
'''simple docstring'''



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


UpperCamelCase__       							=  logging.get_logger(__name__)

UpperCamelCase__       							=  {
    "Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}

class    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  'dpt'






       def __init__(							self       ,							A__=7_68       ,							A__=12       ,							A__=12       ,							A__=30_72       ,							A__="gelu"       ,							A__=0.0       ,							A__=0.0       ,							A__=0.02       ,							A__=1E-12       ,							A__=3_84       ,							A__=16       ,							A__=3       ,							A__=False       ,							A__=True       ,							A__=[2, 5, 8, 11]       ,							A__="project"       ,							A__=[4, 2, 1, 0.5]       ,							A__=[96, 1_92, 3_84, 7_68]       ,							A__=2_56       ,							A__=-1       ,							A__=False       ,							A__=True       ,							A__=0.4       ,							A__=2_55       ,							A__=0.1       ,							A__=[1, 10_24, 24, 24]       ,							A__=[0, 1]       ,							A__=None       ,							**A__       ,							)    ->			str:
          super().__init__(**A__      )

          _SCREAMING_SNAKE_CASE      =				hidden_size
          _SCREAMING_SNAKE_CASE      =				is_hybrid

          if self.is_hybrid:
             if backbone_config is None:
                logger.info("""Initializing the config with a `BiT` backbone."""      )
                _SCREAMING_SNAKE_CASE      =				{
                    """global_padding""": """same""",
                    """layer_type""": """bottleneck""",
                    """depths""": [3, 4, 9],
                    """out_features""": ["""stage1""", """stage2""", """stage3"""],
                    """embedding_dynamic_padding""": True,
                }
                _SCREAMING_SNAKE_CASE      =				BitConfig(**A__      )
             elif isinstance(A__       ,							A__      ):
                logger.info("""Initializing the config with a `BiT` backbone."""      )
                _SCREAMING_SNAKE_CASE      =				BitConfig(**A__      )
             elif isinstance(A__       ,							A__      ):
                _SCREAMING_SNAKE_CASE      =				backbone_config
             else:
                raise ValueError(
                    F"backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."      )

             _SCREAMING_SNAKE_CASE      =				backbone_featmap_shape
             _SCREAMING_SNAKE_CASE      =				neck_ignore_stages

             if readout_type != "project":
                raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode."""      )
          else:
             _SCREAMING_SNAKE_CASE      =				None
             _SCREAMING_SNAKE_CASE      =				None
             _SCREAMING_SNAKE_CASE      =				[]

          _SCREAMING_SNAKE_CASE      =				num_hidden_layers
          _SCREAMING_SNAKE_CASE      =				num_attention_heads
          _SCREAMING_SNAKE_CASE      =				intermediate_size
          _SCREAMING_SNAKE_CASE      =				hidden_act
          _SCREAMING_SNAKE_CASE      =				hidden_dropout_prob
          _SCREAMING_SNAKE_CASE      =				attention_probs_dropout_prob
          _SCREAMING_SNAKE_CASE      =				initializer_range
          _SCREAMING_SNAKE_CASE      =				layer_norm_eps
          _SCREAMING_SNAKE_CASE      =				image_size
          _SCREAMING_SNAKE_CASE      =				patch_size
          _SCREAMING_SNAKE_CASE      =				num_channels
          _SCREAMING_SNAKE_CASE      =				qkv_bias
          _SCREAMING_SNAKE_CASE      =				backbone_out_indices
          if readout_type not in ["ignore", "add", "project"]:
             raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']"""      )
          _SCREAMING_SNAKE_CASE      =				readout_type
          _SCREAMING_SNAKE_CASE      =				reassemble_factors
          _SCREAMING_SNAKE_CASE      =				neck_hidden_sizes
          _SCREAMING_SNAKE_CASE      =				fusion_hidden_size
          _SCREAMING_SNAKE_CASE      =				head_in_index
          _SCREAMING_SNAKE_CASE      =				use_batch_norm_in_fusion_residual
          # auxiliary head attributes (semantic segmentation)
          _SCREAMING_SNAKE_CASE      =				use_auxiliary_head
          _SCREAMING_SNAKE_CASE      =				auxiliary_loss_weight
          _SCREAMING_SNAKE_CASE      =				semantic_loss_ignore_index
          _SCREAMING_SNAKE_CASE      =				semantic_classifier_dropout






       def        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				copy.deepcopy(self.__dict__      )

          if output["backbone_config"] is not None:
             _SCREAMING_SNAKE_CASE      =				self.backbone_config.to_dict()

          _SCREAMING_SNAKE_CASE      =				self.__class__.model_type
          return output








'''simple docstring'''



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

from utils import logger

class    _a				(_lowerCamelCase):





       """simple docstring"""




       def __init__(							self       ,							A__       ,							A__      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				params

          _SCREAMING_SNAKE_CASE      =				np.array(A__      )
          _SCREAMING_SNAKE_CASE      =				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       ,							A__      )    ->			Dict:
          return (self.token_ids[index], self.lengths[index])






       def __len__(							self      )    ->			Tuple:
          return len(self.lengths      )






       def        UpperCamelCase				(							self      )    ->			Dict:
          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        UpperCamelCase				(							self      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				self.params.max_model_input_size
          _SCREAMING_SNAKE_CASE      =				self.lengths > max_len
          logger.info(F"Splitting {sum(A__      )} too long sequences."      )

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

          _SCREAMING_SNAKE_CASE      =				[]
          _SCREAMING_SNAKE_CASE      =				[]
          if self.params.mlm:
             _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
          else:
             _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				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:
                _SCREAMING_SNAKE_CASE      =				[]
                for sub_s in divide_chunks(seq_       ,							max_len - 2      ):
                   if sub_s[0] != cls_id:
                      _SCREAMING_SNAKE_CASE      =				np.insert(A__       ,							0       ,							A__      )
                   if sub_s[-1] != sep_id:
                      _SCREAMING_SNAKE_CASE      =				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]      )

          _SCREAMING_SNAKE_CASE      =				np.array(A__      )
          _SCREAMING_SNAKE_CASE      =				np.array(A__      )






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






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






       def        UpperCamelCase				(							self      )    ->			Optional[Any]:
          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        UpperCamelCase				(							self       ,							A__      )    ->			Any:
          _SCREAMING_SNAKE_CASE      =				[t[0] for t in batch]
          _SCREAMING_SNAKE_CASE      =				[t[1] for t in batch]
          assert len(A__      ) == len(A__      )

          # Max for paddings
          _SCREAMING_SNAKE_CASE      =				max(A__      )

          # Pad token ids
          if self.params.mlm:
             _SCREAMING_SNAKE_CASE      =				self.params.special_tok_ids["""pad_token"""]
          else:
             _SCREAMING_SNAKE_CASE      =				self.params.special_tok_ids["""unk_token"""]
          _SCREAMING_SNAKE_CASE      =				[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_      )

          _SCREAMING_SNAKE_CASE      =				torch.tensor(tk_      )  # (bs, max_seq_len_)
          _SCREAMING_SNAKE_CASE      =				torch.tensor(A__      )  # (bs)
          return tk_t, lg_t






