code: 
  '''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   a_  (_a    ):
         __lowerCAmelCase    :				torch.FloatTensor
  
  
  
  class   a_  (_a							,			_a    ):
  
  
  
  
         @register_to_config
         def __init__(     self   ,   snake_case_ = None   ,   snake_case_ = 3   ,   snake_case_ = 3   ,   snake_case_ = False   ,   snake_case_ = "positional"   ,   snake_case_ = 0   ,   snake_case_ = True   ,   snake_case_ = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")   ,   snake_case_ = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")   ,   snake_case_ = (2_2_4, 4_4_8, 6_7_2, 8_9_6)   ,   snake_case_ = 2   ,   snake_case_ = 1   ,   snake_case_ = 1   ,   snake_case_ = "conv"   ,   snake_case_ = "conv"   ,   snake_case_ = "silu"   ,   snake_case_ = 8   ,   snake_case_ = 3_2   ,   snake_case_ = 1E-5   ,   snake_case_ = "default"   ,   snake_case_ = True   ,   snake_case_ = None   ,   snake_case_ = None   ,   ):
           super().__init__()
  
           _lowerCAmelCase       :  Tuple   				=					sample_size
           _lowerCAmelCase       :  Union[str, Any]   				=					block_out_channels[0] * 4
  
           # Check inputs
           if len(snake_case_  ) != len(snake_case_  ):
             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(snake_case_  ) != len(snake_case_  ):
             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       :  Optional[Any]   				=					nn.Convad(snake_case_   ,   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=1_6  )
             _lowerCAmelCase       :  List[str]   				=					2 * block_out_channels[0]
           elif time_embedding_type == "positional":
             _lowerCAmelCase       :  List[Any]   				=					Timesteps(block_out_channels[0]   ,   snake_case_   ,   snake_case_  )
             _lowerCAmelCase       :  int   				=					block_out_channels[0]
  
           _lowerCAmelCase       :  Optional[int]   				=					TimestepEmbedding(snake_case_   ,   snake_case_  )
  
           # class embedding
           if class_embed_type is None and num_class_embeds is not None:
             _lowerCAmelCase       :  List[Any]   				=					nn.Embedding(snake_case_   ,   snake_case_  )
           elif class_embed_type == "timestep":
             _lowerCAmelCase       :  List[str]   				=					TimestepEmbedding(snake_case_   ,   snake_case_  )
           elif class_embed_type == "identity":
             _lowerCAmelCase       :  Any   				=					nn.Identity(snake_case_   ,   snake_case_  )
           else:
             _lowerCAmelCase       :  List[str]   				=					None
  
           _lowerCAmelCase       :  Optional[Any]   				=					nn.ModuleList([]  )
           _lowerCAmelCase       :  Optional[Any]   				=					None
           _lowerCAmelCase       :  List[str]   				=					nn.ModuleList([]  )
  
           # down
           _lowerCAmelCase       :  Optional[int]   				=					block_out_channels[0]
           for i, down_block_type in enumerate(snake_case_  ):
             _lowerCAmelCase       :  Dict   				=					output_channel
             _lowerCAmelCase       :  Dict   				=					block_out_channels[i]
             _lowerCAmelCase       :  Any   				=					i == len(snake_case_  ) - 1
  
             _lowerCAmelCase       :  str   				=					get_down_block(
                 snake_case_   ,   num_layers=snake_case_   ,   in_channels=snake_case_   ,   out_channels=snake_case_   ,   temb_channels=snake_case_   ,   add_downsample=not is_final_block   ,   resnet_eps=snake_case_   ,   resnet_act_fn=snake_case_   ,   resnet_groups=snake_case_   ,   attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel   ,   downsample_padding=snake_case_   ,   resnet_time_scale_shift=snake_case_   ,   downsample_type=snake_case_   ,   )
             self.down_blocks.append(snake_case_  )
  
