




import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__a          =			logging.get_logger(__name__)

@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys()       )}       )
    lowerCAmelCase        =				field(
        metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}       )
    lowerCAmelCase        =				field(
        default=128    ,						metadata={
            '''help''': (
                '''The maximum total input sequence length after tokenization. Sequences longer '''
                '''than this will be truncated, sequences shorter will be padded.'''
            )
        }    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}       )





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Tuple       =      self.task_name.lower()


class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''train'''
    lowerCAmelCase        =				'''dev'''
    lowerCAmelCase        =				'''test'''


class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				42
    lowerCAmelCase        =				42
    lowerCAmelCase        =				42





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = Split.train       ,_SCREAMING_SNAKE_CASE = None       ,)			->		str:
         warnings.warn(
             '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
             '''library. You can have a look at this example script for pointers: '''
             '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''       ,_SCREAMING_SNAKE_CASE       ,)
         UpperCAmelCase_      :							Union[str, Any]       =      args
         UpperCAmelCase_      :							Optional[Any]       =      glue_processors[args.task_name]()
         UpperCAmelCase_      :							List[Any]       =      glue_output_modes[args.task_name]
         if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              try:
                   UpperCAmelCase_      :							Any       =      Split[mode]
              except KeyError:
                   raise KeyError('''mode is not a valid split name'''  )
        # Load data features from cache or dataset file
         UpperCAmelCase_      :							Tuple       =      os.path.join(
             cache_dir if cache_dir is not None else args.data_dir       ,f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''       ,)
         UpperCAmelCase_      :							str       =      self.processor.get_labels()
         if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
             "RobertaTokenizer",
             "RobertaTokenizerFast",
             "XLMRobertaTokenizer",
             "BartTokenizer",
             "BartTokenizerFast",
         ):
              # HACK(label indices are swapped in RoBERTa pretrained model)
              UpperCAmelCase_,   UpperCAmelCase_      :							Any       =      label_list[2], label_list[1]
         UpperCAmelCase_      :							int       =      label_list

         # Make sure only the first process in distributed training processes the dataset,
         # and the others will use the cache.
         UpperCAmelCase_      :							Optional[int]       =      cached_features_file + '''.lock'''
         with FileLock(_SCREAMING_SNAKE_CASE  ):
              if os.path.exists(_SCREAMING_SNAKE_CASE  ) and not args.overwrite_cache:
                   UpperCAmelCase_      :							List[str]       =      time.time()
                   UpperCAmelCase_      :							int       =      torch.load(_SCREAMING_SNAKE_CASE  )
                   logger.info(
                       f'''Loading features from cached file {cached_features_file} [took %.3f s]'''       ,time.time() - start  )
              else:
                   logger.info(f'''Creating features from dataset file at {args.data_dir}'''  )

                   if mode == Split.dev:
                        UpperCAmelCase_      :							Union[str, Any]       =      self.processor.get_dev_examples(args.data_dir  )
                   elif mode == Split.test:
                        UpperCAmelCase_      :							List[Any]       =      self.processor.get_test_examples(args.data_dir  )
                   else:
                        UpperCAmelCase_      :							int       =      self.processor.get_train_examples(args.data_dir  )
                   if limit_length is not None:
                        UpperCAmelCase_      :							Optional[Any]       =      examples[:limit_length]
                   UpperCAmelCase_      :							Optional[Any]       =      glue_convert_examples_to_features(
                       _SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,max_length=args.max_seq_length       ,label_list=_SCREAMING_SNAKE_CASE       ,output_mode=self.output_mode       ,)
                   UpperCAmelCase_      :							List[str]       =      time.time()
                   torch.save(self.features       ,_SCREAMING_SNAKE_CASE  )
                   # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                   logger.info(
                       f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''  )





    def __len__(				self  )			->		Optional[Any]:
         return len(self.features  )





    def __getitem__(				self       ,_SCREAMING_SNAKE_CASE  )			->		InputFeatures:
         return self.features[i]





    def 							a__				(				self  )			->		Tuple:
         return self.label_list





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   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				42


class   __a(					_a    ,						_a       ):






