





'''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


_a					: Dict      =    logging.get_logger(__name__)





@dataclass
class     _UpperCAmelCase							:
		a   :   str
		a   :   str
		a   :   Optional[str]     =None
		a   :   Optional[str]     =None
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

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





@dataclass(frozen=lowerCAmelCase_   )
class     _UpperCAmelCase							:
		a   :   List[int]
		a   :   Optional[List[int]]     =None
		a   :   Optional[List[int]]     =None
		a   :   Optional[Union[int, float]]     =None
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

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





class     _UpperCAmelCase							:
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					raise NotImplementedError()
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					raise NotImplementedError()
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					raise NotImplementedError()
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					raise NotImplementedError()
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					raise NotImplementedError()
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

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







		@classmethod
		def   lowerCamelCase__			(  cls,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None			):



					'''simple docstring'''

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





class     _UpperCAmelCase							(						lowerCAmelCase_   ):
		def __init__(  self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="classification",__SCREAMING_SNAKE_CASE=False			):



					'''simple docstring'''

					__lowerCAmelCase											=				[] if labels is None else labels
					__lowerCAmelCase											=				[] if examples is None else examples
					__lowerCAmelCase											=				mode
					__lowerCAmelCase											=				verbose
		def __len__(  self			):



					'''simple docstring'''

					return len(self.examples			)
		def __getitem__(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):
								return SingleSentenceClassificationProcessor(labels=self.labels,examples=self.examples[idx]			)
					return self.examples[idx]
		@classmethod
		def   lowerCamelCase__			(  cls,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE="",__SCREAMING_SNAKE_CASE=0,__SCREAMING_SNAKE_CASE=1,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=False,**__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				cls(**__SCREAMING_SNAKE_CASE			)
					processor.add_examples_from_csv(
					    __SCREAMING_SNAKE_CASE,split_name=__SCREAMING_SNAKE_CASE,column_label=__SCREAMING_SNAKE_CASE,column_text=__SCREAMING_SNAKE_CASE,column_id=__SCREAMING_SNAKE_CASE,skip_first_row=__SCREAMING_SNAKE_CASE,overwrite_labels=__SCREAMING_SNAKE_CASE,overwrite_examples=__SCREAMING_SNAKE_CASE,)
					return processor
		@classmethod
		def   lowerCamelCase__			(  cls,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				cls(**__SCREAMING_SNAKE_CASE			)
					processor.add_examples(__SCREAMING_SNAKE_CASE,labels=__SCREAMING_SNAKE_CASE			)
					return processor
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE="",__SCREAMING_SNAKE_CASE=0,__SCREAMING_SNAKE_CASE=1,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=False,):



					'''simple docstring'''

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

					return self.add_examples(
					    __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,overwrite_labels=__SCREAMING_SNAKE_CASE,overwrite_examples=__SCREAMING_SNAKE_CASE			)
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=False			):



					'''simple docstring'''

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

					# Update examples
					if overwrite_examples:
								__lowerCAmelCase											=				examples
					else:
								self.examples.extend(__SCREAMING_SNAKE_CASE			)

					# Update labels
					if overwrite_labels:
								__lowerCAmelCase											=				list(__SCREAMING_SNAKE_CASE			)
					else:
								__lowerCAmelCase											=				list(set(self.labels			).union(__SCREAMING_SNAKE_CASE			)			)

					return self.examples







		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=0,__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=None,):



					'''simple docstring'''

					if max_length is None:
								__lowerCAmelCase											=				tokenizer.max_len

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

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

								__lowerCAmelCase											=				tokenizer.encode(
								    example.text_a,add_special_tokens=__SCREAMING_SNAKE_CASE,max_length=min(__SCREAMING_SNAKE_CASE,tokenizer.max_len			),)
								all_input_ids.append(__SCREAMING_SNAKE_CASE			)

					__lowerCAmelCase											=				max(len(__SCREAMING_SNAKE_CASE			) for input_ids in all_input_ids			)

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

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

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

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

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

					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)

								__lowerCAmelCase											=				tf.data.Dataset.from_generator(
								    __SCREAMING_SNAKE_CASE,({"""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

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

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








'''simple docstring'''







from collections import namedtuple

_a					: Any      =    namedtuple("""from_to""", """from_ to""")

_a					: Dict      =    {
    """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""",
}

_a					: Optional[Any]      =    {
    """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, 3.28084),
    """yd""": from_to(0.9144, 1.09361),
    """mi""": from_to(1609.34, 0.000621371),
}





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				from_type.lower().rstrip("""s"""	)
			__lowerCAmelCase											=				TYPE_CONVERSION.get(lowercase					,	lowercase	)
			__lowerCAmelCase											=				to_type.lower().rstrip("""s"""	)
			__lowerCAmelCase											=				TYPE_CONVERSION.get(lowercase					,	lowercase	)
			if new_from not in METRIC_CONVERSION:
						__lowerCAmelCase											=				(
						    f'Invalid \'from_type\' value: {from_type!r}.\n'
						    f'Conversion abbreviations are: {", ".join(lowercase	)}'
						)
						raise ValueError(lowercase	)
			if new_to not in METRIC_CONVERSION:
						__lowerCAmelCase											=				(
						    f'Invalid \'to_type\' value: {to_type!r}.\n'
						    f'Conversion abbreviations are: {", ".join(lowercase	)}'
						)
						raise ValueError(lowercase	)
			return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
	import doctest

	doctest.testmod()