           # mid
           _lowerCAmelCase       :  List[str]   				=					UNetMidBlockaD(
               in_channels=block_out_channels[-1]   ,   temb_channels=snake_case_   ,   resnet_eps=snake_case_   ,   resnet_act_fn=snake_case_   ,   output_scale_factor=snake_case_   ,   resnet_time_scale_shift=snake_case_   ,   attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]   ,   resnet_groups=snake_case_   ,   add_attention=snake_case_   ,   )
  
           # up
           _lowerCAmelCase       :  List[Any]   				=					list(reversed(snake_case_  )  )
           _lowerCAmelCase       :  List[str]   				=					reversed_block_out_channels[0]
           for i, up_block_type in enumerate(snake_case_  ):
             _lowerCAmelCase       :  Tuple   				=					output_channel
             _lowerCAmelCase       :  List[Any]   				=					reversed_block_out_channels[i]
             _lowerCAmelCase       :  int   				=					reversed_block_out_channels[min(i + 1   ,   len(snake_case_  ) - 1  )]
  
             _lowerCAmelCase       :  Tuple   				=					i == len(snake_case_  ) - 1
  
             _lowerCAmelCase       :  Any   				=					get_up_block(
                 snake_case_   ,   num_layers=layers_per_block + 1   ,   in_channels=snake_case_   ,   out_channels=snake_case_   ,   prev_output_channel=snake_case_   ,   temb_channels=snake_case_   ,   add_upsample=not is_final_block   ,   resnet_eps=snake_case_   ,   resnet_act_fn=snake_case_   ,   resnet_groups=snake_case_   ,   attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel   ,   resnet_time_scale_shift=snake_case_   ,   upsample_type=snake_case_   ,   )
             self.up_blocks.append(snake_case_  )
             _lowerCAmelCase       :  Optional[Any]   				=					output_channel
  
           # out
           _lowerCAmelCase       :  Optional[int]   				=					norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4   ,   3_2  )
           _lowerCAmelCase       :  Dict   				=					nn.GroupNorm(num_channels=block_out_channels[0]   ,   num_groups=snake_case_   ,   eps=snake_case_  )
           _lowerCAmelCase       :  Dict   				=					nn.SiLU()
           _lowerCAmelCase       :  Optional[Any]   				=					nn.Convad(block_out_channels[0]   ,   snake_case_   ,   kernel_size=3   ,   padding=1  )
  
  
  
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   snake_case_   ,   snake_case_   ,   snake_case_ = None   ,   snake_case_ = True   ,   ):
           # 0. center input if necessary
           if self.config.center_input_sample:
             _lowerCAmelCase       :  List[str]   				=					2 * sample - 1.0
  
           # 1. time
           _lowerCAmelCase       :  Optional[int]   				=					timestep
           if not torch.is_tensor(snake_case_  ):
             _lowerCAmelCase       :  int   				=					torch.tensor([timesteps]   ,   dtype=torch.long   ,   device=sample.device  )
           elif torch.is_tensor(snake_case_  ) and len(timesteps.shape  ) == 0:
             _lowerCAmelCase       :  Any   				=					timesteps[None].to(sample.device  )
  
           # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
           _lowerCAmelCase       :  List[str]   				=					timesteps * torch.ones(sample.shape[0]   ,   dtype=timesteps.dtype   ,   device=timesteps.device  )
  
           _lowerCAmelCase       :  Union[str, Any]   				=					self.time_proj(snake_case_  )
  
           # 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       :  int   				=					t_emb.to(dtype=self.dtype  )
           _lowerCAmelCase       :  List[Any]   				=					self.time_embedding(snake_case_  )
  
           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       :  Tuple   				=					self.time_proj(snake_case_  )
  
             _lowerCAmelCase       :  Union[str, Any]   				=					self.class_embedding(snake_case_  ).to(dtype=self.dtype  )
             _lowerCAmelCase       :  Optional[int]   				=					emb + class_emb
  
