





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

from ..utils.generic import ModelOutput



class       A__    ( snake_case__    ):





      def __init__(				self ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     dataset
       UpperCamelCase			:      Union[str, Any] 			=     process
       UpperCamelCase			:      List[Any] 			=     params





      def __len__(				self     ):
       '''simple docstring'''




       return len(self.dataset     )





      def __getitem__(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     self.dataset[i]
       UpperCamelCase			:      str 			=     self.process(UpperCAmelCase_ ,							**self.params     )
       return processed



class       A__    ( snake_case__    ):





      def __init__(				self ,							A_ ,							A_ ,							A_ ,							A_=None     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     loader
       UpperCamelCase			:      Optional[Any] 			=     infer
       UpperCamelCase			:      int 			=     params
       if loader_batch_size == 1:
        # Let's spare some time by deactivating altogether
        UpperCamelCase			:      List[str] 			=     None
       UpperCamelCase			:      str 			=     loader_batch_size

       # Internal bookkeeping
       UpperCamelCase			:      Tuple 			=     None
       UpperCamelCase			:      str 			=     None





      def __len__(				self     ):
       '''simple docstring'''




       return len(self.loader     )





      def __iter__(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     iter(self.loader     )
       return self





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self     ):
       '''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			:      Optional[int] 			=     next(self.iterator     )
       UpperCamelCase			:      Tuple 			=     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     ):
         UpperCamelCase			:      Tuple 			=     processed
        else:
         UpperCamelCase			:      Any 			=     list(processed.keys()     )[0]
         UpperCamelCase			:      Optional[Any] 			=     processed[key]
        if isinstance(UpperCAmelCase_ ,							UpperCAmelCase_     ):
         UpperCamelCase			:      Union[str, Any] 			=     len(UpperCAmelCase_     )
        else:
         UpperCamelCase			:      List[Any] 			=     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			:      int 			=     observed_batch_size
        # Setting internal index to unwrap the batch
        UpperCamelCase			:      List[str] 			=     processed
        UpperCamelCase			:      Optional[Any] 			=     0
        return self.loader_batch_item()
       else:
        # We're not unrolling batches
        return processed



class       A__    ( snake_case__    ):





      def __init__(				self ,							A_ ,							A_ ,							A_ ,							A_=None     ):
       '''simple docstring'''




       super().__init__(UpperCAmelCase_ ,							UpperCAmelCase_ ,							UpperCAmelCase_     )





      def __iter__(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     iter(self.loader     )
       UpperCamelCase			:      List[Any] 			=     None
       return self





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       if self.subiterator is None:
        UpperCamelCase			:      int 			=     self.infer(next(self.iterator     ) ,							**self.params     )
       try:
        # Try to return next item
        UpperCamelCase			:      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
        UpperCamelCase			:      List[str] 			=     self.infer(next(self.iterator     ) ,							**self.params     )
        UpperCamelCase			:      str 			=     next(self.subiterator     )
       return processed



class       A__    ( snake_case__    ):





      def __iter__(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     iter(self.loader     )
       return self





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     False
       UpperCamelCase			:      Optional[Any] 			=     []
       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			:      Dict 			=     self.loader_batch_item()
         UpperCamelCase			:      Dict 			=     item.pop("is_last"     )
         accumulator.append(UpperCAmelCase_     )
         if is_last:
          return accumulator

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



class       A__    ( snake_case__    ):





      def __init__(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     dataset
       UpperCamelCase			:      int 			=     key





      def __len__(				self     ):
       '''simple docstring'''




       return len(self.dataset     )





      def __getitem__(				self ,							A_     ):
       '''simple docstring'''




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



class       A__    ( snake_case__    ):





      def __init__(				self ,							A_ ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     dataset
       UpperCamelCase			:      Optional[int] 			=     keya
       UpperCamelCase			:      Tuple 			=     keya





      def __len__(				self     ):
       '''simple docstring'''




       return len(self.dataset     )





      def __getitem__(				self ,							A_     ):
       '''simple docstring'''




