




from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder




@dataclass
class 					snake_case__							(_A     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_											=      42




class 					snake_case__							(_A					, _A     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_											=      True





				@register_to_config
				def __init__(   self       :  int      ,   __lowerCamelCase       :  Optional[int] = 3      ,   __lowerCamelCase       :  Dict = 3      ,   __lowerCamelCase       :  Tuple = ("DownEncoderBlock2D",)      ,   __lowerCamelCase       :  Optional[int] = ("UpDecoderBlock2D",)      ,   __lowerCamelCase       :  Optional[int] = (64,)      ,   __lowerCamelCase       :  Tuple = 1      ,   __lowerCamelCase       :  List[str] = "silu"      ,   __lowerCamelCase       :  Any = 4      ,   __lowerCamelCase       :  Union[str, Any] = 32      ,   __lowerCamelCase       :  Optional[Any] = 32      ,   __lowerCamelCase       :  Optional[Any] = 0.18_215      ,   )      ->  int:
							super().__init__()

							# pass init params to Encoder
							a					=    Encoder(
							    in_channels=__lowerCamelCase      ,   out_channels=__lowerCamelCase      ,   down_block_types=__lowerCamelCase      ,   block_out_channels=__lowerCamelCase      ,   layers_per_block=__lowerCamelCase      ,   act_fn=__lowerCamelCase      ,   norm_num_groups=__lowerCamelCase      ,   double_z=__lowerCamelCase      ,   )

							# pass init params to Decoder
							a					=    Decoder(
							    in_channels=__lowerCamelCase      ,   out_channels=__lowerCamelCase      ,   up_block_types=__lowerCamelCase      ,   block_out_channels=__lowerCamelCase      ,   layers_per_block=__lowerCamelCase      ,   norm_num_groups=__lowerCamelCase      ,   act_fn=__lowerCamelCase      ,   )

							a					=    nn.Convad(2 * latent_channels      ,   2 * latent_channels      ,   1							)
							a					=    nn.Convad(__lowerCamelCase      ,   __lowerCamelCase      ,   1							)

							a					=    False
							a					=    False

							# only relevant if vae tiling is enabled
							a					=    self.config.sample_size
							a					=    (
							    self.config.sample_size[0]
							    if isinstance(self.config.sample_size      ,   (list, tuple)							)
							    else self.config.sample_size
							)
							a					=    int(sample_size / (2 ** (len(self.config.block_out_channels							) - 1))							)
							a					=    0.25





				def 							__UpperCAmelCase							(   self       :  Tuple      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Any=False							)      ->  str:
							if isinstance(__lowerCamelCase      ,   (Encoder, Decoder)							):
										a					=    value





				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   __lowerCamelCase       :  List[Any] = True							)      ->  List[str]:
							a					=    use_tiling





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  List[str]:
							self.enable_tiling(__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  Dict:
							a					=    True





				def 							__UpperCAmelCase							(   self       :  str							)      ->  Tuple:
							a					=    False





				@property
				# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
				def 							__UpperCAmelCase							(   self       :  int							)      ->  Dict[str, AttentionProcessor]:
							a					=    {}

							def fn_recursive_add_processors(__lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  Any							):
										if hasattr(__lowerCamelCase      ,   "set_processor"							):
													a					=    module.processor

										for sub_name, child in module.named_children():
													fn_recursive_add_processors(f"""{name}.{sub_name}"""      ,   __lowerCamelCase      ,   __lowerCamelCase							)

										return processors

							for name, module in self.named_children():
										fn_recursive_add_processors(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase							)

							return processors





				def 							__UpperCAmelCase							(   self       :  Optional[int]      ,   __lowerCamelCase       :  Tuple							)      ->  Optional[Any]:
							a					=    len(self.attn_processors.keys()							)

							if isinstance(__lowerCamelCase      ,   __lowerCamelCase							) and len(__lowerCamelCase							) != count:
										raise ValueError(
										    f"""A dict of processors was passed, but the number of processors {len(__lowerCamelCase							)} does not match the"""
										    f""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""							)

