




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     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	torch.FloatTensor







class     lowercase		( SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__		):




							@register_to_config
							def __init__(    self       ,A__ = 6_5_5_3_6       ,A__ = None       ,A__ = 2       ,A__ = 2       ,A__ = 0       ,A__ = "fourier"       ,A__ = True       ,A__ = False       ,A__ = 0.0       ,A__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")       ,A__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")       ,A__ = "UNetMidBlock1D"       ,A__ = None       ,A__ = (3_2, 3_2, 6_4)       ,A__ = None       ,A__ = 8       ,A__ = 1       ,A__ = False       ,):
												super().__init__()
												lowercase						     =						sample_size

												# time
												if time_embedding_type == "fourier":
																	lowercase						     =						GaussianFourierProjection(
																	    embedding_size=8       ,set_W_to_weight=A__       ,log=A__       ,flip_sin_to_cos=A__)
																	lowercase						     =						2 * block_out_channels[0]
												elif time_embedding_type == "positional":
																	lowercase						     =						Timesteps(
																	    block_out_channels[0]       ,flip_sin_to_cos=A__       ,downscale_freq_shift=A__)
																	lowercase						     =						block_out_channels[0]

												if use_timestep_embedding:
																	lowercase						     =						block_out_channels[0] * 4
																	lowercase						     =						TimestepEmbedding(
																	    in_channels=A__       ,time_embed_dim=A__       ,act_fn=A__       ,out_dim=block_out_channels[0]       ,)

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

												# down
												lowercase						     =						in_channels
												for i, down_block_type in enumerate(A__):
																	lowercase						     =						output_channel
																	lowercase						     =						block_out_channels[i]

																	if i == 0:
																						input_channel += extra_in_channels

																	lowercase						     =						i == len(A__) - 1

																	lowercase						     =						get_down_block(
																	    A__       ,num_layers=A__       ,in_channels=A__       ,out_channels=A__       ,temb_channels=block_out_channels[0]       ,add_downsample=not is_final_block or downsample_each_block       ,)
																	self.down_blocks.append(A__)

												# mid
												lowercase						     =						get_mid_block(
												    A__       ,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=A__       ,add_downsample=A__       ,)

												# up
												lowercase						     =						list(reversed(A__))
												lowercase						     =						reversed_block_out_channels[0]
												if out_block_type is None:
																	lowercase						     =						out_channels
												else:
																	lowercase						     =						block_out_channels[0]

												for i, up_block_type in enumerate(A__):
																	lowercase						     =						output_channel
																	lowercase						     =						(
																	    reversed_block_out_channels[i + 1] if i < len(A__) - 1 else final_upsample_channels
																	)

																	lowercase						     =						i == len(A__) - 1

																	lowercase						     =						get_up_block(
																	    A__       ,num_layers=A__       ,in_channels=A__       ,out_channels=A__       ,temb_channels=block_out_channels[0]       ,add_upsample=not is_final_block       ,)
																	self.up_blocks.append(A__)
																	lowercase						     =						output_channel

												# out
												lowercase						     =						norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4       ,3_2)
												lowercase						     =						get_out_block(
												    out_block_type=A__       ,num_groups_out=A__       ,embed_dim=block_out_channels[0]       ,out_channels=A__       ,act_fn=A__       ,fc_dim=block_out_channels[-1] // 4       ,)





							def 			A__				(    self       ,A__       ,A__       ,A__ = True       ,):
												lowercase						     =						timestep
												if not torch.is_tensor(A__):
																	lowercase						     =						torch.tensor([timesteps]       ,dtype=torch.long       ,device=sample.device)
												elif torch.is_tensor(A__) and len(timesteps.shape) == 0:
																	lowercase						     =						timesteps[None].to(sample.device)

												lowercase						     =						self.time_proj(A__)
												if self.config.use_timestep_embedding:
																	lowercase						     =						self.time_mlp(A__)
												else:
																	lowercase						     =						timestep_embed[..., None]
																	lowercase						     =						timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
																	lowercase						     =						timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

												# 2. down
												lowercase						     =						()
												for downsample_block in self.down_blocks:
																	lowercase     ,						lowercase						     =						downsample_block(hidden_states=A__       ,temb=A__)
																	down_block_res_samples += res_samples

												# 3. mid
												if self.mid_block:
																	lowercase						     =						self.mid_block(A__       ,A__)

												# 4. up
												for i, upsample_block in enumerate(self.up_blocks):
																	lowercase						     =						down_block_res_samples[-1:]
																	lowercase						     =						down_block_res_samples[:-1]
																	lowercase						     =						upsample_block(A__       ,res_hidden_states_tuple=A__       ,temb=A__)

												# 5. post-process
												if self.out_block:
																	lowercase						     =						self.out_block(A__       ,A__)

												if not return_dict:
																	return (sample,)

												return UNetaDOutput(sample=A__)






from collections import OrderedDict
from typing import Mapping

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


lowercase__		:Optional[int]			  =   logging.get_logger(__name__)

lowercase__		:List[Any]			  =   {
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	List[Any]	 ='''camembert'''




							def __init__(    self       ,A__=3_0_5_2_2       ,A__=7_6_8       ,A__=1_2       ,A__=1_2       ,A__=3_0_7_2       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=5_1_2       ,A__=2       ,A__=0.02       ,A__=1E-12       ,A__=1       ,A__=0       ,A__=2       ,A__="absolute"       ,A__=True       ,A__=None       ,**A__       ,):
												super().__init__(pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__       ,**A__)

												lowercase						     =						vocab_size
												lowercase						     =						hidden_size
												lowercase						     =						num_hidden_layers
												lowercase						     =						num_attention_heads
												lowercase						     =						hidden_act
												lowercase						     =						intermediate_size
												lowercase						     =						hidden_dropout_prob
												lowercase						     =						attention_probs_dropout_prob
												lowercase						     =						max_position_embeddings
												lowercase						     =						type_vocab_size
												lowercase						     =						initializer_range
												lowercase						     =						layer_norm_eps
												lowercase						     =						position_embedding_type
												lowercase						     =						use_cache
												lowercase						     =						classifier_dropout







class     lowercase		( SCREAMING_SNAKE_CASE__		):




							@property
							def 			A__				(    self):
												if self.task == "multiple-choice":
																	lowercase						     =						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																	lowercase						     =						{0: '''batch''', 1: '''sequence'''}
												return OrderedDict(
												    [
												        ('''input_ids''', dynamic_axis),
												        ('''attention_mask''', dynamic_axis),
												    ])