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







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import VivitImageProcessor



class       A__     ( unittest.TestCase    ):





      def __init__(				self ,							A_ ,							A_=7 ,							A_=3 ,							A_=10 ,							A_=18 ,							A_=30 ,							A_=400 ,							A_=True ,							A_=None ,							A_=True ,							A_=[0.5, 0.5, 0.5] ,							A_=[0.5, 0.5, 0.5] ,							A_=None ,							):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     size if size is not None else {"shortest_edge": 18}
       UpperCamelCase			:      Tuple 			=     crop_size if crop_size is not None else {"height": 18, "width": 18}

       UpperCamelCase			:      Optional[Any] 			=     parent
       UpperCamelCase			:      Optional[int] 			=     batch_size
       UpperCamelCase			:      List[Any] 			=     num_channels
       UpperCamelCase			:      Union[str, Any] 			=     num_frames
       UpperCamelCase			:      Any 			=     image_size
       UpperCamelCase			:      Tuple 			=     min_resolution
       UpperCamelCase			:      Optional[Any] 			=     max_resolution
       UpperCamelCase			:      Any 			=     do_resize
       UpperCamelCase			:      Tuple 			=     size
       UpperCamelCase			:      List[Any] 			=     do_normalize
       UpperCamelCase			:      Optional[int] 			=     image_mean
       UpperCamelCase			:      Any 			=     image_std
       UpperCamelCase			:      str 			=     crop_size





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_size": self.crop_size,
       }



@require_torch
@require_vision
class       A__     ( __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:List[str]							=     VivitImageProcessor if is_vision_available() else None





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     VivitImageProcessingTester(self     )





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.image_processor_tester.prepare_image_processor_dict()





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     self.image_processing_class(**self.image_processor_dict     )
       self.assertTrue(hasattr(A_ ,							"image_mean"     )     )
       self.assertTrue(hasattr(A_ ,							"image_std"     )     )
       self.assertTrue(hasattr(A_ ,							"do_normalize"     )     )
       self.assertTrue(hasattr(A_ ,							"do_resize"     )     )
       self.assertTrue(hasattr(A_ ,							"do_center_crop"     )     )
       self.assertTrue(hasattr(A_ ,							"size"     )     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     self.image_processing_class.from_dict(self.image_processor_dict     )
       self.assertEqual(image_processor.size ,							{"shortest_edge": 18}     )
       self.assertEqual(image_processor.crop_size ,							{"height": 18, "width": 18}     )

       UpperCamelCase			:      Dict 			=     self.image_processing_class.from_dict(self.image_processor_dict ,							size=42 ,							crop_size=84     )
       self.assertEqual(image_processor.size ,							{"shortest_edge": 42}     )
       self.assertEqual(image_processor.crop_size ,							{"height": 84, "width": 84}     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random PIL videos
       UpperCamelCase			:      Union[str, Any] 			=     prepare_video_inputs(self.image_processor_tester ,							equal_resolution=A_     )
       for video in video_inputs:
        self.assertIsInstance(A_ ,							A_     )
        self.assertIsInstance(video[0] ,							Image.Image     )

       # Test not batched input
       UpperCamelCase			:      Any 			=     image_processing(video_inputs[0] ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)

       # Test batched
       UpperCamelCase			:      str 			=     image_processing(A_ ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random numpy tensors
       UpperCamelCase			:      str 			=     prepare_video_inputs(self.image_processor_tester ,							equal_resolution=A_ ,							numpify=A_     )
       for video in video_inputs:
        self.assertIsInstance(A_ ,							A_     )
        self.assertIsInstance(video[0] ,							np.ndarray     )

       # Test not batched input
       UpperCamelCase			:      Tuple 			=     image_processing(video_inputs[0] ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)

       # Test batched
       UpperCamelCase			:      Any 			=     image_processing(A_ ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random PyTorch tensors
       UpperCamelCase			:      Union[str, Any] 			=     prepare_video_inputs(self.image_processor_tester ,							equal_resolution=A_ ,							torchify=A_     )
       for video in video_inputs:
        self.assertIsInstance(A_ ,							A_     )
        self.assertIsInstance(video[0] ,							torch.Tensor     )

       # Test not batched input
       UpperCamelCase			:      Tuple 			=     image_processing(video_inputs[0] ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)

       # Test batched
       UpperCamelCase			:      List[Any] 			=     image_processing(A_ ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_videos.shape ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           ) ,							)
