





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

from ..utils.generic import ModelOutput
class SCREAMING_SNAKE_CASE_						(		lowercase__					):






						def __init__(      self      :      Any		,   _A      :      Any		,   _A      :      Any		,   _A      :      int	)							->    int:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	dataset
							snake_case_		:			int											=	process
							snake_case_		:			Optional[int]											=	params






						def __len__(      self      :      List[Any]	)							->    int:







							"""simple docstring"""


							return len(self.dataset	)






						def __getitem__(      self      :      Optional[Any]		,   _A      :      Union[str, Any]	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Dict											=	self.dataset[i]
							snake_case_		:			Any											=	self.process(_A		,   **self.params	)
							return processed
class SCREAMING_SNAKE_CASE_						(		lowercase__					):






						def __init__(      self      :      Tuple		,   _A      :      List[Any]		,   _A      :      Dict		,   _A      :      int		,   _A      :      str=None	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			str											=	loader
							snake_case_		:			int											=	infer
							snake_case_		:			Union[str, Any]											=	params
							if loader_batch_size == 1:
								# Let's spare some time by deactivating altogether
								snake_case_		:			str											=	None
							snake_case_		:			Union[str, Any]											=	loader_batch_size

							# Internal bookkeeping
							snake_case_		:			List[Any]											=	None
							snake_case_		:			Union[str, Any]											=	None






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







							"""simple docstring"""


							return len(self.loader	)






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







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	iter(self.loader	)
							return self






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







							"""simple docstring"""


							if isinstance(self._loader_batch_data		,   torch.Tensor	):
								# Batch data is simple tensor, just fetch the slice
								snake_case_		:			Optional[Any]											=	self._loader_batch_data[self._loader_batch_index]
							else:
								# Batch data is assumed to be BaseModelOutput (or dict)
								snake_case_		:			List[Any]											=	{}
								for k, element in self._loader_batch_data.items():
									if isinstance(_A		,   _A	):
										# Convert ModelOutput to tuple first
										snake_case_		:			Tuple											=	element.to_tuple()
										if isinstance(element[0]		,   torch.Tensor	):
											snake_case_		:			int											=	tuple(el[self._loader_batch_index].unsqueeze(0	) for el in element	)
										elif isinstance(element[0]		,   np.ndarray	):
											snake_case_		:			str											=	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(_A		,   _A	):
										# Those are stored as lists of tensors so need specific unbatching.
										if isinstance(element[0]		,   torch.Tensor	):
											snake_case_		:			Tuple											=	tuple(el[self._loader_batch_index].unsqueeze(0	) for el in element	)
										elif isinstance(element[0]		,   np.ndarray	):
											snake_case_		:			Dict											=	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
										snake_case_		:			Tuple											=	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

										snake_case_		:			Optional[int]											=	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
										snake_case_		:			Dict											=	np.expand_dims(element[self._loader_batch_index]		,   0	)
									else:
										# This is typically a list, so no need to `unsqueeze`.
										snake_case_		:			Union[str, Any]											=	element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
								snake_case_		:			Optional[int]											=	self._loader_batch_data.__class__(_A	)
							self._loader_batch_index += 1
							return result






						def UpperCAmelCase_				(      self      :      str	)							->    int:







							"""simple docstring"""


							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
							snake_case_		:			Tuple											=	next(self.iterator	)
							snake_case_		:			Any											=	self.infer(_A		,   **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(_A		,   torch.Tensor	):
									snake_case_		:			Optional[Any]											=	processed
								else:
									snake_case_		:			Tuple											=	list(processed.keys()	)[0]
									snake_case_		:			Tuple											=	processed[key]
								if isinstance(_A		,   _A	):
									snake_case_		:			List[str]											=	len(_A	)
								else:
									snake_case_		:			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.
									snake_case_		:			Optional[int]											=	observed_batch_size
								# Setting internal index to unwrap the batch
								snake_case_		:			List[str]											=	processed
								snake_case_		:			Optional[Any]											=	0
								return self.loader_batch_item()
							else:
								# We're not unrolling batches
								return processed
class SCREAMING_SNAKE_CASE_						(		lowercase__					):






						def __init__(      self      :      Optional[Any]		,   _A      :      List[Any]		,   _A      :      Any		,   _A      :      Optional[Any]		,   _A      :      str=None	)							->    Optional[Any]:







