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

from ..utils.generic import ModelOutput


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		def __init__(				self       ,   A_       ,   A_       ,   A_ )						->      int:
				"""simple docstring"""
				UpperCamelCase					=  dataset
				UpperCamelCase					=  process
				UpperCamelCase					=  params



		def __len__(				self )						->      int:
				"""simple docstring"""
				return len(self.dataset )



		def __getitem__(				self       ,   A_ )						->      str:
				"""simple docstring"""
				UpperCamelCase					=  self.dataset[i]
				UpperCamelCase					=  self.process(A_       ,   **self.params )
				return processed






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		def __init__(				self       ,   A_       ,   A_       ,   A_       ,   A_=None )						->      Optional[Any]:
				"""simple docstring"""
				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 )						->      int:
				"""simple docstring"""
				return len(self.loader )



		def __iter__(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase					=  iter(self.loader )
				return self



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				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(A_       ,   A_ ):
										# 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(A_       ,   A_ ):
										# 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__(A_ )
				self._loader_batch_index += 1
				return result



		def __UpperCamelCase				(				self )						->      Tuple:
				"""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
				UpperCamelCase					=  next(self.iterator )
				UpperCamelCase					=  self.infer(A_       ,   **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(A_       ,   torch.Tensor ):
								UpperCamelCase					=  processed
						else:
								UpperCamelCase					=  list(processed.keys() )[0]
								UpperCamelCase					=  processed[key]
						if isinstance(A_       ,   A_ ):
								UpperCamelCase					=  len(A_ )
						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        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		def __init__(				self       ,   A_       ,   A_       ,   A_       ,   A_=None )						->      Optional[Any]:
				"""simple docstring"""
				super().__init__(A_       ,   A_       ,   A_ )



		def __iter__(				self )						->      str:
				"""simple docstring"""
				UpperCamelCase					=  iter(self.loader )
				UpperCamelCase					=  None
				return self



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				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        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		def __iter__(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase					=  iter(self.loader )
				return self



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				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(A_ )
								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(A_       ,   torch.Tensor ):
										UpperCamelCase					=  processed
								else:
										UpperCamelCase					=  list(processed.keys() )[0]
										UpperCamelCase					=  processed[key]
								if isinstance(A_       ,   A_ ):
										UpperCamelCase					=  len(A_ )
								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(A_ )
										if is_last:
												return accumulator
						else:
								UpperCamelCase					=  processed
								UpperCamelCase					=  item.pop('is_last' )
								accumulator.append(A_ )
				return accumulator






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		def __init__(				self       ,   A_       ,   A_ )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase					=  dataset
				UpperCamelCase					=  key



		def __len__(				self )						->      Union[str, Any]:
				"""simple docstring"""
				return len(self.dataset )



		def __getitem__(				self       ,   A_ )						->      Optional[Any]:
				"""simple docstring"""
				return self.dataset[i][self.key]






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		def __init__(				self       ,   A_       ,   A_       ,   A_ )						->      str:
				"""simple docstring"""
				UpperCamelCase					=  dataset
				UpperCamelCase					=  keya
				UpperCamelCase					=  keya



		def __len__(				self )						->      Any:
				"""simple docstring"""
				return len(self.dataset )



		def __getitem__(				self       ,   A_ )						->      int:
				"""simple docstring"""
				return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase      :				Tuple          = logging.get_logger(__name__)

_UpperCAmelCase      :				Union[str, Any]          = {
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Dict			  =       "data2vec-text"



		def __init__(				self       ,   A_=30_522       ,   A_=768       ,   A_=12       ,   A_=12       ,   A_=3_072       ,   A_="gelu"       ,   A_=0.1       ,   A_=0.1       ,   A_=512       ,   A_=2       ,   A_=0.02       ,   A_=1e-12       ,   A_=1       ,   A_=0       ,   A_=2       ,   A_="absolute"       ,   A_=True       ,   A_=None       ,   **A_       ,   )						->      Any:
				"""simple docstring"""
				super().__init__(pad_token_id=A_       ,   bos_token_id=A_       ,   eos_token_id=A_       ,   **A_ )

				UpperCamelCase						=  vocab_size
				UpperCamelCase						=  hidden_size
				UpperCamelCase						=  num_hidden_layers
				UpperCamelCase						=  num_attention_heads
				UpperCamelCase						=  hidden_act
				UpperCamelCase						=  intermediate_size
				UpperCamelCase						=  hidden_dropout_prob
				UpperCamelCase						=  attention_probs_dropout_prob
				UpperCamelCase						=  max_position_embeddings
				UpperCamelCase						=  type_vocab_size
				UpperCamelCase						=  initializer_range
				UpperCamelCase						=  layer_norm_eps
				UpperCamelCase						=  position_embedding_type
				UpperCamelCase						=  use_cache
				UpperCamelCase						=  classifier_dropout






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		@property
		def __UpperCamelCase				(				self )						->      Mapping[str, Mapping[int, str]]:
				"""simple docstring"""
				if self.task == "multiple-choice":
						UpperCamelCase						=  {0: 'batch', 1: 'choice', 2: 'sequence'}
				else:
						UpperCamelCase						=  {0: 'batch', 1: 'sequence'}
				return OrderedDict(
				    [
				        ('input_ids', dynamic_axis),
				        ('attention_mask', dynamic_axis),
				    ] )
