




'''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


UpperCAmelCase_       				=      logging.get_logger(__name__)

@dataclass
class 						lowerCAmelCase_   :







		'''simple docstring'''







		lowerCAmelCase_				:					str
		lowerCAmelCase_				:					str
		lowerCAmelCase_				:					Optional[str]				     =						None
		lowerCAmelCase_				:					Optional[str]				     =						None


		def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






			"""simple docstring"""






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

@dataclass(frozen=lowerCamelCase_   )
class 						lowerCAmelCase_   :







		'''simple docstring'''







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


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any] ):






			"""simple docstring"""






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

class 						lowerCAmelCase_   :







		'''simple docstring'''







		def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	Dict ):






			"""simple docstring"""






			raise NotImplementedError()


		def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	int ):






			"""simple docstring"""






			raise NotImplementedError()


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple						,		_UpperCAmelCase    :	Optional[int] ):






			"""simple docstring"""






			raise NotImplementedError()


		def 							SCREAMING_SNAKE_CASE__			(						self    :	int						,		_UpperCAmelCase    :	int ):






			"""simple docstring"""






			raise NotImplementedError()


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int] ):






			"""simple docstring"""






			raise NotImplementedError()


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int]						,		_UpperCAmelCase    :	List[Any] ):






			"""simple docstring"""






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



		@classmethod
		def 							SCREAMING_SNAKE_CASE__			(						cls    :	List[Any]						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	str=None ):






			"""simple docstring"""






			with open(_UpperCAmelCase						,		"""r"""						,		encoding="""utf-8-sig""" ) as f:
				return list(csv.reader(_UpperCAmelCase						,		delimiter="""\t"""						,		quotechar=_UpperCAmelCase ) )

class 						lowerCAmelCase_   ( lowerCamelCase_   ):







		'''simple docstring'''







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






			"""simple docstring"""






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


		def __len__(						self    :	Optional[Any] ):






			"""simple docstring"""






			return len(self.examples )


		def __getitem__(						self    :	List[str]						,		_UpperCAmelCase    :	List[str] ):






			"""simple docstring"""






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


		@classmethod
		def 							SCREAMING_SNAKE_CASE__			(						cls    :	int						,		_UpperCAmelCase    :	Union[str, Any]						,		_UpperCAmelCase    :	Any=""						,		_UpperCAmelCase    :	Tuple=0						,		_UpperCAmelCase    :	List[str]=1						,		_UpperCAmelCase    :	Any=None						,		_UpperCAmelCase    :	str=False						,		**_UpperCAmelCase    :	List[str] ):






			"""simple docstring"""






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


		@classmethod
		def 							SCREAMING_SNAKE_CASE__			(						cls    :	Tuple						,		_UpperCAmelCase    :	Union[str, Any]						,		_UpperCAmelCase    :	str=None						,		**_UpperCAmelCase    :	Any ):






			"""simple docstring"""






			UpperCAmelCase__										=    cls(**_UpperCAmelCase )
			processor.add_examples(_UpperCAmelCase						,		labels=_UpperCAmelCase )
			return processor


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[int]						,		_UpperCAmelCase    :	List[Any]						,		_UpperCAmelCase    :	Dict=""						,		_UpperCAmelCase    :	Union[str, Any]=0						,		_UpperCAmelCase    :	List[Any]=1						,		_UpperCAmelCase    :	int=None						,		_UpperCAmelCase    :	List[str]=False						,		_UpperCAmelCase    :	Any=False						,		_UpperCAmelCase    :	Any=False						,		):






			"""simple docstring"""






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

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


		def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any]						,		_UpperCAmelCase    :	List[Any]						,		_UpperCAmelCase    :	Tuple=None						,		_UpperCAmelCase    :	Optional[Any]=None						,		_UpperCAmelCase    :	Dict=False						,		_UpperCAmelCase    :	List[str]=False ):






			"""simple docstring"""






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

			# Update examples
			if overwrite_examples:
				UpperCAmelCase__										=    examples
			else:
				self.examples.extend(_UpperCAmelCase )

