



import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_lowercase     							=						logging.getLogger(__name__)
@dataclass
class 				__A					:
			UpperCamelCase					:str
			UpperCamelCase					:List[str]
			UpperCamelCase					:Optional[List[str]]







@dataclass
class 				__A					:
			UpperCamelCase					:List[int]
			UpperCamelCase					:List[int]
			UpperCamelCase					:Optional[List[int]]     =			None
			UpperCamelCase					:Optional[List[int]]     =			None







class 				__A					( A_      ):
			UpperCamelCase					:List[str]     =			'''train'''
			UpperCamelCase					:Dict     =			'''dev'''
			UpperCamelCase					:List[str]     =			'''test'''







class 				__A					:





			@staticmethod
			def        _snake_case    (__magic_name__							,    __magic_name__				):
						raise NotImplementedError





			@staticmethod
			def        _snake_case    (__magic_name__				):
						raise NotImplementedError





			@staticmethod
			def        _snake_case    (__magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__=False							,    __magic_name__="[CLS]"							,    __magic_name__=1							,    __magic_name__="[SEP]"							,    __magic_name__=False							,    __magic_name__=False							,    __magic_name__=0							,    __magic_name__=0							,    __magic_name__=-100							,    __magic_name__=0							,    __magic_name__=True							,    ):
						lowerCamelCase__ :      Tuple								=		{label: i for i, label in enumerate(__magic_name__				)}

						lowerCamelCase__ :      str								=		[]
						for ex_index, example in enumerate(__magic_name__				):
									if ex_index % 10000 == 0:
												logger.info("""Writing example %d of %d"""							,    __magic_name__							,    len(__magic_name__				)				)

									lowerCamelCase__ :      Optional[int]								=		[]
									lowerCamelCase__ :      Optional[Any]								=		[]
									for word, label in zip(example.words							,    example.labels				):
												lowerCamelCase__ :      List[Any]								=		tokenizer.tokenize(__magic_name__				)

												# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
												if len(__magic_name__				) > 0:
															tokens.extend(__magic_name__				)
															# Use the real label id for the first token of the word, and padding ids for the remaining tokens
															label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__magic_name__				) - 1)				)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
									lowerCamelCase__ :      List[str]								=		tokenizer.num_special_tokens_to_add()
									if len(__magic_name__				) > max_seq_length - special_tokens_count:
												lowerCamelCase__ :      Optional[int]								=		tokens[: (max_seq_length - special_tokens_count)]
												lowerCamelCase__ :      List[Any]								=		label_ids[: (max_seq_length - special_tokens_count)]

									# The convention in BERT is:
									# (a) For sequence pairs:
									#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
									#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
									# (b) For single sequences:
									#  tokens:   [CLS] the dog is hairy . [SEP]
									#  type_ids:   0   0   0   0  0     0   0
									#
									# Where "type_ids" are used to indicate whether this is the first
									# sequence or the second sequence. The embedding vectors for `type=0` and
									# `type=1` were learned during pre-training and are added to the wordpiece
									# embedding vector (and position vector). This is not *strictly* necessary
									# since the [SEP] token unambiguously separates the sequences, but it makes
									# it easier for the model to learn the concept of sequences.
									#
									# For classification tasks, the first vector (corresponding to [CLS]) is
									# used as the "sentence vector". Note that this only makes sense because
									# the entire model is fine-tuned.
									tokens += [sep_token]
									label_ids += [pad_token_label_id]
									if sep_token_extra:
												# roberta uses an extra separator b/w pairs of sentences
												tokens += [sep_token]
												label_ids += [pad_token_label_id]
									lowerCamelCase__ :      Union[str, Any]								=		[sequence_a_segment_id] * len(__magic_name__				)

									if cls_token_at_end:
												tokens += [cls_token]
												label_ids += [pad_token_label_id]
												segment_ids += [cls_token_segment_id]
									else:
												lowerCamelCase__ :      str								=		[cls_token] + tokens
												lowerCamelCase__ :      Union[str, Any]								=		[pad_token_label_id] + label_ids
												lowerCamelCase__ :      Optional[int]								=		[cls_token_segment_id] + segment_ids

									lowerCamelCase__ :      Optional[Any]								=		tokenizer.convert_tokens_to_ids(__magic_name__				)

