"""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      _lowercase						(				__lowerCamelCase					):
				lowerCamelCase__									=    42


class      _lowercase						(				__lowerCamelCase , __lowerCamelCase					):

				@register_to_config
				def __init__(       self    :      List[Any]    ,      _lowercase    :      Optional[Union[int, Tuple[int, int]]] = None    ,      _lowercase    :      int = 3    ,      _lowercase    :      int = 3    ,      _lowercase    :      bool = False    ,      _lowercase    :      str = "positional"    ,      _lowercase    :      int = 0    ,      _lowercase    :      bool = True    ,      _lowercase    :      Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")    ,      _lowercase    :      Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")    ,      _lowercase    :      Tuple[int] = (2_2_4, 4_4_8, 6_7_2, 8_9_6)    ,      _lowercase    :      int = 2    ,      _lowercase    :      float = 1    ,      _lowercase    :      int = 1    ,      _lowercase    :      str = "conv"    ,      _lowercase    :      str = "conv"    ,      _lowercase    :      str = "silu"    ,      _lowercase    :      Optional[int] = 8    ,      _lowercase    :      int = 3_2    ,      _lowercase    :      float = 1e-5    ,      _lowercase    :      str = "default"    ,      _lowercase    :      bool = True    ,      _lowercase    :      Optional[str] = None    ,      _lowercase    :      Optional[int] = None    ,      ):


										'''simple docstring'''
										super().__init__()

										UpperCAmelCase__   		=						sample_size
										UpperCAmelCase__   		=						block_out_channels[0] * 4

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

										# time
										if time_embedding_type == "fourier":
																UpperCAmelCase__   		=						GaussianFourierProjection(embedding_size=block_out_channels[0]    ,      scale=1_6       )
																UpperCAmelCase__   		=						2 * block_out_channels[0]
										elif time_embedding_type == "positional":
																UpperCAmelCase__   		=						Timesteps(block_out_channels[0]    ,      _lowercase    ,      _lowercase       )
																UpperCAmelCase__   		=						block_out_channels[0]

										UpperCAmelCase__   		=						TimestepEmbedding(_lowercase    ,      _lowercase       )

										# class embedding
										if class_embed_type is None and num_class_embeds is not None:
																UpperCAmelCase__   		=						nn.Embedding(_lowercase    ,      _lowercase       )
										elif class_embed_type == "timestep":
																UpperCAmelCase__   		=						TimestepEmbedding(_lowercase    ,      _lowercase       )
										elif class_embed_type == "identity":
																UpperCAmelCase__   		=						nn.Identity(_lowercase    ,      _lowercase       )
										else:
																UpperCAmelCase__   		=						None

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

										# down
										UpperCAmelCase__   		=						block_out_channels[0]
										for i, down_block_type in enumerate(_lowercase       ):
																UpperCAmelCase__   		=						output_channel
																UpperCAmelCase__   		=						block_out_channels[i]
																UpperCAmelCase__   		=						i == len(_lowercase       ) - 1

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

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

										# up
										UpperCAmelCase__   		=						list(reversed(_lowercase       )       )
										UpperCAmelCase__   		=						reversed_block_out_channels[0]
										for i, up_block_type in enumerate(_lowercase       ):
																UpperCAmelCase__   		=						output_channel
																UpperCAmelCase__   		=						reversed_block_out_channels[i]
																UpperCAmelCase__   		=						reversed_block_out_channels[min(i + 1    ,      len(_lowercase       ) - 1       )]

																UpperCAmelCase__   		=						i == len(_lowercase       ) - 1

																UpperCAmelCase__   		=						get_up_block(
																    _lowercase    ,      num_layers=layers_per_block + 1    ,      in_channels=_lowercase    ,      out_channels=_lowercase    ,      prev_output_channel=_lowercase    ,      temb_channels=_lowercase    ,      add_upsample=not is_final_block    ,      resnet_eps=_lowercase    ,      resnet_act_fn=_lowercase    ,      resnet_groups=_lowercase    ,      attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel    ,      resnet_time_scale_shift=_lowercase    ,      upsample_type=_lowercase    ,      )
																self.up_blocks.append(_lowercase       )
																UpperCAmelCase__   		=						output_channel

										# out
										UpperCAmelCase__   		=						norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4    ,      3_2       )
										UpperCAmelCase__   		=						nn.GroupNorm(num_channels=block_out_channels[0]    ,      num_groups=_lowercase    ,      eps=_lowercase       )
										UpperCAmelCase__   		=						nn.SiLU()
										UpperCAmelCase__   		=						nn.Convad(block_out_channels[0]    ,      _lowercase    ,      kernel_size=3    ,      padding=1       )

				def     A	(       self    :      Dict    ,      _lowercase    :      torch.FloatTensor    ,      _lowercase    :      Union[torch.Tensor, float, int]    ,      _lowercase    :      Optional[torch.Tensor] = None    ,      _lowercase    :      bool = True    ,      ):


										'''simple docstring'''
										# 0. center input if necessary
										if self.config.center_input_sample:
																UpperCAmelCase__   		=						2 * sample - 1.0

