



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 SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			torch.FloatTensor






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    @register_to_config
    def __init__(	self,		lowerCamelCase__ = 6_5536,		lowerCamelCase__ = None,		lowerCamelCase__ = 2,		lowerCamelCase__ = 2,		lowerCamelCase__ = 0,		lowerCamelCase__ = "fourier",		lowerCamelCase__ = True,		lowerCamelCase__ = False,		lowerCamelCase__ = 0.0,		lowerCamelCase__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D"),		lowerCamelCase__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip"),		lowerCamelCase__ = "UNetMidBlock1D",		lowerCamelCase__ = None,		lowerCamelCase__ = (32, 32, 64),		lowerCamelCase__ = None,		lowerCamelCase__ = 8,		lowerCamelCase__ = 1,		lowerCamelCase__ = False,		):
           super().__init__()
           A			:	int										=       sample_size

           # time
           if time_embedding_type == "fourier":
                  A			:	str										=       GaussianFourierProjection(
                      embedding_size=8,		set_W_to_weight=lowerCamelCase__,		log=lowerCamelCase__,		flip_sin_to_cos=lowerCamelCase__		)
                  A			:	List[str]										=       2 * block_out_channels[0]
           elif time_embedding_type == "positional":
                  A			:	Any										=       Timesteps(
                      block_out_channels[0],		flip_sin_to_cos=lowerCamelCase__,		downscale_freq_shift=lowerCamelCase__		)
                  A			:	Optional[Any]										=       block_out_channels[0]

           if use_timestep_embedding:
                  A			:	List[Any]										=       block_out_channels[0] * 4
                  A			:	Optional[int]										=       TimestepEmbedding(
                      in_channels=lowerCamelCase__,		time_embed_dim=lowerCamelCase__,		act_fn=lowerCamelCase__,		out_dim=block_out_channels[0],		)

           A			:	Any										=       nn.ModuleList([]		)
           A			:	List[Any]										=       None
           A			:	List[str]										=       nn.ModuleList([]		)
           A			:	Optional[int]										=       None

           # down
           A			:	int										=       in_channels
           for i, down_block_type in enumerate(lowerCamelCase__		):
                  A			:	Tuple										=       output_channel
                  A			:	Union[str, Any]										=       block_out_channels[i]

                  if i == 0:
                         input_channel += extra_in_channels

                  A			:	int										=       i == len(lowerCamelCase__		) - 1

                  A			:	List[Any]										=       get_down_block(
                      lowerCamelCase__,		num_layers=lowerCamelCase__,		in_channels=lowerCamelCase__,		out_channels=lowerCamelCase__,		temb_channels=block_out_channels[0],		add_downsample=not is_final_block or downsample_each_block,		)
                  self.down_blocks.append(lowerCamelCase__		)

           # mid
           A			:	List[str]										=       get_mid_block(
               lowerCamelCase__,		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=lowerCamelCase__,		add_downsample=lowerCamelCase__,		)

           # up
           A			:	int										=       list(reversed(lowerCamelCase__		)		)
           A			:	str										=       reversed_block_out_channels[0]
           if out_block_type is None:
                  A			:	Optional[int]										=       out_channels
           else:
                  A			:	str										=       block_out_channels[0]

           for i, up_block_type in enumerate(lowerCamelCase__		):
                  A			:	Tuple										=       output_channel
                  A			:	Union[str, Any]										=       (
                      reversed_block_out_channels[i + 1] if i < len(lowerCamelCase__		) - 1 else final_upsample_channels
                  )

                  A			:	Any										=       i == len(lowerCamelCase__		) - 1

                  A			:	str										=       get_up_block(
                      lowerCamelCase__,		num_layers=lowerCamelCase__,		in_channels=lowerCamelCase__,		out_channels=lowerCamelCase__,		temb_channels=block_out_channels[0],		add_upsample=not is_final_block,		)
                  self.up_blocks.append(lowerCamelCase__		)
                  A			:	str										=       output_channel

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






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = True,		):
           A			:	str										=       timestep
           if not torch.is_tensor(lowerCamelCase__		):
                  A			:	Any										=       torch.tensor([timesteps],		dtype=torch.long,		device=sample.device		)
           elif torch.is_tensor(lowerCamelCase__		) and len(timesteps.shape		) == 0:
                  A			:	Dict										=       timesteps[None].to(sample.device		)

           A			:	Any										=       self.time_proj(lowerCamelCase__		)
           if self.config.use_timestep_embedding:
                  A			:	Any										=       self.time_mlp(lowerCamelCase__		)
           else:
                  A			:	List[Any]										=       timestep_embed[..., None]
                  A			:	Dict										=       timestep_embed.repeat([1, 1, sample.shape[2]]		).to(sample.dtype		)
                  A			:	str										=       timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])		)

           # 2. down
           A			:	Optional[Any]										=       ()
           for downsample_block in self.down_blocks:
                  A			:	str										=       downsample_block(hidden_states=lowerCamelCase__,		temb=lowerCamelCase__		)
                  down_block_res_samples += res_samples

           # 3. mid
           if self.mid_block:
                  A			:	Optional[Any]										=       self.mid_block(lowerCamelCase__,		lowerCamelCase__		)

           # 4. up
           for i, upsample_block in enumerate(self.up_blocks		):
                  A			:	Dict										=       down_block_res_samples[-1:]
                  A			:	Optional[int]										=       down_block_res_samples[:-1]
                  A			:	Any										=       upsample_block(lowerCamelCase__,		res_hidden_states_tuple=lowerCamelCase__,		temb=lowerCamelCase__		)

           # 5. post-process
           if self.out_block:
                  A			:	str										=       self.out_block(lowerCamelCase__,		lowerCamelCase__		)

           if not return_dict:
                  return (sample,)

           return UNetaDOutput(sample=lowerCamelCase__		)







from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_:int					=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Any					=						{
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Any =				"roberta"






    def __init__(	self,		lowerCamelCase__=5_0265,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A			:	List[str]										=       vocab_size
        A			:	List[Any]										=       hidden_size
        A			:	List[Any]										=       num_hidden_layers
        A			:	Optional[Any]										=       num_attention_heads
        A			:	Union[str, Any]										=       hidden_act
        A			:	Union[str, Any]										=       intermediate_size
        A			:	Optional[int]										=       hidden_dropout_prob
        A			:	List[str]										=       attention_probs_dropout_prob
        A			:	int										=       max_position_embeddings
        A			:	List[str]										=       type_vocab_size
        A			:	List[str]										=       initializer_range
        A			:	Dict										=       layer_norm_eps
        A			:	Optional[int]										=       position_embedding_type
        A			:	Any										=       use_cache
        A			:	Optional[int]										=       classifier_dropout






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    @property
    def       _lowerCAmelCase (	self		):
        if self.task == "multiple-choice":
            A			:	Optional[Any]										=       {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A			:	int										=       {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


