

'''simple docstring'''
import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


lowerCamelCase				:   str      				=  logging.get_logger(__name__)







@dataclass
class 							A__					:
  A__					=       42
  A__					=       42
  A__					=       None
  A__					=       None





  def 		A						(	self   :			Union[str, Any]				)			->					List[str]:





       '''simple docstring'''






       return json.dumps(dataclasses.asdict(self				)							,      indent=2				) + "\n"







@dataclass(frozen=A__						)
class 							A__					:
  A__					=       42
  A__					=       None
  A__					=       None
  A__					=       None





  def 		A						(	self   :			int				)			->					int:





       '''simple docstring'''






       return json.dumps(dataclasses.asdict(self				)				) + "\n"







class 							A__					:





  def 		A						(	self   :			Dict							,      _a   :			str				)			->					Optional[int]:





       '''simple docstring'''






       raise NotImplementedError()





  def 		A						(	self   :			List[Any]							,      _a   :			Optional[int]				)			->					List[str]:





       '''simple docstring'''






       raise NotImplementedError()





  def 		A						(	self   :			Optional[int]							,      _a   :			str				)			->					Optional[Any]:





       '''simple docstring'''






       raise NotImplementedError()





  def 		A						(	self   :			Optional[int]							,      _a   :			Tuple				)			->					Optional[Any]:





       '''simple docstring'''






       raise NotImplementedError()





  def 		A						(	self   :			Optional[int]				)			->					Tuple:





       '''simple docstring'''






       raise NotImplementedError()





  def 		A						(	self   :			List[str]							,      _a   :			str				)			->					int:





       '''simple docstring'''






       if len(self.get_labels()				) > 1:
            _SCREAMING_SNAKE_CASE												=self.get_labels()[int(example.label				)]
       return example





  @classmethod
  def 		A						(	cls   :			List[str]							,      _a   :			List[str]							,      _a   :			Any=None				)			->					List[Any]:





       '''simple docstring'''






       with open(_a							,      'r'							,      encoding='utf-8-sig'				) as f:
            return list(csv.reader(_a							,      delimiter='\t'							,      quotechar=_a				)				)







class 							A__					( A__						):





  def __init__(	self   :			Optional[Any]							,      _a   :			List[str]=None							,      _a   :			Tuple=None							,      _a   :			str="classification"							,      _a   :			int=False				)			->					int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=[] if labels is None else labels
       _SCREAMING_SNAKE_CASE												=[] if examples is None else examples
       _SCREAMING_SNAKE_CASE												=mode
       _SCREAMING_SNAKE_CASE												=verbose





  def __len__(	self   :			List[Any]				)			->					Any:





       '''simple docstring'''






       return len(self.examples				)





  def __getitem__(	self   :			Union[str, Any]							,      _a   :			str				)			->					Union[str, Any]:





       '''simple docstring'''






       if isinstance(_a							,      _a				):
            return SingleSentenceClassificationProcessor(labels=self.labels							,      examples=self.examples[idx]				)
       return self.examples[idx]





  @classmethod
  def 		A						(	cls   :			Any							,      _a   :			List[Any]							,      _a   :			Any=""							,      _a   :			Optional[int]=0							,      _a   :			int=1							,      _a   :			str=None							,      _a   :			Dict=False							,      **_a   :			List[str]				)			->					List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=cls(**_a				)
       processor.add_examples_from_csv(
           _a							,      split_name=_a							,      column_label=_a							,      column_text=_a							,      column_id=_a							,      skip_first_row=_a							,      overwrite_labels=_a							,      overwrite_examples=_a							,      )
       return processor





  @classmethod
  def 		A						(	cls   :			Any							,      _a   :			Optional[Any]							,      _a   :			str=None							,      **_a   :			Union[str, Any]				)			->					int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=cls(**_a				)
       processor.add_examples(_a							,      labels=_a				)
       return processor





  def 		A						(	self   :			Any							,      _a   :			List[Any]							,      _a   :			List[Any]=""							,      _a   :			List[str]=0							,      _a   :			Optional[int]=1							,      _a   :			List[Any]=None							,      _a   :			List[Any]=False							,      _a   :			List[str]=False							,      _a   :			str=False							,      )			->					Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=self._read_tsv(_a				)
       if skip_first_row:
            _SCREAMING_SNAKE_CASE												=lines[1:]
       _SCREAMING_SNAKE_CASE												=[]
       _SCREAMING_SNAKE_CASE												=[]
       _SCREAMING_SNAKE_CASE												=[]
       for i, line in enumerate(_a				):
            texts.append(line[column_text]				)
            labels.append(line[column_label]				)
            if column_id is not None:
                 ids.append(line[column_id]				)
            else:
                 _SCREAMING_SNAKE_CASE												=f"{split_name}-{i}" if split_name else str(_a				)
                 ids.append(_a				)

