






"""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


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import TvltImageProcessor




def 					a			(  UpperCamelCase_				:					int  ,						UpperCamelCase_				:					Dict=1_0  ,						UpperCamelCase_				:					Any=1_0  ,						UpperCamelCase_				:					List[str]=False  ,						UpperCamelCase_				:					Union[str, Any]=False		)	->		Union[str, Any]:
 __A						:							Tuple      =						[]
 for i in range(image_processor_tester.num_frames		):
  video.append(np.random.randint(2_5_5  ,						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
  __A						:							Any      =						[Image.fromarray(np.moveaxis(UpperCamelCase_  ,						0  ,						-1		)		) for frame in video]

 if torchify:
  __A						:							str      =						[torch.from_numpy(UpperCamelCase_		) for frame in video]

 return video




def 					a			(  UpperCamelCase_				:					Optional[Any]  ,						UpperCamelCase_				:					Optional[Any]=False  ,						UpperCamelCase_				:					Optional[int]=False  ,						UpperCamelCase_				:					List[Any]=False		)	->		int:
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 __A						:							int      =						[]
 for i in range(image_processor_tester.batch_size		):
  if equal_resolution:
   __A						:							List[str]      =						image_processor_tester.max_resolution
  else:
   __A     ,       __A						:							Optional[Any]      =						np.random.choice(
       np.arange(image_processor_tester.min_resolution  ,						image_processor_tester.max_resolution		)  ,						2		)
   __A						:							Dict      =						prepare_video(
       image_processor_tester=UpperCamelCase_  ,						width=UpperCamelCase_  ,						height=UpperCamelCase_  ,						numpify=UpperCamelCase_  ,						torchify=UpperCamelCase_  ,						)
  video_inputs.append(UpperCamelCase_		)

 return video_inputs






class __a ( unittest.TestCase					):



  def __init__(self	:		Union[str, Any]					,      lowercase_	:		List[Any]					,      lowercase_	:		List[str]=7					,      lowercase_	:		List[Any]=3					,      lowercase_	:		List[str]=4					,      lowercase_	:		List[Any]=18					,      lowercase_	:		List[Any]=30					,      lowercase_	:		Dict=400					,      lowercase_	:		Dict=True					,      lowercase_	:		Optional[int]=None					,      lowercase_	:		int=True					,      lowercase_	:		Dict=[0.5, 0.5, 0.5]					,      lowercase_	:		Union[str, Any]=[0.5, 0.5, 0.5]					,      lowercase_	:		List[str]=True					,      lowercase_	:		Any=None					,      )		->				Any:
   __A						:							Union[str, Any]      =						size if size is not None else {'shortest_edge': 18}
   __A						:							Optional[Any]      =						crop_size if crop_size is not None else {'height': 18, 'width': 18}

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



  def     A_					(self	:		Optional[Any] )		->				Dict:
   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 __a ( snake_case_			,unittest.TestCase					):
  __lowercase     :     Tuple       						=       TvltImageProcessor if is_vision_available() else None



  def     A_					(self	:		Optional[int] )		->				Union[str, Any]:
   __A						:							List[str]      =						TvltImageProcessorTester(self )



  @property
  def     A_					(self	:		Optional[int] )		->				Dict:
   return self.image_processor_tester.prepare_image_processor_dict()



  def     A_					(self	:		Optional[int] )		->				Any:
   __A						:							List[str]      =						self.image_processing_class(**self.image_processor_dict )
   self.assertTrue(hasattr(lowercase_					,      'image_mean' ) )
   self.assertTrue(hasattr(lowercase_					,      'image_std' ) )
   self.assertTrue(hasattr(lowercase_					,      'do_normalize' ) )
   self.assertTrue(hasattr(lowercase_					,      'do_resize' ) )
   self.assertTrue(hasattr(lowercase_					,      'do_center_crop' ) )
   self.assertTrue(hasattr(lowercase_					,      'size' ) )



  def     A_					(self	:		Tuple )		->				List[str]:
   # Initialize image_processor
   __A						:							Optional[Any]      =						self.image_processing_class(**self.image_processor_dict )
   # create random PIL videos
   __A						:							str      =						prepare_video_inputs(self.image_processor_tester					,      equal_resolution=lowercase_ )
   for video in video_inputs:
    self.assertIsInstance(lowercase_					,      lowercase_ )
    self.assertIsInstance(video[0]					,      Image.Image )

   # Test not batched input
   __A						:							Any      =						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
   __A						:							List[str]      =						image_processor(lowercase_					,      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	:		Optional[Any] )		->				str:
   # Initialize image_processor
   __A						:							List[Any]      =						self.image_processing_class(**self.image_processor_dict )
   # create random numpy tensors
   __A						:							Optional[Any]      =						prepare_video_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      numpify=lowercase_ )
   for video in video_inputs:
    self.assertIsInstance(lowercase_					,      lowercase_ )
    self.assertIsInstance(video[0]					,      np.ndarray )

