


"""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_       (     _UpperCAmelCase   ):





  """simple docstring"""

  lowercase		:	torch.FloatTensor



class 	A_       (     _UpperCAmelCase      ,  _UpperCAmelCase   ):





  """simple docstring"""

  @register_to_config
  def __init__(						self  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = 3  ,					__UpperCAmelCase = 3  ,					__UpperCAmelCase = False  ,					__UpperCAmelCase = "positional"  ,					__UpperCAmelCase = 0  ,					__UpperCAmelCase = True  ,					__UpperCAmelCase = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")  ,					__UpperCAmelCase = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")  ,					__UpperCAmelCase = (2_24, 4_48, 6_72, 8_96)  ,					__UpperCAmelCase = 2  ,					__UpperCAmelCase = 1  ,					__UpperCAmelCase = 1  ,					__UpperCAmelCase = "conv"  ,					__UpperCAmelCase = "conv"  ,					__UpperCAmelCase = "silu"  ,					__UpperCAmelCase = 8  ,					__UpperCAmelCase = 32  ,					__UpperCAmelCase = 1E-5  ,					__UpperCAmelCase = "default"  ,					__UpperCAmelCase = True  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					)				->					List[Any]:
     super().__init__()

     a   :	Optional[int]						=    sample_size
     a   :	str						=    block_out_channels[0] * 4

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

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

     a   :	int						=    TimestepEmbedding(__UpperCAmelCase  ,					__UpperCAmelCase						)

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

     a   :	Union[str, Any]						=    nn.ModuleList([]						)
     a   :	Any						=    None
     a   :	Union[str, Any]						=    nn.ModuleList([]						)

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

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

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

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

        a   :	str						=    i == len(__UpperCAmelCase						) - 1

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

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






  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = True  ,					)				->					Union[UNetaDOutput, Tuple]:
     # 0. center input if necessary
     if self.config.center_input_sample:
        a   :	Any						=    2 * sample - 1.0

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

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

     a   :	Optional[Any]						=    self.time_proj(__UpperCAmelCase						)

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

     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":
           a   :	Optional[Any]						=    self.time_proj(__UpperCAmelCase						)

        a   :	Optional[int]						=    self.class_embedding(__UpperCAmelCase						).to(dtype=self.dtype						)
        a   :	int						=    emb + class_emb

     # 2. pre-process
     a   :	Union[str, Any]						=    sample
     a   :	Union[str, Any]						=    self.conv_in(__UpperCAmelCase						)

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

        down_block_res_samples += res_samples

     # 4. mid
     a   :	Optional[int]						=    self.mid_block(__UpperCAmelCase  ,					__UpperCAmelCase						)

     # 5. up
     a   :	int						=    None
     for upsample_block in self.up_blocks:
        a   :	Dict						=    down_block_res_samples[-len(upsample_block.resnets						) :]
        a   :	Tuple						=    down_block_res_samples[: -len(upsample_block.resnets						)]

        if hasattr(__UpperCAmelCase  ,					'skip_conv'						):
           a	, a   :	List[str]						=    upsample_block(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)
        else:
           a   :	Any						=    upsample_block(__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)

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

     if skip_sample is not None:
        sample += skip_sample

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

     if not return_dict:
        return (sample,)

     return UNetaDOutput(sample=__UpperCAmelCase						)









"""simple docstring"""






import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property


class 	A_       (     unittest.TestCase   ):





  """simple docstring"""

  @cached_property
  def 							lowercase_     (						self						)				->					Optional[Any]:
     return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'						)
  @cached_property
  def 							lowercase_     (						self						)				->					Optional[int]:
     return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'						)
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     a   :	List[Any]						=    self.tokenizer_ab
     a   :	int						=    ' I am a small frog.'
     a   :	Any						=    tok([src_text]  ,					padding=__UpperCAmelCase  ,					truncation=__UpperCAmelCase						)['input_ids']
     a   :	List[str]						=    tok.batch_decode(__UpperCAmelCase  ,					skip_special_tokens=__UpperCAmelCase  ,					clean_up_tokenization_spaces=__UpperCAmelCase						)[0]
     assert src_text == decoded
  def 							lowercase_     (						self						)				->					Optional[Any]:
     a   :	Optional[int]						=    self.rust_tokenizer_ab
     a   :	List[Any]						=    ' I am a small frog.'
     a   :	Union[str, Any]						=    tok([src_text]  ,					padding=__UpperCAmelCase  ,					truncation=__UpperCAmelCase						)['input_ids']
     a   :	Optional[Any]						=    tok.batch_decode(__UpperCAmelCase  ,					skip_special_tokens=__UpperCAmelCase  ,					clean_up_tokenization_spaces=__UpperCAmelCase						)[0]
     assert src_text == decoded
  def 							lowercase_     (						self						)				->					Tuple:
     assert self.tokenizer_ab.add_prefix_space
     assert self.tokenizer_ab([' Sam', 'Sam']						).input_ids == [[55_02, 2], [55_02, 2]]






  def 							lowercase_     (						self						)				->					Tuple:
     assert self.rust_tokenizer_ab.add_prefix_space
     assert self.rust_tokenizer_ab([' Sam', 'Sam']						).input_ids == [[55_02, 2], [55_02, 2]]





