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

from ..utils.generic import ModelOutput




class  lowerCamelCase__							(				_UpperCAmelCase):




	'''simple docstring'''




	def __init__(					self		:str     ,   a		:str     ,   a		:Optional[Any]     ,   a		:Optional[Any]						)		-> Optional[int]:
								__UpperCamelCase	:   Any			   =					dataset
								__UpperCamelCase	:   Dict			   =					process
								__UpperCamelCase	:   List[Any]			   =					params







	def __len__(					self		:Optional[int]						)		-> Tuple:
								return len(self.dataset						)







	def __getitem__(					self		:List[Any]     ,   a		:Optional[int]						)		-> List[Any]:
								__UpperCamelCase	:   List[str]			   =					self.dataset[i]
								__UpperCamelCase	:   Optional[Any]			   =					self.process(_UpperCAmelCase     ,   **self.params						)
								return processed




class  lowerCamelCase__							(				_UpperCAmelCase):




	'''simple docstring'''




	def __init__(					self		:Tuple     ,   a		:int     ,   a		:Optional[int]     ,   a		:Dict     ,   a		:List[Any]=None						)		-> List[Any]:
								__UpperCamelCase	:   Union[str, Any]			   =					loader
								__UpperCamelCase	:   Dict			   =					infer
								__UpperCamelCase	:   str			   =					params
								if loader_batch_size == 1:
															# Let's spare some time by deactivating altogether
															__UpperCamelCase	:   Any			   =					None
								__UpperCamelCase	:   Any			   =					loader_batch_size

								# Internal bookkeeping
								__UpperCamelCase	:   Any			   =					None
								__UpperCamelCase	:   List[str]			   =					None







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







	def __iter__(					self		:List[str]						)		-> List[Any]:
								__UpperCamelCase	:   Tuple			   =					iter(self.loader						)
								return self







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Tuple:
								if isinstance(self._loader_batch_data     ,   torch.Tensor						):
															# Batch data is simple tensor, just fetch the slice
															__UpperCamelCase	:   int			   =					self._loader_batch_data[self._loader_batch_index]
								else:
															# Batch data is assumed to be BaseModelOutput (or dict)
															__UpperCamelCase	:   Dict			   =					{}
															for k, element in self._loader_batch_data.items():
																						if isinstance(_UpperCAmelCase     ,   _UpperCAmelCase						):
																													# Convert ModelOutput to tuple first
																													__UpperCamelCase	:   Union[str, Any]			   =					element.to_tuple()
																													if isinstance(element[0]     ,   torch.Tensor						):
																																				__UpperCamelCase	:   Any			   =					tuple(el[self._loader_batch_index].unsqueeze(0						) for el in element						)
																													elif isinstance(element[0]     ,   np.ndarray						):
																																				__UpperCamelCase	:   Optional[int]			   =					tuple(np.expand_dims(el[self._loader_batch_index]     ,   0						) for el in element						)
																													continue
																						if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_UpperCAmelCase     ,   _UpperCAmelCase						):
																													# Those are stored as lists of tensors so need specific unbatching.
																													if isinstance(element[0]     ,   torch.Tensor						):
																																				__UpperCamelCase	:   List[str]			   =					tuple(el[self._loader_batch_index].unsqueeze(0						) for el in element						)
																													elif isinstance(element[0]     ,   np.ndarray						):
																																				__UpperCamelCase	:   str			   =					tuple(np.expand_dims(el[self._loader_batch_index]     ,   0						) for el in element						)
																													continue
																						if element is None:
																													# This can happen for optional data that get passed around
																													__UpperCamelCase	:   Optional[int]			   =					None
																						elif isinstance(element[self._loader_batch_index]     ,   torch.Tensor						):
																													# Take correct batch data, but make it looked like batch_size=1
																													# For compatibility with other methods within transformers