       return self.add_examples(
           _a							,      _a							,      _a							,      overwrite_labels=_a							,      overwrite_examples=_a				)





  def 		A						(	self   :			List[str]							,      _a   :			Optional[Any]							,      _a   :			Any=None							,      _a   :			Dict=None							,      _a   :			Optional[Any]=False							,      _a   :			Optional[Any]=False				)			->					Any:





       '''simple docstring'''






       if labels is not None and len(_a				) != len(_a				):
            raise ValueError(
                f"Text and labels have mismatched lengths {len(_a				)} and {len(_a				)}"				)
       if ids is not None and len(_a				) != len(_a				):
            raise ValueError(f"Text and ids have mismatched lengths {len(_a				)} and {len(_a				)}"				)
       if ids is None:
            _SCREAMING_SNAKE_CASE												=[None] * len(_a				)
       if labels is None:
            _SCREAMING_SNAKE_CASE												=[None] * len(_a				)
       _SCREAMING_SNAKE_CASE												=[]
       _SCREAMING_SNAKE_CASE												=set()
       for text_or_text_and_label, label, guid in zip(_a							,      _a							,      _a				):
            if isinstance(_a							,      (tuple, list)				) and label is None:
                 _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=text_or_text_and_label
            else:
                 _SCREAMING_SNAKE_CASE												=text_or_text_and_label
            added_labels.add(_a				)
            examples.append(InputExample(guid=_a							,      text_a=_a							,      text_b=_a							,      label=_a				)				)

       # Update examples
       if overwrite_examples:
            _SCREAMING_SNAKE_CASE												=examples
       else:
            self.examples.extend(_a				)

       # Update labels
       if overwrite_labels:
            _SCREAMING_SNAKE_CASE												=list(_a				)
       else:
            _SCREAMING_SNAKE_CASE												=list(set(self.labels				).union(_a				)				)

       return self.examples





  def 		A						(	self   :			Union[str, Any]							,      _a   :			int							,      _a   :			Dict=None							,      _a   :			int=False							,      _a   :			str=0							,      _a   :			List[str]=True							,      _a   :			Optional[Any]=None							,      )			->					Union[str, Any]:





       '''simple docstring'''






       if max_length is None:
            _SCREAMING_SNAKE_CASE												=tokenizer.max_len

       _SCREAMING_SNAKE_CASE												={label: i for i, label in enumerate(self.labels				)}

       _SCREAMING_SNAKE_CASE												=[]
       for ex_index, example in enumerate(self.examples				):
            if ex_index % 1_0000 == 0:
                 logger.info(f"Tokenizing example {ex_index}"				)

            _SCREAMING_SNAKE_CASE												=tokenizer.encode(
                example.text_a							,      add_special_tokens=_a							,      max_length=min(_a							,      tokenizer.max_len				)							,      )
            all_input_ids.append(_a				)

       _SCREAMING_SNAKE_CASE												=max(len(_a				) for input_ids in all_input_ids				)

       _SCREAMING_SNAKE_CASE												=[]
       for ex_index, (input_ids, example) in enumerate(zip(_a							,      self.examples				)				):
            if ex_index % 1_0000 == 0:
                 logger.info(f"Writing example {ex_index}/{len(self.examples				)}"				)
            # The mask has 1 for real tokens and 0 for padding tokens. Only real
            # tokens are attended to.
            _SCREAMING_SNAKE_CASE												=[1 if mask_padding_with_zero else 0] * len(_a				)

