






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__   :      Union[str, Any]				=		logging.getLogger(__name__)
@dataclass
class     lowerCamelCase_					:
				a__       							=	42
				a__       							=	42
				a__       							=	42







@dataclass
class     lowerCamelCase_					:
				a__       							=	42
				a__       							=	42
				a__       							=	None
				a__       							=	None







class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	'''train'''
				a__       							=	'''dev'''
				a__       							=	'''test'''







class     lowerCamelCase_					:







				@staticmethod
				def       A						(    __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								raise NotImplementedError







				@staticmethod
				def       A						(    __lowerCAmelCase  ):
								"""simple docstring"""
								raise NotImplementedError







				@staticmethod
				def       A						(    __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase=False							,       __lowerCAmelCase="[CLS]"							,       __lowerCAmelCase=1							,       __lowerCAmelCase="[SEP]"							,       __lowerCAmelCase=False							,       __lowerCAmelCase=False							,       __lowerCAmelCase=0							,       __lowerCAmelCase=0							,       __lowerCAmelCase=-1_0_0							,       __lowerCAmelCase=0							,       __lowerCAmelCase=True							,       ):
								"""simple docstring"""
								__magic_name__		:str   =   {label: i for i, label in enumerate(__lowerCAmelCase  )}

								__magic_name__		:int   =   []
								for ex_index, example in enumerate(__lowerCAmelCase  ):
												if ex_index % 1_0_0_0_0 == 0:
																logger.info('''Writing example %d of %d'''							,       __lowerCAmelCase							,       len(__lowerCAmelCase  )  )

												__magic_name__		:Tuple   =   []
												__magic_name__		:str   =   []
												for word, label in zip(example.words							,       example.labels  ):
																__magic_name__		:Tuple   =   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.
												__magic_name__		:List[Any]   =   tokenizer.num_special_tokens_to_add()
												if len(__lowerCAmelCase  ) > max_seq_length - special_tokens_count:
																__magic_name__		:Optional[int]   =   tokens[: (max_seq_length - special_tokens_count)]
																__magic_name__		:Tuple   =   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]
												__magic_name__		:Dict   =   [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:
																__magic_name__		:Tuple   =   [cls_token] + tokens
																__magic_name__		:Any   =   [pad_token_label_id] + label_ids
																__magic_name__		:Optional[int]   =   [cls_token_segment_id] + segment_ids

												__magic_name__		:int   =   tokenizer.convert_tokens_to_ids(__lowerCAmelCase  )

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

												# Zero-pad up to the sequence length.
												__magic_name__		:Any   =   max_seq_length - len(__lowerCAmelCase  )
												if pad_on_left:
																__magic_name__		:Optional[Any]   =   ([pad_token] * padding_length) + input_ids
																__magic_name__		:Optional[Any]   =   ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																__magic_name__		:Optional[int]   =   ([pad_token_segment_id] * padding_length) + segment_ids
																__magic_name__		:Union[str, 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(__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:
																__magic_name__		:Tuple   =   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_					(   lowerCamelCase			):
									a__       							=	42
									a__       							=	nn.CrossEntropyLoss().ignore_index







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







									def __getitem__(    self							,       __lowerCAmelCase  ):
													"""simple docstring"""
													return self.features[i]


if is_tf_available():
					import tensorflow as tf
					class     lowerCamelCase_					:
									a__       							=	42
									a__       							=	-1_00







									def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = None							,       __lowerCAmelCase=False							,       __lowerCAmelCase = Split.train							,       ):
													"""simple docstring"""
													__magic_name__		:Union[str, Any]   =   token_classification_task.read_examples_from_file(__lowerCAmelCase							,       __lowerCAmelCase  )
													# TODO clean up all this to leverage built-in features of tokenizers
													__magic_name__		:Tuple   =   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:
																	__magic_name__		: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:
																	__magic_name__		:Optional[int]   =   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       A						(    self  ):
													"""simple docstring"""
													__magic_name__		:Dict   =   self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

													return self.dataset







									def __len__(    self  ):
													"""simple docstring"""
													return len(self.features  )







									def __getitem__(    self							,       __lowerCAmelCase  ):
													"""simple docstring"""
													return self.features[i]














import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline

@pytest.mark.integration
def   __lowercase       (			):







				"""simple docstring"""






				with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT			):
								with pytest.raises(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   __lowercase       (			):







				"""simple docstring"""






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

def   __lowercase       (			):







				"""simple docstring"""






				with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1			):
								with pytest.raises(snake_case			):
												http_head('''https://huggingface.co'''			)






