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      UpperCAmelCase_						:
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=12    ,  SCREAMING_SNAKE_CASE_=7    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=99    ,  SCREAMING_SNAKE_CASE_=32    ,  SCREAMING_SNAKE_CASE_=32    ,  SCREAMING_SNAKE_CASE_=5    ,  SCREAMING_SNAKE_CASE_=4    ,  SCREAMING_SNAKE_CASE_=37    ,  SCREAMING_SNAKE_CASE_=0.1    ,  SCREAMING_SNAKE_CASE_=0.1    ,  SCREAMING_SNAKE_CASE_=5_12    ,  SCREAMING_SNAKE_CASE_=0.0_2    ,  SCREAMING_SNAKE_CASE_=0    ,  SCREAMING_SNAKE_CASE_=None    ,  )       ->  Any:
           __lowerCamelCase     :			int              =	parent
           __lowerCamelCase     :			Dict              =	batch_size
           __lowerCamelCase     :			List[str]              =	seq_length
           __lowerCamelCase     :			str              =	is_training
           __lowerCamelCase     :			int              =	use_input_mask
           __lowerCamelCase     :			Any              =	use_labels
           __lowerCamelCase     :			Optional[int]              =	vocab_size
           __lowerCamelCase     :			Tuple              =	hidden_size
           __lowerCamelCase     :			Tuple              =	projection_dim
           __lowerCamelCase     :			Optional[int]              =	num_hidden_layers
           __lowerCamelCase     :			Tuple              =	num_attention_heads
           __lowerCamelCase     :			List[str]              =	intermediate_size
           __lowerCamelCase     :			Optional[Any]              =	dropout
           __lowerCamelCase     :			List[Any]              =	attention_dropout
           __lowerCamelCase     :			Dict              =	max_position_embeddings
           __lowerCamelCase     :			str              =	initializer_range
           __lowerCamelCase     :			Union[str, Any]              =	scope
           __lowerCamelCase     :			Any              =	bos_token_id
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Union[str, Any]:
           __lowerCamelCase     :			Tuple              =	ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size			)
  
           __lowerCamelCase     :			List[str]              =	None
           if self.use_input_mask:
                __lowerCamelCase     :			Union[str, Any]              =	random_attention_mask([self.batch_size, self.seq_length]			)
  
           if input_mask is not None:
                __lowerCamelCase       , __lowerCamelCase     :			Optional[Any]              =	input_mask.shape
                __lowerCamelCase     :			Dict              =	np.random.randint(1    ,  seq_length - 1    ,  size=(batch_size,)			)
                for batch_idx, start_index in enumerate(a_			):
                     __lowerCamelCase     :			Union[str, Any]              =	1
                     __lowerCamelCase     :			Tuple              =	0
  
           __lowerCamelCase     :			int              =	self.get_config()
  
           return config, input_ids, input_mask
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Dict:
           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    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  Dict:
           __lowerCamelCase     :			List[str]              =	BlipTextModel(config=a_			)
           model.to(a_			)
           model.eval()
           with torch.no_grad():
                __lowerCamelCase     :			str              =	model(a_    ,  attention_mask=a_			)
                __lowerCamelCase     :			str              =	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    lowercase_       (							self			)       ->  int:
           __lowerCamelCase     :			str              =	self.prepare_config_and_inputs()
           __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase     :			Any              =	config_and_inputs
           __lowerCamelCase     :			List[Any]              =	{'input_ids': input_ids, 'attention_mask': input_mask}
           return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class      UpperCAmelCase_						(SCREAMING_SNAKE_CASE__      ,	unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Any							  =    (BlipTextModel,) if is_torch_available() else ()
      lowerCamelCase						:      List[Any]							  =    False
      lowerCamelCase						:      Dict							  =    False
      lowerCamelCase						:      int							  =    False
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           __lowerCamelCase     :			Any              =	BlipTextModelTester(self			)
           __lowerCamelCase     :			int              =	ConfigTester(self    ,  config_class=a_    ,  hidden_size=37			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Dict:
           self.config_tester.run_common_tests()
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[Any]:
           __lowerCamelCase     :			Optional[int]              =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*a_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           pass
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  int:
           pass
  
  
  
  
  
  
  
      @unittest.skip(reason='Blip does not use inputs_embeds'			)
      def    lowercase_       (							self			)       ->  Any:
           pass
  
  
  
  
  
  
  
