



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__		:     Any       						=logging.getLogger(__name__)

@dataclass
class    UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__			: str
						UpperCamelCase__			: List[str]
						UpperCamelCase__			: Optional[List[str]]







@dataclass
class    UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__			: List[int]
						UpperCamelCase__			: List[int]
						UpperCamelCase__			: Optional[List[int]]				  =  None
						UpperCamelCase__			: Optional[List[int]]				  =  None







class    UpperCAmelCase_  (   __a       ):

						'''simple docstring'''




						UpperCamelCase__			: Union[str, Any]				  =  '''train'''
						UpperCamelCase__			: List[Any]				  =  '''dev'''
						UpperCamelCase__			: Optional[int]				  =  '''test'''







class    UpperCAmelCase_  :

						'''simple docstring'''




						@staticmethod
						def        _A	(    _A      ,							_A		):







													'''simple docstring'''

													raise NotImplementedError






						@staticmethod
						def        _A	(    _A		):







													'''simple docstring'''

													raise NotImplementedError






						@staticmethod
						def        _A	(    _A      ,							_A      ,							_A      ,							_A      ,							_A=False      ,							_A="[CLS]"      ,							_A=1      ,							_A="[SEP]"      ,							_A=False      ,							_A=False      ,							_A=0      ,							_A=0      ,							_A=-100      ,							_A=0      ,							_A=True      ,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									=					{label: i for i, label in enumerate(__a		)}

													__SCREAMING_SNAKE_CASE									=					[]
													for ex_index, example in enumerate(__a		):
																				if ex_index % 10_000 == 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    UpperCAmelCase_  (   __a       ):

									'''simple docstring'''




									UpperCamelCase__			: List[InputFeatures]
									UpperCamelCase__			: int				  =  nn.CrossEntropyLoss().ignore_index






									def __init__(    self      ,							_A      ,							_A      ,							_A      ,							_A      ,							_A      ,							_A = None      ,							_A=False      ,							_A = Split.train      ,							):







																'''simple docstring'''

																__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		):







																'''simple docstring'''

																return len(self.features		)






									def __getitem__(    self      ,							_A		):







																'''simple docstring'''

																return self.features[i]


if is_tf_available():
			import tensorflow as tf

			class    UpperCAmelCase_  :

									'''simple docstring'''




									UpperCamelCase__			: List[InputFeatures]
									UpperCamelCase__			: int				  =  -100






									def __init__(    self      ,							_A      ,							_A      ,							_A      ,							_A      ,							_A      ,							_A = None      ,							_A=False      ,							_A = Split.train      ,							):







																'''simple docstring'''

																__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        _A	(    self		):







																'''simple docstring'''

																__SCREAMING_SNAKE_CASE									=					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      ,							_A		):







																'''simple docstring'''

																return self.features[i]














from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase       :       Optional[Any]        =				logging.get_logger(__name__)

lowerCamelCase       :       List[str]        =				{
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	List[Any]     =					'''canine'''



					def __init__(     self :		List[str]						,   __a :		Optional[int]=768						,   __a :		Any=12						,   __a :		Any=12						,   __a :		Dict=3072						,   __a :		Dict="gelu"						,   __a :		List[Any]=0.1						,   __a :		List[Any]=0.1						,   __a :		Tuple=16384						,   __a :		List[Any]=16						,   __a :		List[Any]=0.02						,   __a :		Optional[Any]=1E-12						,   __a :		Dict=0						,   __a :		List[Any]=0xe_0_0_0						,   __a :		Optional[int]=0xe_0_0_1						,   __a :		Any=4						,   __a :		Dict=4						,   __a :		Optional[int]=8						,   __a :		Any=16384						,   __a :		Optional[Any]=128						,   **__a :		List[str]						,   )	->     Union[str, Any]:


												"""simple docstring"""

												super().__init__(pad_token_id=__a						,   bos_token_id=__a						,   eos_token_id=__a						,   **__a							)

												__lowercase					:							int			=   max_position_embeddings
												__lowercase					:							List[str]			=   hidden_size
												__lowercase					:							List[Any]			=   num_hidden_layers
												__lowercase					:							Dict			=   num_attention_heads
												__lowercase					:							int			=   intermediate_size
												__lowercase					:							Dict			=   hidden_act
												__lowercase					:							Tuple			=   hidden_dropout_prob
												__lowercase					:							Union[str, Any]			=   attention_probs_dropout_prob
												__lowercase					:							Union[str, Any]			=   initializer_range
												__lowercase					:							Any			=   type_vocab_size
												__lowercase					:							int			=   layer_norm_eps

												# Character config:
												__lowercase					:							int			=   downsampling_rate
												__lowercase					:							str			=   upsampling_kernel_size
												__lowercase					:							Union[str, Any]			=   num_hash_functions
												__lowercase					:							Optional[Any]			=   num_hash_buckets
												__lowercase					:							Optional[int]			=   local_transformer_stride