

"""simple docstring"""





from typing import TYPE_CHECKING

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


__UpperCamelCase  :      str							      =		{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Any							      =		['''MvpTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Dict							      =		[
							    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''MvpForCausalLM''',
							    '''MvpForConditionalGeneration''',
							    '''MvpForQuestionAnswering''',
							    '''MvpForSequenceClassification''',
							    '''MvpModel''',
							    '''MvpPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
							from .tokenization_mvp import MvpTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mvp_fast import MvpTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mvp import (
														    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MvpForCausalLM,
														    MvpForConditionalGeneration,
														    MvpForQuestionAnswering,
														    MvpForSequenceClassification,
														    MvpModel,
														    MvpPreTrainedModel,
														)

else:
							import sys

							__UpperCamelCase  :      str							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







"""simple docstring"""





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

from ..utils.generic import ModelOutput







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				List[Any]   ,lowercase_			:				Tuple   ,lowercase_			:				Dict   ,lowercase_			:				str		):
						lowerCAmelCase__	:						int  				=	dataset
						lowerCAmelCase__	:						List[str]  				=	process
						lowerCAmelCase__	:						Dict  				=	params







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







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







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



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

						# Internal bookkeeping
						lowerCAmelCase__	:						Optional[Any]  				=	None
						lowerCAmelCase__	:						Optional[int]  				=	None







					def __len__(       self			:				Union[str, Any]		):
						return len(self.loader		)







					def __iter__(       self			:				List[Any]		):
						lowerCAmelCase__	:						List[Any]  				=	iter(self.loader		)
						return self







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







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						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__	:						Dict  				=	next(self.iterator		)
						lowerCAmelCase__	:						List[Any]  				=	self.infer(lowercase_   ,**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(lowercase_   ,torch.Tensor		):
								lowerCAmelCase__	:						int  				=	processed
							else:
								lowerCAmelCase__	:						Union[str, Any]  				=	list(processed.keys()		)[0]
								lowerCAmelCase__	:						Union[str, Any]  				=	processed[key]
							if isinstance(lowercase_   ,lowercase_		):
								lowerCAmelCase__	:						List[Any]  				=	len(lowercase_		)
							else:
								lowerCAmelCase__	:						List[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.
								lowerCAmelCase__	:						Optional[Any]  				=	observed_batch_size
							# Setting internal index to unwrap the batch
							lowerCAmelCase__	:						str  				=	processed
							lowerCAmelCase__	:						Any  				=	0
							return self.loader_batch_item()
						else:
							# We're not unrolling batches
							return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				int   ,lowercase_			:				str   ,lowercase_			:				str   ,lowercase_			:				Union[str, Any]   ,lowercase_			:				int=None		):
						super().__init__(lowercase_   ,lowercase_   ,lowercase_		)







					def __iter__(       self			:				List[Any]		):
						lowerCAmelCase__	:						Dict  				=	iter(self.loader		)
						lowerCAmelCase__	:						Tuple  				=	None
						return self







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						if self.subiterator is None:
							lowerCAmelCase__	:						List[Any]  				=	self.infer(next(self.iterator		)   ,**self.params		)
						try:
							# Try to return next item
							lowerCAmelCase__	:						Optional[int]  				=	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__	:						Any  				=	self.infer(next(self.iterator		)   ,**self.params		)
							lowerCAmelCase__	:						int  				=	next(self.subiterator		)
						return processed







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



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







					def        __lowerCAmelCase (       self			:				List[Any]		):
						# 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.
						lowerCAmelCase__	:						Tuple  				=	False
						lowerCAmelCase__	:						str  				=	[]
						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__	:						Dict  				=	self.loader_batch_item()
								lowerCAmelCase__	:						Optional[Any]  				=	item.pop('''is_last'''		)
								accumulator.append(lowercase_		)
								if is_last:
									return accumulator

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







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				int   ,lowercase_			:				Dataset   ,lowercase_			:				str		):
						lowerCAmelCase__	:						List[Any]  				=	dataset
						lowerCAmelCase__	:						List[Any]  				=	key







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







					def __getitem__(       self			:				str   ,lowercase_			:				Union[str, Any]		):
						return self.dataset[i][self.key]







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					def __init__(       self			:				Dict   ,lowercase_			:				Dataset   ,lowercase_			:				str   ,lowercase_			:				str		):
						lowerCAmelCase__	:						str  				=	dataset
						lowerCAmelCase__	:						List[str]  				=	keya
						lowerCAmelCase__	:						Optional[Any]  				=	keya







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







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




