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

from ..utils.generic import ModelOutput






class      __lowercase					(    UpperCamelCase_    ):

						def __init__(	self				:      str							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      Optional[Any]   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		dataset
									lowercase							=		process
									lowercase							=		params

						def __len__(	self				:      Tuple   )							->		Tuple:


									'''simple docstring'''

									return len(self.dataset   )



						def __getitem__(	self				:      List[str]							,							__lowerCamelCase				:      Optional[Any]   )							->		str:


									'''simple docstring'''

									lowercase							=		self.dataset[i]
									lowercase							=		self.process(UpperCamelCase__							,							**self.params   )
									return processed






class      __lowercase					(    UpperCamelCase_    ):

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


									'''simple docstring'''

									lowercase							=		loader
									lowercase							=		infer
									lowercase							=		params
									if loader_batch_size == 1:
												# Let's spare some time by deactivating altogether
												lowercase							=		None
									lowercase							=		loader_batch_size

									# Internal bookkeeping
									lowercase							=		None
									lowercase							=		None

						def __len__(	self				:      int   )							->		int:


									'''simple docstring'''

									return len(self.loader   )

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


									'''simple docstring'''

									lowercase							=		iter(self.loader   )
									return self

						def        __a		(	self				:      List[str]   )							->		Optional[int]:


									'''simple docstring'''

									if isinstance(self._loader_batch_data							,							torch.Tensor   ):
												# Batch data is simple tensor, just fetch the slice
												lowercase							=		self._loader_batch_data[self._loader_batch_index]
									else:
												# Batch data is assumed to be BaseModelOutput (or dict)
												lowercase							=		{}
												for k, element in self._loader_batch_data.items():
															if isinstance(UpperCamelCase__							,							UpperCamelCase__   ):
																		# Convert ModelOutput to tuple first
																		lowercase							=		element.to_tuple()
																		if isinstance(element[0]							,							torch.Tensor   ):
																					lowercase							=		tuple(el[self._loader_batch_index].unsqueeze(0   ) for el in element   )
																		elif isinstance(element[0]							,							np.ndarray   ):
																					lowercase							=		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   ):
																					lowercase							=		tuple(el[self._loader_batch_index].unsqueeze(0   ) for el in element   )
																		elif isinstance(element[0]							,							np.ndarray   ):
																					lowercase							=		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
																		lowercase							=		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

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



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


									'''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
									lowercase							=		next(self.iterator   )
									lowercase							=		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   ):
															lowercase							=		processed
												else:
															lowercase							=		list(processed.keys()   )[0]
															lowercase							=		processed[key]
												if isinstance(UpperCamelCase__							,							UpperCamelCase__   ):
															lowercase							=		len(UpperCamelCase__   )
												else:
															lowercase							=		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.
															lowercase							=		observed_batch_size
												# Setting internal index to unwrap the batch
												lowercase							=		processed
												lowercase							=		0
												return self.loader_batch_item()
									else:
												# We're not unrolling batches
												return processed






class      __lowercase					(    UpperCamelCase_    ):

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


									'''simple docstring'''

									super().__init__(UpperCamelCase__							,							UpperCamelCase__							,							UpperCamelCase__   )

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


									'''simple docstring'''

									lowercase							=		iter(self.loader   )
									lowercase							=		None
									return self



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


									'''simple docstring'''

									if self.subiterator is None:
												lowercase							=		self.infer(next(self.iterator   )							,							**self.params   )
									try:
												# Try to return next item
												lowercase							=		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
												lowercase							=		self.infer(next(self.iterator   )							,							**self.params   )
												lowercase							=		next(self.subiterator   )
									return processed






class      __lowercase					(    UpperCamelCase_    ):

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


									'''simple docstring'''

									lowercase							=		iter(self.loader   )
									return self



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


									'''simple docstring'''

									lowercase							=		False
									lowercase							=		[]
									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:
															lowercase							=		self.loader_batch_item()
															lowercase							=		item.pop('''is_last'''   )
															accumulator.append(UpperCamelCase__   )
															if is_last:
																		return accumulator

									while not is_last:
												lowercase							=		self.infer(next(self.iterator   )							,							**self.params   )
												if self.loader_batch_size is not None:
															if isinstance(UpperCamelCase__							,							torch.Tensor   ):
																		lowercase							=		processed
															else:
																		lowercase							=		list(processed.keys()   )[0]
																		lowercase							=		processed[key]
															if isinstance(UpperCamelCase__							,							UpperCamelCase__   ):
																		lowercase							=		len(UpperCamelCase__   )
															else:
																		lowercase							=		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.
																		lowercase							=		observed_batch_size
															lowercase							=		processed
															lowercase							=		0
															while self._loader_batch_index < self.loader_batch_size:
																		lowercase							=		self.loader_batch_item()
																		lowercase							=		item.pop('''is_last'''   )
																		accumulator.append(UpperCamelCase__   )
																		if is_last:
																					return accumulator
												else:
															lowercase							=		processed
															lowercase							=		item.pop('''is_last'''   )
															accumulator.append(UpperCamelCase__   )
									return accumulator






class      __lowercase					(    UpperCamelCase_    ):

						def __init__(	self				:      Optional[Any]							,							__lowerCamelCase				:      Dataset							,							__lowerCamelCase				:      str   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		dataset
									lowercase							=		key

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


									'''simple docstring'''

									return len(self.dataset   )



						def __getitem__(	self				:      Dict							,							__lowerCamelCase				:      Tuple   )							->		Tuple:


									'''simple docstring'''

									return self.dataset[i][self.key]






class      __lowercase					(    UpperCamelCase_    ):

						def __init__(	self				:      List[Any]							,							__lowerCamelCase				:      Dataset							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      str   )							->		Union[str, Any]:


									'''simple docstring'''

									lowercase							=		dataset
									lowercase							=		keya
									lowercase							=		keya

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


									'''simple docstring'''

									return len(self.dataset   )



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


									'''simple docstring'''

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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       int:



			"""simple docstring"""







			while a != 0:
						lowercase		,lowercase							=		b % a, a
			return b






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       int:



			"""simple docstring"""







			if gcd(UpperCAmelCase,		UpperCAmelCase       ) != 1:
						lowercase							=		f'mod inverse of {a!r} and {m!r} does not exist'
						raise ValueError(UpperCAmelCase       )
			lowercase		,lowercase		,lowercase							=		1, 0, a
			lowercase		,lowercase		,lowercase							=		0, 1, m
			while va != 0:
						lowercase							=		ua // va
						lowercase		,lowercase		,lowercase		,lowercase		,lowercase		,lowercase							=		(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
			return ua % m