			# Update labels
			if overwrite_labels:
				UpperCAmelCase__										=    list(_UpperCAmelCase )
			else:
				UpperCAmelCase__										=    list(set(self.labels ).union(_UpperCAmelCase ) )

			return self.examples



		def 							SCREAMING_SNAKE_CASE__			(						self    :	List[Any]						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	Any=None						,		_UpperCAmelCase    :	List[str]=False						,		_UpperCAmelCase    :	Tuple=0						,		_UpperCAmelCase    :	List[Any]=True						,		_UpperCAmelCase    :	Dict=None						,		):






			"""simple docstring"""






			if max_length is None:
				UpperCAmelCase__										=    tokenizer.max_len

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

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

				UpperCAmelCase__										=    tokenizer.encode(
				    example.text_a						,		add_special_tokens=_UpperCAmelCase						,		max_length=min(_UpperCAmelCase						,		tokenizer.max_len )						,		)
				all_input_ids.append(_UpperCAmelCase )

			UpperCAmelCase__										=    max(len(_UpperCAmelCase ) for input_ids in all_input_ids )

			UpperCAmelCase__										=    []
			for ex_index, (input_ids, example) in enumerate(zip(_UpperCAmelCase						,		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.
				UpperCAmelCase__										=    [1 if mask_padding_with_zero else 0] * len(_UpperCAmelCase )

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

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

				if self.mode == "classification":
					UpperCAmelCase__										=    label_map[example.label]
				elif self.mode == "regression":
					UpperCAmelCase__										=    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(_UpperCAmelCase ) for x in input_ids] )}''' )
					logger.info(f'''attention_mask: {' '.join([str(_UpperCAmelCase ) for x in attention_mask] )}''' )
					logger.info(f'''label: {example.label} (id = {label})''' )

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

			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)

				UpperCAmelCase__										=    tf.data.Dataset.from_generator(
				    _UpperCAmelCase						,		({"""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

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

				UpperCAmelCase__										=    TensorDataset(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase )
				return dataset
			else:
				raise ValueError("""return_tensors should be one of 'tf' or 'pt'""" )






'''simple docstring'''




from collections import namedtuple

UpperCAmelCase_       				=      namedtuple('from_to', 'from_ to')

UpperCAmelCase_       				=      {
    'millimeter': 'mm',
    'centimeter': 'cm',
    'meter': 'm',
    'kilometer': 'km',
    'inch': 'in',
    'inche': 'in',  # Trailing 's' has been stripped off
    'feet': 'ft',
    'foot': 'ft',
    'yard': 'yd',
    'mile': 'mi',
}

UpperCAmelCase_       				=      {
    'mm': from_to(0.001, 1_0_0_0),
    'cm': from_to(0.01, 1_0_0),
    'm': from_to(1, 1),
    'km': from_to(1_0_0_0, 0.001),
    'in': from_to(0.0254, 39.3701),
    'ft': from_to(0.3048, 3.2_8084),
    'yd': from_to(0.9144, 1.0_9361),
    'mi': from_to(1609.34, 0.0_0062_1371),
}

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      float       ,						SCREAMING_SNAKE_CASE__			:      str       ,						SCREAMING_SNAKE_CASE__			:      str       ):
 '''simple docstring'''





 UpperCAmelCase__										=    from_type.lower().rstrip("""s"""       )
 UpperCAmelCase__										=    TYPE_CONVERSION.get(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       )
 UpperCAmelCase__										=    to_type.lower().rstrip("""s"""       )
 UpperCAmelCase__										=    TYPE_CONVERSION.get(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       )
 if new_from not in METRIC_CONVERSION:
  UpperCAmelCase__										=    (
      F'''Invalid \'from_type\' value: {from_type!r}.\n'''
      F'''Conversion abbreviations are: {', '.join(SCREAMING_SNAKE_CASE__       )}'''
  )
  raise ValueError(SCREAMING_SNAKE_CASE__       )
 if new_to not in METRIC_CONVERSION:
  UpperCAmelCase__										=    (
      F'''Invalid \'to_type\' value: {to_type!r}.\n'''
      F'''Conversion abbreviations are: {', '.join(SCREAMING_SNAKE_CASE__       )}'''
  )
  raise ValueError(SCREAMING_SNAKE_CASE__       )
 return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
    import doctest

    doctest.testmod()
