

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    SCREAMING_SNAKE_CASE__      (__snake_case  ):
    __lowerCamelCase		: torch.FloatTensor



class    SCREAMING_SNAKE_CASE__      (__snake_case	, __snake_case  ):





    @register_to_config
    def __init__(   self		,     a = None		,     a = 3		,     a = 3		,     a = False		,     a = "positional"		,     a = 0		,     a = True		,     a = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")		,     a = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")		,     a = (224, 448, 672, 896)		,     a = 2		,     a = 1		,     a = 1		,     a = "conv"		,     a = "conv"		,     a = "silu"		,     a = 8		,     a = 32		,     a = 1e-5		,     a = "default"		,     a = True		,     a = None		,     a = None		,     ):
         super().__init__()

         lowercase__					:     List[Any]   = sample_size
         lowercase__					:     List[str]   = block_out_channels[0] * 4

         # Check inputs
         if len(a) != len(a):
              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(a) != len(a):
              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
         lowercase__					:     Dict   = nn.Convad(a		,     block_out_channels[0]		,     kernel_size=3		,     padding=(1, 1))

         # time
         if time_embedding_type == "fourier":
              lowercase__					:     str   = GaussianFourierProjection(embedding_size=block_out_channels[0]		,     scale=16)
              lowercase__					:     List[Any]   = 2 * block_out_channels[0]
         elif time_embedding_type == "positional":
              lowercase__					:     List[Any]   = Timesteps(block_out_channels[0]		,     a		,     a)
              lowercase__					:     Optional[int]   = block_out_channels[0]

         lowercase__					:     Tuple   = TimestepEmbedding(a		,     a)

         # class embedding
         if class_embed_type is None and num_class_embeds is not None:
              lowercase__					:     int   = nn.Embedding(a		,     a)
         elif class_embed_type == "timestep":
              lowercase__					:     Optional[Any]   = TimestepEmbedding(a		,     a)
         elif class_embed_type == "identity":
              lowercase__					:     str   = nn.Identity(a		,     a)
         else:
              lowercase__					:     Tuple   = None

         lowercase__					:     List[str]   = nn.ModuleList([])
         lowercase__					:     List[Any]   = None
         lowercase__					:     Dict   = nn.ModuleList([])

         # down
         lowercase__					:     Optional[int]   = block_out_channels[0]
         for i, down_block_type in enumerate(a):
              lowercase__					:     Dict   = output_channel
              lowercase__					:     Optional[Any]   = block_out_channels[i]
              lowercase__					:     str   = i == len(a) - 1

              lowercase__					:     Any   = get_down_block(
                  a		,     num_layers=a		,     in_channels=a		,     out_channels=a		,     temb_channels=a		,     add_downsample=not is_final_block		,     resnet_eps=a		,     resnet_act_fn=a		,     resnet_groups=a		,     attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel		,     downsample_padding=a		,     resnet_time_scale_shift=a		,     downsample_type=a		,     )
              self.down_blocks.append(a)

         # mid
         lowercase__					:     List[Any]   = UNetMidBlockaD(
             in_channels=block_out_channels[-1]		,     temb_channels=a		,     resnet_eps=a		,     resnet_act_fn=a		,     output_scale_factor=a		,     resnet_time_scale_shift=a		,     attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]		,     resnet_groups=a		,     add_attention=a		,     )

         # up
         lowercase__					:     List[Any]   = list(reversed(a))
         lowercase__					:     Tuple   = reversed_block_out_channels[0]
         for i, up_block_type in enumerate(a):
              lowercase__					:     List[str]   = output_channel
              lowercase__					:     List[Any]   = reversed_block_out_channels[i]
              lowercase__					:     Any   = reversed_block_out_channels[min(i + 1		,     len(a) - 1)]

              lowercase__					:     Union[str, Any]   = i == len(a) - 1

              lowercase__					:     Union[str, Any]   = get_up_block(
                  a		,     num_layers=layers_per_block + 1		,     in_channels=a		,     out_channels=a		,     prev_output_channel=a		,     temb_channels=a		,     add_upsample=not is_final_block		,     resnet_eps=a		,     resnet_act_fn=a		,     resnet_groups=a		,     attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel		,     resnet_time_scale_shift=a		,     upsample_type=a		,     )
              self.up_blocks.append(a)
              lowercase__					:     List[Any]   = output_channel