           # 2. pre-process
           _lowerCAmelCase       :  Any   				=					sample
           _lowerCAmelCase       :  Dict   				=					self.conv_in(snake_case_  )
  
           # 3. down
           _lowerCAmelCase       :  List[str]   				=					(sample,)
           for downsample_block in self.down_blocks:
             if hasattr(snake_case_   ,   """skip_conv"""  ):
               _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase       :  Optional[Any]   				=					downsample_block(
                   hidden_states=snake_case_   ,   temb=snake_case_   ,   skip_sample=snake_case_  )
             else:
               _lowerCAmelCase				,					_lowerCAmelCase       :  Union[str, Any]   				=					downsample_block(hidden_states=snake_case_   ,   temb=snake_case_  )
  
             down_block_res_samples += res_samples
  
           # 4. mid
           _lowerCAmelCase       :  str   				=					self.mid_block(snake_case_   ,   snake_case_  )
  
           # 5. up
           _lowerCAmelCase       :  Optional[Any]   				=					None
           for upsample_block in self.up_blocks:
             _lowerCAmelCase       :  Optional[Any]   				=					down_block_res_samples[-len(upsample_block.resnets  ) :]
             _lowerCAmelCase       :  Any   				=					down_block_res_samples[: -len(upsample_block.resnets  )]
  
             if hasattr(snake_case_   ,   """skip_conv"""  ):
               _lowerCAmelCase				,					_lowerCAmelCase       :  Any   				=					upsample_block(snake_case_   ,   snake_case_   ,   snake_case_   ,   snake_case_  )
             else:
               _lowerCAmelCase       :  List[Any]   				=					upsample_block(snake_case_   ,   snake_case_   ,   snake_case_  )
  
          # 6. post-process
           _lowerCAmelCase       :  int   				=					self.conv_norm_out(snake_case_  )
           _lowerCAmelCase       :  List[Any]   				=					self.conv_act(snake_case_  )
           _lowerCAmelCase       :  int   				=					self.conv_out(snake_case_  )
  
           if skip_sample is not None:
             sample += skip_sample
  
           if self.config.time_embedding_type == "fourier":
             _lowerCAmelCase       :  Union[str, Any]   				=					timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]  )))  )
             _lowerCAmelCase       :  int   				=					sample / timesteps
  
           if not return_dict:
             return (sample,)
  
           return UNetaDOutput(sample=snake_case_  )
  
  
  
style_context: 
  '''simple docstring'''
  
  import json
  import os
  import unittest
  
  from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
      VOCAB_FILES_NAMES,
      GPTNeoXJapaneseTokenizer,
  )
  from transformers.testing_utils import require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  @require_tokenizers
  class   a_  (_a							,			unittest.TestCase    ):
         __lowerCAmelCase    :				List[Any]    =      GPTNeoXJapaneseTokenizer
         __lowerCAmelCase    :				Any    =      False
         __lowerCAmelCase    :				List[Any]    =      {"""do_clean_text""": False, """add_prefix_space""": False}
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           super().setUp()
  
           _lowerCAmelCase       :  Any   				=					[
               """こん""",
               """こんに""",
               """にちは""",
               """ばんは""",
               """世界,㔺界""",
               """、""",
               """。""",
               """<BR>""",
               """<SP>""",
               """<TAB>""",
               """<URL>""",
               """<EMAIL>""",
               """<TEL>""",
               """<DATE>""",
               """<PRICE>""",
               """<BLOCK>""",
               """<KIGOU>""",
               """<U2000U2BFF>""",
               """<|emoji1|>""",
               """<unk>""",
               """<|startoftext|>""",
               """<|endoftext|>""",
           ]
           _lowerCAmelCase       :  Union[str, Any]   				=					{"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
           _lowerCAmelCase       :  Any   				=					{"""unk_token""": """<unk>"""}
  
