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 			A							:
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__=12       ,     lowerCamelCase__=7       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     lowerCamelCase__=99       ,     lowerCamelCase__=32       ,     lowerCamelCase__=32       ,     lowerCamelCase__=5       ,     lowerCamelCase__=4       ,     lowerCamelCase__=37       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=512       ,     lowerCamelCase__=0.02       ,     lowerCamelCase__=0       ,     lowerCamelCase__=None       ,     )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       parent
  									lowercase__							 =       batch_size
  									lowercase__							 =       seq_length
  									lowercase__							 =       is_training
  									lowercase__							 =       use_input_mask
  									lowercase__							 =       use_labels
  									lowercase__							 =       vocab_size
  									lowercase__							 =       hidden_size
  									lowercase__							 =       projection_dim
  									lowercase__							 =       num_hidden_layers
  									lowercase__							 =       num_attention_heads
  									lowercase__							 =       intermediate_size
  									lowercase__							 =       dropout
  									lowercase__							 =       attention_dropout
  									lowercase__							 =       max_position_embeddings
  									lowercase__							 =       initializer_range
  									lowercase__							 =       scope
  									lowercase__							 =       bos_token_id
  
  
  
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )
  
  									lowercase__							 =       None
  									if self.use_input_mask:
  															lowercase__							 =       random_attention_mask([self.batch_size, self.seq_length]       )
  
  									if input_mask is not None:
  															lowercase__ ,						lowercase__							 =       input_mask.shape
  															lowercase__							 =       np.random.randint(1       ,     seq_length - 1       ,     size=(batch_size,)       )
  															for batch_idx, start_index in enumerate(lowerCamelCase__       ):
  																					lowercase__							 =       1
  																					lowercase__							 =       0
  
  									lowercase__							 =       self.get_config()
  
  									return config, input_ids, input_mask
  
  
  
  			def A__ (					self       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									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 A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       BlipTextModel(config=lowerCamelCase__       )
  									model.to(lowerCamelCase__       )
  									model.eval()
  									with torch.no_grad():
  															lowercase__							 =       model(lowerCamelCase__       ,     attention_mask=lowerCamelCase__       )
  															lowercase__							 =       model(lowerCamelCase__       )
  									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 A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.prepare_config_and_inputs()
  									lowercase__ ,						lowercase__ ,						lowercase__							 =       config_and_inputs
  									lowercase__							 =       {"""input_ids""": input_ids, """attention_mask""": input_mask}
  									return config, inputs_dict
  
  
  
  
  
  @require_torch
  class 			A							(       __UpperCAmelCase		,						unittest.TestCase				):
  			lowerCamelCase							:							List[Any]       		=  (BlipTextModel,) if is_torch_available() else ()
  			lowerCamelCase							:							Optional[int]       		=  False
  			lowerCamelCase							:							str       		=  False
  			lowerCamelCase							:							Optional[int]       		=  False
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       BlipTextModelTester(self       )
  									lowercase__							 =       ConfigTester(self       ,     config_class=lowerCamelCase__       ,     hidden_size=37       )
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									self.config_tester.run_common_tests()
  
  
  
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_model(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			@unittest.skip(reason="""Blip does not use inputs_embeds"""       )
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""       )
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""       )
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			@slow
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  															lowercase__							 =       BlipTextModel.from_pretrained(lowerCamelCase__       )
  															self.assertIsNotNone(lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									super().test_pt_tf_model_equivalence(allow_missing_keys=lowerCamelCase__       )
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  __A          =    {"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__A          =    ["DetrFeatureExtractor"]
  				__A          =    ["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__A          =    [
  				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DetrForObjectDetection",
  				    "DetrForSegmentation",
  				    "DetrModel",
  				    "DetrPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_detr import DetrFeatureExtractor
  								from .image_processing_detr import DetrImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_detr import (
  								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DetrForObjectDetection,
  								    DetrForSegmentation,
  								    DetrModel,
  								    DetrPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				__A          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  