
     lowercase__           =  n_layer
     lowercase__           =  n_head
     if d_model % n_head != 0:
       raise ValueError(f"""'d_model % n_head' ({d_model % n_head}) should be equal to 0"""	)
     if "d_head" in kwargs:
       if kwargs["d_head"] != d_model // n_head:
         raise ValueError(
             f"""`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})"""	)
     lowercase__           =  d_model // n_head
     lowercase__           =  ff_activation
     lowercase__           =  d_inner
     lowercase__           =  untie_r
     lowercase__           =  attn_type

     lowercase__           =  initializer_range
     lowercase__           =  layer_norm_eps

     lowercase__           =  dropout
     lowercase__           =  mem_len
     lowercase__           =  reuse_len
     lowercase__           =  bi_data
     lowercase__           =  clamp_len
     lowercase__           =  same_length

     lowercase__           =  summary_type
     lowercase__           =  summary_use_proj
     lowercase__           =  summary_activation
     lowercase__           =  summary_last_dropout
     lowercase__           =  start_n_top
     lowercase__           =  end_n_top

     lowercase__           =  bos_token_id
     lowercase__           =  pad_token_id
     lowercase__           =  eos_token_id

     if "use_cache" in kwargs:
       warnings.warn(
           'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
           ' instead.'      ,					a      ,					)
       lowercase__           =  kwargs['use_cache']

     lowercase__           =  use_mems_eval
     lowercase__           =  use_mems_train
     super().__init__(pad_token_id=a      ,					bos_token_id=a      ,					eos_token_id=a      ,					**a	)


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]	)->       List[str]:





     """simple docstring"""







     logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""	)
     return -1


   @max_position_embeddings.setter
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    Union[str, Any]	)->       List[Any]:





     """simple docstring"""







     raise NotImplementedError(
         f"""The model {self.model_type} is one of the few models that has no sequence length limit."""	)


