






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

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

												__magic_name__		:List[Any]        =      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__		:Any        =      [1 if mask_padding_with_zero else 0] * len(__lowerCAmelCase     )

												# Zero-pad up to the sequence length.
												__magic_name__		:Dict        =      max_seq_length - len(__lowerCAmelCase     )
												if pad_on_left:
																__magic_name__		:Optional[Any]        =      ([pad_token] * padding_length) + input_ids
																__magic_name__		:Optional[int]        =      ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																__magic_name__		:Tuple        =      ([pad_token_segment_id] * padding_length) + segment_ids
																__magic_name__		: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__		:List[str]        =      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__		:Union[str, 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__		:Tuple        =      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__		:Any        =      torch.load(__lowerCAmelCase     )
																	else:
																					logger.info(F'''Creating features from dataset file at {data_dir}'''     )
																					__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       ,					)
																					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__		:Tuple        =      token_classification_task.read_examples_from_file(__lowerCAmelCase       ,					__lowerCAmelCase     )
													# TODO clean up all this to leverage built-in features of tokenizers
													__magic_name__		:List[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       ,					)

													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__		:Union[str, 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[Any]        =      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__		:Union[str, Any]        =      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]














from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url

@pytest.mark.parametrize('''repo_id''',  ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''',  ['''filename.csv''', '''filename with blanks.csv'''] )
@pytest.mark.parametrize('''revision''',  [None, '''v2'''] )
def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Tuple        =      hf_hub_url(repo_id=snake_case,  path=snake_case,  revision=snake_case )
				assert url == f'''https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(snake_case )}'''