         # out
         lowercase__					:     Optional[int]   = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,     32)
         lowercase__					:     str   = nn.GroupNorm(num_channels=block_out_channels[0]		,     num_groups=a		,     eps=a)
         lowercase__					:     Optional[int]   = nn.SiLU()
         lowercase__					:     Optional[Any]   = nn.Convad(block_out_channels[0]		,     a		,     kernel_size=3		,     padding=1)





    def    snake_case_       (   self		,     a		,     a		,     a = None		,     a = True		,     ):
         # 0. center input if necessary
         if self.config.center_input_sample:
              lowercase__					:     List[Any]   = 2 * sample - 1.0

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

         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         lowercase__					:     Union[str, Any]   = timesteps * torch.ones(sample.shape[0]		,     dtype=timesteps.dtype		,     device=timesteps.device)

         lowercase__					:     Optional[int]   = self.time_proj(a)

         # 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.
         lowercase__					:     List[str]   = t_emb.to(dtype=self.dtype)
         lowercase__					:     List[Any]   = self.time_embedding(a)

         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":
                   lowercase__					:     Dict   = self.time_proj(a)

              lowercase__					:     str   = self.class_embedding(a).to(dtype=self.dtype)
              lowercase__					:     List[Any]   = emb + class_emb

         # 2. pre-process
         lowercase__					:     List[Any]   = sample
         lowercase__					:     List[Any]   = self.conv_in(a)

         # 3. down
         lowercase__					:     str   = (sample,)
         for downsample_block in self.down_blocks:
              if hasattr(a		,     'skip_conv'):
                   lowercase__   ,				lowercase__   ,				lowercase__					:     Optional[int]   = downsample_block(
                       hidden_states=a		,     temb=a		,     skip_sample=a)
              else:
                   lowercase__   ,				lowercase__					:     List[Any]   = downsample_block(hidden_states=a		,     temb=a)

              down_block_res_samples += res_samples

         # 4. mid
         lowercase__					:     Tuple   = self.mid_block(a		,     a)

         # 5. up
         lowercase__					:     List[str]   = None
         for upsample_block in self.up_blocks:
              lowercase__					:     Any   = down_block_res_samples[-len(upsample_block.resnets) :]
              lowercase__					:     Optional[Any]   = down_block_res_samples[: -len(upsample_block.resnets)]

              if hasattr(a		,     'skip_conv'):
                   lowercase__   ,				lowercase__					:     Optional[int]   = upsample_block(a		,     a		,     a		,     a)
              else:
                   lowercase__					:     List[Any]   = upsample_block(a		,     a		,     a)

        # 6. post-process
         lowercase__					:     Optional[int]   = self.conv_norm_out(a)
         lowercase__					:     List[str]   = self.conv_act(a)
         lowercase__					:     Optional[Any]   = self.conv_out(a)

         if skip_sample is not None:
              sample += skip_sample

         if self.config.time_embedding_type == "fourier":
              lowercase__					:     List[Any]   = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]))))
              lowercase__					:     Any   = sample / timesteps

         if not return_dict:
              return (sample,)

         return UNetaDOutput(sample=a)




from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest


@custom_tokenizers
class    SCREAMING_SNAKE_CASE__      (__snake_case  ):





    def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a):
         return True





    def    snake_case_       (   self):
         lowercase__					:     Tuple   = CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate')
         lowercase__					:     str   = 'Hugging Face大法好，谁用谁知道。'
         lowercase__					:     Optional[int]   = 'Hugging Face大法好,谁用谁知道。<unk>'
         lowercase__					:     Any   = '▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

         lowercase__					:     Any   = tokenizer.tokenize(a)
         self.assertListEqual(a		,     a)

         lowercase__					:     List[Any]   = tokens + [tokenizer.unk_token]

         lowercase__					:     Any   = [1_3789, 1_3283, 1421, 8, 10, 1164, 1_3608, 1_6528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a)		,     a)

         lowercase__					:     Optional[Any]   = tokenizer.decode(a)
         self.assertEqual(a		,     a)