   # Test not batched input
   __A						:							List[Any]      =						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
   __A						:							Optional[Any]      =						image_processor(lowercase_					,      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] )		->				List[str]:
   # Initialize image_processor
   __A						:							Tuple      =						self.image_processing_class(**self.image_processor_dict )
   # create random PyTorch tensors
   __A						:							str      =						prepare_video_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      torchify=lowercase_ )
   for video in video_inputs:
    self.assertIsInstance(lowercase_					,      lowercase_ )
    self.assertIsInstance(video[0]					,      torch.Tensor )

   # Test not batched input
   __A						:							int      =						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
   __A						:							Dict      =						image_processor(lowercase_					,      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 math




def 					a			(  UpperCamelCase_				:					int		)	->		str:
 if number == 0:
  __A						:							Dict      =						'Zero'
  return words
 else:
  __A						:							List[str]      =						math.logaa(UpperCamelCase_		)
  __A						:							Union[str, Any]      =						digits + 1
  __A						:							List[str]      =						{}
  __A						:							int      =						''
  __A						:							Any      =						'One'
  __A						:							Tuple      =						'Two'
  __A						:							Any      =						'Three'
  __A						:							Any      =						'Four'
  __A						:							Optional[Any]      =						'Five'
  __A						:							List[Any]      =						'Six'
  __A						:							List[Any]      =						'Seven'
  __A						:							Dict      =						'Eight'
  __A						:							Union[str, Any]      =						'Nine'

  __A						:							List[Any]      =						{}
  __A						:							Tuple      =						''
  __A						:							List[Any]      =						'Twenty'
  __A						:							Optional[Any]      =						'Thirty'
  __A						:							Any      =						'Forty'
  __A						:							Optional[int]      =						'Fifty'
  __A						:							Dict      =						'Sixty'
  __A						:							Dict      =						'Seventy'
  __A						:							Optional[Any]      =						'Eighty'
  __A						:							Any      =						'Ninety'

  __A						:							int      =						{}
  __A						:							List[str]      =						'Ten'
  __A						:							Optional[Any]      =						'Eleven'
  __A						:							Dict      =						'Twelve'
  __A						:							Union[str, Any]      =						'Thirteen'
  __A						:							Union[str, Any]      =						'Fourteen'
  __A						:							Dict      =						'Fifteen'
  __A						:							str      =						'Sixteen'
  __A						:							str      =						'Seventeen'
  __A						:							Union[str, Any]      =						'Eighteen'
  __A						:							Union[str, Any]      =						'Nineteen'

  __A						:							Optional[Any]      =						{}
  __A						:							Optional[int]      =						'Hundred,'
  __A						:							Union[str, Any]      =						'Thousand,'
  __A						:							Union[str, Any]      =						'Lakh,'
  __A						:							Union[str, Any]      =						'Crore,'

  __A						:							Optional[Any]      =						number
  __A						:							Optional[Any]      =						''
  __A						:							List[str]      =						0
  __A						:							List[str]      =						int(UpperCamelCase_		)
  while counter < digits:
   __A						:							Any      =						temp_num % 1_0
   if counter % 2 == 0:
    __A						:							Optional[Any]      =						''
    if counter in placevalue and current != 0:
     __A						:							Union[str, Any]      =						placevalue[counter]
    if counter == 2:
     __A						:							Dict      =						singles[current] + addition + words
    elif counter == 0:
     if ((temp_num % 1_0_0) // 1_0) == 1:
      __A						:							Any      =						teens[current] + addition + words
      __A						:							int      =						temp_num // 1_0
      counter += 1
     else:
      __A						:							Optional[Any]      =						singles[current] + addition + words

    else:
     __A						:							str      =						doubles[current] + addition + words

   else:
    if counter == 1:
     if current == 1:
      __A						:							List[Any]      =						teens[number % 1_0] + words
     else:
      __A						:							Union[str, Any]      =						''
      if counter in placevalue:
       __A						:							int      =						placevalue[counter]
      __A						:							Optional[Any]      =						doubles[current] + addition + words
    else:
     __A						:							Any      =						''
     if counter in placevalue:
      if current == 0 and ((temp_num % 1_0_0) // 1_0) == 0:
       __A						:							Optional[int]      =						''
      else:
       __A						:							Optional[int]      =						placevalue[counter]
     if ((temp_num % 1_0_0) // 1_0) == 1:
      __A						:							Dict      =						teens[current] + addition + words
      __A						:							Any      =						temp_num // 1_0
      counter += 1
     else:
      __A						:							List[str]      =						singles[current] + addition + words
   counter += 1
   __A						:							Dict      =						temp_num // 1_0
 return words


if __name__ == "__main__":
   import doctest

   doctest.testmod()





