code: 
  
  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     UpperCAmelCase__				(    _A   :				Optional[int]			,   _A   :				List[Any]=10			,   _A   :				Optional[int]=10			,   _A   :				Dict=False			,   _A   :				Any=False							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=[]
    for i in range(image_processor_tester.num_frames							):
      video.append(np.random.randint(2_55			,   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__						=[Image.fromarray(np.moveaxis(_A			,   0			,   -1							)							) for frame in video]
  
    if torchify:
      a__						=[torch.from_numpy(_A							) for frame in video]
  
    return video
  
  
  
  
  def     UpperCAmelCase__				(    _A   :				List[str]			,   _A   :				Optional[int]=False			,   _A   :				Dict=False			,   _A   :				Optional[Any]=False							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
    a__						=[]
    for i in range(image_processor_tester.batch_size							):
      if equal_resolution:
        a__						=a__						=image_processor_tester.max_resolution
      else:
        a__,   a__						=np.random.choice(
            np.arange(image_processor_tester.min_resolution			,   image_processor_tester.max_resolution							)			,   2							)
        a__						=prepare_video(
            image_processor_tester=_A			,   width=_A			,   height=_A			,   numpify=_A			,   torchify=_A			,   )
      video_inputs.append(_A							)
  
    return video_inputs
  
  
  
  
  
  
  
  class 		__magic_name__    (							unittest.TestCase							):
  
     '''simple docstring'''
  
  
  
  
     def __init__(       self,					lowercase_,					lowercase_=7,					lowercase_=3,					lowercase_=4,					lowercase_=18,					lowercase_=30,					lowercase_=400,					lowercase_=True,					lowercase_=None,					lowercase_=True,					lowercase_=[0.5, 0.5, 0.5],					lowercase_=[0.5, 0.5, 0.5],					lowercase_=True,					lowercase_=None,					)							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=size if size is not None else {'''shortest_edge''': 18}
       a__						=crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
  
       a__						=parent
       a__						=batch_size
       a__						=num_channels
       a__						=num_frames
       a__						=image_size
       a__						=min_resolution
       a__						=max_resolution
       a__						=do_resize
       a__						=size
       a__						=do_normalize
       a__						=image_mean
       a__						=image_std
       a__						=do_center_crop
       a__						=crop_size
  
  
  
     def 				_UpperCAmelCase				(       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,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
       }
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 		__magic_name__    (							lowerCamelCase__	, unittest.TestCase							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			str												=     TvltImageProcessor if is_vision_available() else None
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=TvltImageProcessorTester(self )
  
  
  
     @property
     def 				_UpperCAmelCase				(       self )							->					Optional[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
     def 				_UpperCAmelCase				(       self )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=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 				_UpperCAmelCase				(       self )							->					Tuple:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.image_processing_class(**self.image_processor_dict )
       # create random PIL videos
       a__						=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__						=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__						=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 				_UpperCAmelCase				(       self )							->					int:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       a__						=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__						=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__						=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 				_UpperCAmelCase				(       self )							->					int:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       a__						=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__						=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__						=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'''],
           ),					)
  
style_context: 
  
  import unittest
  
  from transformers import ErnieMConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
         import torch
  
         from transformers import (
             ErnieMForInformationExtraction,
             ErnieMForMultipleChoice,
             ErnieMForQuestionAnswering,
             ErnieMForSequenceClassification,
             ErnieMForTokenClassification,
             ErnieMModel,
         )
         from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  
  
  
  
  
  class 		__magic_name__    :
  
     '''simple docstring'''
  
  
  
  
     def __init__(       self,					lowercase_,					lowercase_=13,					lowercase_=7,					lowercase_=True,					lowercase_=True,					lowercase_=True,					lowercase_=99,					lowercase_=32,					lowercase_=5,					lowercase_=4,					lowercase_=37,					lowercase_="gelu",					lowercase_=0.1,					lowercase_=0.1,					lowercase_=512,					lowercase_=16,					lowercase_=2,					lowercase_=0.02,					lowercase_=3,					lowercase_=4,					lowercase_=None,					)							->					List[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=parent
       a__						=batch_size
       a__						=seq_length
       a__						=is_training
       a__						=use_input_mask
       a__						=use_labels
       a__						=vocab_size
       a__						=hidden_size
       a__						=num_hidden_layers
       a__						=num_attention_heads
       a__						=intermediate_size
       a__						=hidden_act
       a__						=hidden_dropout_prob
       a__						=attention_probs_dropout_prob
       a__						=max_position_embeddings
       a__						=type_vocab_size
       a__						=type_sequence_label_size
       a__						=initializer_range
       a__						=num_labels
       a__						=num_choices
       a__						=scope
  
  
  
     def 				_UpperCAmelCase				(       self )							->					List[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=ids_tensor([self.batch_size, self.seq_length],					self.vocab_size )
  
       a__						=None
       if self.use_input_mask:
         a__						=random_attention_mask([self.batch_size, self.seq_length] )
  
       a__						=None
       a__						=None
       a__						=None
       if self.use_labels:
         a__						=ids_tensor([self.batch_size],					self.type_sequence_label_size )
         a__						=ids_tensor([self.batch_size, self.seq_length],					self.num_labels )
         a__						=ids_tensor([self.batch_size],					self.num_choices )
  
       a__						=self.get_config()
  
       return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=ids_tensor([self.batch_size, self.seq_length],					self.vocab_size )
  
       a__						=None
       if self.use_input_mask:
         a__						=random_attention_mask([self.batch_size, self.seq_length] )
       a__						=self.get_config()
  
