
"""simple docstring"""




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

from utils import logger



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     params

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     np.array(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     np.array([len(SCREAMING_SNAKE_CASE__					) 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     , SCREAMING_SNAKE_CASE__					)      ->	Any:



											"""simple docstring"""




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



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



											"""simple docstring"""




											return len(self.lengths					)



				def 			__magic_name__					(self					)      ->	List[str]:



											"""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 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.params.max_model_input_size
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self.lengths > max_len
											logger.info(F'''Splitting {sum(SCREAMING_SNAKE_CASE__					)} too long sequences.'''					)

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

											SCREAMING_SNAKE_CASE__		: int     =     []
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     []
											if self.params.mlm:
																		SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: List[str]     =     self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
											else:
																		SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Tuple     =     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__		: Optional[int]     =     []
																									for sub_s in divide_chunks(seq_     , max_len - 2					):
																																if sub_s[0] != cls_id:
																																							SCREAMING_SNAKE_CASE__		: Tuple     =     np.insert(SCREAMING_SNAKE_CASE__     , 0     , SCREAMING_SNAKE_CASE__					)
																																if sub_s[-1] != sep_id:
																																							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     np.insert(SCREAMING_SNAKE_CASE__     , len(SCREAMING_SNAKE_CASE__					)     , SCREAMING_SNAKE_CASE__					)
																																assert len(SCREAMING_SNAKE_CASE__					) <= max_len
																																assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																																sub_seqs.append(SCREAMING_SNAKE_CASE__					)

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

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     np.array(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     np.array(SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     len(self					)
											SCREAMING_SNAKE_CASE__		: str     =     self.lengths > 11
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.token_ids[indices]
											SCREAMING_SNAKE_CASE__		: str     =     self.lengths[indices]
											SCREAMING_SNAKE_CASE__		: List[str]     =     len(self					)
											logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''					)



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



											"""simple docstring"""




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



				def 			__magic_name__					(self					)      ->	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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: int     =     [t[0] for t in batch]
											SCREAMING_SNAKE_CASE__		: str     =     [t[1] for t in batch]
											assert len(SCREAMING_SNAKE_CASE__					) == len(SCREAMING_SNAKE_CASE__					)

											# Max for paddings
											SCREAMING_SNAKE_CASE__		: int     =     max(SCREAMING_SNAKE_CASE__					)

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

											SCREAMING_SNAKE_CASE__		: int     =     torch.tensor(tk_					)  # (bs, max_seq_len_)
											SCREAMING_SNAKE_CASE__		: str     =     torch.tensor(SCREAMING_SNAKE_CASE__					)  # (bs)
											return tk_t, lg_t






"""simple docstring"""




import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase__   :					Optional[Any]												=				logging.get_logger(__name__)

UpperCAmelCase__   :					str												=				{
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		List[Any]       =     '''detr'''
				__UpperCamelCase   :		List[Any]       =     ['''past_key_values''']
				__UpperCamelCase   :		List[Any]       =     {
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}



				def __init__(self     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=3     , SCREAMING_SNAKE_CASE__=1_00     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=20_48     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=20_48     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__="relu"     , SCREAMING_SNAKE_CASE__=2_56     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=0.02     , SCREAMING_SNAKE_CASE__=1.0     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__="sine"     , SCREAMING_SNAKE_CASE__="resnet50"     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=1     , SCREAMING_SNAKE_CASE__=5     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=1     , SCREAMING_SNAKE_CASE__=1     , SCREAMING_SNAKE_CASE__=5     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=0.1     , **SCREAMING_SNAKE_CASE__     , )      ->	Optional[Any]:



											"""simple docstring"""




											if backbone_config is not None and use_timm_backbone:
																		raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""					)

											if not use_timm_backbone:
																		if backbone_config is None:
																									logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""					)
																									SCREAMING_SNAKE_CASE__		: Any     =     CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]					)
																		elif isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																									SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     backbone_config.get("""model_type"""					)
																									SCREAMING_SNAKE_CASE__		: int     =     CONFIG_MAPPING[backbone_model_type]
																									SCREAMING_SNAKE_CASE__		: str     =     config_class.from_dict(SCREAMING_SNAKE_CASE__					)
																		# set timm attributes to None
																		SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: List[str]     =     None, None, None

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     use_timm_backbone
											SCREAMING_SNAKE_CASE__		: Tuple     =     backbone_config
											SCREAMING_SNAKE_CASE__		: List[Any]     =     num_channels
											SCREAMING_SNAKE_CASE__		: Tuple     =     num_queries
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     d_model
											SCREAMING_SNAKE_CASE__		: str     =     encoder_ffn_dim
											SCREAMING_SNAKE_CASE__		: str     =     encoder_layers
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     encoder_attention_heads
											SCREAMING_SNAKE_CASE__		: Any     =     decoder_ffn_dim
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     decoder_layers
											SCREAMING_SNAKE_CASE__		: Dict     =     decoder_attention_heads
											SCREAMING_SNAKE_CASE__		: List[str]     =     dropout
											SCREAMING_SNAKE_CASE__		: Tuple     =     attention_dropout
											SCREAMING_SNAKE_CASE__		: Tuple     =     activation_dropout
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     activation_function
											SCREAMING_SNAKE_CASE__		: Any     =     init_std
											SCREAMING_SNAKE_CASE__		: Dict     =     init_xavier_std
											SCREAMING_SNAKE_CASE__		: Any     =     encoder_layerdrop
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     decoder_layerdrop
											SCREAMING_SNAKE_CASE__		: Dict     =     encoder_layers
											SCREAMING_SNAKE_CASE__		: List[str]     =     auxiliary_loss
											SCREAMING_SNAKE_CASE__		: List[Any]     =     position_embedding_type
											SCREAMING_SNAKE_CASE__		: List[str]     =     backbone
											SCREAMING_SNAKE_CASE__		: Dict     =     use_pretrained_backbone
											SCREAMING_SNAKE_CASE__		: Any     =     dilation
											# Hungarian matcher
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     class_cost
											SCREAMING_SNAKE_CASE__		: Tuple     =     bbox_cost
											SCREAMING_SNAKE_CASE__		: List[Any]     =     giou_cost
											# Loss coefficients
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     mask_loss_coefficient
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     dice_loss_coefficient
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     bbox_loss_coefficient
											SCREAMING_SNAKE_CASE__		: Any     =     giou_loss_coefficient
											SCREAMING_SNAKE_CASE__		: List[str]     =     eos_coefficient
											super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)



				@property
				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											return self.encoder_attention_heads



				@property
				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											return self.d_model



				@classmethod
				def 			__magic_name__					(cls     , SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)      ->	Tuple:



											"""simple docstring"""




											return cls(backbone_config=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Dict[str, any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     copy.deepcopy(self.__dict__					)
											if output["backbone_config"] is not None:
																		SCREAMING_SNAKE_CASE__		: Any     =     self.backbone_config.to_dict()
											SCREAMING_SNAKE_CASE__		: Dict     =     self.__class__.model_type
											return output






class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		Any       =     version.parse('''1.11'''						)



				@property
				def 			__magic_name__					(self					)      ->	Mapping[str, Mapping[int, str]]:



											"""simple docstring"""




											return OrderedDict(
											    [
											        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
											        ("""pixel_mask""", {0: """batch"""}),
											    ]					)



				@property
				def 			__magic_name__					(self					)      ->	float:



											"""simple docstring"""




											return 1E-5



				@property
				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											return 12




