'''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        __magic_name__		(       _UpperCAmelCase):
			UpperCamelCase__							=			42





class        __magic_name__		(       _UpperCAmelCase,	_UpperCAmelCase):







			@register_to_config
			def __init__(      self							:    Optional[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] = (224, 448, 672, 896)							,     lowercase_							:    int = 2							,     lowercase_							:    float = 1							,     lowercase_							:    int = 1							,     lowercase_							:    str = "conv"							,     lowercase_							:    str = "conv"							,     lowercase_							:    str = "silu"							,     lowercase_							:    Optional[int] = 8							,     lowercase_							:    int = 32							,     lowercase_							:    float = 1E-5							,     lowercase_							:    str = "default"							,     lowercase_							:    bool = True							,     lowercase_							:    Optional[str] = None							,     lowercase_							:    Optional[int] = None							,     ):
										super().__init__()

										lowercase_   :     Optional[Any]									=			sample_size
										lowercase_   :     str									=			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
										lowercase_   :     str									=			nn.Convad(lowercase_							,     block_out_channels[0]							,     kernel_size=3							,     padding=(1, 1)       )

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

										lowercase_   :     int									=			TimestepEmbedding(lowercase_							,     lowercase_       )

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

										lowercase_   :     int									=			nn.ModuleList([]       )
										lowercase_   :     Dict									=			None
										lowercase_   :     List[str]									=			nn.ModuleList([]       )

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

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

																	lowercase_   :     Optional[int]									=			i == len(lowercase_       ) - 1

																	lowercase_   :     Dict									=			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_       )
																	lowercase_   :     Optional[int]									=			output_channel

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







			def       SCREAMING_SNAKE_CASE_	(      self							:    str							,     lowercase_							:    torch.FloatTensor							,     lowercase_							:    Union[torch.Tensor, float, int]							,     lowercase_							:    Optional[torch.Tensor] = None							,     lowercase_							:    bool = True							,     ):
										# 0. center input if necessary
										if self.config.center_input_sample:
																	lowercase_   :     List[Any]									=			2 * sample - 1.0

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

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

										lowercase_   :     List[Any]									=			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.
										lowercase_   :     Optional[Any]									=			t_emb.to(dtype=self.dtype       )
										lowercase_   :     List[Any]									=			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":
																								lowercase_   :     Dict									=			self.time_proj(lowercase_       )

																	lowercase_   :     int									=			self.class_embedding(lowercase_       ).to(dtype=self.dtype       )
																	lowercase_   :     Dict									=			emb + class_emb

										# 2. pre-process
										lowercase_   :     Optional[Any]									=			sample
										lowercase_   :     Tuple									=			self.conv_in(lowercase_       )

										# 3. down
										lowercase_   :     int									=			(sample,)
										for downsample_block in self.down_blocks:
																	if hasattr(lowercase_							,     """skip_conv"""       ):
																								lowercase_		,		lowercase_		,		lowercase_   :     Dict									=			downsample_block(
																								    hidden_states=lowercase_							,     temb=lowercase_							,     skip_sample=lowercase_       )
																	else:
																								lowercase_		,		lowercase_   :     Optional[int]									=			downsample_block(hidden_states=lowercase_							,     temb=lowercase_       )

																	down_block_res_samples += res_samples

										# 4. mid
										lowercase_   :     Tuple									=			self.mid_block(lowercase_							,     lowercase_       )

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

																	if hasattr(lowercase_							,     """skip_conv"""       ):
																								lowercase_		,		lowercase_   :     List[Any]									=			upsample_block(lowercase_							,     lowercase_							,     lowercase_							,     lowercase_       )
																	else:
																								lowercase_   :     Tuple									=			upsample_block(lowercase_							,     lowercase_							,     lowercase_       )

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

										if skip_sample is not None:
																	sample += skip_sample

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

										if not return_dict:
																	return (sample,)

										return UNetaDOutput(sample=lowercase_       )






'''simple docstring'''




import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


_lowercase		:					str													=		logging.getLogger(__name__)




def 				lowerCamelCase      ( )     -> Tuple:
							lowercase_   :     Any									=			argparse.ArgumentParser(description="""Train a unigram tokenizer on the wikitext dataset."""					)
							parser.add_argument(
							    """--dataset_name""" , type=UpperCAmelCase__ , default="""wikitext""" , help="""Name of the training. Explore datasets at: hf.co/datasets.""" , )
							parser.add_argument(
							    """--dataset_config""" , type=UpperCAmelCase__ , default="""wikitext-103-raw-v1""" , help="""Configuration name of the dataset."""					)
							parser.add_argument(
							    """--batch_size""" , type=UpperCAmelCase__ , default=1000 , help="""Batch size during training.""" , )
							parser.add_argument(
							    """--vocab_size""" , type=UpperCAmelCase__ , default=10048 , help="""Size of the desired vocabulary.""" , )
							parser.add_argument(
							    """--limit""" , default=UpperCAmelCase__ , type=UpperCAmelCase__ , help="""Limit the number of shards (used for debugging).""" , )
							parser.add_argument(
							    """--export_to_hub""" , action="""store_true""" , )

							lowercase_   :     List[Any]									=			parser.parse_args()
							return args




def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[int]					)     -> List[Any]:
							lowercase_   :     Any									=			datasets.load_dataset(args.dataset_name , args.dataset_config , split="""train"""					)

							if args.limit is not None:
														lowercase_   :     Optional[Any]									=			min(len(UpperCAmelCase__					) , args.limit					)
														lowercase_   :     Dict									=			dataset.select(range(UpperCAmelCase__					)					)
														logger.info(F'''Limiting the dataset to {args.limit} entries.'''					)

							def batch_iterator():
														for i in range(0 , len(UpperCAmelCase__					) , args.batch_size					):
																					yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
							lowercase_   :     Optional[int]									=			Tokenizer(Unigram()					)
							lowercase_   :     Optional[int]									=			normalizers.Sequence([normalizers.Replace("""``""" , """\""""					), normalizers.Replace("""''""" , """\""""					)]					)
							lowercase_   :     List[Any]									=			pre_tokenizers.Metaspace()

							# Prepare the trainer.
							lowercase_   :     str									=			UnigramTrainer(
							    unk_token="""<unk>""" , special_tokens=["""[CLS]""", """[SEP]""", """<unk>""", """<pad>""", """[MASK]"""] , vocab_size=args.vocab_size , )

							logger.info("""Training the tokenizer."""					)
							tokenizer.train_from_iterator(batch_iterator() , trainer=UpperCAmelCase__					)
							logger.info("""Tokenizer training complete!"""					)

							lowercase_   :     Dict									=			tokenizer.token_to_id("""[CLS]"""					)
							lowercase_   :     Any									=			tokenizer.token_to_id("""[SEP]"""					)
							lowercase_   :     Optional[Any]									=			processors.TemplateProcessing(
							    single="""[CLS]:0 $A:0 [SEP]:0""" , pair="""[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1""" , special_tokens=[
							        ("""[CLS]""", cls_token_id),
							        ("""[SEP]""", sep_token_id),
							    ] , )
							lowercase_   :     int									=			decoders.Metaspace()

							if args.export_to_hub:
														logger.info("""Exporting the trained tokenzier to Hub."""					)
														lowercase_   :     Any									=			AlbertTokenizerFast(tokenizer_object=UpperCAmelCase__					)
														new_tokenizer.push_to_hub("""unigram-tokenizer-dataset"""					)


if __name__ == "__main__":
						_lowercase		:					Dict													=		parse_args()
						main(args)





