


"""simple docstring"""


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

from utils import logger


class 			A__	(						__lowercase):


     """simple docstring"""

     def __init__(	self:						Tuple     ,		__a:						Any     ,		__a:						Optional[Any]	)->    List[Any]:
           lowerCamelCase					:					Any        =	params

           lowerCamelCase					:					List[str]        =	np.array(__a	)
           lowerCamelCase					:					List[Any]        =	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:						Any     ,		__a:						int	)->    List[Any]:
           return (self.token_ids[index], self.lengths[index])

     def __len__(	self:						List[Any]	)->    Union[str, Any]:
           return len(self.lengths	)

     def 		a__						(	self:						str	)->    List[str]:
           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:						int	)->    Dict:
           lowerCamelCase					:					Tuple        =	self.params.max_model_input_size
           lowerCamelCase					:					Union[str, Any]        =	self.lengths > max_len
           logger.info(f'Splitting {sum(__a	)} too long sequences.'	)

           def divide_chunks(__a:						str     ,		__a:						Optional[Any]	):
                 return [l[i : i + n] for i in range(0     ,		len(__a	)     ,		__a	)]

           lowerCamelCase					:					int        =	[]
           lowerCamelCase					:					Any        =	[]
           if self.params.mlm:
                 lowerCamelCase     ,			lowerCamelCase					:					Union[str, Any]        =	self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
           else:
                 lowerCamelCase     ,			lowerCamelCase					:					Union[str, Any]        =	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					:					int        =	[]
                       for sub_s in divide_chunks(seq_     ,		max_len - 2	):
                             if sub_s[0] != cls_id:
                                   lowerCamelCase					:					Tuple        =	np.insert(__a     ,		0     ,		__a	)
                             if sub_s[-1] != sep_id:
                                   lowerCamelCase					:					int        =	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]	)

           lowerCamelCase					:					Dict        =	np.array(__a	)
           lowerCamelCase					:					str        =	np.array(__a	)

     def 		a__						(	self:						Dict	)->    Optional[Any]:
           lowerCamelCase					:					Optional[int]        =	len(self	)
           lowerCamelCase					:					Union[str, Any]        =	self.lengths > 11
           lowerCamelCase					:					Optional[int]        =	self.token_ids[indices]
           lowerCamelCase					:					Tuple        =	self.lengths[indices]
           lowerCamelCase					:					Any        =	len(self	)
           logger.info(f'Remove {init_size - new_size} too short (<=11 tokens) sequences.'	)

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

     def 		a__						(	self:						List[Any]	)->    Optional[int]:
           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:						List[str]     ,		__a:						Optional[int]	)->    Dict:
           lowerCamelCase					:					Union[str, Any]        =	[t[0] for t in batch]
           lowerCamelCase					:					Dict        =	[t[1] for t in batch]
           assert len(__a	) == len(__a	)

           # Max for paddings
           lowerCamelCase					:					Any        =	max(__a	)

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

           lowerCamelCase					:					str        =	torch.tensor(tk_	)  # (bs, max_seq_len_)
           lowerCamelCase					:					List[str]        =	torch.tensor(__a	)  # (bs)
           return tk_t, lg_t








"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase      :Tuple	    =		logging.get_logger(__name__)

__lowerCamelCase      :Any	    =		{
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Tuple													='''glpn'''

     def __init__(	self:						Dict     ,		__a:						List[str]=3     ,		__a:						Optional[int]=4     ,		__a:						Dict=[2, 2, 2, 2]     ,		__a:						str=[8, 4, 2, 1]     ,		__a:						Optional[int]=[32, 64, 160, 256]     ,		__a:						Dict=[7, 3, 3, 3]     ,		__a:						Dict=[4, 2, 2, 2]     ,		__a:						Optional[Any]=[1, 2, 5, 8]     ,		__a:						Tuple=[4, 4, 4, 4]     ,		__a:						int="gelu"     ,		__a:						Union[str, Any]=0.0     ,		__a:						str=0.0     ,		__a:						Union[str, Any]=0.02     ,		__a:						str=0.1     ,		__a:						Union[str, Any]=1e-6     ,		__a:						Any=64     ,		__a:						Dict=10     ,		__a:						Union[str, Any]=-1     ,		**__a:						Optional[Any]     ,		)->    Dict:
           super().__init__(**__a	)

           lowerCamelCase					:					Dict        =	num_channels
           lowerCamelCase					:					Any        =	num_encoder_blocks
           lowerCamelCase					:					Dict        =	depths
           lowerCamelCase					:					List[str]        =	sr_ratios
           lowerCamelCase					:					Dict        =	hidden_sizes
           lowerCamelCase					:					Tuple        =	patch_sizes
           lowerCamelCase					:					Optional[int]        =	strides
           lowerCamelCase					:					Optional[Any]        =	mlp_ratios
           lowerCamelCase					:					Union[str, Any]        =	num_attention_heads
           lowerCamelCase					:					List[str]        =	hidden_act
           lowerCamelCase					:					Any        =	hidden_dropout_prob
           lowerCamelCase					:					Optional[int]        =	attention_probs_dropout_prob
           lowerCamelCase					:					List[Any]        =	initializer_range
           lowerCamelCase					:					Dict        =	drop_path_rate
           lowerCamelCase					:					Any        =	layer_norm_eps
           lowerCamelCase					:					Optional[Any]        =	decoder_hidden_size
           lowerCamelCase					:					Tuple        =	max_depth
           lowerCamelCase					:					Optional[Any]        =	head_in_index