           _lowerCAmelCase       :  Optional[int]   				=					os.path.join(self.tmpdirname   ,   VOCAB_FILES_NAMES["""vocab_file"""]  )
           _lowerCAmelCase       :  Any   				=					os.path.join(self.tmpdirname   ,   VOCAB_FILES_NAMES["""emoji_file"""]  )
           with open(self.vocab_file   ,   """w"""   ,   encoding="""utf-8"""  ) as vocab_writer:
             vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]  )  )
           with open(self.emoji_file   ,   """w"""  ) as emoji_writer:
             emoji_writer.write(json.dumps(snake_case_  )  )
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   **snake_case_  ):
           kwargs.update(self.special_tokens_map  )
           return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname   ,   **snake_case_  )
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   snake_case_  ):
           _lowerCAmelCase       :  Tuple   				=					"""こんにちは、世界。 \nこんばんは、㔺界。😀"""
           _lowerCAmelCase       :  str   				=					"""こんにちは、世界。 \nこんばんは、世界。😀"""
           return input_text, output_text
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   snake_case_  ):
           _lowerCAmelCase				,					_lowerCAmelCase       :  str   				=					self.get_input_output_texts(snake_case_  )
           _lowerCAmelCase       :  Tuple   				=					tokenizer.encode(snake_case_   ,   add_special_tokens=snake_case_  )
           _lowerCAmelCase       :  Optional[Any]   				=					tokenizer.decode(snake_case_   ,   clean_up_tokenization_spaces=snake_case_  )
           return text, ids
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           pass  # TODO add if relevant
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           pass  # TODO add if relevant
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           pass  # TODO add if relevant
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           _lowerCAmelCase       :  List[Any]   				=					self.get_tokenizer()
  
           # Testing tokenization
           _lowerCAmelCase       :  Tuple   				=					"""こんにちは、世界。　こんばんは、㔺界。"""
           _lowerCAmelCase       :  Optional[Any]   				=					["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
           _lowerCAmelCase       :  Tuple   				=					tokenizer.tokenize(snake_case_  )
           self.assertListEqual(snake_case_   ,   snake_case_  )
  
           # Testing conversion to ids without special tokens
           _lowerCAmelCase       :  Optional[int]   				=					[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
           _lowerCAmelCase       :  Optional[int]   				=					tokenizer.convert_tokens_to_ids(snake_case_  )
           self.assertListEqual(snake_case_   ,   snake_case_  )
  
           # Testing conversion to ids with special tokens
           _lowerCAmelCase       :  Optional[Any]   				=					tokens + [tokenizer.unk_token]
           _lowerCAmelCase       :  List[str]   				=					[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
           _lowerCAmelCase       :  Dict   				=					tokenizer.convert_tokens_to_ids(snake_case_  )
           self.assertListEqual(snake_case_   ,   snake_case_  )
  
  
  
  
         @slow
         def 							__UpperCamelCase       (     self  ):
           _lowerCAmelCase       :  int   				=					self.tokenizer_class.from_pretrained("""abeja/gpt-neox-japanese-2.7b"""  )
  
           _lowerCAmelCase       :  List[Any]   				=					tokenizer.encode("""ありがとう。"""   ,   add_special_tokens=snake_case_  )
           _lowerCAmelCase       :  List[str]   				=					tokenizer.encode("""どういたしまして。"""   ,   add_special_tokens=snake_case_  )
  
           _lowerCAmelCase       :  int   				=					tokenizer.build_inputs_with_special_tokens(snake_case_  )
           _lowerCAmelCase       :  Any   				=					tokenizer.build_inputs_with_special_tokens(snake_case_   ,   snake_case_  )
  
           assert encoded_sentence == ids_a
           assert encoded_pair == ids_a + ids_a
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           # Intentionally convert some words to accommodate character fluctuations unique to Japanese
           pass
  
  
  
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           # tokenizer has no padding token
           pass
  
  
  