



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

from ..utils.generic import ModelOutput



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				dataset
							UpperCAmelCase_						=				process
							UpperCAmelCase_						=				params



					def __len__(			self     ):
							return len(self.dataset     )



					def __getitem__(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				self.dataset[i]
							UpperCAmelCase_						=				self.process(lowerCAmelCase    ,	**self.params     )
							return processed





class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase=None     ):
							UpperCAmelCase_						=				loader
							UpperCAmelCase_						=				infer
							UpperCAmelCase_						=				params
							if loader_batch_size == 1:
									# Let's spare some time by deactivating altogether
									UpperCAmelCase_						=				None
							UpperCAmelCase_						=				loader_batch_size

							# Internal bookkeeping
							UpperCAmelCase_						=				None
							UpperCAmelCase_						=				None



					def __len__(			self     ):
							return len(self.loader     )



					def __iter__(			self     ):
							UpperCAmelCase_						=				iter(self.loader     )
							return self



					def    A__		(			self     ):
							if isinstance(self._loader_batch_data    ,	torch.Tensor     ):
									# Batch data is simple tensor, just fetch the slice
									UpperCAmelCase_						=				self._loader_batch_data[self._loader_batch_index]
							else:
									# Batch data is assumed to be BaseModelOutput (or dict)
									UpperCAmelCase_						=				{}
									for k, element in self._loader_batch_data.items():
											if isinstance(lowerCAmelCase    ,	lowerCAmelCase     ):
													# Convert ModelOutput to tuple first
													UpperCAmelCase_						=				element.to_tuple()
													if isinstance(element[0]    ,	torch.Tensor     ):
															UpperCAmelCase_						=				tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
													elif isinstance(element[0]    ,	np.ndarray     ):
															UpperCAmelCase_						=				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(lowerCAmelCase    ,	lowerCAmelCase     ):
													# Those are stored as lists of tensors so need specific unbatching.
													if isinstance(element[0]    ,	torch.Tensor     ):
															UpperCAmelCase_						=				tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
													elif isinstance(element[0]    ,	np.ndarray     ):
															UpperCAmelCase_						=				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_						=				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_						=				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_						=				np.expand_dims(element[self._loader_batch_index]    ,	0     )
											else:
													# This is typically a list, so no need to `unsqueeze`.
													UpperCAmelCase_						=				element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
									UpperCAmelCase_						=				self._loader_batch_data.__class__(lowerCAmelCase     )
							self._loader_batch_index += 1
							return result



					def    A__		(			self     ):
							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_						=				next(self.iterator     )
							UpperCAmelCase_						=				self.infer(lowerCAmelCase    ,	**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(lowerCAmelCase    ,	torch.Tensor     ):
											UpperCAmelCase_						=				processed
									else:
											UpperCAmelCase_						=				list(processed.keys()     )[0]
											UpperCAmelCase_						=				processed[key]
									if isinstance(lowerCAmelCase    ,	lowerCAmelCase     ):
											UpperCAmelCase_						=				len(lowerCAmelCase     )
									else:
											UpperCAmelCase_						=				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_						=				observed_batch_size
									# Setting internal index to unwrap the batch
									UpperCAmelCase_						=				processed
									UpperCAmelCase_						=				0
									return self.loader_batch_item()
							else:
									# We're not unrolling batches
									return processed





class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase=None     ):
							super().__init__(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     )



					def __iter__(			self     ):
							UpperCAmelCase_						=				iter(self.loader     )
							UpperCAmelCase_						=				None
							return self



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





class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					def __iter__(			self     ):
							UpperCAmelCase_						=				iter(self.loader     )
							return self



					def    A__		(			self     ):
							# 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_						=				False
							UpperCAmelCase_						=				[]
							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_						=				self.loader_batch_item()
											UpperCAmelCase_						=				item.pop("is_last"     )
											accumulator.append(lowerCAmelCase     )
											if is_last:
													return accumulator

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





class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				dataset
							UpperCAmelCase_						=				key



					def __len__(			self     ):
							return len(self.dataset     )



					def __getitem__(			self    ,	lowerCAmelCase     ):
							return self.dataset[i][self.key]





class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				dataset
							UpperCAmelCase_						=				keya
							UpperCAmelCase_						=				keya



					def __len__(			self     ):
							return len(self.dataset     )



					def __getitem__(			self    ,	lowerCAmelCase     ):
							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_tokenizers_available


SCREAMING_SNAKE_CASE     				=						{"tokenization_herbert": ["HerbertTokenizer"]}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["HerbertTokenizerFast"]


if TYPE_CHECKING:
			from .tokenization_herbert import HerbertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_herbert_fast import HerbertTokenizerFast

else:
			import sys

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

