






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


lowerCamelCase_  :	List[Any] 				=					logging.getLogger(__name__)

@dataclass
class     _UpperCamelCase			:






			'''simple docstring'''

			__UpperCamelCase :  Tuple    =	42
			__UpperCamelCase :  str    =	42
			__UpperCamelCase :  Optional[Any]    =	42




@dataclass
class     _UpperCamelCase			:






			'''simple docstring'''

			__UpperCamelCase :  Optional[int]    =	42
			__UpperCamelCase :  Dict    =	42
			__UpperCamelCase :  Tuple    =	None
			__UpperCamelCase :  Optional[Any]    =	None




class     _UpperCamelCase			(   A_					):






			'''simple docstring'''

			__UpperCamelCase :  int    =	"""train"""
			__UpperCamelCase :  List[Any]    =	"""dev"""
			__UpperCamelCase :  Union[str, Any]    =	"""test"""




class     _UpperCamelCase			:






			'''simple docstring'''

			@staticmethod
			def  lowerCAmelCase__		(    snake_case_	:    Optional[Any]  ,		snake_case_	:    Union[Split, str] ):
							raise NotImplementedError

			@staticmethod
			def  lowerCAmelCase__		(    snake_case_	:    str ):
							raise NotImplementedError




			@staticmethod
			def  lowerCAmelCase__		(    snake_case_	:    List[InputExample]  ,		snake_case_	:    List[str]  ,		snake_case_	:    int  ,		snake_case_	:    PreTrainedTokenizer  ,		snake_case_	:    str=False  ,		snake_case_	:    List[Any]="[CLS]"  ,		snake_case_	:    Optional[int]=1  ,		snake_case_	:    List[str]="[SEP]"  ,		snake_case_	:    int=False  ,		snake_case_	:    List[Any]=False  ,		snake_case_	:    List[Any]=0  ,		snake_case_	:    Dict=0  ,		snake_case_	:    List[str]=-100  ,		snake_case_	:    Optional[Any]=0  ,		snake_case_	:    Any=True  ,		):
							UpperCamelCase_:							Dict		=  {label: i for i, label in enumerate(_lowerCamelCase )}

							UpperCamelCase_:							Union[str, Any]		=  []
							for ex_index, example in enumerate(_lowerCamelCase ):
											if ex_index % 1_0000 == 0:
															logger.info("""Writing example %d of %d"""  ,		_lowerCamelCase  ,		len(_lowerCamelCase ) )

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

											UpperCamelCase_:							Union[str, 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.
											UpperCamelCase_:							Any		=  [1 if mask_padding_with_zero else 0] * len(_lowerCamelCase )

											# Zero-pad up to the sequence length.
											UpperCamelCase_:							Union[str, Any]		=  max_seq_length - len(_lowerCamelCase )
											if pad_on_left:
															UpperCamelCase_:							Any		=  ([pad_token] * padding_length) + input_ids
															UpperCamelCase_:							Dict		=  ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
															UpperCamelCase_:							List[Any]		=  ([pad_token_segment_id] * padding_length) + segment_ids
															UpperCamelCase_:							Optional[int]		=  ([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:
															UpperCamelCase_:							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     _UpperCamelCase			(   A_					):






										'''simple docstring'''

										__UpperCamelCase :  int    =	42
										__UpperCamelCase :  Any    =	nn.CrossEntropyLoss().ignore_index

										def __init__(    self	:    Tuple  ,		snake_case_	:    TokenClassificationTask  ,		snake_case_	:    str  ,		snake_case_	:    PreTrainedTokenizer  ,		snake_case_	:    List[str]  ,		snake_case_	:    str  ,		snake_case_	:    Optional[int] = None  ,		snake_case_	:    Tuple=False  ,		snake_case_	:    Split = Split.train  ,		):
														# Load data features from cache or dataset file
														UpperCamelCase_:							Dict		=  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.
														UpperCamelCase_:							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}''' )
																						UpperCamelCase_:							List[str]		=  torch.load(_lowerCamelCase )
																		else:
																						logger.info(f'''Creating features from dataset file at {data_dir}''' )
																						UpperCamelCase_:							Dict		=  token_classification_task.read_examples_from_file(_lowerCamelCase  ,		_lowerCamelCase )
																						# TODO clean up all this to leverage built-in features of tokenizers
																						UpperCamelCase_:							Optional[Any]		=  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	:    Any ):
														return len(self.features )




										def __getitem__(    self	:    List[Any]  ,		snake_case_	:    List[Any] ):
														return self.features[i]


if is_tf_available():
							import tensorflow as tf

							class     _UpperCamelCase			:






										'''simple docstring'''

										__UpperCamelCase :  List[str]    =	42
										__UpperCamelCase :  Any    =	-100

										def __init__(    self	:    str  ,		snake_case_	:    TokenClassificationTask  ,		snake_case_	:    str  ,		snake_case_	:    PreTrainedTokenizer  ,		snake_case_	:    List[str]  ,		snake_case_	:    str  ,		snake_case_	:    Optional[int] = None  ,		snake_case_	:    Union[str, Any]=False  ,		snake_case_	:    Split = Split.train  ,		):
														UpperCamelCase_:							Any		=  token_classification_task.read_examples_from_file(_lowerCamelCase  ,		_lowerCamelCase )
														# TODO clean up all this to leverage built-in features of tokenizers
														UpperCamelCase_:							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:
																		UpperCamelCase_:							Optional[int]		=  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:
																		UpperCamelCase_:							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  lowerCAmelCase__		(    self	:    Optional[int] ):
														UpperCamelCase_:							Any		=  self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features ) ) )

														return self.dataset

										def __len__(    self	:    Any ):
														return len(self.features )




										def __getitem__(    self	:    List[Any]  ,		snake_case_	:    Tuple ):
														return self.features[i]








from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching







def     A__   (  )					->		Union[str, Any]:
				import os as original_os
				from os import path as original_path
				from os import rename as original_rename
				from os.path import dirname as original_dirname
				from os.path import join as original_join

				assert _test_patching.os is original_os
				assert _test_patching.path is original_path
				assert _test_patching.join is original_join

				assert _test_patching.renamed_os is original_os
				assert _test_patching.renamed_path is original_path
				assert _test_patching.renamed_join is original_join

				UpperCamelCase_:							Optional[int]		=  """__test_patch_submodule_mock__"""
				with patch_submodule(_test_patching       ,      """os.path.join"""       ,      lowerCamelCase  ):
								# Every way to access os.path.join must be patched, and the rest must stay untouched

								# check os.path.join
								assert isinstance(_test_patching.os       ,      _PatchedModuleObj  )
								assert isinstance(_test_patching.os.path       ,      _PatchedModuleObj  )
								assert _test_patching.os.path.join is mock

								# check path.join
								assert isinstance(_test_patching.path       ,      _PatchedModuleObj  )
								assert _test_patching.path.join is mock

								# check join
								assert _test_patching.join is mock

								# check that the other attributes are untouched
								assert _test_patching.os.rename is original_rename
								assert _test_patching.path.dirname is original_dirname
								assert _test_patching.os.path.dirname is original_dirname

								# Even renamed modules or objects must be patched

								# check renamed_os.path.join
								assert isinstance(_test_patching.renamed_os       ,      _PatchedModuleObj  )
								assert isinstance(_test_patching.renamed_os.path       ,      _PatchedModuleObj  )
								assert _test_patching.renamed_os.path.join is mock

								# check renamed_path.join
								assert isinstance(_test_patching.renamed_path       ,      _PatchedModuleObj  )
								assert _test_patching.renamed_path.join is mock

								# check renamed_join
								assert _test_patching.renamed_join is mock

								# check that the other attributes are untouched
								assert _test_patching.renamed_os.rename is original_rename
								assert _test_patching.renamed_path.dirname is original_dirname
								assert _test_patching.renamed_os.path.dirname is original_dirname

