


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			: Optional[Any]				   =					logging.getLogger(__name__)






@dataclass
class _lowerCamelCase:
       lowercase_					:      str
       lowercase_					:      List[str]
       lowercase_					:      Optional[List[str]]






@dataclass
class _lowerCamelCase:
       lowercase_					:      List[int]
       lowercase_					:      List[int]
       lowercase_					:      Optional[List[int]]       					=     None
       lowercase_					:      Optional[List[int]]       					=     None






class _lowerCamelCase(		_a  ):
       lowercase_					:      List[str]       					=     """train"""
       lowercase_					:      List[Any]       					=     """dev"""
       lowercase_					:      List[str]       					=     """test"""






class _lowerCamelCase:
       @staticmethod
       def      UpperCamelCase	(   lowerCamelCase, lowerCamelCase)			->  List[InputExample]:




           """simple docstring"""

           raise NotImplementedError
       @staticmethod
       def      UpperCamelCase	(   lowerCamelCase)			->  List[str]:




           """simple docstring"""

           raise NotImplementedError




       @staticmethod
       def      UpperCamelCase	(   lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase=False, lowerCamelCase="[CLS]", lowerCamelCase=1, lowerCamelCase="[SEP]", lowerCamelCase=False, lowerCamelCase=False, lowerCamelCase=0, lowerCamelCase=0, lowerCamelCase=-1_00, lowerCamelCase=0, lowerCamelCase=True, )			->  List[InputFeatures]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						{label: i for i, label in enumerate(lowerCamelCase)}

           _lowercase			:				Union[str, Any] 						=						[]
           for ex_index, example in enumerate(lowerCamelCase):
               if ex_index % 1_00_00 == 0:
                   logger.info('Writing example %d of %d', lowerCamelCase, len(lowerCamelCase))

               _lowercase			:				str 						=						[]
               _lowercase			:				Tuple 						=						[]
               for word, label in zip(example.words, example.labels):
                   _lowercase			:				Dict 						=						tokenizer.tokenize(lowerCamelCase)

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

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
               _lowercase			:				int 						=						tokenizer.num_special_tokens_to_add()
               if len(lowerCamelCase) > max_seq_length - special_tokens_count:
                   _lowercase			:				Optional[int] 						=						tokens[: (max_seq_length - special_tokens_count)]
                   _lowercase			:				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]
               _lowercase			:				List[str] 						=						[sequence_a_segment_id] * len(lowerCamelCase)

               if cls_token_at_end:
                   tokens += [cls_token]
                   label_ids += [pad_token_label_id]
                   segment_ids += [cls_token_segment_id]
               else:
                   _lowercase			:				Union[str, Any] 						=						[cls_token] + tokens
                   _lowercase			:				Any 						=						[pad_token_label_id] + label_ids
                   _lowercase			:				List[str] 						=						[cls_token_segment_id] + segment_ids

               _lowercase			:				Dict 						=						tokenizer.convert_tokens_to_ids(lowerCamelCase)

               # The mask has 1 for real tokens and 0 for padding tokens. Only real
               # tokens are attended to.
               _lowercase			:				Optional[int] 						=						[1 if mask_padding_with_zero else 0] * len(lowerCamelCase)

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

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

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

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


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






 class _lowerCamelCase(		_a  ):
        lowercase_					:      List[InputFeatures]
        lowercase_					:      int       					=     nn.CrossEntropyLoss().ignore_index
        def __init__(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, lowerCamelCase=False, lowerCamelCase = Split.train, )			->  int:




            """simple docstring"""

            _lowercase			:				List[str] 						=						os.path.join(
                lowerCamelCase, 'cached_{}_{}_{}'.format(mode.value, tokenizer.__class__.__name__, str(lowerCamelCase)), )

            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.
            _lowercase			:				List[Any] 						=						cached_features_file + '.lock'
            with FileLock(lowerCamelCase):
                if os.path.exists(lowerCamelCase) and not overwrite_cache:
                    logger.info(F'''Loading features from cached file {cached_features_file}''')
                    _lowercase			:				str 						=						torch.load(lowerCamelCase)
                else:
                    logger.info(F'''Creating features from dataset file at {data_dir}''')
                    _lowercase			:				Optional[int] 						=						token_classification_task.read_examples_from_file(lowerCamelCase, lowerCamelCase)
                    # TODO clean up all this to leverage built-in features of tokenizers
                    _lowercase			:				Optional[int] 						=						token_classification_task.convert_examples_to_features(
                        lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, 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=lowerCamelCase, 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, lowerCamelCase)
        def __len__(   self)			->  int:




            """simple docstring"""

            return len(self.features)




        def __getitem__(   self, lowerCamelCase)			->  InputFeatures:




            """simple docstring"""

            return self.features[i]


if is_tf_available():
 import tensorflow as tf






 class _lowerCamelCase:
        lowercase_					:      List[InputFeatures]
        lowercase_					:      int       					=     -1_00
        def __init__(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, lowerCamelCase=False, lowerCamelCase = Split.train, )			->  Optional[Any]:




            """simple docstring"""

            _lowercase			:				List[str] 						=						token_classification_task.read_examples_from_file(lowerCamelCase, lowerCamelCase)
            # TODO clean up all this to leverage built-in features of tokenizers
            _lowercase			:				int 						=						token_classification_task.convert_examples_to_features(
                lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase, 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=lowerCamelCase, 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			:				Optional[Any] 						=						tf.data.Dataset.from_generator(
                    lowerCamelCase, ({'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			:				Dict 						=						tf.data.Dataset.from_generator(
                    lowerCamelCase, ({'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)			->  int:




            """simple docstring"""

            _lowercase			:				Optional[Any] 						=						self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))

            return self.dataset
        def __len__(   self)			->  Any:




            """simple docstring"""

            return len(self.features)




        def __getitem__(   self, lowerCamelCase)			->  InputFeatures:




            """simple docstring"""

            return self.features[i]





'''simple docstring'''




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : Union[str, Any]    ):
  # "extended trapezoidal rule"
  # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
  UpperCAmelCase    				=						(boundary[1] - boundary[0]) / steps
  UpperCAmelCase    				=						boundary[0]
  UpperCAmelCase    				=						boundary[1]
  UpperCAmelCase    				=						make_points(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						0.0
  y += (h / 2.0) * f(SCREAMING_SNAKE_CASE    )
  for i in x_i:
    # print(i)
    y += h * f(SCREAMING_SNAKE_CASE    )
  y += (h / 2.0) * f(SCREAMING_SNAKE_CASE    )
  return y


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Tuple							, SCREAMING_SNAKE_CASE   : Dict							, SCREAMING_SNAKE_CASE   : Tuple    ):
  UpperCAmelCase    				=						a + h
  while x < (b - h):
    yield x
    UpperCAmelCase    				=						x + h


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[Any]    ):  # enter your function here
  UpperCAmelCase    				=						(x - 0) * (x - 0)
  return y


def    lowerCamelCase__		(    ):
  UpperCAmelCase    				=						0.0  # Lower bound of integration
  UpperCAmelCase    				=						1.0  # Upper bound of integration
  UpperCAmelCase    				=						10.0  # define number of steps or resolution
  UpperCAmelCase    				=						[a, b]  # define boundary of integration
  UpperCAmelCase    				=						method_a(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  print(f'''y = {y}'''    )


if __name__ == "__main__":
  main()


