
'''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


UpperCamelCase__      :		List[str]       							=  logging.getLogger(__name__)

@dataclass
class    _a				:





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  42
       SCREAMING_SNAKE_CASE      =  42
       SCREAMING_SNAKE_CASE      =  42






@dataclass
class    _a				:





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  42
       SCREAMING_SNAKE_CASE      =  42
       SCREAMING_SNAKE_CASE      =  None
       SCREAMING_SNAKE_CASE      =  None






class    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  'train'
       SCREAMING_SNAKE_CASE      =  'dev'
       SCREAMING_SNAKE_CASE      =  'test'






class    _a				:





       """simple docstring"""




       @staticmethod
       def        UpperCamelCase				(							A__       ,							A__      )    ->			List[InputExample]:
          raise NotImplementedError






       @staticmethod
       def        UpperCamelCase				(							A__      )    ->			List[str]:
          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__=-1_00       ,							A__=0       ,							A__=True       ,							)    ->			List[InputFeatures]:
          _SCREAMING_SNAKE_CASE      =				{label: i for i, label in enumerate(A__      )}

          _SCREAMING_SNAKE_CASE      =				[]
          for ex_index, example in enumerate(A__      ):
             if ex_index % 1_00_00 == 0:
                logger.info("""Writing example %d of %d"""       ,							A__       ,							len(A__      )      )

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

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

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

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

             _SCREAMING_SNAKE_CASE      =				tokenizer.convert_tokens_to_ids(A__      )

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

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

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

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

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


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

     class    _a				(_lowerCamelCase):





            """simple docstring"""




            SCREAMING_SNAKE_CASE      =  42
            SCREAMING_SNAKE_CASE      =  nn.CrossEntropyLoss().ignore_index






            def __init__(							self       ,							A__       ,							A__       ,							A__       ,							A__       ,							A__       ,							A__ = None       ,							A__=False       ,							A__ = Split.train       ,							)    ->			List[Any]:
               # Load data features from cache or dataset file
               _SCREAMING_SNAKE_CASE      =				os.path.join(
                   A__       ,							"""cached_{}_{}_{}""".format(mode.value       ,							tokenizer.__class__.__name__       ,							str(A__      )      )       ,							)

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






            def __len__(							self      )    ->			str:
               return len(self.features      )






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


if is_tf_available():
     import tensorflow as tf

     class    _a				:





            """simple docstring"""




            SCREAMING_SNAKE_CASE      =  42
            SCREAMING_SNAKE_CASE      =  -1_00






            def __init__(							self       ,							A__       ,							A__       ,							A__       ,							A__       ,							A__       ,							A__ = None       ,							A__=False       ,							A__ = Split.train       ,							)    ->			Tuple:
               _SCREAMING_SNAKE_CASE      =				token_classification_task.read_examples_from_file(A__       ,							A__      )
               # TODO clean up all this to leverage built-in features of tokenizers
               _SCREAMING_SNAKE_CASE      =				token_classification_task.convert_examples_to_features(
                   A__       ,							A__       ,							A__       ,							A__       ,							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=A__       ,							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:
                  _SCREAMING_SNAKE_CASE      =				tf.data.Dataset.from_generator(
                      A__       ,							({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)       ,							(
                          {"""input_ids""": tf.TensorShape([None]      ), """attention_mask""": tf.TensorShape([None]      )},
                          tf.TensorShape([None]      ),
                      )       ,							)
               else:
                  _SCREAMING_SNAKE_CASE      =				tf.data.Dataset.from_generator(
                      A__       ,							({"""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      )    ->			Any:
               _SCREAMING_SNAKE_CASE      =				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       ,							A__      )    ->			InputFeatures:
               return self.features[i]








'''simple docstring'''



import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def    lowerCAmelCase_	(				)     ->     List[Any]:




   """simple docstring"""
   with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT						):
      with pytest.raises(SCREAMING_SNAKE_CASE_						):
         requests.request("""GET"""    ,      """https://huggingface.co"""						)
      with pytest.raises(requests.exceptions.ConnectTimeout						):
         requests.request("""GET"""    ,      """https://huggingface.co"""    ,      timeout=1.0						)
@pytest.mark.integration
def    lowerCAmelCase_	(				)     ->     int:




   """simple docstring"""
   with offline(OfflineSimulationMode.CONNECTION_FAILS						):
      with pytest.raises(requests.exceptions.ConnectionError						):
         requests.request("""GET"""    ,      """https://huggingface.co"""						)







def    lowerCAmelCase_	(				)     ->     Optional[Any]:




   """simple docstring"""
   with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1						):
      with pytest.raises(SCREAMING_SNAKE_CASE_						):
         http_head("""https://huggingface.co"""						)






