"""simple docstring"""


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

from ..utils.generic import ModelOutput




class  UpperCamelCase					(				lowerCAmelCase__   ):







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)      ->      List[Any]:
            snake_case_					       =					dataset
            snake_case_					       =					process
            snake_case_					       =					params







       def __len__( self)      ->      Optional[Any]:
            return len(self.dataset)







       def __getitem__( self,							lowerCAmelCase__)      ->      Optional[int]:
            snake_case_					       =					self.dataset[i]
            snake_case_					       =					self.process(lowerCAmelCase__,							**self.params)
            return processed




class  UpperCamelCase					(				lowerCAmelCase__   ):







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__=None)      ->      Optional[Any]:
            snake_case_					       =					loader
            snake_case_					       =					infer
            snake_case_					       =					params
            if loader_batch_size == 1:
                 # Let's spare some time by deactivating altogether
                 snake_case_					       =					None
            snake_case_					       =					loader_batch_size

            # Internal bookkeeping
            snake_case_					       =					None
            snake_case_					       =					None







       def __len__( self)      ->      str:
            return len(self.loader)







       def __iter__( self)      ->      List[Any]:
            snake_case_					       =					iter(self.loader)
            return self







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







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




class  UpperCamelCase					(				lowerCAmelCase__   ):







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__=None)      ->      Any:
            super().__init__(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)







       def __iter__( self)      ->      str:
            snake_case_					       =					iter(self.loader)
            snake_case_					       =					None
            return self







       def 		a_						( self)      ->      Optional[int]:
            if self.subiterator is None:
                 snake_case_					       =					self.infer(next(self.iterator),							**self.params)
            try:
                 # Try to return next item
                 snake_case_					       =					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_					       =					self.infer(next(self.iterator),							**self.params)
                 snake_case_					       =					next(self.subiterator)
            return processed




class  UpperCamelCase					(				lowerCAmelCase__   ):







       def __iter__( self)      ->      Any:
            snake_case_					       =					iter(self.loader)
            return self







       def 		a_						( self)      ->      Tuple:
            # 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.
            snake_case_					       =					False
            snake_case_					       =					[]
            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_					       =					self.loader_batch_item()
                      snake_case_					       =					item.pop('is_last')
                      accumulator.append(lowerCAmelCase__)
                      if is_last:
                           return accumulator

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




class  UpperCamelCase					(				lowerCAmelCase__   ):







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__)      ->      Tuple:
            snake_case_					       =					dataset
            snake_case_					       =					key







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







       def __getitem__( self,							lowerCAmelCase__)      ->      List[Any]:
            return self.dataset[i][self.key]




class  UpperCamelCase					(				lowerCAmelCase__   ):







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)      ->      List[str]:
            snake_case_					       =					dataset
            snake_case_					       =					keya
            snake_case_					       =					keya







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







       def __getitem__( self,							lowerCAmelCase__)      ->      Any:
            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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :	Dict    = {
    'configuration_xlm_roberta': [
        'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaConfig',
        'XLMRobertaOnnxConfig',
    ],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Union[str, Any]    = ['XLMRobertaTokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = ['XLMRobertaTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	List[Any]    = [
					    'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'XLMRobertaForCausalLM',
					    'XLMRobertaForMaskedLM',
					    'XLMRobertaForMultipleChoice',
					    'XLMRobertaForQuestionAnswering',
					    'XLMRobertaForSequenceClassification',
					    'XLMRobertaForTokenClassification',
					    'XLMRobertaModel',
					    'XLMRobertaPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	int    = [
					    'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFXLMRobertaForCausalLM',
					    'TFXLMRobertaForMaskedLM',
					    'TFXLMRobertaForMultipleChoice',
					    'TFXLMRobertaForQuestionAnswering',
					    'TFXLMRobertaForSequenceClassification',
					    'TFXLMRobertaForTokenClassification',
					    'TFXLMRobertaModel',
					    'TFXLMRobertaPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A     :	Tuple    = [
					    'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'FlaxXLMRobertaForMaskedLM',
					    'FlaxXLMRobertaForCausalLM',
					    'FlaxXLMRobertaForMultipleChoice',
					    'FlaxXLMRobertaForQuestionAnswering',
					    'FlaxXLMRobertaForSequenceClassification',
					    'FlaxXLMRobertaForTokenClassification',
					    'FlaxXLMRobertaModel',
					    'FlaxXLMRobertaPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_xlm_roberta import (
					    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    XLMRobertaConfig,
					    XLMRobertaOnnxConfig,
					)

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlm_roberta import XLMRobertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm_roberta import (
										    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMRobertaForCausalLM,
										    XLMRobertaForMaskedLM,
										    XLMRobertaForMultipleChoice,
										    XLMRobertaForQuestionAnswering,
										    XLMRobertaForSequenceClassification,
										    XLMRobertaForTokenClassification,
										    XLMRobertaModel,
										    XLMRobertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm_roberta import (
										    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMRobertaForCausalLM,
										    TFXLMRobertaForMaskedLM,
										    TFXLMRobertaForMultipleChoice,
										    TFXLMRobertaForQuestionAnswering,
										    TFXLMRobertaForSequenceClassification,
										    TFXLMRobertaForTokenClassification,
										    TFXLMRobertaModel,
										    TFXLMRobertaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_xlm_roberta import (
										    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaxXLMRobertaForCausalLM,
										    FlaxXLMRobertaForMaskedLM,
										    FlaxXLMRobertaForMultipleChoice,
										    FlaxXLMRobertaForQuestionAnswering,
										    FlaxXLMRobertaForSequenceClassification,
										    FlaxXLMRobertaForTokenClassification,
										    FlaxXLMRobertaModel,
										    FlaxXLMRobertaPreTrainedModel,
										)

else:
					import sys

					A     :	Tuple    = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)