
'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__ :			str											=		logging.get_logger(__name__)

UpperCAmelCase__ :			int											=		{
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}






class  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :str   =  'roc_bert'





	def __init__(       self      :							int   ,				__magic_name__      :							Optional[int]=30522   ,				__magic_name__      :							Optional[Any]=768   ,				__magic_name__      :							Dict=12   ,				__magic_name__      :							int=12   ,				__magic_name__      :							Tuple=3072   ,				__magic_name__      :							Optional[int]="gelu"   ,				__magic_name__      :							List[Any]=0.1   ,				__magic_name__      :							Dict=0.1   ,				__magic_name__      :							Optional[Any]=512   ,				__magic_name__      :							List[Any]=2   ,				__magic_name__      :							Optional[Any]=0.02   ,				__magic_name__      :							Tuple=1E-12   ,				__magic_name__      :							List[Any]=True   ,				__magic_name__      :							List[Any]=0   ,				__magic_name__      :							List[str]="absolute"   ,				__magic_name__      :							Dict=None   ,				__magic_name__      :							List[Any]=True   ,				__magic_name__      :							Optional[Any]=True   ,				__magic_name__      :							Dict=768   ,				__magic_name__      :							int=910   ,				__magic_name__      :							Optional[Any]=512   ,				__magic_name__      :							int=24858   ,				__magic_name__      :							Dict=True   ,				**__magic_name__      :							Union[str, Any]   ,				):


				"""simple docstring"""

				lowerCAmelCase__										=				vocab_size
				lowerCAmelCase__										=				max_position_embeddings
				lowerCAmelCase__										=				hidden_size
				lowerCAmelCase__										=				num_hidden_layers
				lowerCAmelCase__										=				num_attention_heads
				lowerCAmelCase__										=				intermediate_size
				lowerCAmelCase__										=				hidden_act
				lowerCAmelCase__										=				hidden_dropout_prob
				lowerCAmelCase__										=				attention_probs_dropout_prob
				lowerCAmelCase__										=				initializer_range
				lowerCAmelCase__										=				type_vocab_size
				lowerCAmelCase__										=				layer_norm_eps
				lowerCAmelCase__										=				use_cache
				lowerCAmelCase__										=				enable_pronunciation
				lowerCAmelCase__										=				enable_shape
				lowerCAmelCase__										=				pronunciation_embed_dim
				lowerCAmelCase__										=				pronunciation_vocab_size
				lowerCAmelCase__										=				shape_embed_dim
				lowerCAmelCase__										=				shape_vocab_size
				lowerCAmelCase__										=				concat_input
				lowerCAmelCase__										=				position_embedding_type
				lowerCAmelCase__										=				classifier_dropout
				super().__init__(pad_token_id=__magic_name__   ,				**__magic_name__				)








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     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







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






              """simple docstring"""


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

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






       def 			a		(		self   )      ->							Optional[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     __snake_case    (				snake_case__			,	unittest.TestCase  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							VivitImageProcessor if is_vision_available() else None


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              __snake_case       	=							VivitImageProcessingTester(self   )


       @property
       def 			a		(		self   )      ->							str:






              """simple docstring"""


              return self.image_processor_tester.prepare_image_processor_dict()


       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


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


       def 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case       	=							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}   )

              __snake_case       	=							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[str]:






              """simple docstring"""


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

              # Test not batched input
              __snake_case       	=							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
              __snake_case       	=							image_processing(_UpperCamelCase      , 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   )      ->							Union[str, Any]:






              """simple docstring"""


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

              # Test not batched input
              __snake_case       	=							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
              __snake_case       	=							image_processing(_UpperCamelCase      , 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"""


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

              # Test not batched input
              __snake_case       	=							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
              __snake_case       	=							image_processing(_UpperCamelCase      , 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"""],
                  )      , )

