


'''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_			)










'''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()






