

import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger





class     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A							):
											__lowerCAmelCase					=							params

											__lowerCAmelCase					=							np.array(UpperCAmelCase__							)
											__lowerCAmelCase					=							np.array([len(UpperCAmelCase__							) for t in data]							)

											self.check()
											self.remove_long_sequences()
											self.remove_empty_sequences()
											self.remove_unknown_sequences()
											self.check()
											self.print_statistics()




							def __getitem__( self					,						_A							):
											return (self.token_ids[index], self.lengths[index])




							def __len__( self							):
											return len(self.lengths							)




							def        A__  ( self							):
											assert len(self.token_ids							) == len(self.lengths							)
											assert all(self.lengths[i] == len(self.token_ids[i]							) for i in range(len(self.lengths							)							)							)




							def        A__  ( self							):
											__lowerCAmelCase					=							self.params.max_model_input_size
											__lowerCAmelCase					=							self.lengths > max_len
											logger.info(F"""Splitting {sum(UpperCAmelCase__							)} too long sequences."""							)

											def divide_chunks(_A					,						_A							):
															return [l[i : i + n] for i in range(0					,						len(UpperCAmelCase__							)					,						UpperCAmelCase__							)]

											__lowerCAmelCase					=							[]
											__lowerCAmelCase					=							[]
											if self.params.mlm:
															__lowerCAmelCase					=							self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
											else:
															__lowerCAmelCase					=							self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

											for seq_, len_ in zip(self.token_ids					,						self.lengths							):
															assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
															if len_ <= max_len:
																			new_tok_ids.append(seq_							)
																			new_lengths.append(len_							)
															else:
																			__lowerCAmelCase					=							[]
																			for sub_s in divide_chunks(seq_					,						max_len - 2							):
																							if sub_s[0] != cls_id:
																											__lowerCAmelCase					=							np.insert(UpperCAmelCase__					,						0					,						UpperCAmelCase__							)
																							if sub_s[-1] != sep_id:
																											__lowerCAmelCase					=							np.insert(UpperCAmelCase__					,						len(UpperCAmelCase__							)					,						UpperCAmelCase__							)
																							assert len(UpperCAmelCase__							) <= max_len
																							assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																							sub_seqs.append(UpperCAmelCase__							)

																			new_tok_ids.extend(UpperCAmelCase__							)
																			new_lengths.extend([len(UpperCAmelCase__							) for l in sub_seqs]							)

											__lowerCAmelCase					=							np.array(UpperCAmelCase__							)
											__lowerCAmelCase					=							np.array(UpperCAmelCase__							)




							def        A__  ( self							):
											__lowerCAmelCase					=							len(self							)
											__lowerCAmelCase					=							self.lengths > 1_1
											__lowerCAmelCase					=							self.token_ids[indices]
											__lowerCAmelCase					=							self.lengths[indices]
											__lowerCAmelCase					=							len(self							)
											logger.info(F"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""							)




							def        A__  ( self							):
											if "unk_token" not in self.params.special_tok_ids:
															return
											else:
															__lowerCAmelCase					=							self.params.special_tok_ids['''unk_token''']
											__lowerCAmelCase					=							len(self							)
											__lowerCAmelCase					=							np.array([np.count_nonzero(a == unk_token_id							) for a in self.token_ids]							)
											__lowerCAmelCase					=							(unk_occs / self.lengths) < 0.5
											__lowerCAmelCase					=							self.token_ids[indices]
											__lowerCAmelCase					=							self.lengths[indices]
											__lowerCAmelCase					=							len(self							)
											logger.info(F"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."""							)




							def        A__  ( self							):
											if not self.params.is_master:
															return
											logger.info(F"""{len(self							)} sequences"""							)
											# data_len = sum(self.lengths)
											# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
											# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

											# unk_idx = self.params.special_tok_ids['unk_token']
											# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
											# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')




							def        A__  ( self					,						_A							):
											__lowerCAmelCase					=							[t[0] for t in batch]
											__lowerCAmelCase					=							[t[1] for t in batch]
											assert len(UpperCAmelCase__							) == len(UpperCAmelCase__							)

											# Max for paddings
											__lowerCAmelCase					=							max(UpperCAmelCase__							)

											# Pad token ids
											if self.params.mlm:
															__lowerCAmelCase					=							self.params.special_tok_ids['''pad_token''']
											else:
															__lowerCAmelCase					=							self.params.special_tok_ids['''unk_token''']
											__lowerCAmelCase					=							[list(t.astype(UpperCAmelCase__							)							) + [pad_idx] * (max_seq_len_ - len(UpperCAmelCase__							)) for t in token_ids]
											assert len(tk_							) == len(UpperCAmelCase__							)
											assert all(len(UpperCAmelCase__							) == max_seq_len_ for t in tk_							)

											__lowerCAmelCase					=							torch.tensor(tk_							)  # (bs, max_seq_len_)
											__lowerCAmelCase					=							torch.tensor(UpperCAmelCase__							)  # (bs)
											return tk_t, lg_t









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase      				=     {
    '''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase      				=     [
		    '''LILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LiltForQuestionAnswering''',
		    '''LiltForSequenceClassification''',
		    '''LiltForTokenClassification''',
		    '''LiltModel''',
		    '''LiltPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_lilt import (
				    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LiltForQuestionAnswering,
				    LiltForSequenceClassification,
				    LiltForTokenClassification,
				    LiltModel,
				    LiltPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase      				=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