    """simple docstring"""





    lowerCAmelCase        =				True





    @register_to_config
    def __init__(				self       ,_SCREAMING_SNAKE_CASE = 3       ,_SCREAMING_SNAKE_CASE = 3       ,_SCREAMING_SNAKE_CASE = ("DownEncoderBlock2D",)       ,_SCREAMING_SNAKE_CASE = ("UpDecoderBlock2D",)       ,_SCREAMING_SNAKE_CASE = (64,)       ,_SCREAMING_SNAKE_CASE = 1       ,_SCREAMING_SNAKE_CASE = "silu"       ,_SCREAMING_SNAKE_CASE = 4       ,_SCREAMING_SNAKE_CASE = 32       ,_SCREAMING_SNAKE_CASE = 32       ,_SCREAMING_SNAKE_CASE = 0.1_82_15       ,)			->		Optional[int]:
         super().__init__()

         # pass init params to Encoder
         UpperCAmelCase_      :							List[Any]       =      Encoder(
             in_channels=_SCREAMING_SNAKE_CASE       ,out_channels=_SCREAMING_SNAKE_CASE       ,down_block_types=_SCREAMING_SNAKE_CASE       ,block_out_channels=_SCREAMING_SNAKE_CASE       ,layers_per_block=_SCREAMING_SNAKE_CASE       ,act_fn=_SCREAMING_SNAKE_CASE       ,norm_num_groups=_SCREAMING_SNAKE_CASE       ,double_z=_SCREAMING_SNAKE_CASE       ,)

         # pass init params to Decoder
         UpperCAmelCase_      :							List[str]       =      Decoder(
             in_channels=_SCREAMING_SNAKE_CASE       ,out_channels=_SCREAMING_SNAKE_CASE       ,up_block_types=_SCREAMING_SNAKE_CASE       ,block_out_channels=_SCREAMING_SNAKE_CASE       ,layers_per_block=_SCREAMING_SNAKE_CASE       ,norm_num_groups=_SCREAMING_SNAKE_CASE       ,act_fn=_SCREAMING_SNAKE_CASE       ,)

         UpperCAmelCase_      :							int       =      nn.Convad(2 * latent_channels       ,2 * latent_channels       ,1  )
         UpperCAmelCase_      :							Union[str, Any]       =      nn.Convad(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,1  )

         UpperCAmelCase_      :							Optional[Any]       =      False
         UpperCAmelCase_      :							Dict       =      False

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





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=False  )			->		int:
         if isinstance(_SCREAMING_SNAKE_CASE       ,(Encoder, Decoder)  ):
              UpperCAmelCase_      :							Union[str, Any]       =      value





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE = True  )			->		Optional[Any]:
         UpperCAmelCase_      :							Dict       =      use_tiling





    def 							a__				(				self  )			->		Optional[Any]:
         self.enable_tiling(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							str       =      True





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							Any       =      False





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

         def fn_recursive_add_processors(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              if hasattr(_SCREAMING_SNAKE_CASE       ,'''set_processor'''  ):
                   UpperCAmelCase_      :							Tuple       =      module.processor

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

              return processors

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

         return processors





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Optional[int]       =      len(self.attn_processors.keys()  )

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

         def fn_recursive_attn_processor(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              if hasattr(_SCREAMING_SNAKE_CASE       ,'''set_processor'''  ):
                   if not isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
                        module.set_processor(_SCREAMING_SNAKE_CASE  )
                   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}'''       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

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





    def 							a__				(				self  )			->		Tuple:
         self.set_attn_processor(AttnProcessor()  )





    @apply_forward_hook
    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = 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(_SCREAMING_SNAKE_CASE       ,return_dict=_SCREAMING_SNAKE_CASE  )

         if self.use_slicing and x.shape[0] > 1:
              UpperCAmelCase_      :							int       =      [self.encoder(_SCREAMING_SNAKE_CASE  ) for x_slice in x.split(1  )]
              UpperCAmelCase_      :							List[str]       =      torch.cat(_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							Optional[Any]       =      self.encoder(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Any       =      self.quant_conv(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      DiagonalGaussianDistribution(_SCREAMING_SNAKE_CASE  )

         if not return_dict:
              return (posterior,)

         return AutoencoderKLOutput(latent_dist=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = 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(_SCREAMING_SNAKE_CASE       ,return_dict=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Tuple       =      self.post_quant_conv(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Tuple       =      self.decoder(_SCREAMING_SNAKE_CASE  )

         if not return_dict:
              return (dec,)

         return DecoderOutput(sample=_SCREAMING_SNAKE_CASE  )





