


'''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 		_snake_case    (				snake_case_		):






						'''simple docstring'''







						__snake_case   =      42






class 		_snake_case    (				snake_case_  ,  snake_case_		):






						'''simple docstring'''







						@register_to_config
						def __init__(  self:	Dict      ,							__UpperCamelCase:	Optional[Union[int, Tuple[int, int]]] = None      ,							__UpperCamelCase:	int = 3      ,							__UpperCamelCase:	int = 3      ,							__UpperCamelCase:	bool = False      ,							__UpperCamelCase:	str = "positional"      ,							__UpperCamelCase:	int = 0      ,							__UpperCamelCase:	bool = True      ,							__UpperCamelCase:	Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")      ,							__UpperCamelCase:	Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")      ,							__UpperCamelCase:	Tuple[int] = (224, 448, 672, 896)      ,							__UpperCamelCase:	int = 2      ,							__UpperCamelCase:	float = 1      ,							__UpperCamelCase:	int = 1      ,							__UpperCamelCase:	str = "conv"      ,							__UpperCamelCase:	str = "conv"      ,							__UpperCamelCase:	str = "silu"      ,							__UpperCamelCase:	Optional[int] = 8      ,							__UpperCamelCase:	int = 32      ,							__UpperCamelCase:	float = 1E-5      ,							__UpperCamelCase:	str = "default"      ,							__UpperCamelCase:	bool = True      ,							__UpperCamelCase:	Optional[str] = None      ,							__UpperCamelCase:	Optional[int] = None      ,							)							->	List[str]:
										super().__init__()

										__magic_name__				:					Optional[Any]						       =       sample_size
										__magic_name__				:					List[Any]						       =       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
										__magic_name__				:					Tuple						       =       nn.Convad(__UpperCamelCase      ,							block_out_channels[0]      ,							kernel_size=3      ,							padding=(1, 1)     )

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

										__magic_name__				:					str						       =       TimestepEmbedding(__UpperCamelCase      ,							__UpperCamelCase     )

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

										__magic_name__				:					List[Any]						       =       nn.ModuleList([]     )
										__magic_name__				:					List[Any]						       =       None
										__magic_name__				:					List[Any]						       =       nn.ModuleList([]     )

										# down
										__magic_name__				:					Union[str, Any]						       =       block_out_channels[0]
										for i, down_block_type in enumerate(__UpperCamelCase     ):
														__magic_name__				:					List[Any]						       =       output_channel
														__magic_name__				:					List[Any]						       =       block_out_channels[i]
														__magic_name__				:					Any						       =       i == len(__UpperCamelCase     ) - 1

														__magic_name__				:					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
										__magic_name__				:					str						       =       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
										__magic_name__				:					Dict						       =       list(reversed(__UpperCamelCase     )     )
										__magic_name__				:					Any						       =       reversed_block_out_channels[0]
										for i, up_block_type in enumerate(__UpperCamelCase     ):
														__magic_name__				:					Optional[int]						       =       output_channel
														__magic_name__				:					Optional[int]						       =       reversed_block_out_channels[i]
														__magic_name__				:					Union[str, Any]						       =       reversed_block_out_channels[min(i + 1      ,							len(__UpperCamelCase     ) - 1     )]

														__magic_name__				:					Optional[Any]						       =       i == len(__UpperCamelCase     ) - 1

														__magic_name__				:					str						       =       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     )
														__magic_name__				:					Optional[int]						       =       output_channel

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







						def lowerCAmelCase__ (  self:	List[str]      ,							__UpperCamelCase:	torch.FloatTensor      ,							__UpperCamelCase:	Union[torch.Tensor, float, int]      ,							__UpperCamelCase:	Optional[torch.Tensor] = None      ,							__UpperCamelCase:	bool = True      ,							)							->	Union[UNetaDOutput, Tuple]:
										# 0. center input if necessary
										if self.config.center_input_sample:
														__magic_name__				:					Dict						       =       2 * sample - 1.0

