






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 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					List[str]=10			,  SCREAMING_SNAKE_CASE_	:					str=10			,  SCREAMING_SNAKE_CASE_	:					int=False			,  SCREAMING_SNAKE_CASE_	:					Any=False    ):



     '''simple docstring'''





     lowercase__					:     List[Any]   = []
     for i in range(image_processor_tester.num_frames    ):
          video.append(np.random.randint(255			,  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
          lowercase__					:     Optional[Any]   = [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_			,  0			,  -1    )    ) for frame in video]

     if torchify:
          lowercase__					:     Optional[int]   = [torch.from_numpy(SCREAMING_SNAKE_CASE_    ) for frame in video]

     return video

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					Optional[Any]=False			,  SCREAMING_SNAKE_CASE_	:					Optional[int]=False			,  SCREAMING_SNAKE_CASE_	:					Optional[Any]=False    ):



     '''simple docstring'''





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

     lowercase__					:     Optional[int]   = []
     for i in range(image_processor_tester.batch_size    ):
          if equal_resolution:
               lowercase__					:     List[str]   = image_processor_tester.max_resolution
          else:
               lowercase__					:     Tuple   = np.random.choice(
                   np.arange(image_processor_tester.min_resolution			,  image_processor_tester.max_resolution    )			,  2    )
               lowercase__					:     Any   = 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    SCREAMING_SNAKE_CASE__      (unittest.TestCase  ):





    def __init__(   self		,     a		,     a=7		,     a=3		,     a=4		,     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=True		,     a=None		,     ):
         lowercase__					:     Optional[Any]   = size if size is not None else {'shortest_edge': 18}
         lowercase__					:     Tuple   = crop_size if crop_size is not None else {'height': 18, 'width': 18}

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





    def    snake_case_       (   self):
         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    SCREAMING_SNAKE_CASE__      (__snake_case	, unittest.TestCase  ):
    __lowerCamelCase		: List[Any]														=  TvltImageProcessor if is_vision_available() else None





    def    snake_case_       (   self):
         lowercase__					:     str   = TvltImageProcessorTester(self)





    @property
    def    snake_case_       (   self):
         return self.image_processor_tester.prepare_image_processor_dict()





    def    snake_case_       (   self):
         lowercase__					:     List[str]   = 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    snake_case_       (   self):
         # Initialize image_processor
         lowercase__					:     List[str]   = self.image_processing_class(**self.image_processor_dict)
         # create random PIL videos
         lowercase__					:     Optional[int]   = 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
         lowercase__					:     Optional[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
         lowercase__					:     int   = image_processor(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    snake_case_       (   self):
         # Initialize image_processor
         lowercase__					:     List[Any]   = self.image_processing_class(**self.image_processor_dict)
         # create random numpy tensors
         lowercase__					:     Any   = 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
         lowercase__					:     Dict   = 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
         lowercase__					:     int   = image_processor(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    snake_case_       (   self):
         # Initialize image_processor
         lowercase__					:     Any   = self.image_processing_class(**self.image_processor_dict)
         # create random PyTorch tensors
         lowercase__					:     int   = 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
         lowercase__					:     Optional[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
         lowercase__					:     str   = image_processor(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'],
             )		,     )




from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest


@custom_tokenizers
class    SCREAMING_SNAKE_CASE__      (__snake_case  ):





    def    snake_case_       (   self		,     a		,     a		,     a		,     a		,     a):
         return True





    def    snake_case_       (   self):
         lowercase__					:     Tuple   = CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate')
         lowercase__					:     str   = 'Hugging Face大法好，谁用谁知道。'
         lowercase__					:     Optional[int]   = 'Hugging Face大法好,谁用谁知道。<unk>'
         lowercase__					:     Any   = '▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

         lowercase__					:     Any   = tokenizer.tokenize(a)
         self.assertListEqual(a		,     a)

         lowercase__					:     List[Any]   = tokens + [tokenizer.unk_token]

         lowercase__					:     Any   = [1_3789, 1_3283, 1421, 8, 10, 1164, 1_3608, 1_6528, 63, 8, 9, 440, 108, 440, 121, 90, 8, 12, 0]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a)		,     a)

         lowercase__					:     Optional[Any]   = tokenizer.decode(a)
         self.assertEqual(a		,     a)

