



"""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


lowerCAmelCase_   						=   logging.getLogger(__name__)
@dataclass
class        __A       :







		'''simple docstring'''





		lowerCAmelCase			:					int   	=				4_2
		lowerCAmelCase			:					int   	=				4_2
		lowerCAmelCase			:					int   	=				4_2





@dataclass
class        __A       :







		'''simple docstring'''





		lowerCAmelCase			:					Any   	=				4_2
		lowerCAmelCase			:					Tuple   	=				4_2
		lowerCAmelCase			:					Union[str, Any]   	=				None
		lowerCAmelCase			:					int   	=				None





class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					Optional[Any]   	=				"train"
		lowerCAmelCase			:					List[str]   	=				"dev"
		lowerCAmelCase			:					List[str]   	=				"test"





class        __A       :







		'''simple docstring'''





		@staticmethod
		def 					UpperCAmelCase    (	_snake_case						:  Optional[int]			,_snake_case						:  Union[Split, str]						)					->		List[InputExample]:






				"""simple docstring"""



				raise NotImplementedError







		@staticmethod
		def 					UpperCAmelCase    (	_snake_case						:  str						)					->		List[str]:






				"""simple docstring"""



				raise NotImplementedError







		@staticmethod
		def 					UpperCAmelCase    (	_snake_case						:  List[InputExample]			,_snake_case						:  List[str]			,_snake_case						:  int			,_snake_case						:  PreTrainedTokenizer			,_snake_case						:  Optional[int]=False			,_snake_case						:  int="[CLS]"			,_snake_case						:  Optional[int]=1			,_snake_case						:  Optional[int]="[SEP]"			,_snake_case						:  int=False			,_snake_case						:  Optional[Any]=False			,_snake_case						:  Dict=0			,_snake_case						:  List[str]=0			,_snake_case						:  Optional[int]=-100			,_snake_case						:  Union[str, Any]=0			,_snake_case						:  Union[str, Any]=True			,)					->		List[InputFeatures]:






				"""simple docstring"""



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

				lowercase__   :    Optional[Any]				=			[]
				for ex_index, example in enumerate(__SCREAMING_SNAKE_CASE						):
						if ex_index % 10_000 == 0:
								logger.info('''Writing example %d of %d'''			,__SCREAMING_SNAKE_CASE			,len(__SCREAMING_SNAKE_CASE						)						)

						lowercase__   :    List[Any]				=			[]
						lowercase__   :    List[Any]				=			[]
						for word, label in zip(example.words			,example.labels						):
								lowercase__   :    Union[str, Any]				=			tokenizer.tokenize(__SCREAMING_SNAKE_CASE						)

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

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
						lowercase__   :    int				=			tokenizer.num_special_tokens_to_add()
						if len(__SCREAMING_SNAKE_CASE						) > max_seq_length - special_tokens_count:
								lowercase__   :    Union[str, Any]				=			tokens[: (max_seq_length - special_tokens_count)]
								lowercase__   :    List[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__   :    Dict				=			[sequence_a_segment_id] * len(__SCREAMING_SNAKE_CASE						)

						if cls_token_at_end:
								tokens += [cls_token]
								label_ids += [pad_token_label_id]
								segment_ids += [cls_token_segment_id]
						else:
								lowercase__   :    Tuple				=			[cls_token] + tokens
								lowercase__   :    Optional[int]				=			[pad_token_label_id] + label_ids
								lowercase__   :    str				=			[cls_token_segment_id] + segment_ids

						lowercase__   :    Any				=			tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE						)

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

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

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

						if "token_type_ids" not in tokenizer.model_input_names:
								lowercase__   :    str				=			None

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


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







									'''simple docstring'''





									lowerCAmelCase			:					List[Any]   	=				4_2
									lowerCAmelCase			:					Union[str, Any]   	=				nn.CrossEntropyLoss().ignore_index







									def __init__(	self						:  List[Any]			,_snake_case						:  TokenClassificationTask			,_snake_case						:  str			,_snake_case						:  PreTrainedTokenizer			,_snake_case						:  List[str]			,_snake_case						:  str			,_snake_case						:  Optional[int] = None			,_snake_case						:  List[Any]=False			,_snake_case						:  Split = Split.train			,)					->		Union[str, Any]:






											"""simple docstring"""



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

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







									def __len__(	self						:  Tuple						)					->		str:






											"""simple docstring"""



											return len(self.features						)







									def __getitem__(	self						:  Union[str, Any]			,_snake_case						:  Union[str, Any]						)					->		InputFeatures:






											"""simple docstring"""



											return self.features[i]


if is_tf_available():
							import tensorflow as tf
							class        __A       :







									'''simple docstring'''





									lowerCAmelCase			:					Any   	=				4_2
									lowerCAmelCase			:					Tuple   	=				-1_0_0







									def __init__(	self						:  Any			,_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			,)					->		Optional[Any]:






											"""simple docstring"""



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






											"""simple docstring"""



											lowercase__   :    List[str]				=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features						)						)						)

											return self.dataset







									def __len__(	self						:  List[Any]						)					->		Union[str, Any]:






											"""simple docstring"""



											return len(self.features						)







									def __getitem__(	self						:  Union[str, Any]			,_snake_case						:  Optional[int]						)					->		InputFeatures:






											"""simple docstring"""



											return self.features[i]



'''simple docstring'''



class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       int			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      n
      __SCREAMING_SNAKE_CASE     =      [None] * self.n
      __SCREAMING_SNAKE_CASE     =      0  # index of the first element
      __SCREAMING_SNAKE_CASE     =      0
      __SCREAMING_SNAKE_CASE     =      0
 def __len__(		self					:       Tuple			)  -> int:


      """simple docstring"""




      return self.size
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> bool:


      """simple docstring"""




      return self.size == 0
 def       UpperCAmelCase__		(		self					:       Any			)  -> int:


      """simple docstring"""




      return False if self.is_empty() else self.array[self.front]
 def       UpperCAmelCase__		(		self					:       Dict					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]			)  -> Dict:


      """simple docstring"""




      if self.size >= self.n:
           raise Exception("""QUEUE IS FULL"""			)

      __SCREAMING_SNAKE_CASE     =      data
      __SCREAMING_SNAKE_CASE     =      (self.rear + 1) % self.n
      self.size += 1
      return self


 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Optional[Any]:


      """simple docstring"""




      if self.size == 0:
           raise Exception("""UNDERFLOW"""			)

      __SCREAMING_SNAKE_CASE     =      self.array[self.front]
      __SCREAMING_SNAKE_CASE     =      None
      __SCREAMING_SNAKE_CASE     =      (self.front + 1) % self.n
      self.size -= 1
      return temp






