

"""simple docstring"""




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_							:			Dict											= logging.get_logger(__name__)





@dataclass
class    lowerCamelCase						:
			lowerCamelCase__   :		str							=							field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()			)}			)
			lowerCamelCase__   :		str							=							field(
			    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}			)
			lowerCamelCase__   :		int							=							field(
			    default=1_2_8			,metadata={
			        'help': (
			            'The maximum total input sequence length after tokenization. Sequences longer '
			            'than this will be truncated, sequences shorter will be padded.'
			        )
			    }			,)
			lowerCamelCase__   :		bool							=							field(
			    default=A__			,metadata={'help': 'Overwrite the cached training and evaluation sets'}			)

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      self.task_name.lower()





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		Dict							=							'train'
			lowerCamelCase__   :		Tuple							=							'dev'
			lowerCamelCase__   :		Any							=							'test'





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		GlueDataTrainingArguments
			lowerCamelCase__   :		str
			lowerCamelCase__   :		List[InputFeatures]

			def __init__(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						GlueDataTrainingArguments    ,				__UpperCAmelCase				:						PreTrainedTokenizerBase    ,				__UpperCAmelCase				:						Optional[int] = None    ,				__UpperCAmelCase				:						Union[str, Split] = Split.train    ,				__UpperCAmelCase				:						Optional[str] = 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"""    ,				__UpperCAmelCase    ,				)
						SCREAMING_SNAKE_CASE__								=      args
						SCREAMING_SNAKE_CASE__								=      glue_processors[args.task_name]()
						SCREAMING_SNAKE_CASE__								=      glue_output_modes[args.task_name]
						if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ):
									try:
												SCREAMING_SNAKE_CASE__								=      Split[mode]
									except KeyError:
												raise KeyError("""mode is not a valid split name"""       )
        # Load data features from cache or dataset file
						SCREAMING_SNAKE_CASE__								=      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}"""    ,				)
						SCREAMING_SNAKE_CASE__								=      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)
									SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      label_list[2], label_list[1]
						SCREAMING_SNAKE_CASE__								=      label_list

						# Make sure only the first process in distributed training processes the dataset,
						# and the others will use the cache.
						SCREAMING_SNAKE_CASE__								=      cached_features_file + """.lock"""
						with FileLock(__UpperCAmelCase       ):
									if os.path.exists(__UpperCAmelCase       ) and not args.overwrite_cache:
												SCREAMING_SNAKE_CASE__								=      time.time()
												SCREAMING_SNAKE_CASE__								=      torch.load(__UpperCAmelCase       )
												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:
															SCREAMING_SNAKE_CASE__								=      self.processor.get_dev_examples(args.data_dir       )
												elif mode == Split.test:
															SCREAMING_SNAKE_CASE__								=      self.processor.get_test_examples(args.data_dir       )
												else:
															SCREAMING_SNAKE_CASE__								=      self.processor.get_train_examples(args.data_dir       )
												if limit_length is not None:
															SCREAMING_SNAKE_CASE__								=      examples[:limit_length]
												SCREAMING_SNAKE_CASE__								=      glue_convert_examples_to_features(
												    __UpperCAmelCase    ,				__UpperCAmelCase    ,				max_length=args.max_seq_length    ,				label_list=__UpperCAmelCase    ,				output_mode=self.output_mode    ,				)
												SCREAMING_SNAKE_CASE__								=      time.time()
												torch.save(self.features    ,				__UpperCAmelCase       )
												# ^ 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				:						Tuple       )   ->    Optional[Any]:
						return len(self.features       )

			def __getitem__(					self				:						Tuple    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    InputFeatures:
						return self.features[i]





			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    str:
						return self.label_list





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_							:			Union[str, Any]											= logging.get_logger(__name__)

A_							:			int											= {
    "caidas/swin2sr-classicalsr-x2-64": (
        "https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"
    ),
}





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		List[str]							=							'swin2sr'

			lowerCamelCase__   :		Optional[Any]							=							{
			    'hidden_size': 'embed_dim',
			    'num_attention_heads': 'num_heads',
			    'num_hidden_layers': 'num_layers',
			}

			def __init__(					self				:						Tuple    ,				__UpperCAmelCase				:						Optional[int]=6_4    ,				__UpperCAmelCase				:						Tuple=1    ,				__UpperCAmelCase				:						Optional[Any]=3    ,				__UpperCAmelCase				:						List[str]=1_8_0    ,				__UpperCAmelCase				:						List[str]=[6, 6, 6, 6, 6, 6]    ,				__UpperCAmelCase				:						Optional[Any]=[6, 6, 6, 6, 6, 6]    ,				__UpperCAmelCase				:						Tuple=8    ,				__UpperCAmelCase				:						Union[str, Any]=2.0    ,				__UpperCAmelCase				:						int=True    ,				__UpperCAmelCase				:						Optional[Any]=0.0    ,				__UpperCAmelCase				:						str=0.0    ,				__UpperCAmelCase				:						Dict=0.1    ,				__UpperCAmelCase				:						Dict="gelu"    ,				__UpperCAmelCase				:						Tuple=False    ,				__UpperCAmelCase				:						List[str]=0.02    ,				__UpperCAmelCase				:						List[Any]=1e-5    ,				__UpperCAmelCase				:						str=2    ,				__UpperCAmelCase				:						Optional[int]=1.0    ,				__UpperCAmelCase				:						Union[str, Any]="1conv"    ,				__UpperCAmelCase				:						List[Any]="pixelshuffle"    ,				**__UpperCAmelCase				:						Any    ,				)   ->    Optional[int]:
						super().__init__(**__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      image_size
						SCREAMING_SNAKE_CASE__								=      patch_size
						SCREAMING_SNAKE_CASE__								=      num_channels
						SCREAMING_SNAKE_CASE__								=      embed_dim
						SCREAMING_SNAKE_CASE__								=      depths
						SCREAMING_SNAKE_CASE__								=      len(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      num_heads
						SCREAMING_SNAKE_CASE__								=      window_size
						SCREAMING_SNAKE_CASE__								=      mlp_ratio
						SCREAMING_SNAKE_CASE__								=      qkv_bias
						SCREAMING_SNAKE_CASE__								=      hidden_dropout_prob
						SCREAMING_SNAKE_CASE__								=      attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__								=      drop_path_rate
						SCREAMING_SNAKE_CASE__								=      hidden_act
						SCREAMING_SNAKE_CASE__								=      use_absolute_embeddings
						SCREAMING_SNAKE_CASE__								=      layer_norm_eps
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      upscale
						SCREAMING_SNAKE_CASE__								=      img_range
						SCREAMING_SNAKE_CASE__								=      resi_connection
						SCREAMING_SNAKE_CASE__								=      upsampler