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

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

										UpperCAmelCase__   		=						self.time_proj(_lowercase       )

										# 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.
										UpperCAmelCase__   		=						t_emb.to(dtype=self.dtype       )
										UpperCAmelCase__   		=						self.time_embedding(_lowercase       )

										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":
																						UpperCAmelCase__   		=						self.time_proj(_lowercase       )

																UpperCAmelCase__   		=						self.class_embedding(_lowercase       ).to(dtype=self.dtype       )
																UpperCAmelCase__   		=						emb + class_emb

										# 2. pre-process
										UpperCAmelCase__   		=						sample
										UpperCAmelCase__   		=						self.conv_in(_lowercase       )

										# 3. down
										UpperCAmelCase__   		=						(sample,)
										for downsample_block in self.down_blocks:
																if hasattr(_lowercase    ,      """skip_conv"""       ):
																						UpperCAmelCase__		, UpperCAmelCase__		, UpperCAmelCase__   		=						downsample_block(
																						    hidden_states=_lowercase    ,      temb=_lowercase    ,      skip_sample=_lowercase       )
																else:
																						UpperCAmelCase__		, UpperCAmelCase__   		=						downsample_block(hidden_states=_lowercase    ,      temb=_lowercase       )

																down_block_res_samples += res_samples

										# 4. mid
										UpperCAmelCase__   		=						self.mid_block(_lowercase    ,      _lowercase       )

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

																if hasattr(_lowercase    ,      """skip_conv"""       ):
																						UpperCAmelCase__		, UpperCAmelCase__   		=						upsample_block(_lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase       )
																else:
																						UpperCAmelCase__   		=						upsample_block(_lowercase    ,      _lowercase    ,      _lowercase       )

        # 6. post-process
										UpperCAmelCase__   		=						self.conv_norm_out(_lowercase       )
										UpperCAmelCase__   		=						self.conv_act(_lowercase       )
										UpperCAmelCase__   		=						self.conv_out(_lowercase       )

										if skip_sample is not None:
																sample += skip_sample

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

										if not return_dict:
																return (sample,)

										return UNetaDOutput(sample=_lowercase       )


"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_    :    Optional[Any]											=						logging.get_logger(__name__)

UpperCamelCase_    :    Optional[int]											=						{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}


class      _lowercase						(				__lowerCamelCase					):
				lowerCamelCase__									=    'rembert'

				def __init__(       self    :      List[Any]    ,      _lowercase    :      Tuple=2_5_0_3_0_0    ,      _lowercase    :      Tuple=1_1_5_2    ,      _lowercase    :      List[Any]=3_2    ,      _lowercase    :      Dict=1_8    ,      _lowercase    :      str=2_5_6    ,      _lowercase    :      Optional[Any]=1_6_6_4    ,      _lowercase    :      Union[str, Any]=4_6_0_8    ,      _lowercase    :      Optional[Any]="gelu"    ,      _lowercase    :      Optional[Any]=0.0    ,      _lowercase    :      Tuple=0.0    ,      _lowercase    :      Dict=0.1    ,      _lowercase    :      str=5_1_2    ,      _lowercase    :      Tuple=2    ,      _lowercase    :      int=0.02    ,      _lowercase    :      Tuple=1e-12    ,      _lowercase    :      Union[str, Any]=True    ,      _lowercase    :      Optional[int]=0    ,      _lowercase    :      Any=3_1_2    ,      _lowercase    :      Optional[Any]=3_1_3    ,      **_lowercase    :      str    ,      ):


										'''simple docstring'''
										super().__init__(pad_token_id=_lowercase    ,      bos_token_id=_lowercase    ,      eos_token_id=_lowercase    ,      **_lowercase       )

										UpperCAmelCase__   		=						vocab_size
										UpperCAmelCase__   		=						input_embedding_size
										UpperCAmelCase__   		=						output_embedding_size
										UpperCAmelCase__   		=						max_position_embeddings
										UpperCAmelCase__   		=						hidden_size
										UpperCAmelCase__   		=						num_hidden_layers
										UpperCAmelCase__   		=						num_attention_heads
										UpperCAmelCase__   		=						intermediate_size
										UpperCAmelCase__   		=						hidden_act
										UpperCAmelCase__   		=						hidden_dropout_prob
										UpperCAmelCase__   		=						attention_probs_dropout_prob
										UpperCAmelCase__   		=						classifier_dropout_prob
										UpperCAmelCase__   		=						initializer_range
										UpperCAmelCase__   		=						type_vocab_size
										UpperCAmelCase__   		=						layer_norm_eps
										UpperCAmelCase__   		=						use_cache
										UpperCAmelCase__   		=						False


class      _lowercase						(				__lowerCamelCase					):

				@property
				def     A	(       self    :      str       ):


										'''simple docstring'''
										if self.task == "multiple-choice":
																UpperCAmelCase__   		=						{0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
																UpperCAmelCase__   		=						{0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										        ("""token_type_ids""", dynamic_axis),
										    ]       )

				@property
				def     A	(       self    :      Tuple       ):


										'''simple docstring'''
										return 1e-4

