


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        __lowercase       (							unittest.TestCase  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A=7		,      A=3		,      A=10		,      A=18		,      A=30		,      A=4_00		,      A=True		,      A=None		,      A=True		,      A=[0.5, 0.5, 0.5]		,      A=[0.5, 0.5, 0.5]		,      A=None		,      )  -> Dict:







					'''simple docstring'''







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

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







		def 					__A   ( self	)  -> List[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       (							a_			,							unittest.TestCase  ):



		"""simple docstring"""

		UpperCamelCase     :					List[str]			       =		VivitImageProcessor if is_vision_available() else None







		def 					__A   ( self	)  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  VivitImageProcessingTester(self	)







		@property
		def 					__A   ( self	)  -> str:







					'''simple docstring'''







					return self.image_processor_tester.prepare_image_processor_dict()







		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					lowerCamelCase    		=  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 					__A   ( self	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  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}	)

					lowerCamelCase    		=  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	)  -> str:







					'''simple docstring'''







					lowerCamelCase    		=  self.image_processing_class(**self.image_processor_dict	)
					# create random PIL videos
					lowerCamelCase    		=  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
					lowerCamelCase    		=  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
					lowerCamelCase    		=  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 					__A   ( self	)  -> List[Any]:







					'''simple docstring'''







					lowerCamelCase    		=  self.image_processing_class(**self.image_processor_dict	)
					# create random numpy tensors
					lowerCamelCase    		=  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
					lowerCamelCase    		=  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
					lowerCamelCase    		=  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 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  self.image_processing_class(**self.image_processor_dict	)
					# create random PyTorch tensors
					lowerCamelCase    		=  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
					lowerCamelCase    		=  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
					lowerCamelCase    		=  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"""],
					    )		,      )





# Lint as: python3
import itertools
import os
import re


UpperCAmelCase   :    Any     =		re.compile(r"([A-Z]+)([A-Z][a-z])")
UpperCAmelCase   :    Optional[Any]     =		re.compile(r"([a-z\d])([A-Z])")

UpperCAmelCase   :    Optional[Any]     =		re.compile(r"(?<!_)_(?!_)")
UpperCAmelCase   :    Any     =		re.compile(r"(_{2,})")

UpperCAmelCase   :    List[Any]     =		r"^\w+(\.\w+)*$"

UpperCAmelCase   :    Any     =		r"<>:/\|?*"






def 	__lowerCamelCase						(							lowerCamelCase__		:						Tuple      ):





			'''simple docstring'''




			lowerCamelCase    		=  _uppercase_uppercase_re.sub(R"""\1_\2"""  ,							lowerCamelCase__      )
			lowerCamelCase    		=  _lowercase_uppercase_re.sub(R"""\1_\2"""  ,							lowerCamelCase__      )
			return name.lower()






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[str]      ):





			'''simple docstring'''




			lowerCamelCase    		=  _single_underscore_re.split(lowerCamelCase__      )
			lowerCamelCase    		=  [_multiple_underscores_re.split(lowerCamelCase__      ) for n in name]
			return "".join(n.capitalize() for n in itertools.chain.from_iterable(lowerCamelCase__      ) if n != """"""      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any      ):





			'''simple docstring'''




			if os.path.basename(lowerCamelCase__      ) != name:
						raise ValueError(f'Should be a dataset name, not a path: {name}'      )
			return camelcase_to_snakecase(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[str]  ,							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			if os.path.basename(lowerCamelCase__      ) != name:
						raise ValueError(f'Should be a dataset name, not a path: {name}'      )
			if not re.match(_split_re  ,							lowerCamelCase__      ):
						raise ValueError(f'Split name should match \'{_split_re}\'\' but got \'{split}\'.'      )
			return f'{filename_prefix_for_name(lowerCamelCase__      )}-{split}'






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[Any]  ,							lowerCamelCase__		:						Optional[Any]  ,							lowerCamelCase__		:						List[Any]  ,							lowerCamelCase__		:						int=None      ):





			'''simple docstring'''




			lowerCamelCase    		=  filename_prefix_for_split(lowerCamelCase__  ,							lowerCamelCase__      )
			if filetype_suffix:
						prefix += f'.{filetype_suffix}'
			lowerCamelCase    		=  os.path.join(lowerCamelCase__  ,							lowerCamelCase__      )
			return f'{filepath}*'






def 	__lowerCamelCase						(							lowerCamelCase__		:						Dict  ,							lowerCamelCase__		:						List[Any]  ,							lowerCamelCase__		:						List[Any]  ,							lowerCamelCase__		:						int=None  ,							lowerCamelCase__		:						str=None      ):





			'''simple docstring'''




			lowerCamelCase    		=  filename_prefix_for_split(lowerCamelCase__  ,							lowerCamelCase__      )
			lowerCamelCase    		=  os.path.join(lowerCamelCase__  ,							lowerCamelCase__      )

			if shard_lengths:
						lowerCamelCase    		=  len(lowerCamelCase__      )
						lowerCamelCase    		=  [f'{prefix}-{shard_id:05d}-of-{num_shards:05d}' for shard_id in range(lowerCamelCase__      )]
						if filetype_suffix:
									lowerCamelCase    		=  [filename + f'.{filetype_suffix}' for filename in filenames]
						return filenames
			else:
						lowerCamelCase    		=  prefix
						if filetype_suffix:
									filename += f'.{filetype_suffix}'
						return [filename]

