




"""simple docstring"""







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

from ..utils.generic import ModelOutput






class       SCREAMING_SNAKE_CASE__					(					UpperCamelCase_					):


						def __init__(							self				:			str						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			Optional[Any]					):
										lowerCAmelCase  =				dataset
										lowerCAmelCase  =				process
										lowerCAmelCase  =				params


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




						def __getitem__(							self				:			List[str]						,						lowerCAmelCase				:			Optional[Any]					):
										lowerCAmelCase  =				self.dataset[i]
										lowerCAmelCase  =				self.process(UpperCamelCase__						,						**self.params					)
										return processed






class       SCREAMING_SNAKE_CASE__					(					UpperCamelCase_					):


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

										# Internal bookkeeping
										lowerCAmelCase  =				None
										lowerCAmelCase  =				None


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


						def __iter__(							self				:			List[str]					):
										lowerCAmelCase  =				iter(self.loader					)
										return self


						def   __lowercase (							self				:			List[str]					):





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

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




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






class       SCREAMING_SNAKE_CASE__					(					UpperCamelCase_					):


						def __init__(							self				:			str						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			Union[str, Any]=None					):
										super().__init__(UpperCamelCase__						,						UpperCamelCase__						,						UpperCamelCase__					)


						def __iter__(							self				:			Union[str, Any]					):
										lowerCAmelCase  =				iter(self.loader					)
										lowerCAmelCase  =				None
										return self




						def   __lowercase (							self				:			List[str]					):





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






class       SCREAMING_SNAKE_CASE__					(					UpperCamelCase_					):


						def __iter__(							self				:			Tuple					):
										lowerCAmelCase  =				iter(self.loader					)
										return self




						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				False
										lowerCAmelCase  =				[]
										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:
																		lowerCAmelCase  =				self.loader_batch_item()
																		lowerCAmelCase  =				item.pop("""is_last"""					)
																		accumulator.append(UpperCamelCase__					)
																		if is_last:
																						return accumulator

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






class       SCREAMING_SNAKE_CASE__					(					UpperCamelCase_					):


						def __init__(							self				:			Optional[Any]						,						lowerCAmelCase				:			Dataset						,						lowerCAmelCase				:			str					):
										lowerCAmelCase  =				dataset
										lowerCAmelCase  =				key


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




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






class       SCREAMING_SNAKE_CASE__					(					UpperCamelCase_					):


						def __init__(							self				:			List[Any]						,						lowerCAmelCase				:			Dataset						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			str					):
										lowerCAmelCase  =				dataset
										lowerCAmelCase  =				keya
										lowerCAmelCase  =				keya


						def __len__(							self				:			List[str]					):
										return len(self.dataset					)




						def __getitem__(							self				:			Union[str, Any]						,						lowerCAmelCase				:			Any					):
										return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}











"""simple docstring"""







from typing import TYPE_CHECKING

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


a      =							{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BioGptForCausalLM',
		    'BioGptForTokenClassification',
		    'BioGptForSequenceClassification',
		    'BioGptModel',
		    'BioGptPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
		from .tokenization_biogpt import BioGptTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_biogpt import (
				    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptPreTrainedModel,
				)


else:
		import sys

		a      =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





