




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

from ..utils.generic import ModelOutput







class 							A__   (	A__  ):





  def __init__(		self				:     str							, _a				:     Dict							, _a				:     int							, _a				:     str			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =dataset
       _SCREAMING_SNAKE_CASE							   =process
       _SCREAMING_SNAKE_CASE							   =params





  def __len__(		self				:     int			)     ->   Tuple:





       '''simple docstring'''






       return len(self.dataset			)





  def __getitem__(		self				:     Optional[Any]							, _a				:     int			)     ->   List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.dataset[i]
       _SCREAMING_SNAKE_CASE							   =self.process(_a							, **self.params			)
       return processed







class 							A__   (	A__  ):





  def __init__(		self				:     str							, _a				:     Optional[Any]							, _a				:     Tuple							, _a				:     int							, _a				:     Any=None			)     ->   Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =loader
       _SCREAMING_SNAKE_CASE							   =infer
       _SCREAMING_SNAKE_CASE							   =params
       if loader_batch_size == 1:
            # Let's spare some time by deactivating altogether
            _SCREAMING_SNAKE_CASE							   =None
       _SCREAMING_SNAKE_CASE							   =loader_batch_size

       # Internal bookkeeping
       _SCREAMING_SNAKE_CASE							   =None
       _SCREAMING_SNAKE_CASE							   =None





  def __len__(		self				:     Union[str, Any]			)     ->   List[Any]:





       '''simple docstring'''






       return len(self.loader			)





  def __iter__(		self				:     Any			)     ->   List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =iter(self.loader			)
       return self





  def 						A  (		self				:     Union[str, Any]			)     ->   Any:





       '''simple docstring'''






       if isinstance(self._loader_batch_data							, torch.Tensor			):
            # Batch data is simple tensor, just fetch the slice
            _SCREAMING_SNAKE_CASE							   =self._loader_batch_data[self._loader_batch_index]
       else:
            # Batch data is assumed to be BaseModelOutput (or dict)
            _SCREAMING_SNAKE_CASE							   ={}
            for k, element in self._loader_batch_data.items():
                 if isinstance(_a							, _a			):
                      # Convert ModelOutput to tuple first
                      _SCREAMING_SNAKE_CASE							   =element.to_tuple()
                      if isinstance(element[0]							, torch.Tensor			):
                           _SCREAMING_SNAKE_CASE							   =tuple(el[self._loader_batch_index].unsqueeze(0			) for el in element			)
                      elif isinstance(element[0]							, np.ndarray			):
                           _SCREAMING_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(_a							, _a			):
                      # Those are stored as lists of tensors so need specific unbatching.
                      if isinstance(element[0]							, torch.Tensor			):
                           _SCREAMING_SNAKE_CASE							   =tuple(el[self._loader_batch_index].unsqueeze(0			) for el in element			)
                      elif isinstance(element[0]							, np.ndarray			):
                           _SCREAMING_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
                      _SCREAMING_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

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





  def 						A  (		self				:     Tuple			)     ->   Optional[int]:





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







class 							A__   (	A__  ):





  def __init__(		self				:     List[str]							, _a				:     Any							, _a				:     Any							, _a				:     int							, _a				:     Optional[Any]=None			)     ->   str:





       '''simple docstring'''






       super().__init__(_a							, _a							, _a			)





  def __iter__(		self				:     List[str]			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =iter(self.loader			)
       _SCREAMING_SNAKE_CASE							   =None
       return self





  def 						A  (		self				:     Any			)     ->   str:





       '''simple docstring'''






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







class 							A__   (	A__  ):





  def __iter__(		self				:     Optional[int]			)     ->   Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =iter(self.loader			)
       return self





  def 						A  (		self				:     List[Any]			)     ->   int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =False
       _SCREAMING_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:
                 _SCREAMING_SNAKE_CASE							   =self.loader_batch_item()
                 _SCREAMING_SNAKE_CASE							   =item.pop('is_last'			)
                 accumulator.append(_a			)
                 if is_last:
                      return accumulator

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







class 							A__   (	A__  ):





  def __init__(		self				:     List[Any]							, _a				:     Dataset							, _a				:     str			)     ->   str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =dataset
       _SCREAMING_SNAKE_CASE							   =key





  def __len__(		self				:     Optional[int]			)     ->   str:





       '''simple docstring'''






       return len(self.dataset			)





  def __getitem__(		self				:     Optional[Any]							, _a				:     Dict			)     ->   Optional[Any]:





       '''simple docstring'''






       return self.dataset[i][self.key]







class 							A__   (	A__  ):





  def __init__(		self				:     Any							, _a				:     Dataset							, _a				:     str							, _a				:     str			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =dataset
       _SCREAMING_SNAKE_CASE							   =keya
       _SCREAMING_SNAKE_CASE							   =keya





  def __len__(		self				:     List[Any]			)     ->   Union[str, Any]:





       '''simple docstring'''






       return len(self.dataset			)





  def __getitem__(		self				:     Optional[int]							, _a				:     Dict			)     ->   List[str]:





       '''simple docstring'''






       return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}



'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowerCamelCase			:				Any 			=					logging.get_logger(__name__)

lowerCamelCase			:				int 			=					{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}







class 							A__   (	A__  ):
  A__					=    'data2vec-vision'





  def __init__(		self				:     str							, _a				:     Optional[int]=768							, _a				:     List[str]=12							, _a				:     Any=12							, _a				:     Tuple=3072							, _a				:     Optional[Any]="gelu"							, _a				:     Tuple=0.0							, _a				:     str=0.0							, _a				:     Dict=0.02							, _a				:     int=1e-12							, _a				:     List[str]=224							, _a				:     str=16							, _a				:     Dict=3							, _a				:     int=False							, _a				:     List[str]=False							, _a				:     str=False							, _a				:     str=False							, _a				:     Optional[int]=0.1							, _a				:     List[str]=0.1							, _a				:     Dict=True							, _a				:     str=[3, 5, 7, 11]							, _a				:     List[Any]=[1, 2, 3, 6]							, _a				:     Any=True							, _a				:     Optional[Any]=0.4							, _a				:     str=256							, _a				:     Dict=1							, _a				:     List[str]=False							, _a				:     List[Any]=255							, **_a				:     Any							, )     ->   Optional[Any]:





       '''simple docstring'''






       super().__init__(**_a			)

       _SCREAMING_SNAKE_CASE							   =hidden_size
       _SCREAMING_SNAKE_CASE							   =num_hidden_layers
       _SCREAMING_SNAKE_CASE							   =num_attention_heads
       _SCREAMING_SNAKE_CASE							   =intermediate_size
       _SCREAMING_SNAKE_CASE							   =hidden_act
       _SCREAMING_SNAKE_CASE							   =hidden_dropout_prob
       _SCREAMING_SNAKE_CASE							   =attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE							   =initializer_range
       _SCREAMING_SNAKE_CASE							   =layer_norm_eps

       _SCREAMING_SNAKE_CASE							   =image_size
       _SCREAMING_SNAKE_CASE							   =patch_size
       _SCREAMING_SNAKE_CASE							   =num_channels
       _SCREAMING_SNAKE_CASE							   =use_mask_token
       _SCREAMING_SNAKE_CASE							   =use_absolute_position_embeddings
       _SCREAMING_SNAKE_CASE							   =use_relative_position_bias
       _SCREAMING_SNAKE_CASE							   =use_shared_relative_position_bias
       _SCREAMING_SNAKE_CASE							   =layer_scale_init_value
       _SCREAMING_SNAKE_CASE							   =drop_path_rate
       _SCREAMING_SNAKE_CASE							   =use_mean_pooling
       # decode head attributes (semantic segmentation)
       _SCREAMING_SNAKE_CASE							   =out_indices
       _SCREAMING_SNAKE_CASE							   =pool_scales
       # auxiliary head attributes (semantic segmentation)
       _SCREAMING_SNAKE_CASE							   =use_auxiliary_head
       _SCREAMING_SNAKE_CASE							   =auxiliary_loss_weight
       _SCREAMING_SNAKE_CASE							   =auxiliary_channels
       _SCREAMING_SNAKE_CASE							   =auxiliary_num_convs
       _SCREAMING_SNAKE_CASE							   =auxiliary_concat_input
       _SCREAMING_SNAKE_CASE							   =semantic_loss_ignore_index







class 							A__   (	A__  ):
  A__					=    version.parse('1.11'  )





  @property
  def 						A  (		self				:     str			)     ->   Mapping[str, Mapping[int, str]]:





       '''simple docstring'''






       return OrderedDict(
           [
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
           ]			)





  @property
  def 						A  (		self				:     List[Any]			)     ->   float:





       '''simple docstring'''






       return 1e-4