      @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'			)
      def    lowercase_       (							self			)       ->  Any:
           pass
  
  
  
  
  
  
  
      @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'			)
      def    lowercase_       (							self			)       ->  List[str]:
           pass
  
  
  
  
  
  
  
      @slow
      def    lowercase_       (							self			)       ->  List[str]:
  
  
  
           for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                __lowerCamelCase     :			Tuple              =	BlipTextModel.from_pretrained(a_			)
                self.assertIsNotNone(a_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           super().test_pt_tf_model_equivalence(allow_missing_keys=a_			)
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  A__	:      List[Any]				=						[0.3_3, 0.4_4, 0.5_5, 0.4_4, 0.3_3]
  A__	:      Dict				=						3
  A__	:      Union[str, Any]				=						5
  
  # Buffer Space (with Parameters Space)
  A__	:      list[float]				=						[]
  A__	:      list[float]				=						[]
  
  # Machine Time
  A__	:      Dict				=						0
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       List[str]							)  ->	str:
       global buffer_space, params_space, machine_time, K, m, t
  
       # Choosing Dynamical Systems (All)
       for key, value in enumerate(UpperCAmelCase_							):
            # Evolution Parameter
            __lowerCamelCase     :			List[str]              =	float(seed / value							)
  
            # Control Theory: Orbit Change
            __lowerCamelCase     :			Optional[int]              =	(buffer_space[(key + 1) % m] + e) % 1
  
            # Control Theory: Trajectory Change
            __lowerCamelCase     :			str              =	(params_space[key] + e) % 1 + 3
  
            # Modification (Transition Function) - Jumps
            __lowerCamelCase     :			List[Any]              =	round(float(r * value * (1 - value)							)   ,					10							)
            __lowerCamelCase     :			Tuple              =	r  # Saving to Parameters Space
  
       # Logistic Map
       assert max(UpperCAmelCase_							) < 1
       assert max(UpperCAmelCase_							) < 4
  
       # Machine Time
       machine_time += 1
  
  
  
  def     UpperCAmelCase__      (			)  ->	Optional[int]:
       global buffer_space, params_space, machine_time, K, m, t
  
       # PRNG (Xorshift by George Marsaglia)
       def xorshift(UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       Tuple							):
            x ^= y >> 13
            y ^= x << 17
            x ^= y >> 5
            return x
  
       # Choosing Dynamical Systems (Increment)
       __lowerCamelCase     :			List[Any]              =	machine_time % m
  
       # Evolution (Time Length)
       for _ in range(0   ,					UpperCAmelCase_							):
            # Variables (Position + Parameters)
            __lowerCamelCase     :			Union[str, Any]              =	params_space[key]
            __lowerCamelCase     :			Optional[Any]              =	buffer_space[key]
  
            # Modification (Transition Function) - Flow
            __lowerCamelCase     :			str              =	round(float(r * value * (1 - value)							)   ,					10							)
            __lowerCamelCase     :			Dict              =	(machine_time * 0.01 + r * 1.01) % 1 + 3
  
       # Choosing Chaotic Data
       __lowerCamelCase     :			List[Any]              =	int(buffer_space[(key + 2) % m] * (10**10)							)
       __lowerCamelCase     :			List[str]              =	int(buffer_space[(key - 2) % m] * (10**10)							)
  
       # Machine Time
       machine_time += 1
  
       return xorshift(UpperCAmelCase_   ,					UpperCAmelCase_							) % 0xff_ff_ff_ff
  
  
  
  def     UpperCAmelCase__      (			)  ->	Any:
       global buffer_space, params_space, machine_time, K, m, t
  
       __lowerCamelCase     :			Union[str, Any]              =	K
       __lowerCamelCase     :			Optional[int]              =	[0] * m
       __lowerCamelCase     :			Tuple              =	0
  
  
  if __name__ == "__main__":
        # Initialization
        reset()
  
        # Pushing Data (Input)
        import random
  
        A__	:      Optional[Any]				=						random.sample(range(0XFFFFFFFF), 100)
        for chunk in message:
              push(chunk)
  
        # for controlling
        A__	:      Tuple				=						""""""
  
        # Pulling Data (Output)
        while inp in ("e", "E"):
              print(f'''{format(pull(), '#04x')}''')
              print(buffer_space)
              print(params_space)
              A__	:      Optional[Any]				=						input("""(e)exit? """).strip()
  
  
  
  
  
  
  