



import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger




def    UpperCAmelCase					(  lowercase__						:					int				,					lowercase__						:					List[str]	):



			'''simple docstring'''





			a__					=  copy.deepcopy(_SCREAMING_SNAKE_CASE	)
			a__					=  sorted(_SCREAMING_SNAKE_CASE	)
			a__					=  [bisect.bisect_right(_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE	) for y in x]
			return quantized




def    UpperCAmelCase					(  lowercase__						:					Dict				,					lowercase__						:					Tuple=0	):



			'''simple docstring'''





			a__					=  np.arange(start=3				,					stop=_SCREAMING_SNAKE_CASE				,					step=4	).tolist() if k > 0 else [10]
			a__					=  _quantize(_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE	)
			# count number of elements per group
			a__					=  np.unique(_SCREAMING_SNAKE_CASE				,					return_counts=_SCREAMING_SNAKE_CASE	)[1]
			a__					=  [0] + bins + [np.inf]
			logger.info("""Using {} as bins for aspect lengths quantization""".format(_SCREAMING_SNAKE_CASE	)	)
			logger.info("""Count of instances per bin: {}""".format(_SCREAMING_SNAKE_CASE	)	)
			return groups





class lowerCAmelCase_       (   lowerCAmelCase_			):







	'''simple docstring'''




	def __init__(	self   ,       lowerCamelCase   ,       lowerCamelCase   ,       lowerCamelCase       ):



				'''simple docstring'''


				if not isinstance(lowerCamelCase__   ,       lowerCamelCase__       ):
							raise ValueError(
							    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase__       )       )
				a__					=  sampler
				a__					=  group_ids
				a__					=  batch_size

	def __iter__(	self       ):



				'''simple docstring'''


				a__					=  defaultdict(lowerCamelCase__       )
				a__					=  defaultdict(lowerCamelCase__       )

				a__					=  0
				for idx in self.sampler:
							a__					=  self.group_ids[idx]
							buffer_per_group[group_id].append(lowerCamelCase__       )
							samples_per_group[group_id].append(lowerCamelCase__       )
							if len(buffer_per_group[group_id]       ) == self.batch_size:
										yield buffer_per_group[group_id]  # TODO
										num_batches += 1
										del buffer_per_group[group_id]
							assert len(buffer_per_group[group_id]       ) < self.batch_size

				# now we have run out of elements that satisfy
				# the group criteria, let's return the remaining
				# elements so that the size of the sampler is
				# deterministic
				a__					=  len(self       )
				a__					=  expected_num_batches - num_batches
				if num_remaining > 0:
							# for the remaining batches, group the batches by similar lengths
							a__					=  []
							for group_id, idxs in sorted(buffer_per_group.items()   ,       key=lambda lowerCamelCase       : x[0]       ):
										batch_idx.extend(lowerCamelCase__       )
										if len(lowerCamelCase__       ) >= self.batch_size:
													yield batch_idx[: self.batch_size]
													a__					=  batch_idx[self.batch_size :]
													num_remaining -= 1
							if len(lowerCamelCase__       ) > 0:
										yield batch_idx
										num_remaining -= 1
				assert num_remaining == 0







	def __len__(	self       ):



				'''simple docstring'''


				return (len(self.sampler       ) + self.batch_size - 1) // self.batch_size








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


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import TvltImageProcessor



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE=10					,		_SCREAMING_SNAKE_CASE=10					,		_SCREAMING_SNAKE_CASE=False					,		_SCREAMING_SNAKE_CASE=False      )					->  Optional[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		[]
      for i in range(image_processor_tester.num_frames      ):
            video.append(np.random.randint(2_55					,		size=(image_processor_tester.num_channels, width, height)					,		dtype=np.uinta      )      )

      if not numpify and not torchify:
            # PIL expects the channel dimension as last dimension
            SCREAMING_SNAKE_CASE						=		[Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE					,		0					,		-1      )      ) for frame in video]

      if torchify:
            SCREAMING_SNAKE_CASE						=		[torch.from_numpy(_SCREAMING_SNAKE_CASE      ) for frame in video]

      return video



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE=False					,		_SCREAMING_SNAKE_CASE=False					,		_SCREAMING_SNAKE_CASE=False      )					->  Tuple:


      '''simple docstring'''


