


"""simple docstring"""


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

from utils import logger
class snake_case      (  _UpperCamelCase):







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





						'''simple docstring'''







						_A					  =			params

						_A					  =			np.array(a__		)
						_A					  =			np.array([len(a__		) 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      :       Tuple    ,   a__      :       Optional[Any]		)       ->						Tuple:





						'''simple docstring'''







						return (self.token_ids[index], self.lengths[index])







		def __len__(					self      :       Dict		)       ->						Optional[int]:





						'''simple docstring'''







						return len(self.lengths		)







		def  a_ (					self      :       List[Any]		)       ->						Tuple:





						'''simple docstring'''







						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      :       Optional[int]		)       ->						Optional[int]:





						'''simple docstring'''







						_A					  =			self.params.max_model_input_size
						_A					  =			self.lengths > max_len
						logger.info(F"""Splitting {sum(a__		)} too long sequences."""		)

						def divide_chunks(a__      :       Tuple    ,   a__      :       str		):
										return [l[i : i + n] for i in range(0    ,   len(a__		)    ,   a__		)]

						_A					  =			[]
						_A					  =			[]
						if self.params.mlm:
										_A  ,		_A					  =			self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
						else:
										_A  ,		_A					  =			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:
														_A					  =			[]
														for sub_s in divide_chunks(seq_    ,   max_len - 2		):
																		if sub_s[0] != cls_id:
																						_A					  =			np.insert(a__    ,   0    ,   a__		)
																		if sub_s[-1] != sep_id:
																						_A					  =			np.insert(a__    ,   len(a__		)    ,   a__		)
																		assert len(a__		) <= max_len
																		assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																		sub_seqs.append(a__		)

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

						_A					  =			np.array(a__		)
						_A					  =			np.array(a__		)







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





						'''simple docstring'''







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







		def  a_ (					self      :       int		)       ->						Any:





						'''simple docstring'''







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







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





						'''simple docstring'''







						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      :       Any    ,   a__      :       int		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			[t[0] for t in batch]
						_A					  =			[t[1] for t in batch]
						assert len(a__		) == len(a__		)

						# Max for paddings
						_A					  =			max(a__		)

						# Pad token ids
						if self.params.mlm:
										_A					  =			self.params.special_tok_ids["pad_token"]
						else:
										_A					  =			self.params.special_tok_ids["unk_token"]
						_A					  =			[list(t.astype(a__		)		) + [pad_idx] * (max_seq_len_ - len(a__		)) for t in token_ids]
						assert len(tk_		) == len(a__		)
						assert all(len(a__		) == max_seq_len_ for t in tk_		)

						_A					  =			torch.tensor(tk_		)  # (bs, max_seq_len_)
						_A					  =			torch.tensor(a__		)  # (bs)
						return tk_t, lg_t



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_       							=    {
    "configuration_upernet": ["UperNetConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "UperNetForSemanticSegmentation",
		    "UperNetPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_upernet import UperNetConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
		import sys

		a_       							=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)