



'''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


__SCREAMING_SNAKE_CASE :      Optional[Any]             =logging.get_logger(__name__)


@dataclass
class   SCREAMING_SNAKE_CASE__				:
	"""simple docstring"""


	A__    :    str
	A__    :    str
	A__    :    Optional[str]						=				None
	A__    :    Optional[str]						=				None


	def      a__ (		self			) ->							Tuple:
				return json.dumps(dataclasses.asdict(self			)					,   indent=2			) + "\n"







@dataclass(frozen=snake_case_					)
class   SCREAMING_SNAKE_CASE__				:
	"""simple docstring"""


	A__    :    List[int]
	A__    :    Optional[List[int]]						=				None
	A__    :    Optional[List[int]]						=				None
	A__    :    Optional[Union[int, float]]						=				None


	def      a__ (		self			) ->							Tuple:
				return json.dumps(dataclasses.asdict(self			)			) + "\n"







class   SCREAMING_SNAKE_CASE__				:
	"""simple docstring"""


	def      a__ (		self					,   A			) ->							List[str]:
				raise NotImplementedError()


	def      a__ (		self					,   A			) ->							Dict:
				raise NotImplementedError()


	def      a__ (		self					,   A			) ->							List[Any]:
				raise NotImplementedError()


	def      a__ (		self					,   A			) ->							Any:
				raise NotImplementedError()


	def      a__ (		self			) ->							Union[str, Any]:
				raise NotImplementedError()


	def      a__ (		self					,   A			) ->							Any:
				if len(self.get_labels()			) > 1:
							A:      str														=   self.get_labels()[int(example.label			)]
				return example





	@classmethod
	def      a__ (		cls					,   A					,   A=None			) ->							Optional[Any]:
				with open(A					,   """r"""					,   encoding="""utf-8-sig"""			) as f:
							return list(csv.reader(A					,   delimiter="""\t"""					,   quotechar=A			)			)







class   SCREAMING_SNAKE_CASE__				(   snake_case_					):
	"""simple docstring"""


	def __init__(		self					,   A=None					,   A=None					,   A="classification"					,   A=False			) ->							Union[str, Any]:
				A:      List[str]														=   [] if labels is None else labels
				A:      Optional[int]														=   [] if examples is None else examples
				A:      Optional[Any]														=   mode
				A:      Optional[Any]														=   verbose


	def __len__(		self			) ->							str:
				return len(self.examples			)


	def __getitem__(		self					,   A			) ->							Dict:
				if isinstance(A					,   A			):
							return SingleSentenceClassificationProcessor(labels=self.labels					,   examples=self.examples[idx]			)
				return self.examples[idx]


	@classmethod
	def      a__ (		cls					,   A					,   A=""					,   A=0					,   A=1					,   A=None					,   A=False					,   **A			) ->							Optional[Any]:
				A:      List[str]														=   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					,   A					,   A=None					,   **A			) ->							Optional[Any]:
				A:      Dict														=   cls(**A			)
				processor.add_examples(A					,   labels=A			)
				return processor


	def      a__ (		self					,   A					,   A=""					,   A=0					,   A=1					,   A=None					,   A=False					,   A=False					,   A=False					,   ) ->							str:
				A:      int														=   self._read_tsv(A			)
				if skip_first_row:
							A:      List[str]														=   lines[1:]
				A:      Optional[int]														=   []
				A:      str														=   []
				A:      Union[str, Any]														=   []
				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:
										A:      Tuple														=   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					,   A					,   A=None					,   A=None					,   A=False					,   A=False			) ->							List[Any]:
				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:
							A:      Optional[int]														=   [None] * len(A			)
				if labels is None:
							A:      Any														=   [None] * len(A			)
				A:      Dict														=   []
				A:      Optional[Any]														=   set()
				for text_or_text_and_label, label, guid in zip(A					,   A					,   A			):
							if isinstance(A					,   (tuple, list)			) and label is None:
										A							,		A:      Tuple														=   text_or_text_and_label
							else:
										A:      Dict														=   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:
							A:      Tuple														=   examples
				else:
							self.examples.extend(A			)

