



"""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 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				torch.FloatTensor







class 					a	(  UpperCAmelCase__	,      UpperCAmelCase__       ):



  @register_to_config
  def __init__(						self :					int       ,		lowerCAmelCase :					int = 6_5536       ,		lowerCAmelCase :					Optional[int] = None       ,		lowerCAmelCase :					int = 2       ,		lowerCAmelCase :					int = 2       ,		lowerCAmelCase :					int = 0       ,		lowerCAmelCase :					str = "fourier"       ,		lowerCAmelCase :					bool = True       ,		lowerCAmelCase :					bool = False       ,		lowerCAmelCase :					float = 0.0       ,		lowerCAmelCase :					Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")       ,		lowerCAmelCase :					Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")       ,		lowerCAmelCase :					Tuple[str] = "UNetMidBlock1D"       ,		lowerCAmelCase :					str = None       ,		lowerCAmelCase :					Tuple[int] = (32, 32, 64)       ,		lowerCAmelCase :					str = None       ,		lowerCAmelCase :					int = 8       ,		lowerCAmelCase :					int = 1       ,		lowerCAmelCase :					bool = False       ,		)     ->					List[str]:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=sample_size

     # time
     if time_embedding_type == "fourier":
        SCREAMING_SNAKE_CASE_: Tuple     			=GaussianFourierProjection(
            embedding_size=8       ,		set_W_to_weight=lowerCAmelCase       ,		log=lowerCAmelCase       ,		flip_sin_to_cos=lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: Any     			=2 * block_out_channels[0]
     elif time_embedding_type == "positional":
        SCREAMING_SNAKE_CASE_: int     			=Timesteps(
            block_out_channels[0]       ,		flip_sin_to_cos=lowerCAmelCase       ,		downscale_freq_shift=lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=block_out_channels[0]

     if use_timestep_embedding:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=block_out_channels[0] * 4
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=TimestepEmbedding(
            in_channels=lowerCAmelCase       ,		time_embed_dim=lowerCAmelCase       ,		act_fn=lowerCAmelCase       ,		out_dim=block_out_channels[0]       ,		)

     SCREAMING_SNAKE_CASE_: Any     			=nn.ModuleList([]		)
     SCREAMING_SNAKE_CASE_: Dict     			=None
     SCREAMING_SNAKE_CASE_: Tuple     			=nn.ModuleList([]		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=None

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

        if i == 0:
           input_channel += extra_in_channels

        SCREAMING_SNAKE_CASE_: Optional[Any]     			=i == len(lowerCAmelCase		) - 1

        SCREAMING_SNAKE_CASE_: Tuple     			=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
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=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
     SCREAMING_SNAKE_CASE_: List[str]     			=list(reversed(lowerCAmelCase		)		)
     SCREAMING_SNAKE_CASE_: Tuple     			=reversed_block_out_channels[0]
     if out_block_type is None:
        SCREAMING_SNAKE_CASE_: Tuple     			=out_channels
     else:
        SCREAMING_SNAKE_CASE_: Tuple     			=block_out_channels[0]

     for i, up_block_type in enumerate(lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: Dict     			=output_channel
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=(
            reversed_block_out_channels[i + 1] if i < len(lowerCAmelCase		) - 1 else final_upsample_channels
        )

        SCREAMING_SNAKE_CASE_: Dict     			=i == len(lowerCAmelCase		) - 1

        SCREAMING_SNAKE_CASE_: 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		)
        SCREAMING_SNAKE_CASE_: List[Any]     			=output_channel

     # out
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4       ,		32		)
     SCREAMING_SNAKE_CASE_: Optional[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 	lowerCamelCase__       (						self :					Dict       ,		lowerCAmelCase :					torch.FloatTensor       ,		lowerCAmelCase :					Union[torch.Tensor, float, int]       ,		lowerCAmelCase :					bool = True       ,		)     ->					Union[UNetaDOutput, Tuple]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=timestep
     if not torch.is_tensor(lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: List[str]     			=torch.tensor([timesteps]       ,		dtype=torch.long       ,		device=sample.device		)
     elif torch.is_tensor(lowerCAmelCase		) and len(timesteps.shape		) == 0:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=timesteps[None].to(sample.device		)

     SCREAMING_SNAKE_CASE_: int     			=self.time_proj(lowerCAmelCase		)
     if self.config.use_timestep_embedding:
        SCREAMING_SNAKE_CASE_: Dict     			=self.time_mlp(lowerCAmelCase		)
     else:
        SCREAMING_SNAKE_CASE_: Optional[int]     			=timestep_embed[..., None]
        SCREAMING_SNAKE_CASE_: List[str]     			=timestep_embed.repeat([1, 1, sample.shape[2]]		).to(sample.dtype		)
        SCREAMING_SNAKE_CASE_: Dict     			=timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])		)