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

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

										__magic_name__				:					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.
										__magic_name__				:					str						       =       t_emb.to(dtype=self.dtype     )
										__magic_name__				:					str						       =       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":
																		__magic_name__				:					List[str]						       =       self.time_proj(__UpperCamelCase     )

														__magic_name__				:					str						       =       self.class_embedding(__UpperCamelCase     ).to(dtype=self.dtype     )
														__magic_name__				:					Dict						       =       emb + class_emb

										# 2. pre-process
										__magic_name__				:					Optional[Any]						       =       sample
										__magic_name__				:					Union[str, Any]						       =       self.conv_in(__UpperCamelCase     )

										# 3. down
										__magic_name__				:					int						       =       (sample,)
										for downsample_block in self.down_blocks:
														if hasattr(__UpperCamelCase      ,							"skip_conv"     ):
																		__magic_name__       ,     __magic_name__       ,     __magic_name__				:					str						       =       downsample_block(
																		    hidden_states=__UpperCamelCase      ,							temb=__UpperCamelCase      ,							skip_sample=__UpperCamelCase     )
														else:
																		__magic_name__       ,     __magic_name__				:					Any						       =       downsample_block(hidden_states=__UpperCamelCase      ,							temb=__UpperCamelCase     )

														down_block_res_samples += res_samples

										# 4. mid
										__magic_name__				:					Dict						       =       self.mid_block(__UpperCamelCase      ,							__UpperCamelCase     )

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

														if hasattr(__UpperCamelCase      ,							"skip_conv"     ):
																		__magic_name__       ,     __magic_name__				:					Optional[int]						       =       upsample_block(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )
														else:
																		__magic_name__				:					Optional[int]						       =       upsample_block(__UpperCamelCase      ,							__UpperCamelCase      ,							__UpperCamelCase     )

        # 6. post-process
										__magic_name__				:					Optional[int]						       =       self.conv_norm_out(__UpperCamelCase     )
										__magic_name__				:					Optional[int]						       =       self.conv_act(__UpperCamelCase     )
										__magic_name__				:					Any						       =       self.conv_out(__UpperCamelCase     )

										if skip_sample is not None:
														sample += skip_sample

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

										if not return_dict:
														return (sample,)

										return UNetaDOutput(sample=__UpperCamelCase     )



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE       :   Any        = {
    "configuration_deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config", "DebertaV2OnnxConfig"],
    "tokenization_deberta_v2": ["DebertaV2Tokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE       :   List[Any]        = ["DebertaV2TokenizerFast"]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE       :   int        = [
		    "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFDebertaV2ForMaskedLM",
		    "TFDebertaV2ForQuestionAnswering",
		    "TFDebertaV2ForSequenceClassification",
		    "TFDebertaV2ForTokenClassification",
		    "TFDebertaV2Model",
		    "TFDebertaV2PreTrainedModel",
		]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE       :   Union[str, Any]        = [
		    "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "DebertaV2ForMaskedLM",
		    "DebertaV2ForMultipleChoice",
		    "DebertaV2ForQuestionAnswering",
		    "DebertaV2ForSequenceClassification",
		    "DebertaV2ForTokenClassification",
		    "DebertaV2Model",
		    "DebertaV2PreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_deberta_va import (
		    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DebertaVaConfig,
		    DebertaVaOnnxConfig,
		)
		from .tokenization_deberta_va import DebertaVaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deberta_va import (
				    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDebertaVaForMaskedLM,
				    TFDebertaVaForQuestionAnswering,
				    TFDebertaVaForSequenceClassification,
				    TFDebertaVaForTokenClassification,
				    TFDebertaVaModel,
				    TFDebertaVaPreTrainedModel,
				)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deberta_va import (
				    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DebertaVaForMaskedLM,
				    DebertaVaForMultipleChoice,
				    DebertaVaForQuestionAnswering,
				    DebertaVaForSequenceClassification,
				    DebertaVaForTokenClassification,
				    DebertaVaModel,
				    DebertaVaPreTrainedModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE       :   Dict        = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)