																													__UpperCamelCase	:   Any			   =					element[self._loader_batch_index].unsqueeze(0						)
																						elif isinstance(element[self._loader_batch_index]     ,   np.ndarray						):
																													# Take correct batch data, but make it looked like batch_size=1
																													# For compatibility with other methods within transformers
																													__UpperCamelCase	:   List[str]			   =					np.expand_dims(element[self._loader_batch_index]     ,   0						)
																						else:
																													# This is typically a list, so no need to `unsqueeze`.
																													__UpperCamelCase	:   List[Any]			   =					element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
															__UpperCamelCase	:   Any			   =					self._loader_batch_data.__class__(_UpperCAmelCase						)
								self._loader_batch_index += 1
								return result







	def    _lowerCamelCase				(					self		:Any						)		-> Optional[Any]:
								if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
															# We are currently unrolling a batch so we just need to return
															# the current item within a batch
															return self.loader_batch_item()

								# We're out of items within a batch
								__UpperCamelCase	:   Dict			   =					next(self.iterator						)
								__UpperCamelCase	:   Any			   =					self.infer(_UpperCAmelCase     ,   **self.params						)
								# We now have a batch of "inferred things".
								if self.loader_batch_size is not None:
															# Try to infer the size of the batch
															if isinstance(_UpperCAmelCase     ,   torch.Tensor						):
																						__UpperCamelCase	:   Union[str, Any]			   =					processed
															else:
																						__UpperCamelCase	:   Dict			   =					list(processed.keys()						)[0]
																						__UpperCamelCase	:   Union[str, Any]			   =					processed[key]
															if isinstance(_UpperCAmelCase     ,   _UpperCAmelCase						):
																						__UpperCamelCase	:   Any			   =					len(_UpperCAmelCase						)
															else:
																						__UpperCamelCase	:   str			   =					first_tensor.shape[0]
															if 0 < observed_batch_size < self.loader_batch_size:
																						# could be last batch so we can't unroll as many
																						# elements.
																						__UpperCamelCase	:   Union[str, Any]			   =					observed_batch_size
															# Setting internal index to unwrap the batch
															__UpperCamelCase	:   Tuple			   =					processed
															__UpperCamelCase	:   Optional[int]			   =					0
															return self.loader_batch_item()
								else:
															# We're not unrolling batches
															return processed




class  lowerCamelCase__							(				_UpperCAmelCase):




	'''simple docstring'''




	def __init__(					self		:Any     ,   a		:str     ,   a		:List[str]     ,   a		:Union[str, Any]     ,   a		:Tuple=None						)		-> Optional[int]:
								super().__init__(_UpperCAmelCase     ,   _UpperCAmelCase     ,   _UpperCAmelCase						)







	def __iter__(					self		:Optional[Any]						)		-> Tuple:
								__UpperCamelCase	:   Any			   =					iter(self.loader						)
								__UpperCamelCase	:   Optional[Any]			   =					None
								return self







	def    _lowerCamelCase				(					self		:List[str]						)		-> Union[str, Any]:
								if self.subiterator is None:
															__UpperCamelCase	:   Optional[int]			   =					self.infer(next(self.iterator						)     ,   **self.params						)
								try:
															# Try to return next item
															__UpperCamelCase	:   str			   =					next(self.subiterator						)
								except StopIteration:
															# When a preprocess iterator ends, we can start lookig at the next item
															# ChunkIterator will keep feeding until ALL elements of iterator
															# all have created their subiterator and have been iterating against.
															#
															# Another way to look at it, is we're basically flattening lists of lists
															# into a single list, but with generators
															__UpperCamelCase	:   Any			   =					self.infer(next(self.iterator						)     ,   **self.params						)
															__UpperCamelCase	:   int			   =					next(self.subiterator						)
								return processed




class  lowerCamelCase__							(				_UpperCAmelCase):




	'''simple docstring'''




	def __iter__(					self		:Union[str, Any]						)		-> Optional[int]:
								__UpperCamelCase	:   Optional[int]			   =					iter(self.loader						)
								return self







	def    _lowerCamelCase				(					self		:Tuple						)		-> Dict:
								# Extremely similar to PipelineIterator in its unpacking mechanism
								# BUT, we have an extra required item which is the presence of `is_last`
								# That is because everything is flattened by `PipelineChunkIterator` we
								# need to keep track of how to regroup here in the original `process`
								# boundaries so that `process` and `postprocess` see the same data.

