


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


A	:		List[str]    =       logging.getLogger(__name__)



@dataclass
class 							A   :
					'''simple docstring'''






					A__      =			42
					A__      =			42
					A__      =			42






@dataclass
class 							A   :
					'''simple docstring'''






					A__      =			42
					A__      =			42
					A__      =			None
					A__      =			None






class 							A   (	__lowercase							):
					'''simple docstring'''






					A__      =			"train"
					A__      =			"dev"
					A__      =			"test"






class 							A   :
					'''simple docstring'''






					@staticmethod
					def   lowerCamelCase__					(_UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						Dict		)						->       List[InputExample]:




							"""simple docstring"""


							raise NotImplementedError

					@staticmethod
					def   lowerCamelCase__					(_UpperCAmelCase   :						List[str]		)						->       List[str]:




							"""simple docstring"""


							raise NotImplementedError


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




							"""simple docstring"""


							lowercase__					      =		{label: i for i, label in enumerate(_a		)}

							lowercase__					      =		[]
							for ex_index, example in enumerate(_a		):
									if ex_index % 1_0000 == 0:
											logger.info("""Writing example %d of %d"""				, _a				, len(_a		)		)

									lowercase__					      =		[]
									lowercase__					      =		[]
									for word, label in zip(example.words				, example.labels		):
											lowercase__					      =		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.
									lowercase__					      =		tokenizer.num_special_tokens_to_add()
									if len(_a		) > max_seq_length - special_tokens_count:
											lowercase__					      =		tokens[: (max_seq_length - special_tokens_count)]
											lowercase__					      =		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__					      =		[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:
											lowercase__					      =		[cls_token] + tokens
											lowercase__					      =		[pad_token_label_id] + label_ids
											lowercase__					      =		[cls_token_segment_id] + segment_ids

									lowercase__					      =		tokenizer.convert_tokens_to_ids(_a		)

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

									# Zero-pad up to the sequence length.
									lowercase__					      =		max_seq_length - len(_a		)
									if pad_on_left:
											lowercase__					      =		([pad_token] * padding_length) + input_ids
											lowercase__					      =		([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
											lowercase__					      =		([pad_token_segment_id] * padding_length) + segment_ids
											lowercase__					      =		([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:
											lowercase__					      =		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 							A   (	__lowercase							):
						'''simple docstring'''






						A__      =			42
						A__      =			nn.CrossEntropyLoss().ignore_index

						def __init__(self   :						Optional[Any]				, _UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						List[Any]				, _UpperCAmelCase   :						str = None				, _UpperCAmelCase   :						Dict=False				, _UpperCAmelCase   :						Union[str, Any] = Split.train				, )						->       List[Any]:




								"""simple docstring"""


								lowercase__					      =		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.
								lowercase__					      =		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}'''		)
												lowercase__					      =		torch.load(_a		)
										else:
												logger.info(f'''Creating features from dataset file at {data_dir}'''		)
												lowercase__					      =		token_classification_task.read_examples_from_file(_a				, _a		)
												# TODO clean up all this to leverage built-in features of tokenizers
												lowercase__					      =		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   :						str		)						->       Union[str, Any]:




								"""simple docstring"""


								return len(self.features		)


						def __getitem__(self   :						Optional[Any]				, _UpperCAmelCase   :						Tuple		)						->       InputFeatures:




								"""simple docstring"""


								return self.features[i]


if is_tf_available():
	import tensorflow as tf



	class 							A   :
						'''simple docstring'''






						A__      =			42
						A__      =			-1_00

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




								"""simple docstring"""


								lowercase__					      =		token_classification_task.read_examples_from_file(_a				, _a		)
								# TODO clean up all this to leverage built-in features of tokenizers
								lowercase__					      =		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:
										lowercase__					      =		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:
										lowercase__					      =		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   lowerCamelCase__					(self   :						Tuple		)						->       Optional[int]:




								"""simple docstring"""


								lowercase__					      =		self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

								return self.dataset

						def __len__(self   :						Tuple		)						->       int:




								"""simple docstring"""


								return len(self.features		)


						def __getitem__(self   :						List[str]				, _UpperCAmelCase   :						Any		)						->       InputFeatures:




								"""simple docstring"""


								return self.features[i]









from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A	:		Union[str, Any]    =       logging.get_logger(__name__)

A	:		int    =       {
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					A__      =			'''roberta-prelayernorm'''

					def __init__(self   :						Dict				, _UpperCAmelCase   :						List[Any]=5_0265				, _UpperCAmelCase   :						List[Any]=768				, _UpperCAmelCase   :						str=12				, _UpperCAmelCase   :						Any=12				, _UpperCAmelCase   :						Any=3072				, _UpperCAmelCase   :						int="gelu"				, _UpperCAmelCase   :						Tuple=0.1				, _UpperCAmelCase   :						Tuple=0.1				, _UpperCAmelCase   :						Any=512				, _UpperCAmelCase   :						Dict=2				, _UpperCAmelCase   :						int=0.02				, _UpperCAmelCase   :						List[Any]=1E-1_2				, _UpperCAmelCase   :						Tuple=1				, _UpperCAmelCase   :						int=0				, _UpperCAmelCase   :						int=2				, _UpperCAmelCase   :						Optional[Any]="absolute"				, _UpperCAmelCase   :						int=True				, _UpperCAmelCase   :						Optional[int]=None				, **_UpperCAmelCase   :						Any				, )						->       List[str]:




							"""simple docstring"""


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

							lowercase__					      =		vocab_size
							lowercase__					      =		hidden_size
							lowercase__					      =		num_hidden_layers
							lowercase__					      =		num_attention_heads
							lowercase__					      =		hidden_act
							lowercase__					      =		intermediate_size
							lowercase__					      =		hidden_dropout_prob
							lowercase__					      =		attention_probs_dropout_prob
							lowercase__					      =		max_position_embeddings
							lowercase__					      =		type_vocab_size
							lowercase__					      =		initializer_range
							lowercase__					      =		layer_norm_eps
							lowercase__					      =		position_embedding_type
							lowercase__					      =		use_cache
							lowercase__					      =		classifier_dropout






class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					@property
					def   lowerCamelCase__					(self   :						Any		)						->       Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							if self.task == "multiple-choice":
									lowercase__					      =		{0: """batch""", 1: """choice""", 2: """sequence"""}
							else:
									lowercase__					      =		{0: """batch""", 1: """sequence"""}
							return OrderedDict(
							    [
							        ("""input_ids""", dynamic_axis),
							        ("""attention_mask""", dynamic_axis),
							    ]		)