							def fn_recursive_attn_processor(__lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  str							):
										if hasattr(__lowerCamelCase      ,   "set_processor"							):
													if not isinstance(__lowerCamelCase      ,   __lowerCamelCase							):
																module.set_processor(__lowerCamelCase							)
													else:
																module.set_processor(processor.pop(f"""{name}.processor"""							)							)

										for sub_name, child in module.named_children():
													fn_recursive_attn_processor(f"""{name}.{sub_name}"""      ,   __lowerCamelCase      ,   __lowerCamelCase							)

							for name, module in self.named_children():
										fn_recursive_attn_processor(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Optional[int]:
							self.set_attn_processor(AttnProcessor()							)





				@apply_forward_hook
				def 							__UpperCAmelCase							(   self       :  Optional[int]      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  int = True							)      ->  AutoencoderKLOutput:
							if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
										return self.tiled_encode(__lowerCamelCase      ,   return_dict=__lowerCamelCase							)

							if self.use_slicing and x.shape[0] > 1:
										a					=    [self.encoder(__lowerCamelCase							) for x_slice in x.split(1							)]
										a					=    torch.cat(__lowerCamelCase							)
							else:
										a					=    self.encoder(__lowerCamelCase							)

							a					=    self.quant_conv(__lowerCamelCase							)
							a					=    DiagonalGaussianDistribution(__lowerCamelCase							)

							if not return_dict:
										return (posterior,)

							return AutoencoderKLOutput(latent_dist=__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  Dict = True							)      ->  Union[DecoderOutput, torch.FloatTensor]:
							if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
										return self.tiled_decode(__lowerCamelCase      ,   return_dict=__lowerCamelCase							)

							a					=    self.post_quant_conv(__lowerCamelCase							)
							a					=    self.decoder(__lowerCamelCase							)

							if not return_dict:
										return (dec,)

							return DecoderOutput(sample=__lowerCamelCase							)





				@apply_forward_hook
				def 							__UpperCAmelCase							(   self       :  List[Any]      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  List[Any] = True							)      ->  Union[DecoderOutput, torch.FloatTensor]:
							if self.use_slicing and z.shape[0] > 1:
										a					=    [self._decode(__lowerCamelCase							).sample for z_slice in z.split(1							)]
										a					=    torch.cat(__lowerCamelCase							)
							else:
										a					=    self._decode(__lowerCamelCase							).sample

							if not return_dict:
										return (decoded,)

							return DecoderOutput(sample=__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Tuple      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  int							)      ->  Dict:
							a					=    min(a.shape[2]      ,   b.shape[2]      ,   __lowerCamelCase							)
							for y in range(__lowerCamelCase							):
										a					=    a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
							return b





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  Any							)      ->  Dict:
							a					=    min(a.shape[3]      ,   b.shape[3]      ,   __lowerCamelCase							)
							for x in range(__lowerCamelCase							):
										a					=    a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
							return b





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  Any = True							)      ->  AutoencoderKLOutput:
							a					=    int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)							)
							a					=    int(self.tile_latent_min_size * self.tile_overlap_factor							)
							a					=    self.tile_latent_min_size - blend_extent

							# Split the image into 512x512 tiles and encode them separately.
							a					=    []
							for i in range(0      ,   x.shape[2]      ,   __lowerCamelCase							):
										a					=    []
										for j in range(0      ,   x.shape[3]      ,   __lowerCamelCase							):
													a					=    x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
													a					=    self.encoder(__lowerCamelCase							)
													a					=    self.quant_conv(__lowerCamelCase							)
													row.append(__lowerCamelCase							)
										rows.append(__lowerCamelCase							)
							a					=    []
							for i, row in enumerate(__lowerCamelCase							):
										a					=    []
										for j, tile in enumerate(__lowerCamelCase							):
													# blend the above tile and the left tile
													# to the current tile and add the current tile to the result row
													if i > 0:
																a					=    self.blend_v(rows[i - 1][j]      ,   __lowerCamelCase      ,   __lowerCamelCase							)
													if j > 0:
																a					=    self.blend_h(row[j - 1]      ,   __lowerCamelCase      ,   __lowerCamelCase							)
													result_row.append(tile[:, :, :row_limit, :row_limit]							)
										result_rows.append(torch.cat(__lowerCamelCase      ,   dim=3							)							)