								# This iterator accumulates items (possibly while unbatching) until it
								# its a `is_last` and then just passes it on to the caller.
								__UpperCamelCase	:   int			   =					False
								__UpperCamelCase	:   Any			   =					[]
								if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
															while self._loader_batch_index < self.loader_batch_size:
																						__UpperCamelCase	:   Union[str, Any]			   =					self.loader_batch_item()
																						__UpperCamelCase	:   Tuple			   =					item.pop("is_last"						)
																						accumulator.append(_UpperCAmelCase						)
																						if is_last:
																													return accumulator

								while not is_last:
															__UpperCamelCase	:   str			   =					self.infer(next(self.iterator						)     ,   **self.params						)
															if self.loader_batch_size is not None:
																						if isinstance(_UpperCAmelCase     ,   torch.Tensor						):
																													__UpperCamelCase	:   str			   =					processed
																						else:
																													__UpperCamelCase	:   Union[str, Any]			   =					list(processed.keys()						)[0]
																													__UpperCamelCase	:   Optional[Any]			   =					processed[key]
																						if isinstance(_UpperCAmelCase     ,   _UpperCAmelCase						):
																													__UpperCamelCase	:   Tuple			   =					len(_UpperCAmelCase						)
																						else:
																													__UpperCamelCase	:   Optional[Any]			   =					first_tensor.shape[0]
																						if 0 < observed_batch_size < self.loader_batch_size:
																													# could be last batch so we can't unroll as many
																													# elements.
																													__UpperCamelCase	:   Any			   =					observed_batch_size
																						__UpperCamelCase	:   int			   =					processed
																						__UpperCamelCase	:   Dict			   =					0
																						while self._loader_batch_index < self.loader_batch_size:
																													__UpperCamelCase	:   Union[str, Any]			   =					self.loader_batch_item()
																													__UpperCamelCase	:   Union[str, Any]			   =					item.pop("is_last"						)
																													accumulator.append(_UpperCAmelCase						)
																													if is_last:
																																				return accumulator
															else:
																						__UpperCamelCase	:   Union[str, Any]			   =					processed
																						__UpperCamelCase	:   List[str]			   =					item.pop("is_last"						)
																						accumulator.append(_UpperCAmelCase						)
								return accumulator




class  lowerCamelCase__							(				_UpperCAmelCase):




	'''simple docstring'''




	def __init__(					self		:Union[str, Any]     ,   a		:int     ,   a		:List[Any]						)		-> Tuple:
								__UpperCamelCase	:   Union[str, Any]			   =					dataset
								__UpperCamelCase	:   str			   =					key







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







	def __getitem__(					self		:Optional[int]     ,   a		:Any						)		-> str:
								return self.dataset[i][self.key]




class  lowerCamelCase__							(				_UpperCAmelCase):




	'''simple docstring'''




	def __init__(					self		:Dict     ,   a		:Optional[Any]     ,   a		:str     ,   a		:Tuple						)		-> str:
								__UpperCamelCase	:   Optional[int]			   =					dataset
								__UpperCamelCase	:   List[str]			   =					keya
								__UpperCamelCase	:   List[str]			   =					keya







	def __len__(					self		:Optional[Any]						)		-> List[str]:
								return len(self.dataset						)







	def __getitem__(					self		:Optional[Any]     ,   a		:Tuple						)		-> Optional[Any]:
								return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
"""simple docstring"""