      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      SCREAMING_SNAKE_CASE						=		[]
      for i in range(image_processor_tester.batch_size      ):
            if equal_resolution:
                  SCREAMING_SNAKE_CASE						=		SCREAMING_SNAKE_CASE						=		image_processor_tester.max_resolution
            else:
                  SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		np.random.choice(
                      np.arange(image_processor_tester.min_resolution					,		image_processor_tester.max_resolution      )					,		2      )
                  SCREAMING_SNAKE_CASE						=		prepare_video(
                      image_processor_tester=_SCREAMING_SNAKE_CASE					,		width=_SCREAMING_SNAKE_CASE					,		height=_SCREAMING_SNAKE_CASE					,		numpify=_SCREAMING_SNAKE_CASE					,		torchify=_SCREAMING_SNAKE_CASE					,		)
            video_inputs.append(_SCREAMING_SNAKE_CASE      )

      return video_inputs

class 						UpperCamelCase__					( unittest.TestCase  ):



     '''simple docstring'''

     def __init__(				self		:       Union[str, Any]		,lowerCamelCase__		:       Tuple		,lowerCamelCase__		:       Any=7		,lowerCamelCase__		:       str=3		,lowerCamelCase__		:       Union[str, Any]=4		,lowerCamelCase__		:       Union[str, Any]=18		,lowerCamelCase__		:       Any=30		,lowerCamelCase__		:       Union[str, Any]=400		,lowerCamelCase__		:       Any=True		,lowerCamelCase__		:       Any=None		,lowerCamelCase__		:       Tuple=True		,lowerCamelCase__		:       int=[0.5, 0.5, 0.5]		,lowerCamelCase__		:       Any=[0.5, 0.5, 0.5]		,lowerCamelCase__		:       Dict=True		,lowerCamelCase__		:       Tuple=None		,)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		size if size is not None else {"""shortest_edge""": 18}
           SCREAMING_SNAKE_CASE						=		crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

           SCREAMING_SNAKE_CASE						=		parent
           SCREAMING_SNAKE_CASE						=		batch_size
           SCREAMING_SNAKE_CASE						=		num_channels
           SCREAMING_SNAKE_CASE						=		num_frames
           SCREAMING_SNAKE_CASE						=		image_size
           SCREAMING_SNAKE_CASE						=		min_resolution
           SCREAMING_SNAKE_CASE						=		max_resolution
           SCREAMING_SNAKE_CASE						=		do_resize
           SCREAMING_SNAKE_CASE						=		size
           SCREAMING_SNAKE_CASE						=		do_normalize
           SCREAMING_SNAKE_CASE						=		image_mean
           SCREAMING_SNAKE_CASE						=		image_std
           SCREAMING_SNAKE_CASE						=		do_center_crop
           SCREAMING_SNAKE_CASE						=		crop_size






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




           '''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,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
           }

@require_torch
@require_vision
class 						UpperCamelCase__					( lowerCAmelCase_				, unittest.TestCase  ):



     '''simple docstring'''

     __snake_case       :      int						      =     TvltImageProcessor if is_vision_available() else None






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		TvltImageProcessorTester(self			)






     @property
     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[int]			)	->						Dict:




           '''simple docstring'''


           return self.image_processor_tester.prepare_image_processor_dict()






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any			)	->						List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.image_processing_class(**self.image_processor_dict			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""image_mean"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""image_std"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""do_normalize"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""do_resize"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""do_center_crop"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""size"""			)			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.image_processing_class(**self.image_processor_dict			)
           # create random PIL videos
           SCREAMING_SNAKE_CASE						=		prepare_video_inputs(self.image_processor_tester		,equal_resolution=lowerCamelCase__			)
           for video in video_inputs:
                 self.assertIsInstance(lowerCamelCase__		,lowerCamelCase__			)
                 self.assertIsInstance(video[0]		,Image.Image			)

           # Test not batched input
           SCREAMING_SNAKE_CASE						=		image_processor(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
           SCREAMING_SNAKE_CASE						=		image_processor(lowerCamelCase__		,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 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						int:




           '''simple docstring'''


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

           # Test not batched input
           SCREAMING_SNAKE_CASE						=		image_processor(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
           SCREAMING_SNAKE_CASE						=		image_processor(lowerCamelCase__		,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 							SCREAMING_SNAKE_CASE__    (				self		:       str			)	->						Optional[Any]:




           '''simple docstring'''


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

           # Test not batched input
           SCREAMING_SNAKE_CASE						=		image_processor(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
           SCREAMING_SNAKE_CASE						=		image_processor(lowerCamelCase__		,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"""],
               )		,)


