



'''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


SCREAMING_SNAKE_CASE__          =  logging.getLogger(__name__)


@dataclass
class 					snake_case			:
       lowerCAmelCase__	:str
       lowerCAmelCase__	:List[str]
       lowerCAmelCase__	:Optional[List[str]]





@dataclass
class 					snake_case			:
       lowerCAmelCase__	:List[int]
       lowerCAmelCase__	:List[int]
       lowerCAmelCase__	:Optional[List[int]]     =       None
       lowerCAmelCase__	:Optional[List[int]]     =       None





class 					snake_case			(UpperCamelCase ):
       lowerCAmelCase__	:Any     =       "train"
       lowerCAmelCase__	:Optional[Any]     =       "dev"
       lowerCAmelCase__	:str     =       "test"





class 					snake_case			:



       @staticmethod
       def 				_a   (					UpperCAmelCase_  ,UpperCAmelCase_   )	->				List[InputExample]:
           raise NotImplementedError



       @staticmethod
       def 				_a   (					UpperCAmelCase_   )	->				List[str]:
           raise NotImplementedError







       @staticmethod
       def 				_a   (					UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_=False  ,UpperCAmelCase_="[CLS]"  ,UpperCAmelCase_=1  ,UpperCAmelCase_="[SEP]"  ,UpperCAmelCase_=False  ,UpperCAmelCase_=False  ,UpperCAmelCase_=0  ,UpperCAmelCase_=0  ,UpperCAmelCase_=-100  ,UpperCAmelCase_=0  ,UpperCAmelCase_=True  ,)	->				List[InputFeatures]:
           lowercase__									=    {label: i for i, label in enumerate(UpperCAmelCase_   )}

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

               lowercase__									=    []
               lowercase__									=    []
               for word, label in zip(example.words  ,example.labels   ):
                   lowercase__									=    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.
               lowercase__									=    tokenizer.num_special_tokens_to_add()
               if len(UpperCAmelCase_   ) > max_seq_length - special_tokens_count:
                   lowercase__									=    tokens[: (max_seq_length - special_tokens_count)]
                   lowercase__									=    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]
               lowercase__									=    [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:
                   lowercase__									=    [cls_token] + tokens
                   lowercase__									=    [pad_token_label_id] + label_ids
                   lowercase__									=    [cls_token_segment_id] + segment_ids

               lowercase__									=    tokenizer.convert_tokens_to_ids(UpperCAmelCase_   )

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

               # Zero-pad up to the sequence length.
               lowercase__									=    max_seq_length - len(UpperCAmelCase_   )
               if pad_on_left:
                   lowercase__									=    ([pad_token] * padding_length) + input_ids
                   lowercase__									=    ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                   lowercase__									=    ([pad_token_segment_id] * padding_length) + segment_ids
                   lowercase__									=    ([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:
                   lowercase__									=    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 					snake_case			(UpperCamelCase ):
        lowerCAmelCase__	:List[InputFeatures]
        lowerCAmelCase__	:int     =       nn.CrossEntropyLoss().ignore_index



        def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_ = None  ,UpperCAmelCase_=False  ,UpperCAmelCase_ = Split.train  ,)	->				str:
            # Load data features from cache or dataset file
            lowercase__									=    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.
            lowercase__									=    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}'''   )
                    lowercase__									=    torch.load(UpperCAmelCase_   )
                else:
                    logger.info(F'''Creating features from dataset file at {data_dir}'''   )
                    lowercase__									=    token_classification_task.read_examples_from_file(UpperCAmelCase_  ,UpperCAmelCase_   )
                    # TODO clean up all this to leverage built-in features of tokenizers
                    lowercase__									=    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   )	->				Optional[Any]:
            return len(self.features   )







        def __getitem__(					self  ,UpperCAmelCase_   )	->				InputFeatures:
            return self.features[i]


if is_tf_available():
 import tensorflow as tf


 class 					snake_case			:
        lowerCAmelCase__	:List[InputFeatures]
        lowerCAmelCase__	:int     =       -100



        def __init__(					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_ = None  ,UpperCAmelCase_=False  ,UpperCAmelCase_ = Split.train  ,)	->				List[Any]:
            lowercase__									=    token_classification_task.read_examples_from_file(UpperCAmelCase_  ,UpperCAmelCase_   )
            # TODO clean up all this to leverage built-in features of tokenizers
            lowercase__									=    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:
                lowercase__									=    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:
                lowercase__									=    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 				_a   (					self   )	->				Union[str, Any]:
            lowercase__									=    self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features   )   )   )

            return self.dataset



        def __len__(					self   )	->				Optional[Any]:
            return len(self.features   )







        def __getitem__(					self  ,UpperCAmelCase_   )	->				InputFeatures:
            return self.features[i]









'''simple docstring'''





def 							lowerCamelCase	(					_snake_case	:    list							):





    '''simple docstring'''


    if not isinstance(_snake_case  ,_snake_case							):
        raise ValueError("Input series is not valid, valid series - [2, 4, 6]"							)
    if len(_snake_case							) == 0:
        raise ValueError("Input list must be a non empty list"							)
    if len(_snake_case							) == 1:
        return True
    lowercase__									=    series[1] - series[0]
    for index in range(len(_snake_case							) - 1							):
        if series[index + 1] - series[index] != common_diff:
            return False
    return True




def 							lowerCamelCase	(					_snake_case	:    list							):





    '''simple docstring'''


    if not isinstance(_snake_case  ,_snake_case							):
        raise ValueError("Input series is not valid, valid series - [2, 4, 6]"							)
    if len(_snake_case							) == 0:
        raise ValueError("Input list must be a non empty list"							)
    lowercase__									=    0
    for val in series:
        answer += val
    return answer / len(_snake_case							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