import copy

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


__A			   = logging.get_logger(__name__)

__A			   = {
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = "deta"
    _UpperCAmelCase	:Any						    = {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }






    def __init__(   self    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=900    ,			_UpperCAmelCase=2048    ,			_UpperCAmelCase=6    ,			_UpperCAmelCase=2048    ,			_UpperCAmelCase=8    ,			_UpperCAmelCase=6    ,			_UpperCAmelCase=1024    ,			_UpperCAmelCase=8    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase="relu"    ,			_UpperCAmelCase=256    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=0.02    ,			_UpperCAmelCase=1.0    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase="sine"    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=300    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.25    ,			**_UpperCAmelCase    ,			):
       if backbone_config is None:
          logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''							)
          lowercase__:   str					=      CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']							)
       else:
          if isinstance(_UpperCAmelCase    ,			_UpperCAmelCase							):
             lowercase__:   Tuple					=      backbone_config.pop('''model_type'''							)
             lowercase__:   str					=      CONFIG_MAPPING[backbone_model_type]
             lowercase__:   Optional[int]					=      config_class.from_dict(_UpperCAmelCase							)

       lowercase__:   int					=      backbone_config
       lowercase__:   Any					=      num_queries
       lowercase__:   List[str]					=      max_position_embeddings
       lowercase__:   Optional[Any]					=      d_model
       lowercase__:   List[Any]					=      encoder_ffn_dim
       lowercase__:   Tuple					=      encoder_layers
       lowercase__:   Dict					=      encoder_attention_heads
       lowercase__:   Any					=      decoder_ffn_dim
       lowercase__:   Union[str, Any]					=      decoder_layers
       lowercase__:   List[Any]					=      decoder_attention_heads
       lowercase__:   int					=      dropout
       lowercase__:   List[str]					=      attention_dropout
       lowercase__:   Tuple					=      activation_dropout
       lowercase__:   Tuple					=      activation_function
       lowercase__:   int					=      init_std
       lowercase__:   Optional[Any]					=      init_xavier_std
       lowercase__:   Optional[Any]					=      encoder_layerdrop
       lowercase__:   Optional[int]					=      auxiliary_loss
       lowercase__:   Union[str, Any]					=      position_embedding_type
       # deformable attributes
       lowercase__:   List[str]					=      num_feature_levels
       lowercase__:   Optional[Any]					=      encoder_n_points
       lowercase__:   int					=      decoder_n_points
       lowercase__:   str					=      two_stage
       lowercase__:   Optional[int]					=      two_stage_num_proposals
       lowercase__:   Tuple					=      with_box_refine
       lowercase__:   str					=      assign_first_stage
       if two_stage is True and with_box_refine is False:
          raise ValueError('''If two_stage is True, with_box_refine must be True.'''							)
       # Hungarian matcher
       lowercase__:   Union[str, Any]					=      class_cost
       lowercase__:   Optional[int]					=      bbox_cost
       lowercase__:   int					=      giou_cost
       # Loss coefficients
       lowercase__:   Optional[int]					=      mask_loss_coefficient
       lowercase__:   List[str]					=      dice_loss_coefficient
       lowercase__:   str					=      bbox_loss_coefficient
       lowercase__:   Union[str, Any]					=      giou_loss_coefficient
       lowercase__:   Optional[int]					=      eos_coefficient
       lowercase__:   str					=      focal_alpha
       super().__init__(is_encoder_decoder=_UpperCAmelCase    ,			**_UpperCAmelCase							)






    @property
    def _snake_case			(   self							):
       return self.encoder_attention_heads






    @property
    def _snake_case			(   self							):
       return self.d_model






    def _snake_case			(   self							):
       lowercase__:   Union[str, Any]					=      copy.deepcopy(self.__dict__							)
       lowercase__:   Dict					=      self.backbone_config.to_dict()
       lowercase__:   Union[str, Any]					=      self.__class__.model_type
       return output