									# The mask has 1 for real tokens and 0 for padding tokens. Only real
									# tokens are attended to.
									lowerCamelCase__ :      Dict								=		[1 if mask_padding_with_zero else 0] * len(__magic_name__				)

									# Zero-pad up to the sequence length.
									lowerCamelCase__ :      Optional[int]								=		max_seq_length - len(__magic_name__				)
									if pad_on_left:
												lowerCamelCase__ :      Dict								=		([pad_token] * padding_length) + input_ids
												lowerCamelCase__ :      List[str]								=		([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
												lowerCamelCase__ :      Tuple								=		([pad_token_segment_id] * padding_length) + segment_ids
												lowerCamelCase__ :      Any								=		([pad_token_label_id] * padding_length) + label_ids
									else:
												input_ids += [pad_token] * padding_length
												input_mask += [0 if mask_padding_with_zero else 1] * padding_length
												segment_ids += [pad_token_segment_id] * padding_length
												label_ids += [pad_token_label_id] * padding_length

									assert len(__magic_name__				) == max_seq_length
									assert len(__magic_name__				) == max_seq_length
									assert len(__magic_name__				) == max_seq_length
									assert len(__magic_name__				) == max_seq_length

									if ex_index < 5:
												logger.info("""*** Example ***"""				)
												logger.info("""guid: %s"""							,    example.guid				)
												logger.info("""tokens: %s"""							,    """ """.join([str(__magic_name__				) for x in tokens]				)				)
												logger.info("""input_ids: %s"""							,    """ """.join([str(__magic_name__				) for x in input_ids]				)				)
												logger.info("""input_mask: %s"""							,    """ """.join([str(__magic_name__				) for x in input_mask]				)				)
												logger.info("""segment_ids: %s"""							,    """ """.join([str(__magic_name__				) for x in segment_ids]				)				)
												logger.info("""label_ids: %s"""							,    """ """.join([str(__magic_name__				) for x in label_ids]				)				)

									if "token_type_ids" not in tokenizer.model_input_names:
												lowerCamelCase__ :      Optional[Any]								=		None

									features.append(
									    InputFeatures(
									        input_ids=__magic_name__							,    attention_mask=__magic_name__							,    token_type_ids=__magic_name__							,    label_ids=__magic_name__				)				)
						return features


if is_torch_available():
				import torch
				from torch import nn
				from torch.utils.data import Dataset
				class 				__A					( A_      ):
							UpperCamelCase					:List[InputFeatures]
							UpperCamelCase					:int     =			nn.CrossEntropyLoss().ignore_index





							def __init__(self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__=False							,    __magic_name__ = Split.train							,    ):
										# Load data features from cache or dataset file
										lowerCamelCase__ :      Tuple								=		os.path.join(
										    __magic_name__							,    """cached_{}_{}_{}""".format(mode.value							,    tokenizer.__class__.__name__							,    str(__magic_name__				)				)							,    )

										# Make sure only the first process in distributed training processes the dataset,
										# and the others will use the cache.
										lowerCamelCase__ :      Any								=		cached_features_file + """.lock"""
										with FileLock(__magic_name__				):
													if os.path.exists(__magic_name__				) and not overwrite_cache:
																logger.info(f"Loading features from cached file {cached_features_file}"				)
																lowerCamelCase__ :      Dict								=		torch.load(__magic_name__				)
													else:
																logger.info(f"Creating features from dataset file at {data_dir}"				)
																lowerCamelCase__ :      Any								=		token_classification_task.read_examples_from_file(__magic_name__							,    __magic_name__				)
																# TODO clean up all this to leverage built-in features of tokenizers
																lowerCamelCase__ :      Optional[int]								=		token_classification_task.convert_examples_to_features(
																    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    cls_token_at_end=bool(model_type in ["""xlnet"""]				)							,    cls_token=tokenizer.cls_token							,    cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0							,    sep_token=tokenizer.sep_token							,    sep_token_extra=__magic_name__							,    pad_on_left=bool(tokenizer.padding_side == """left"""				)							,    pad_token=tokenizer.pad_token_id							,    pad_token_segment_id=tokenizer.pad_token_type_id							,    pad_token_label_id=self.pad_token_label_id							,    )
																logger.info(f"Saving features into cached file {cached_features_file}"				)
																torch.save(self.features							,    __magic_name__				)





