




"""simple docstring"""

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 VideoMAEImageProcessor

class     lowercase (      unittest.TestCase):







				def __init__(							self				:   List[str] ,       _lowerCamelCase				:   Tuple ,       _lowerCamelCase				:   Any=7 ,       _lowerCamelCase				:   Dict=3 ,       _lowerCamelCase				:   List[Any]=10 ,       _lowerCamelCase				:   List[Any]=18 ,       _lowerCamelCase				:   List[str]=30 ,       _lowerCamelCase				:   Optional[Any]=4_00 ,       _lowerCamelCase				:   Dict=True ,       _lowerCamelCase				:   Union[str, Any]=None ,       _lowerCamelCase				:   Any=True ,       _lowerCamelCase				:   Dict=[0.5, 0.5, 0.5] ,       _lowerCamelCase				:   List[Any]=[0.5, 0.5, 0.5] ,       _lowerCamelCase				:   int=None ,       ):



					"""simple docstring"""

					A_					:			int						=						size if size is not None else {'''shortest_edge''': 18}
					A_					:			Any						=						crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

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







				def    a_					(							self				:   Any    ):



					"""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     lowercase (      __UpperCAmelCase       ,		unittest.TestCase):
				__lowerCAmelCase      :      List[str]					    =				VideoMAEImageProcessor if is_vision_available() else None







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						VideoMAEImageProcessingTester(self    )







				@property
				def    a_					(							self				:   str    ):



					"""simple docstring"""

					return self.image_processor_tester.prepare_image_processor_dict()







				def    a_					(							self				:   str    ):



					"""simple docstring"""

					A_					:			int						=						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    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						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}    )

					A_					:			Any						=						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    a_					(							self				:   List[Any]    ):



					"""simple docstring"""

					pass







				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						self.image_processing_class(**self.image_processor_dict    )
					# create random PIL videos
					A_					:			Optional[Any]						=						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
					A_					:			int						=						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
					A_					:			int						=						image_processing(_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    a_					(							self				:   List[str]    ):



					"""simple docstring"""

					A_					:			Tuple						=						self.image_processing_class(**self.image_processor_dict    )
					# create random numpy tensors
					A_					:			Union[str, Any]						=						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
					A_					:			int						=						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
					A_					:			int						=						image_processing(_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    a_					(							self				:   List[str]    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						self.image_processing_class(**self.image_processor_dict    )
					# create random PyTorch tensors
					A_					:			List[str]						=						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
					A_					:			Union[str, 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
					A_					:			int						=						image_processing(_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'''],
					    ) ,       )











"""simple docstring"""

import bisect
import copy
from collections import defaultdict

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

from utils import logger

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase		):




	"""simple docstring"""






	A_					:			Optional[int]						=						copy.deepcopy(_UpperCAmelCase		)
	A_					:			int						=						sorted(_UpperCAmelCase		)
	A_					:			Tuple						=						[bisect.bisect_right(_UpperCAmelCase					, _UpperCAmelCase		) for y in x]
	return quantized

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase=0		):




	"""simple docstring"""






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

class     lowercase (      __UpperCAmelCase):







				def __init__(							self				:   Union[str, Any] ,       _lowerCamelCase				:   List[Any] ,       _lowerCamelCase				:   Dict ,       _lowerCamelCase				:   Dict    ):



					"""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_					:			List[str]						=						sampler
					A_					:			str						=						group_ids
					A_					:			Any						=						batch_size







				def __iter__(							self				:   List[str]    ):



					"""simple docstring"""

					A_					:			List[str]						=						defaultdict(_lowerCamelCase    )
					A_					:			Dict						=						defaultdict(_lowerCamelCase    )

					A_					:			List[str]						=						0
					for idx in self.sampler:
						A_					:			Union[str, Any]						=						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_					:			int						=						len(self    )
					A_					:			Dict						=						expected_num_batches - num_batches
					if num_remaining > 0:
						# for the remaining batches, group the batches by similar lengths
						A_					:			Optional[Any]						=						[]
						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_					:			str						=						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				:   List[Any]    ):



					"""simple docstring"""

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