				# Update labels
				if overwrite_labels:
							A:      List[str]														=   list(A			)
				else:
							A:      List[str]														=   list(set(self.labels			).union(A			)			)

				return self.examples





	def      a__ (		self					,   A					,   A=None					,   A=False					,   A=0					,   A=True					,   A=None					,   ) ->							Optional[int]:
				if max_length is None:
							A:      Tuple														=   tokenizer.max_len

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

				A:      Any														=   []
				for ex_index, example in enumerate(self.examples			):
							if ex_index % 1_00_00 == 0:
										logger.info(f'Tokenizing example {ex_index}'			)

							A:      Union[str, Any]														=   tokenizer.encode(
							    example.text_a					,   add_special_tokens=A					,   max_length=min(A					,   tokenizer.max_len			)					,   )
							all_input_ids.append(A			)

				A:      Tuple														=   max(len(A			) for input_ids in all_input_ids			)

				A:      Any														=   []
				for ex_index, (input_ids, example) in enumerate(zip(A					,   self.examples			)			):
							if ex_index % 1_00_00 == 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.
							A:      int														=   [1 if mask_padding_with_zero else 0] * len(A			)

							# Zero-pad up to the sequence length.
							A:      List[Any]														=   batch_length - len(A			)
							if pad_on_left:
										A:      str														=   ([pad_token] * padding_length) + input_ids
										A:      Union[str, Any]														=   ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
							else:
										A:      Tuple														=   input_ids + ([pad_token] * padding_length)
										A:      Any														=   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":
										A:      List[str]														=   label_map[example.label]
							elif self.mode == "regression":
										A:      Any														=   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)

							A:      Tuple														=   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

							A:      Tuple														=   torch.tensor([f.input_ids for f in features]					,   dtype=torch.long			)
							A:      Optional[Any]														=   torch.tensor([f.attention_mask for f in features]					,   dtype=torch.long			)
							if self.mode == "classification":
										A:      List[str]														=   torch.tensor([f.label for f in features]					,   dtype=torch.long			)
							elif self.mode == "regression":
										A:      List[str]														=   torch.tensor([f.label for f in features]					,   dtype=torch.float			)

							A:      List[Any]														=   TensorDataset(A					,   A					,   A			)
							return dataset
				else:
							raise ValueError("""return_tensors should be one of 'tf' or 'pt'"""			)










'''simple docstring'''
from ..utils import DummyObject, requires_backends


class   SCREAMING_SNAKE_CASE__				(   metaclass=snake_case_					):
	"""simple docstring"""


	A__    :    Optional[int]						=				['''torch''', '''transformers''', '''invisible_watermark''']


	def __init__(		self					,   *A					,   **A			) ->							Tuple:
				requires_backends(self					,   ["""torch""", """transformers""", """invisible_watermark"""]			)


	@classmethod
	def      a__ (		cls					,   *A					,   **A			) ->							Optional[int]:
				requires_backends(cls					,   ["""torch""", """transformers""", """invisible_watermark"""]			)





	@classmethod
	def      a__ (		cls					,   *A					,   **A			) ->							List[Any]:
				requires_backends(cls					,   ["""torch""", """transformers""", """invisible_watermark"""]			)







class   SCREAMING_SNAKE_CASE__				(   metaclass=snake_case_					):
	"""simple docstring"""


	A__    :    Optional[int]						=				['''torch''', '''transformers''', '''invisible_watermark''']


	def __init__(		self					,   *A					,   **A			) ->							List[str]:
				requires_backends(self					,   ["""torch""", """transformers""", """invisible_watermark"""]			)


	@classmethod
	def      a__ (		cls					,   *A					,   **A			) ->							Union[str, Any]:
				requires_backends(cls					,   ["""torch""", """transformers""", """invisible_watermark"""]			)





	@classmethod
	def      a__ (		cls					,   *A					,   **A			) ->							Union[str, Any]:
				requires_backends(cls					,   ["""torch""", """transformers""", """invisible_watermark"""]			)