							def __len__(self				):
										return len(self.features				)





							def __getitem__(self							,    __magic_name__				):
										return self.features[i]


if is_tf_available():
				import tensorflow as tf
				class 				__A					:
							UpperCamelCase					:List[InputFeatures]
							UpperCamelCase					:int     =			-100





							def __init__(self							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__ = None							,    __magic_name__=False							,    __magic_name__ = Split.train							,    ):
										lowerCamelCase__ :      List[str]								=		token_classification_task.read_examples_from_file(__magic_name__							,    __magic_name__				)
										# TODO clean up all this to leverage built-in features of tokenizers
										lowerCamelCase__ :      Dict								=		token_classification_task.convert_examples_to_features(
										    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__							,    cls_token_at_end=bool(model_type in ["""xlnet"""]				)							,    cls_token=tokenizer.cls_token							,    cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0							,    sep_token=tokenizer.sep_token							,    sep_token_extra=__magic_name__							,    pad_on_left=bool(tokenizer.padding_side == """left"""				)							,    pad_token=tokenizer.pad_token_id							,    pad_token_segment_id=tokenizer.pad_token_type_id							,    pad_token_label_id=self.pad_token_label_id							,    )

										def gen():
													for ex in self.features:
																if ex.token_type_ids is None:
																			yield (
																			    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																			    ex.label_ids,
																			)
																else:
																			yield (
																			    {
																			        "input_ids": ex.input_ids,
																			        "attention_mask": ex.attention_mask,
																			        "token_type_ids": ex.token_type_ids,
																			    },
																			    ex.label_ids,
																			)

										if "token_type_ids" not in tokenizer.model_input_names:
													lowerCamelCase__ :      List[str]								=		tf.data.Dataset.from_generator(
													    __magic_name__							,    ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)							,    (
													        {"""input_ids""": tf.TensorShape([None]				), """attention_mask""": tf.TensorShape([None]				)},
													        tf.TensorShape([None]				),
													    )							,    )
										else:
													lowerCamelCase__ :      List[str]								=		tf.data.Dataset.from_generator(
													    __magic_name__							,    ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)							,    (
													        {
													            """input_ids""": tf.TensorShape([None]				),
													            """attention_mask""": tf.TensorShape([None]				),
													            """token_type_ids""": tf.TensorShape([None]				),
													        },
													        tf.TensorShape([None]				),
													    )							,    )





							def        _snake_case    (self				):
										lowerCamelCase__ :      Dict								=		self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features				)				)				)

										return self.dataset





							def __len__(self				):
										return len(self.features				)





							def __getitem__(self							,    __magic_name__				):
										return self.features[i]








def      _A				(UpperCamelCase		:      int      ,			UpperCamelCase		:      int )  ->int:



			'''simple docstring'''







			while b:
						lowerCamelCase__							,lowerCamelCase__ :      int								=		b, a % b
			return a






def      _A				(UpperCamelCase		:      int      ,			UpperCamelCase		:      int )  ->int:



			'''simple docstring'''







			return a if b == 0 else euclidean_gcd_recursive(UpperCamelCase      ,			a % b )






def      _A				()  ->str:



			'''simple docstring'''







			print(f"euclidean_gcd(3, 5) = {euclidean_gcd(3      ,			5 )}" )
			print(f"euclidean_gcd(5, 3) = {euclidean_gcd(5      ,			3 )}" )
			print(f"euclidean_gcd(1, 3) = {euclidean_gcd(1      ,			3 )}" )
			print(f"euclidean_gcd(3, 6) = {euclidean_gcd(3      ,			6 )}" )
			print(f"euclidean_gcd(6, 3) = {euclidean_gcd(6      ,			3 )}" )

			print(f"euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3      ,			5 )}" )
			print(f"euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5      ,			3 )}" )
			print(f"euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1      ,			3 )}" )
			print(f"euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3      ,			6 )}" )
			print(f"euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6      ,			3 )}" )


if __name__ == "__main__":
				main()



