


"""simple docstring"""




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 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]										=				4_2
		__UpperCAmelCase		:			Tuple										=				4_2
		__UpperCAmelCase		:			str										=				4_2







@dataclass
class 					__lowerCAmelCase					:


		'''simple docstring'''



		__UpperCAmelCase		:			Union[str, Any]										=				4_2
		__UpperCAmelCase		:			Any										=				4_2
		__UpperCAmelCase		:			List[str]										=				None
		__UpperCAmelCase		:			Dict										=				None







class 					__lowerCAmelCase					(   lowercase_       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Union[str, Any]										=				'train'
		__UpperCAmelCase		:			Optional[int]										=				'dev'
		__UpperCAmelCase		:			int										=				'test'







class 					__lowerCAmelCase					:


		'''simple docstring'''



		@staticmethod
		def 			__UpperCAmelCase       (  _a							, _a      ):
				raise NotImplementedError

		@staticmethod
		def 			__UpperCAmelCase       (  _a      ):
				raise NotImplementedError





		@staticmethod
		def 			__UpperCAmelCase       (  _a							, _a							, _a							, _a							, _a=False							, _a="[CLS]"							, _a=1							, _a="[SEP]"							, _a=False							, _a=False							, _a=0							, _a=0							, _a=-100							, _a=0							, _a=True							, ):
				__a           =			{label: i for i, label in enumerate(__UpperCamelCase      )}

				__a           =			[]
				for ex_index, example in enumerate(__UpperCamelCase      ):
						if ex_index % 10_000 == 0:
								logger.info('''Writing example %d of %d'''							, __UpperCamelCase							, len(__UpperCamelCase      )      )

						__a           =			[]
						__a           =			[]
						for word, label in zip(example.words							, example.labels      ):
								__a           =			tokenizer.tokenize(__UpperCamelCase      )

								# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
								if len(__UpperCamelCase      ) > 0:
										tokens.extend(__UpperCamelCase      )
										# 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(__UpperCamelCase      ) - 1)      )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
						__a           =			tokenizer.num_special_tokens_to_add()
						if len(__UpperCamelCase      ) > max_seq_length - special_tokens_count:
								__a           =			tokens[: (max_seq_length - special_tokens_count)]
								__a           =			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]
						__a           =			[sequence_a_segment_id] * len(__UpperCamelCase      )

						if cls_token_at_end:
								tokens += [cls_token]
								label_ids += [pad_token_label_id]
								segment_ids += [cls_token_segment_id]
						else:
								__a           =			[cls_token] + tokens
								__a           =			[pad_token_label_id] + label_ids
								__a           =			[cls_token_segment_id] + segment_ids

						__a           =			tokenizer.convert_tokens_to_ids(__UpperCamelCase      )

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

						# Zero-pad up to the sequence length.
						__a           =			max_seq_length - len(__UpperCamelCase      )
						if pad_on_left:
								__a           =			([pad_token] * padding_length) + input_ids
								__a           =			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
								__a           =			([pad_token_segment_id] * padding_length) + segment_ids
								__a           =			([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(__UpperCamelCase      ) == max_seq_length
						assert len(__UpperCamelCase      ) == max_seq_length
						assert len(__UpperCamelCase      ) == max_seq_length
						assert len(__UpperCamelCase      ) == max_seq_length

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

						if "token_type_ids" not in tokenizer.model_input_names:
								__a           =			None

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


if is_torch_available():
				import torch
				from torch import nn
				from torch.utils.data import Dataset
				class 					__lowerCAmelCase					(   lowercase_       ):


						'''simple docstring'''



						__UpperCAmelCase		:			Tuple										=				4_2
						__UpperCAmelCase		:			Tuple										=				nn.CrossEntropyLoss().ignore_index

						def __init__(  self							, _a							, _a							, _a							, _a							, _a							, _a = None							, _a=False							, _a = Split.train							, ):
								__a           =			os.path.join(
								    __UpperCamelCase							, '''cached_{}_{}_{}'''.format(mode.value							, tokenizer.__class__.__name__							, str(__UpperCamelCase      )      )							, )

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								__a           =			cached_features_file + '.lock'
								with FileLock(__UpperCamelCase      ):
										if os.path.exists(__UpperCamelCase      ) and not overwrite_cache:
												logger.info(f'''Loading features from cached file {cached_features_file}'''      )
												__a           =			torch.load(__UpperCamelCase      )
										else:
												logger.info(f'''Creating features from dataset file at {data_dir}'''      )
												__a           =			token_classification_task.read_examples_from_file(__UpperCamelCase							, __UpperCamelCase      )
												# TODO clean up all this to leverage built-in features of tokenizers
												__a           =			token_classification_task.convert_examples_to_features(
												    __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, 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=__UpperCamelCase							, 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							, __UpperCamelCase      )

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





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


if is_tf_available():
				import tensorflow as tf
				class 					__lowerCAmelCase					:


						'''simple docstring'''



						__UpperCAmelCase		:			Dict										=				4_2
						__UpperCAmelCase		:			int										=				-1_0_0

						def __init__(  self							, _a							, _a							, _a							, _a							, _a							, _a = None							, _a=False							, _a = Split.train							, ):
								__a           =			token_classification_task.read_examples_from_file(__UpperCamelCase							, __UpperCamelCase      )
								# TODO clean up all this to leverage built-in features of tokenizers
								__a           =			token_classification_task.convert_examples_to_features(
								    __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, __UpperCamelCase							, 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=__UpperCamelCase							, 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:
										__a           =			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([None]      ),
										    )							, )
								else:
										__a           =			tf.data.Dataset.from_generator(
										    __UpperCamelCase							, ({'''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 			__UpperCAmelCase       (  self      ):
								__a           =			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features      )      )      )

								return self.dataset

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





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







"""simple docstring"""







from __future__ import annotations



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     list      ):
     if not nums:
          raise ValueError('List is empty'      )
     return sum(__SCREAMING_SNAKE_CASE      ) / len(__SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

