


'''simple docstring'''

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

from ..utils.generic import ModelOutput
class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      def __init__(self       :       str  ,		UpperCamelCase       :       List[str]  ,		UpperCamelCase       :       Optional[int]  ,		UpperCamelCase       :       List[str]					):

             '''simple docstring'''







             lowercase__      =					dataset
             lowercase__      =					process
             lowercase__      =					params



      def __len__(self       :       Optional[int]					):

             '''simple docstring'''







             return len(self.dataset					)






      def __getitem__(self       :       Optional[int]  ,		UpperCamelCase       :       Optional[Any]					):

             '''simple docstring'''







             lowercase__      =					self.dataset[i]
             lowercase__      =					self.process(UpperCamelCase  ,		**self.params					)
             return processed






class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      def __init__(self       :       Any  ,		UpperCamelCase       :       Dict  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       List[Any]=None					):

             '''simple docstring'''







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

             # Internal bookkeeping
             lowercase__      =					None
             lowercase__      =					None



      def __len__(self       :       Any					):

             '''simple docstring'''







             return len(self.loader					)



      def __iter__(self       :       Optional[Any]					):

             '''simple docstring'''







             lowercase__      =					iter(self.loader					)
             return self



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







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

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






      def  UpperCamelCase__						(self       :       List[str]					):

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






class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      def __init__(self       :       Dict  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       Tuple=None					):

             '''simple docstring'''







             super().__init__(UpperCamelCase  ,		UpperCamelCase  ,		UpperCamelCase					)



      def __iter__(self       :       Optional[Any]					):

             '''simple docstring'''







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






      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







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






class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      def __iter__(self       :       Tuple					):

             '''simple docstring'''







             lowercase__      =					iter(self.loader					)
             return self






      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             lowercase__      =					False
             lowercase__      =					[]
             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:
                           lowercase__      =					self.loader_batch_item()
                           lowercase__      =					item.pop('''is_last'''					)
                           accumulator.append(UpperCamelCase					)
                           if is_last:
                                  return accumulator

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






class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      def __init__(self       :       str  ,		UpperCamelCase       :       Dataset  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







             lowercase__      =					dataset
             lowercase__      =					key



      def __len__(self       :       Optional[int]					):

             '''simple docstring'''







             return len(self.dataset					)






      def __getitem__(self       :       Union[str, Any]  ,		UpperCamelCase       :       List[Any]					):

             '''simple docstring'''







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






class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      def __init__(self       :       List[Any]  ,		UpperCamelCase       :       Dataset  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







             lowercase__      =					dataset
             lowercase__      =					keya
             lowercase__      =					keya



      def __len__(self       :       Any					):

             '''simple docstring'''







             return len(self.dataset					)






      def __getitem__(self       :       Optional[Any]  ,		UpperCamelCase       :       List[Any]					):

             '''simple docstring'''







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








'''simple docstring'''

from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowerCamelCase				:  Optional[int]    		=      logging.get_logger(__name__)
class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      lowerCAmelCase__   :	Optional[int]       =				["""pixel_values"""]



      def __init__(self       :       str  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       Union[int, float] = 1 / 255  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       int = 8  ,		**UpperCamelCase       :       Dict  ,		):

             '''simple docstring'''







             super().__init__(**UpperCamelCase					)

             lowercase__      =					do_rescale
             lowercase__      =					rescale_factor
             lowercase__      =					do_pad
             lowercase__      =					pad_size



      def  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       np.ndarray  ,		UpperCamelCase       :       float  ,		UpperCamelCase       :       Optional[Union[str, ChannelDimension]] = None  ,		**UpperCamelCase       :       Tuple					):

             '''simple docstring'''







             return rescale(UpperCamelCase  ,		scale=UpperCamelCase  ,		data_format=UpperCamelCase  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Tuple  ,		UpperCamelCase       :       np.ndarray  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       Optional[Union[str, ChannelDimension]] = None					):

             '''simple docstring'''







             lowercase__     ,lowercase__      =					get_image_size(UpperCamelCase					)
             lowercase__      =					(old_height // size + 1) * size - old_height
             lowercase__      =					(old_width // size + 1) * size - old_width

             return pad(UpperCamelCase  ,		((0, pad_height), (0, pad_width))  ,		mode='''symmetric'''  ,		data_format=UpperCamelCase					)






      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       ImageInput  ,		UpperCamelCase       :       Optional[bool] = None  ,		UpperCamelCase       :       Optional[float] = None  ,		UpperCamelCase       :       Optional[bool] = None  ,		UpperCamelCase       :       Optional[int] = None  ,		UpperCamelCase       :       Optional[Union[str, TensorType]] = None  ,		UpperCamelCase       :       Union[str, ChannelDimension] = ChannelDimension.FIRST  ,		**UpperCamelCase       :       int  ,		):

             '''simple docstring'''







             lowercase__      =					do_rescale if do_rescale is not None else self.do_rescale
             lowercase__      =					rescale_factor if rescale_factor is not None else self.rescale_factor
             lowercase__      =					do_pad if do_pad is not None else self.do_pad
             lowercase__      =					pad_size if pad_size is not None else self.pad_size

             lowercase__      =					make_list_of_images(UpperCamelCase					)

             if not valid_images(UpperCamelCase					):
                    raise ValueError(
                        '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                        '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)

             if do_rescale and rescale_factor is None:
                    raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

             # All transformations expect numpy arrays.
             lowercase__      =					[to_numpy_array(UpperCamelCase					) for image in images]

             if do_rescale:
                    lowercase__      =					[self.rescale(image=UpperCamelCase  ,		scale=UpperCamelCase					) for image in images]

             if do_pad:
                    lowercase__      =					[self.pad(UpperCamelCase  ,		size=UpperCamelCase					) for image in images]

             lowercase__      =					[to_channel_dimension_format(UpperCamelCase  ,		UpperCamelCase					) for image in images]

             lowercase__      =					{'''pixel_values''': images}
             return BatchFeature(data=UpperCamelCase  ,		tensor_type=UpperCamelCase					)