       return config, input_ids, input_mask
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return ErnieMConfig(
           vocab_size=self.vocab_size,					hidden_size=self.hidden_size,					num_hidden_layers=self.num_hidden_layers,					num_attention_heads=self.num_attention_heads,					intermediate_size=self.intermediate_size,					hidden_act=self.hidden_act,					hidden_dropout_prob=self.hidden_dropout_prob,					attention_probs_dropout_prob=self.attention_probs_dropout_prob,					max_position_embeddings=self.max_position_embeddings,					type_vocab_size=self.type_vocab_size,					initializer_range=self.initializer_range,					)
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					Union[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=ErnieMModel(config=lowercase_ )
       model.to(lowercase_ )
       model.eval()
       a__						=model(lowercase_,					return_dict=lowercase_ )
       self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size) )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					List[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=ErnieMForQuestionAnswering(config=lowercase_ )
       model.to(lowercase_ )
       model.eval()
       a__						=model(
           lowercase_,					attention_mask=lowercase_,					start_positions=lowercase_,					end_positions=lowercase_,					)
       self.parent.assertEqual(result.start_logits.shape,					(self.batch_size, self.seq_length) )
       self.parent.assertEqual(result.end_logits.shape,					(self.batch_size, self.seq_length) )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=ErnieMForInformationExtraction(config=lowercase_ )
       model.to(lowercase_ )
       model.eval()
       a__						=torch.ones_like(lowercase_,					dtype=torch.floataa )
       a__						=model(
           lowercase_,					attention_mask=lowercase_,					start_positions=lowercase_,					end_positions=lowercase_,					)
       self.parent.assertEqual(result.start_logits.shape,					(self.batch_size, self.seq_length) )
       self.parent.assertEqual(result.end_logits.shape,					(self.batch_size, self.seq_length) )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					Union[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.num_labels
       a__						=ErnieMForSequenceClassification(lowercase_ )
       model.to(lowercase_ )
       model.eval()
       a__						=model(lowercase_,					attention_mask=lowercase_,					labels=lowercase_ )
       self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels) )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.num_labels
       a__						=ErnieMForTokenClassification(config=lowercase_ )
       model.to(lowercase_ )
       model.eval()
       input_ids.to(lowercase_ )
       input_mask.to(lowercase_ )
       token_labels.to(lowercase_ )
  
       a__						=model(lowercase_,					attention_mask=lowercase_,					labels=lowercase_ )
  
       self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.num_labels) )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.num_choices
       a__						=ErnieMForMultipleChoice(config=lowercase_ )
       model.to(lowercase_ )
       model.eval()
       a__						=input_ids.unsqueeze(1 ).expand(-1,					self.num_choices,					-1 ).contiguous()
       a__						=input_mask.unsqueeze(1 ).expand(-1,					self.num_choices,					-1 ).contiguous()
       a__						=model(
           lowercase_,					attention_mask=lowercase_,					labels=lowercase_,					)
       self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_choices) )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.prepare_config_and_inputs()
       (
           (
           a__
       ),   (
           a__
       ),   (
           a__
       ),   (
           a__
       ),   (
           a__
       ),   (
           a__
       ),   
       )						=config_and_inputs
       a__						={'''input_ids''': input_ids, '''attention_mask''': input_mask}
       return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 		__magic_name__    (							lowerCamelCase__	, lowerCamelCase__	, unittest.TestCase							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			List[Any]												=     (
         (
             ErnieMModel,
             ErnieMForMultipleChoice,
             ErnieMForQuestionAnswering,
             ErnieMForSequenceClassification,
             ErnieMForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     lowerCamelCase__				:			Union[str, Any]												=     ()
     lowerCamelCase__				:			Union[str, Any]												=     (
         {
             'feature-extraction': ErnieMModel,
             'question-answering': ErnieMForQuestionAnswering,
             'text-classification': ErnieMForSequenceClassification,
             'token-classification': ErnieMForTokenClassification,
             'zero-shot': ErnieMForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     lowerCamelCase__				:			Any												=     False
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					Optional[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if pipeline_test_casse_name == "QAPipelineTests":
         return True
  
       return False
  
  
  
     def 				_UpperCAmelCase				(       self )							->					int:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=ErnieMModelTester(self )
       a__						=ConfigTester(self,					config_class=lowercase_,					hidden_size=37 )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       self.config_tester.run_common_tests()
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Union[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
         a__						=type
         self.model_tester.create_and_check_model(*lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Tuple:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_information_extraction(*lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*lowercase_ )
  
  
  
     @slow
     def 				_UpperCAmelCase				(       self )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a__						=ErnieMModel.from_pretrained(lowercase_ )
         self.assertIsNotNone(lowercase_ )
  
  
  
  
  
  
  
  @require_torch
  class 		__magic_name__    (							unittest.TestCase							):
  
     '''simple docstring'''
  
  
  
  
     @slow
     def 				_UpperCAmelCase				(       self )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=ErnieMModel.from_pretrained('''susnato/ernie-m-base_pytorch''' )
       model.eval()
       a__						=torch.tensor([[0, 1, 2, 3, 4, 5]] )
       a__						=model(lowercase_ )[0]
  
       # TODO Replace vocab size
       a__						=768
  
       a__						=torch.Size((1, 6, hidden_size) )
       self.assertEqual(output.shape,					lowercase_ )
  
       a__						=torch.tensor(
           [[[-0.0012, 0.1245, -0.0214], [-0.0742, 0.0244, -0.0771], [-0.0333, 0.1164, -0.1554]]] )
  
       self.assertTrue(torch.allclose(output[:, :3, :3],					lowercase_,					atol=1E-3 ) )
  