





'''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


a__					:			Optional[int]          =     logging.getLogger(__name__)






@dataclass
class 				__snake_case     :
	__lowerCAmelCase       						=  42
	__lowerCAmelCase       						=  42
	__lowerCAmelCase       						=  42






@dataclass
class 				__snake_case     :
	__lowerCAmelCase       						=  42
	__lowerCAmelCase       						=  42
	__lowerCAmelCase       						=  None
	__lowerCAmelCase       						=  None






class 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  '''train'''
	__lowerCAmelCase       						=  '''dev'''
	__lowerCAmelCase       						=  '''test'''






class 				__snake_case     :





	@staticmethod
	def   _snake_case   (		UpperCamelCase_      ,       UpperCamelCase_		)      ->		List[InputExample]:
		raise NotImplementedError





	@staticmethod
	def   _snake_case   (		UpperCamelCase_		)      ->		List[str]:
		raise NotImplementedError





	@staticmethod
	def   _snake_case   (		UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_=False      ,       UpperCamelCase_="[CLS]"      ,       UpperCamelCase_=1      ,       UpperCamelCase_="[SEP]"      ,       UpperCamelCase_=False      ,       UpperCamelCase_=False      ,       UpperCamelCase_=0      ,       UpperCamelCase_=0      ,       UpperCamelCase_=-100      ,       UpperCamelCase_=0      ,       UpperCamelCase_=True      ,       )      ->		List[InputFeatures]:
		snake_case__	       =   {label: i for i, label in enumerate(UpperCamelCase_		)}

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

			snake_case__	       =   []
			snake_case__	       =   []
			for word, label in zip(example.words      ,       example.labels		):
				snake_case__	       =   tokenizer.tokenize(UpperCamelCase_		)

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

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
			snake_case__	       =   tokenizer.num_special_tokens_to_add()
			if len(UpperCamelCase_		) > max_seq_length - special_tokens_count:
				snake_case__	       =   tokens[: (max_seq_length - special_tokens_count)]
				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]
			snake_case__	       =   [sequence_a_segment_id] * len(UpperCamelCase_		)

			if cls_token_at_end:
				tokens += [cls_token]
				label_ids += [pad_token_label_id]
				segment_ids += [cls_token_segment_id]
			else:
				snake_case__	       =   [cls_token] + tokens
				snake_case__	       =   [pad_token_label_id] + label_ids
				snake_case__	       =   [cls_token_segment_id] + segment_ids

			snake_case__	       =   tokenizer.convert_tokens_to_ids(UpperCamelCase_		)

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

			# Zero-pad up to the sequence length.
			snake_case__	       =   max_seq_length - len(UpperCamelCase_		)
			if pad_on_left:
				snake_case__	       =   ([pad_token] * padding_length) + input_ids
				snake_case__	       =   ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
				snake_case__	       =   ([pad_token_segment_id] * padding_length) + segment_ids
				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(UpperCamelCase_		) == max_seq_length
			assert len(UpperCamelCase_		) == max_seq_length
			assert len(UpperCamelCase_		) == max_seq_length
			assert len(UpperCamelCase_		) == max_seq_length

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

			if "token_type_ids" not in tokenizer.model_input_names:
				snake_case__	       =   None

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


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






			class 				__snake_case     (					__magic_name__							):
				__lowerCAmelCase       						=  42
				__lowerCAmelCase       						=  nn.CrossEntropyLoss().ignore_index





				def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_ = None      ,       UpperCamelCase_=False      ,       UpperCamelCase_ = Split.train      ,       )      ->		Dict:
					# Load data features from cache or dataset file
					snake_case__	       =   os.path.join(
					    UpperCamelCase_      ,       'cached_{}_{}_{}'.format(mode.value      ,       tokenizer.__class__.__name__      ,       str(UpperCamelCase_		)		)      ,       )

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





				def __len__(		self		)      ->		Dict:
					return len(self.features		)





				def __getitem__(		self      ,       UpperCamelCase_		)      ->		InputFeatures:
					return self.features[i]


if is_tf_available():
			import tensorflow as tf






			class 				__snake_case     :
				__lowerCAmelCase       						=  42
				__lowerCAmelCase       						=  -100





				def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_ = None      ,       UpperCamelCase_=False      ,       UpperCamelCase_ = Split.train      ,       )      ->		int:
					snake_case__	       =   token_classification_task.read_examples_from_file(UpperCamelCase_      ,       UpperCamelCase_		)
					# TODO clean up all this to leverage built-in features of tokenizers
					snake_case__	       =   token_classification_task.convert_examples_to_features(
					    UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       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=UpperCamelCase_      ,       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:
						snake_case__	       =   tf.data.Dataset.from_generator(
						    UpperCamelCase_      ,       ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)      ,       (
						        {'input_ids': tf.TensorShape([None]		), 'attention_mask': tf.TensorShape([None]		)},
						        tf.TensorShape([None]		),
						    )      ,       )
					else:
						snake_case__	       =   tf.data.Dataset.from_generator(
						    UpperCamelCase_      ,       ({'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   _snake_case   (		self		)      ->		Optional[Any]:
					snake_case__	       =   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      ,       UpperCamelCase_		)      ->		InputFeatures:
					return self.features[i]










'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


a__					:			int          =     logging.get_logger(__name__)






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       *UpperCamelCase_      ,       **UpperCamelCase_		)      ->		None:
		warnings.warn(
		    'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
		    ' Please use PerceiverImageProcessor instead.'      ,       UpperCamelCase_      ,       )
		super().__init__(*UpperCamelCase_      ,       **UpperCamelCase_		)



