code: 
  
  
  
  
  
  
  """simple docstring"""
  
  import unittest
  
  import numpy as np
  
  from transformers import BlipTextConfig
  from transformers.testing_utils import require_torch, slow, torch_device
  from transformers.utils import is_torch_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  
  
  if is_torch_available():
  					import torch
  
  					from transformers import BlipTextModel
  					from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  class 	__lowerCamelCase       :
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		def __init__(     self							:			Union[str, Any]		,    a_							:			Dict		,    a_							:			Optional[int]=12		,    a_							:			Dict=7		,    a_							:			Union[str, Any]=True		,    a_							:			List[Any]=True		,    a_							:			Any=True		,    a_							:			int=99		,    a_							:			List[str]=32		,    a_							:			Tuple=32		,    a_							:			Optional[int]=5		,    a_							:			Tuple=4		,    a_							:			List[Any]=37		,    a_							:			Optional[Any]=0.1		,    a_							:			str=0.1		,    a_							:			List[Any]=5_12		,    a_							:			Optional[int]=0.02		,    a_							:			List[Any]=0		,    a_							:			Optional[Any]=None		,    ):
  				lowerCAmelCase_     :							Tuple             =    parent
  				lowerCAmelCase_     :							Optional[Any]             =    batch_size
  				lowerCAmelCase_     :							List[Any]             =    seq_length
  				lowerCAmelCase_     :							Optional[Any]             =    is_training
  				lowerCAmelCase_     :							Optional[Any]             =    use_input_mask
  				lowerCAmelCase_     :							int             =    use_labels
  				lowerCAmelCase_     :							Optional[Any]             =    vocab_size
  				lowerCAmelCase_     :							Dict             =    hidden_size
  				lowerCAmelCase_     :							Union[str, Any]             =    projection_dim
  				lowerCAmelCase_     :							Any             =    num_hidden_layers
  				lowerCAmelCase_     :							Any             =    num_attention_heads
  				lowerCAmelCase_     :							int             =    intermediate_size
  				lowerCAmelCase_     :							Dict             =    dropout
  				lowerCAmelCase_     :							List[str]             =    attention_dropout
  				lowerCAmelCase_     :							Dict             =    max_position_embeddings
  				lowerCAmelCase_     :							Union[str, Any]             =    initializer_range
  				lowerCAmelCase_     :							int             =    scope
  				lowerCAmelCase_     :							Optional[int]             =    bos_token_id
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[int]			):
  				lowerCAmelCase_     :							int             =    ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size			)
  
  				lowerCAmelCase_     :							Union[str, Any]             =    None
  				if self.use_input_mask:
  						lowerCAmelCase_     :							int             =    random_attention_mask([self.batch_size, self.seq_length]			)
  
  				if input_mask is not None:
  						lowerCAmelCase_       ,			lowerCAmelCase_     :							Union[str, Any]             =    input_mask.shape
  						lowerCAmelCase_     :							int             =    np.random.randint(1		,    seq_length - 1		,    size=(batch_size,)			)
  						for batch_idx, start_index in enumerate(a_			):
  								lowerCAmelCase_     :							List[Any]             =    1
  								lowerCAmelCase_     :							Optional[Any]             =    0
  
  				lowerCAmelCase_     :							Dict             =    self.get_config()
  
  				return config, input_ids, input_mask
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Tuple			):
  				return BlipTextConfig(
  				    vocab_size=self.vocab_size		,    hidden_size=self.hidden_size		,    projection_dim=self.projection_dim		,    num_hidden_layers=self.num_hidden_layers		,    num_attention_heads=self.num_attention_heads		,    intermediate_size=self.intermediate_size		,    dropout=self.dropout		,    attention_dropout=self.attention_dropout		,    max_position_embeddings=self.max_position_embeddings		,    initializer_range=self.initializer_range		,    bos_token_id=self.bos_token_id		,    )
  
  
  
  
  
  		def      lowerCamelCase (     self							:			str		,    a_							:			Tuple		,    a_							:			Tuple		,    a_							:			Tuple			):
  				lowerCAmelCase_     :							List[Any]             =    BlipTextModel(config=a_			)
  				model.to(a_			)
  				model.eval()
  				with torch.no_grad():
  						lowerCAmelCase_     :							Optional[Any]             =    model(a_		,    attention_mask=a_			)
  						lowerCAmelCase_     :							Any             =    model(a_			)
  				self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, self.seq_length, self.hidden_size)			)
  				self.parent.assertEqual(result.pooler_output.shape		,    (self.batch_size, self.hidden_size)			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			List[Any]			):
  				lowerCAmelCase_     :							Dict             =    self.prepare_config_and_inputs()
  				lowerCAmelCase_       ,			lowerCAmelCase_       ,			lowerCAmelCase_     :							Union[str, Any]             =    config_and_inputs
  				lowerCAmelCase_     :							Dict             =    {"input_ids": input_ids, "attention_mask": input_mask}
  				return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 	__lowerCamelCase       (				A__				,							unittest.TestCase    ):
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  		a_			:       Dict      =							(BlipTextModel,) if is_torch_available() else ()
  		a_			:       Tuple      =							False
  		a_			:       Optional[int]      =							False
  		a_			:       List[Any]      =							False
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any			):
  				lowerCAmelCase_     :							str             =    BlipTextModelTester(self			)
  				lowerCAmelCase_     :							Any             =    ConfigTester(self		,    config_class=a_		,    hidden_size=37			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				self.config_tester.run_common_tests()
  
  
  
  
  
  		def      lowerCamelCase (     self							:			str			):
  				lowerCAmelCase_     :							Union[str, Any]             =    self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_model(*a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				pass
  
  
  
  
  
  		def      lowerCamelCase (     self							:			int			):
  				pass
  
  
  
  
  
  		@unittest.skip(reason="Blip does not use inputs_embeds"			)
  		def      lowerCamelCase (     self							:			Union[str, Any]			):
  				pass
  
  
  
  
  
  		@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"			)
  		def      lowerCamelCase (     self							:			Optional[Any]			):
  				pass
  
  
  
  
  
  		@unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"			)
  		def      lowerCamelCase (     self							:			str			):
  				pass
  
  
  
  
  
  		@slow
  		def      lowerCamelCase (     self							:			Any			):
  				for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  						lowerCAmelCase_     :							str             =    BlipTextModel.from_pretrained(a_			)
  						self.assertIsNotNone(a_			)
  
  
  
  
  
  		def      lowerCamelCase (     self							:			Any			):
  				super().test_pt_tf_model_equivalence(allow_missing_keys=a_			)
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowercase__      =				{
      """configuration_mobilevitv2""": [
          """MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
          """MobileViTV2Config""",
          """MobileViTV2OnnxConfig""",
      ],
  }
  
  
  try:
  					if not is_torch_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					lowercase__      =				[
  					    """MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
  					    """MobileViTV2ForImageClassification""",
  					    """MobileViTV2ForSemanticSegmentation""",
  					    """MobileViTV2Model""",
  					    """MobileViTV2PreTrainedModel""",
  					]
  
  if TYPE_CHECKING:
  					from .configuration_mobilevitva import (
  					    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
  					    MobileViTVaConfig,
  					    MobileViTVaOnnxConfig,
  					)
  
  					try:
  										if not is_torch_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_mobilevitva import (
  										    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    MobileViTVaForImageClassification,
  										    MobileViTVaForSemanticSegmentation,
  										    MobileViTVaModel,
  										    MobileViTVaPreTrainedModel,
  										)
  
  else:
  					import sys
  
  					lowercase__      =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  