     # 2. down
     SCREAMING_SNAKE_CASE_: str     			=()
     for downsample_block in self.down_blocks:
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=downsample_block(hidden_states=lowerCAmelCase       ,		temb=lowerCAmelCase		)
        down_block_res_samples += res_samples

     # 3. mid
     if self.mid_block:
        SCREAMING_SNAKE_CASE_: List[Any]     			=self.mid_block(lowerCAmelCase       ,		lowerCAmelCase		)

     # 4. up
     for i, upsample_block in enumerate(self.up_blocks		):
        SCREAMING_SNAKE_CASE_: Dict     			=down_block_res_samples[-1:]
        SCREAMING_SNAKE_CASE_: int     			=down_block_res_samples[:-1]
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=upsample_block(lowerCAmelCase       ,		res_hidden_states_tuple=lowerCAmelCase       ,		temb=lowerCAmelCase		)

     # 5. post-process
     if self.out_block:
        SCREAMING_SNAKE_CASE_: Any     			=self.out_block(lowerCAmelCase       ,		lowerCAmelCase		)

     if not return_dict:
        return (sample,)

     return UNetaDOutput(sample=lowerCAmelCase		)








"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class 					a	:



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					List[str]=2       ,		lowerCAmelCase :					int=3       ,		lowerCAmelCase :					Optional[Any]=64       ,		lowerCAmelCase :					Union[str, Any]=None		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=np.random.default_rng(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=length
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=rng.normal(size=(length,)		).astype(np.floataa		)
     SCREAMING_SNAKE_CASE_: Tuple     			=a * self.x + b + rng.normal(scale=0.1       ,		size=(length,)		).astype(np.floataa		)



  def __len__(						self :					List[Any]		)     ->					str:


     '''simple docstring'''




     return self.length





  def __getitem__(						self :					Union[str, Any]       ,		lowerCAmelCase :					Any		)     ->					List[str]:


     '''simple docstring'''




     return {"x": self.x[i], "y": self.y[i]}







class 					a	(  torch.nn.Module       ):



  def __init__(						self :					Optional[int]       ,		lowerCAmelCase :					str=0       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					Optional[int]=False		)     ->					Tuple:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: int     			=torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
     SCREAMING_SNAKE_CASE_: int     			=torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
     SCREAMING_SNAKE_CASE_: Dict     			=True





  def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					Tuple=None		)     ->					int:


     '''simple docstring'''




     if self.first_batch:
        print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''		)
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=False
     return x * self.a[0] + self.b[0]







class 					a	(  torch.nn.Module       ):



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					Any=0       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					List[Any]=False		)     ->					str:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: List[str]     			=torch.nn.Parameter(torch.tensor(lowerCAmelCase		).float()		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.nn.Parameter(torch.tensor(lowerCAmelCase		).float()		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=True





  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					int=None		)     ->					Any:


     '''simple docstring'''




     if self.first_batch:
        print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''		)
        SCREAMING_SNAKE_CASE_: Optional[int]     			=False
     return x * self.a + self.b







def      __magic_name__				(							lowercase							,			lowercase = 16 ):
   from datasets import load_dataset
   from transformers import AutoTokenizer

   SCREAMING_SNAKE_CASE_: Optional[Any]     			=AutoTokenizer.from_pretrained("""bert-base-cased""" )
   SCREAMING_SNAKE_CASE_: Optional[int]     			={"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
   SCREAMING_SNAKE_CASE_: Any     			=load_dataset("""csv"""							,			data_files=lowercase )
   SCREAMING_SNAKE_CASE_: Any     			=datasets["""train"""].unique("""label""" )

   SCREAMING_SNAKE_CASE_: List[Any]     			={v: i for i, v in enumerate(lowercase )}

   def tokenize_function(lowercase ):
      # max_length=None => use the model max length (it's actually the default)
      SCREAMING_SNAKE_CASE_: Dict     			=tokenizer(
          examples["""sentence1"""]							,			examples["""sentence2"""]							,			truncation=lowercase							,			max_length=lowercase							,			padding="""max_length""" )
      if "label" in examples:
         SCREAMING_SNAKE_CASE_: Optional[int]     			=[label_to_id[l] for l in examples["""label"""]]
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   SCREAMING_SNAKE_CASE_: List[Any]     			=datasets.map(
       lowercase							,			batched=lowercase							,			remove_columns=["""sentence1""", """sentence2""", """label"""]							,			)

   def collate_fn(lowercase ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(lowercase							,			padding="""max_length"""							,			max_length=128							,			return_tensors="""pt""" )
      return tokenizer.pad(lowercase							,			padding="""longest"""							,			return_tensors="""pt""" )

   # Instantiate dataloaders.
   SCREAMING_SNAKE_CASE_: Optional[int]     			=DataLoader(tokenized_datasets["""train"""]							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=2 )
   SCREAMING_SNAKE_CASE_: Dict     			=DataLoader(tokenized_datasets["""validation"""]							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=1 )

   return train_dataloader, eval_dataloader