							a					=    torch.cat(__lowerCamelCase      ,   dim=2							)
							a					=    DiagonalGaussianDistribution(__lowerCamelCase							)

							if not return_dict:
										return (posterior,)

							return AutoencoderKLOutput(latent_dist=__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  List[str] = True							)      ->  Union[DecoderOutput, torch.FloatTensor]:
							a					=    int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)							)
							a					=    int(self.tile_sample_min_size * self.tile_overlap_factor							)
							a					=    self.tile_sample_min_size - blend_extent

							# Split z into overlapping 64x64 tiles and decode them separately.
							# The tiles have an overlap to avoid seams between tiles.
							a					=    []
							for i in range(0      ,   z.shape[2]      ,   __lowerCamelCase							):
										a					=    []
										for j in range(0      ,   z.shape[3]      ,   __lowerCamelCase							):
													a					=    z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
													a					=    self.post_quant_conv(__lowerCamelCase							)
													a					=    self.decoder(__lowerCamelCase							)
													row.append(__lowerCamelCase							)
										rows.append(__lowerCamelCase							)
							a					=    []
							for i, row in enumerate(__lowerCamelCase							):
										a					=    []
										for j, tile in enumerate(__lowerCamelCase							):
													# blend the above tile and the left tile
													# to the current tile and add the current tile to the result row
													if i > 0:
																a					=    self.blend_v(rows[i - 1][j]      ,   __lowerCamelCase      ,   __lowerCamelCase							)
													if j > 0:
																a					=    self.blend_h(row[j - 1]      ,   __lowerCamelCase      ,   __lowerCamelCase							)
													result_row.append(tile[:, :, :row_limit, :row_limit]							)
										result_rows.append(torch.cat(__lowerCamelCase      ,   dim=3							)							)

							a					=    torch.cat(__lowerCamelCase      ,   dim=2							)
							if not return_dict:
										return (dec,)

							return DecoderOutput(sample=__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Any      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  int = False      ,   __lowerCamelCase       :  str = True      ,   __lowerCamelCase       :  List[str] = None      ,   )      ->  Union[DecoderOutput, torch.FloatTensor]:
							a					=    sample
							a					=    self.encode(__lowerCamelCase							).latent_dist
							if sample_posterior:
										a					=    posterior.sample(generator=__lowerCamelCase							)
							else:
										a					=    posterior.mode()
							a					=    self.decode(__lowerCamelCase							).sample

							if not return_dict:
										return (dec,)

							return DecoderOutput(sample=__lowerCamelCase							)












import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







			a					=    {}
			a					=    tokenizer(example["content"],						truncation=A     )["input_ids"]
			a					=    len(example["content"]     ) / len(output["input_ids"]     )
			return output


__lowerCAmelCase	:       Dict     =	HfArgumentParser(PretokenizationArguments)
__lowerCAmelCase	:       str     =	parser.parse_args()
if args.num_workers is None:
		__lowerCAmelCase	:       List[Any]     =	multiprocessing.cpu_count()
__lowerCAmelCase	:       str     =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

__lowerCAmelCase	:       List[Any]     =	time.time()
__lowerCAmelCase	:       str     =	load_dataset(args.dataset_name, split='train')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       int     =	time.time()
__lowerCAmelCase	:       Optional[int]     =	ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        'repo_name',
        'path',
        'copies',
        'size',
        'content',
        'license',
        'hash',
        'line_mean',
        'line_max',
        'alpha_frac',
        'autogenerated',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       Tuple     =	time.time()
ds.push_to_hub(args.tokenized_data_repo)






print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')