    @apply_forward_hook
    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = True  )			->		Union[DecoderOutput, torch.FloatTensor]:
         if self.use_slicing and z.shape[0] > 1:
              UpperCAmelCase_      :							Tuple       =      [self._decode(_SCREAMING_SNAKE_CASE  ).sample for z_slice in z.split(1  )]
              UpperCAmelCase_      :							str       =      torch.cat(_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							str       =      self._decode(_SCREAMING_SNAKE_CASE  ).sample

         if not return_dict:
              return (decoded,)

         return DecoderOutput(sample=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         UpperCAmelCase_      :							str       =      min(a.shape[2]       ,b.shape[2]       ,_SCREAMING_SNAKE_CASE  )
         for y in range(_SCREAMING_SNAKE_CASE  ):
              UpperCAmelCase_      :							str       =      a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
         return b





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





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = True  )			->		AutoencoderKLOutput:
         UpperCAmelCase_      :							Optional[int]       =      int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)  )
         UpperCAmelCase_      :							Any       =      int(self.tile_latent_min_size * self.tile_overlap_factor  )
         UpperCAmelCase_      :							str       =      self.tile_latent_min_size - blend_extent

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

         UpperCAmelCase_      :							Optional[Any]       =      torch.cat(_SCREAMING_SNAKE_CASE       ,dim=2  )
         UpperCAmelCase_      :							Union[str, Any]       =      DiagonalGaussianDistribution(_SCREAMING_SNAKE_CASE  )

         if not return_dict:
              return (posterior,)

         return AutoencoderKLOutput(latent_dist=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = True  )			->		Union[DecoderOutput, torch.FloatTensor]:
         UpperCAmelCase_      :							Optional[Any]       =      int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)  )
         UpperCAmelCase_      :							str       =      int(self.tile_sample_min_size * self.tile_overlap_factor  )
         UpperCAmelCase_      :							Any       =      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.
         UpperCAmelCase_      :							Tuple       =      []
         for i in range(0       ,z.shape[2]       ,_SCREAMING_SNAKE_CASE  ):
              UpperCAmelCase_      :							Tuple       =      []
              for j in range(0       ,z.shape[3]       ,_SCREAMING_SNAKE_CASE  ):
                   UpperCAmelCase_      :							Optional[int]       =      z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                   UpperCAmelCase_      :							List[str]       =      self.post_quant_conv(_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							Dict       =      self.decoder(_SCREAMING_SNAKE_CASE  )
                   row.append(_SCREAMING_SNAKE_CASE  )
              rows.append(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[Any]       =      []
         for i, row in enumerate(_SCREAMING_SNAKE_CASE  ):
              UpperCAmelCase_      :							int       =      []
              for j, tile in enumerate(_SCREAMING_SNAKE_CASE  ):
                   # blend the above tile and the left tile
                   # to the current tile and add the current tile to the result row
                   if i > 0:
                        UpperCAmelCase_      :							Optional[int]       =      self.blend_v(rows[i - 1][j]       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
                   if j > 0:
                        UpperCAmelCase_      :							Optional[int]       =      self.blend_h(row[j - 1]       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
                   result_row.append(tile[:, :, :row_limit, :row_limit]  )
              result_rows.append(torch.cat(_SCREAMING_SNAKE_CASE       ,dim=3  )  )

         UpperCAmelCase_      :							Dict       =      torch.cat(_SCREAMING_SNAKE_CASE       ,dim=2  )
         if not return_dict:
              return (dec,)

         return DecoderOutput(sample=_SCREAMING_SNAKE_CASE  )





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

         if not return_dict:
              return (dec,)

         return DecoderOutput(sample=_SCREAMING_SNAKE_CASE  )