							"""simple docstring"""


							super().__init__(_A		,   _A		,   _A	)






						def __iter__(      self      :      Tuple	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	iter(self.loader	)
							snake_case_		:			int											=	None
							return self






						def UpperCAmelCase_				(      self      :      Dict	)							->    List[str]:







							"""simple docstring"""


							if self.subiterator is None:
								snake_case_		:			int											=	self.infer(next(self.iterator	)		,   **self.params	)
							try:
								# Try to return next item
								snake_case_		:			List[Any]											=	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
								snake_case_		:			Optional[Any]											=	self.infer(next(self.iterator	)		,   **self.params	)
								snake_case_		:			List[Any]											=	next(self.subiterator	)
							return processed
class SCREAMING_SNAKE_CASE_						(		lowercase__					):






						def __iter__(      self      :      Any	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			List[str]											=	iter(self.loader	)
							return self






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







							"""simple docstring"""


							snake_case_		:			Tuple											=	False
							snake_case_		:			Dict											=	[]
							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:
									snake_case_		:			str											=	self.loader_batch_item()
									snake_case_		:			Any											=	item.pop('is_last'	)
									accumulator.append(_A	)
									if is_last:
										return accumulator

							while not is_last:
								snake_case_		:			Dict											=	self.infer(next(self.iterator	)		,   **self.params	)
								if self.loader_batch_size is not None:
									if isinstance(_A		,   torch.Tensor	):
										snake_case_		:			Optional[Any]											=	processed
									else:
										snake_case_		:			Tuple											=	list(processed.keys()	)[0]
										snake_case_		:			str											=	processed[key]
									if isinstance(_A		,   _A	):
										snake_case_		:			Union[str, Any]											=	len(_A	)
									else:
										snake_case_		:			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.
										snake_case_		:			str											=	observed_batch_size
									snake_case_		:			Optional[int]											=	processed
									snake_case_		:			int											=	0
									while self._loader_batch_index < self.loader_batch_size:
										snake_case_		:			Any											=	self.loader_batch_item()
										snake_case_		:			Union[str, Any]											=	item.pop('is_last'	)
										accumulator.append(_A	)
										if is_last:
											return accumulator
								else:
									snake_case_		:			Optional[Any]											=	processed
									snake_case_		:			Tuple											=	item.pop('is_last'	)
									accumulator.append(_A	)
							return accumulator
class SCREAMING_SNAKE_CASE_						(		lowercase__					):






						def __init__(      self      :      List[str]		,   _A      :      Any		,   _A      :      Dict	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			int											=	dataset
							snake_case_		:			int											=	key






						def __len__(      self      :      int	)							->    Dict:







							"""simple docstring"""


							return len(self.dataset	)






						def __getitem__(      self      :      Optional[int]		,   _A      :      Tuple	)							->    List[str]:







							"""simple docstring"""


							return self.dataset[i][self.key]
class SCREAMING_SNAKE_CASE_						(		lowercase__					):






						def __init__(      self      :      str		,   _A      :      Dict		,   _A      :      Union[str, Any]		,   _A      :      List[Any]	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			int											=	dataset
							snake_case_		:			Tuple											=	keya
							snake_case_		:			Optional[Any]											=	keya






						def __len__(      self      :      int	)							->    Tuple:







							"""simple docstring"""


							return len(self.dataset	)






						def __getitem__(      self      :      List[Any]		,   _A      :      Tuple	)							->    List[str]:







							"""simple docstring"""


							return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE      	=		{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """SwinForImageClassification""",
						    """SwinForMaskedImageModeling""",
						    """SwinModel""",
						    """SwinPreTrainedModel""",
						    """SwinBackbone""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFSwinForImageClassification""",
						    """TFSwinForMaskedImageModeling""",
						    """TFSwinModel""",
						    """TFSwinPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swin import (
												    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwinBackbone,
												    SwinForImageClassification,
												    SwinForMaskedImageModeling,
												    SwinModel,
												    SwinPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_swin import (
												    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFSwinForImageClassification,
												    TFSwinForMaskedImageModeling,
												    TFSwinModel,
												    TFSwinPreTrainedModel,
												)

else:
						import sys

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

