





'''simple docstring'''







def  _lowerCamelCase				( lowercase						:      str    ,							lowercase						:      int						)	->	list[str]:
				return [sentence[i : i + ngram_size] for i in range(len(lowercase						) - ngram_size + 1						)]


if __name__ == "__main__":
					from doctest import testmod

					testmod()




"""simple docstring"""



from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
  import tensorflow as tf

  from transformers import TFBlipTextModel
  from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST







class _lowerCAmelCase				:







   def __init__(	self						,	UpperCamelCase__						,	UpperCamelCase__=12						,	UpperCamelCase__=7						,	UpperCamelCase__=True						,	UpperCamelCase__=True						,	UpperCamelCase__=True						,	UpperCamelCase__=99						,	UpperCamelCase__=32						,	UpperCamelCase__=32						,	UpperCamelCase__=2						,	UpperCamelCase__=4						,	UpperCamelCase__=37						,	UpperCamelCase__=0.1						,	UpperCamelCase__=0.1						,	UpperCamelCase__=512						,	UpperCamelCase__=0.02						,	UpperCamelCase__=0						,	UpperCamelCase__=None						,	)	->		Dict:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    parent
       snake_case    :      Dict      					=    batch_size
       snake_case    :      List[str]      					=    seq_length
       snake_case    :      Dict      					=    is_training
       snake_case    :      Optional[Any]      					=    use_input_mask
       snake_case    :      Optional[int]      					=    use_labels
       snake_case    :      Tuple      					=    vocab_size
       snake_case    :      Optional[Any]      					=    hidden_size
       snake_case    :      Optional[Any]      					=    projection_dim
       snake_case    :      List[Any]      					=    num_hidden_layers
       snake_case    :      List[Any]      					=    num_attention_heads
       snake_case    :      int      					=    intermediate_size
       snake_case    :      str      					=    dropout
       snake_case    :      List[Any]      					=    attention_dropout
       snake_case    :      Any      					=    max_position_embeddings
       snake_case    :      List[Any]      					=    initializer_range
       snake_case    :      Any      					=    scope
       snake_case    :      Union[str, Any]      					=    bos_token_id







   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case    :      str      					=    ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size						)

       snake_case    :      int      					=    None
       if self.use_input_mask:
           snake_case    :      Optional[int]      					=    random_attention_mask([self.batch_size, self.seq_length]						)

       if input_mask is not None:
           snake_case    :      Tuple      					=    input_mask.numpy()
           snake_case					,snake_case    :      str      					=    input_mask.shape
           snake_case    :      Tuple      					=    np.random.randint(1						,	seq_length - 1						,	size=(batch_size,)						)
           for batch_idx, start_index in enumerate(UpperCamelCase__						):
               snake_case    :      int      					=    1
               snake_case    :      Tuple      					=    0

       snake_case    :      Union[str, Any]      					=    self.get_config()

       return config, input_ids, tf.convert_to_tensor(UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''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  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      str      					=    TFBlipTextModel(config=UpperCamelCase__						)
       snake_case    :      Any      					=    model(UpperCamelCase__						,	attention_mask=UpperCamelCase__						,	training=UpperCamelCase__						)
       snake_case    :      Optional[int]      					=    model(UpperCamelCase__						,	training=UpperCamelCase__						)
       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[str]:

       '''simple docstring'''




       snake_case    :      Tuple      					=    self.prepare_config_and_inputs()
       snake_case					,snake_case					,snake_case    :      Tuple      					=    config_and_inputs
       snake_case    :      str      					=    {"input_ids": input_ids, "attention_mask": input_mask}
       return config, inputs_dict







@require_tf
class _lowerCAmelCase				(      snake_case_    ,							unittest.TestCase	):
   __UpperCAmelCase :       Any							  =  (TFBlipTextModel,) if is_tf_available() else ()
   __UpperCAmelCase :       Any							  =  False
   __UpperCAmelCase :       Dict							  =  False
   __UpperCAmelCase :       List[Any]							  =  False







   def  lowerCamelCase		(	self						)	->		List[str]:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    BlipTextModelTester(self						)
       snake_case    :      Optional[int]      					=    ConfigTester(self						,	config_class=UpperCamelCase__						,	hidden_size=37						)







   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       self.config_tester.run_common_tests()







   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case    :      List[str]      					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''simple docstring'''




       pass







   def  lowerCamelCase		(	self						)	->		List[str]:

       '''simple docstring'''




       pass







   @unittest.skip(reason="Blip does not use inputs_embeds"						)
   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       pass







   @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"						)
   def  lowerCamelCase		(	self						)	->		Optional[Any]:

       '''simple docstring'''




       pass







   @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"						)
   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       pass







   @slow
   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           snake_case    :      List[str]      					=    TFBlipTextModel.from_pretrained(UpperCamelCase__						)
           self.assertIsNotNone(UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__=True						)	->		Optional[int]:

       '''simple docstring'''




       super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCamelCase__						)