            # Zero-pad up to the sequence length.
            _SCREAMING_SNAKE_CASE												=batch_length - len(_a				)
            if pad_on_left:
                 _SCREAMING_SNAKE_CASE												=([pad_token] * padding_length) + input_ids
                 _SCREAMING_SNAKE_CASE												=([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
            else:
                 _SCREAMING_SNAKE_CASE												=input_ids + ([pad_token] * padding_length)
                 _SCREAMING_SNAKE_CASE												=attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

            if len(_a				) != batch_length:
                 raise ValueError(f"Error with input length {len(_a				)} vs {batch_length}"				)
            if len(_a				) != batch_length:
                 raise ValueError(f"Error with input length {len(_a				)} vs {batch_length}"				)

            if self.mode == "classification":
                 _SCREAMING_SNAKE_CASE												=label_map[example.label]
            elif self.mode == "regression":
                 _SCREAMING_SNAKE_CASE												=float(example.label				)
            else:
                 raise ValueError(self.mode				)

            if ex_index < 5 and self.verbose:
                 logger.info('*** Example ***'				)
                 logger.info(f"guid: {example.guid}"				)
                 logger.info(f"input_ids: {' '.join([str(_a				) for x in input_ids]				)}"				)
                 logger.info(f"attention_mask: {' '.join([str(_a				) for x in attention_mask]				)}"				)
                 logger.info(f"label: {example.label} (id = {label})"				)

            features.append(InputFeatures(input_ids=_a							,      attention_mask=_a							,      label=_a				)				)

       if return_tensors is None:
            return features
       elif return_tensors == "tf":
            if not is_tf_available():
                 raise RuntimeError('return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'				)
            import tensorflow as tf

            def gen():
                 for ex in features:
                      yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

            _SCREAMING_SNAKE_CASE												=tf.data.Dataset.from_generator(
                _a							,      ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)							,      ({'input_ids': tf.TensorShape([None]				), 'attention_mask': tf.TensorShape([None]				)}, tf.TensorShape([]				))							,      )
            return dataset
       elif return_tensors == "pt":
            if not is_torch_available():
                 raise RuntimeError('return_tensors set to \'pt\' but PyTorch can\'t be imported'				)
            import torch
            from torch.utils.data import TensorDataset

            _SCREAMING_SNAKE_CASE												=torch.tensor([f.input_ids for f in features]							,      dtype=torch.long				)
            _SCREAMING_SNAKE_CASE												=torch.tensor([f.attention_mask for f in features]							,      dtype=torch.long				)
            if self.mode == "classification":
                 _SCREAMING_SNAKE_CASE												=torch.tensor([f.label for f in features]							,      dtype=torch.long				)
            elif self.mode == "regression":
                 _SCREAMING_SNAKE_CASE												=torch.tensor([f.label for f in features]							,      dtype=torch.float				)

            _SCREAMING_SNAKE_CASE												=TensorDataset(_a							,      _a							,      _a				)
            return dataset
       else:
            raise ValueError('return_tensors should be one of \'tf\' or \'pt\''				)



'''simple docstring'''
from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


lowerCamelCase				:   Dict      				=  datasets.utils.logging.get_logger(__name__)







class 							A__					( folder_based_builder.FolderBasedBuilderConfig						):
  A__					=       None
  A__					=       None







class 							A__					( folder_based_builder.FolderBasedBuilder						):
  A__					=       datasets.Image()
  A__					=       'image'
  A__					=       ImageFolderConfig
  A__					=       42  # definition at the bottom of the script
  A__					=       ImageClassification(image_column='image'  ,     label_column='label'						)

lowerCamelCase				:   Optional[int]      				=  [
    ".blp",
    ".bmp",
    ".dib",
    ".bufr",
    ".cur",
    ".pcx",
    ".dcx",
    ".dds",
    ".ps",
    ".eps",
    ".fit",
    ".fits",
    ".fli",
    ".flc",
    ".ftc",
    ".ftu",
    ".gbr",
    ".gif",
    ".grib",
    ".h5",
    ".hdf",
    ".png",
    ".apng",
    ".jp2",
    ".j2k",
    ".jpc",
    ".jpf",
    ".jpx",
    ".j2c",
    ".icns",
    ".ico",
    ".im",
    ".iim",
    ".tif",
    ".tiff",
    ".jfif",
    ".jpe",
    ".jpg",
    ".jpeg",
    ".mpg",
    ".mpeg",
    ".msp",
    ".pcd",
    ".pxr",
    ".pbm",
    ".pgm",
    ".ppm",
    ".pnm",
    ".psd",
    ".bw",
    ".rgb",
    ".rgba",
    ".sgi",
    ".ras",
    ".tga",
    ".icb",
    ".vda",
    ".vst",
    ".webp",
    ".wmf",
    ".emf",
    ".xbm",
    ".xpm",
]
lowerCamelCase				:   Any      				=  IMAGE_EXTENSIONS