				# check that everthing is back to normal when the patch is over

				assert _test_patching.os is original_os
				assert _test_patching.path is original_path
				assert _test_patching.join is original_join

				assert _test_patching.renamed_os is original_os
				assert _test_patching.renamed_path is original_path
				assert _test_patching.renamed_join is original_join







def     A__   (  )					->		Union[str, Any]:
				assert _test_patching.open is open

				UpperCamelCase_:							List[Any]		=  """__test_patch_submodule_builtin_mock__"""
				# _test_patching has "open" in its globals
				assert _test_patching.open is open
				with patch_submodule(_test_patching       ,      """open"""       ,      lowerCamelCase  ):
								assert _test_patching.open is mock

				# check that everthing is back to normal when the patch is over

				assert _test_patching.open is open







def     A__   (  )					->		Optional[Any]:
				# pandas.read_csv is not present in _test_patching
				UpperCamelCase_:							Optional[Any]		=  """__test_patch_submodule_missing_mock__"""
				with patch_submodule(_test_patching       ,      """pandas.read_csv"""       ,      lowerCamelCase  ):
								pass







def     A__   (  )					->		Any:
				# builtin should always be mocked even if they're not in the globals
				# in case they're loaded at one point
				UpperCamelCase_:							List[Any]		=  """__test_patch_submodule_missing_builtin_mock__"""
				# _test_patching doesn't have "len" in its globals
				assert getattr(_test_patching       ,      """len"""       ,      lowerCamelCase  ) is None
				with patch_submodule(_test_patching       ,      """len"""       ,      lowerCamelCase  ):
								assert _test_patching.len is mock
				assert _test_patching.len is len







def     A__   (  )					->		Any:
				UpperCamelCase_:							Dict		=  """__test_patch_submodule_start_and_stop_mock__"""
				UpperCamelCase_:							List[str]		=  patch_submodule(_test_patching       ,      """open"""       ,      lowerCamelCase  )
				assert _test_patching.open is open
				patch.start()
				assert _test_patching.open is mock
				patch.stop()
				assert _test_patching.open is open







def     A__   (  )					->		List[str]:
				from os import rename as original_rename
				from os.path import dirname as original_dirname
				from os.path import join as original_join

				UpperCamelCase_:							Optional[Any]		=  """__test_patch_submodule_successive_join__"""
				UpperCamelCase_:							Any		=  """__test_patch_submodule_successive_dirname__"""
				UpperCamelCase_:							Dict		=  """__test_patch_submodule_successive_rename__"""
				assert _test_patching.os.path.join is original_join
				assert _test_patching.os.path.dirname is original_dirname
				assert _test_patching.os.rename is original_rename

				with patch_submodule(_test_patching       ,      """os.path.join"""       ,      lowerCamelCase  ):
								with patch_submodule(_test_patching       ,      """os.rename"""       ,      lowerCamelCase  ):
												with patch_submodule(_test_patching       ,      """os.path.dirname"""       ,      lowerCamelCase  ):
																assert _test_patching.os.path.join is mock_join
																assert _test_patching.os.path.dirname is mock_dirname
																assert _test_patching.os.rename is mock_rename

    # try another order
				with patch_submodule(_test_patching       ,      """os.rename"""       ,      lowerCamelCase  ):
								with patch_submodule(_test_patching       ,      """os.path.join"""       ,      lowerCamelCase  ):
												with patch_submodule(_test_patching       ,      """os.path.dirname"""       ,      lowerCamelCase  ):
																assert _test_patching.os.path.join is mock_join
																assert _test_patching.os.path.dirname is mock_dirname
																assert _test_patching.os.rename is mock_rename

				assert _test_patching.os.path.join is original_join
				assert _test_patching.os.path.dirname is original_dirname
				assert _test_patching.os.rename is original_rename







def     A__   (  )					->		Union[str, Any]:
				UpperCamelCase_:							Dict		=  """__test_patch_submodule_doesnt_exist_mock__"""
				with patch_submodule(_test_patching       ,      """__module_that_doesn_exist__.__attribute_that_doesn_exist__"""       ,      lowerCamelCase  ):
								pass
				with patch_submodule(_test_patching       ,      """os.__attribute_that_doesn_exist__"""       ,      lowerCamelCase  ):
								pass






