




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 __snake_case			(			a		):
   UpperCAmelCase__  :    torch.FloatTensor







class __snake_case			(			a ,  a		):







   @register_to_config
   def __init__(      self		:						List[str]					,						_snake_case		:						int = 65536					,						_snake_case		:						Optional[int] = None					,						_snake_case		:						int = 2					,						_snake_case		:						int = 2					,						_snake_case		:						int = 0					,						_snake_case		:						str = "fourier"					,						_snake_case		:						bool = True					,						_snake_case		:						bool = False					,						_snake_case		:						float = 0.0					,						_snake_case		:						Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")					,						_snake_case		:						Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")					,						_snake_case		:						Tuple[str] = "UNetMidBlock1D"					,						_snake_case		:						str = None					,						_snake_case		:						Tuple[int] = (32, 32, 64)					,						_snake_case		:						str = None					,						_snake_case		:						int = 8					,						_snake_case		:						int = 1					,						_snake_case		:						bool = False					,						):

        """simple docstring"""


        super().__init__()
        UpperCAmelCase_          =							sample_size

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

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

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

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

             if i == 0:
                  input_channel += extra_in_channels

             UpperCAmelCase_          =							i == len(_snake_case) - 1

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

        # mid
        UpperCAmelCase_          =							get_mid_block(
            _snake_case					,						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=_snake_case					,						add_downsample=_snake_case					,						)

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

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

             UpperCAmelCase_          =							i == len(_snake_case) - 1

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

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







   def lowerCamelCase						(      self		:						str					,						_snake_case		:						torch.FloatTensor					,						_snake_case		:						Union[torch.Tensor, float, int]					,						_snake_case		:						bool = True					,						):

        """simple docstring"""


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

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

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

        # 3. mid
        if self.mid_block:
             UpperCAmelCase_          =							self.mid_block(_snake_case					,						_snake_case)

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

        # 5. post-process
        if self.out_block:
             UpperCAmelCase_          =							self.out_block(_snake_case					,						_snake_case)

        if not return_dict:
             return (sample,)

        return UNetaDOutput(sample=_snake_case)












import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class __snake_case			(			a ,  unittest.TestCase		):
   UpperCAmelCase__  :    Any          =  PhobertTokenizer
   UpperCAmelCase__  :    List[str]          =  False







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        UpperCAmelCase_          =							['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
        UpperCAmelCase_          =							dict(zip(_snake_case					,						range(len(_snake_case))))
        UpperCAmelCase_          =							['''#version: 0.2''', '''l à</w>''']
        UpperCAmelCase_          =							{'''unk_token''': '''<unk>'''}

        UpperCAmelCase_          =							os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES['''vocab_file'''])
        UpperCAmelCase_          =							os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES['''merges_file'''])

        with open(self.vocab_file					,						'''w'''					,						encoding='''utf-8''') as fp:
             for token in vocab_tokens:
                  fp.write(F"""{token} {vocab_tokens[token]}\n""")
        with open(self.merges_file					,						'''w'''					,						encoding='''utf-8''') as fp:
             fp.write('''\n'''.join(_snake_case))







   def lowerCamelCase						(      self		:						int					,						**_snake_case		:						Any):

        """simple docstring"""


        kwargs.update(self.special_tokens_map)
        return PhobertTokenizer.from_pretrained(self.tmpdirname					,						**_snake_case)







   def lowerCamelCase						(      self		:						Optional[Any]					,						_snake_case		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							'''Tôi là VinAI Research'''
        UpperCAmelCase_          =							'''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
        return input_text, output_text







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							PhobertTokenizer(self.vocab_file					,						self.merges_file					,						**self.special_tokens_map)
        UpperCAmelCase_          =							'''Tôi là VinAI Research'''
        UpperCAmelCase_          =							'''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
        UpperCAmelCase_          =							tokenizer.tokenize(_snake_case)
        print(_snake_case)
        self.assertListEqual(_snake_case					,						_snake_case)

        UpperCAmelCase_          =							tokens + [tokenizer.unk_token]

        UpperCAmelCase_          =							[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case)					,						_snake_case)






