
'''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_  )




'''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


