


import numpy as np
def 							_UpperCAmelCase    (UpperCamelCase__					:  Union[str, Any]    ,							UpperCamelCase__					:  int="utf-8"    ,							UpperCamelCase__					:  Union[str, Any]="surrogatepass"						):
       _A				:	List[str]       =			bin(int.from_bytes(text.encode(UpperCamelCase__    ,							UpperCamelCase__						)    ,							"big"						)						)[2:]
       return bits.zfill(8 * ((len(UpperCamelCase__						) + 7) // 8)						)
def 							_UpperCAmelCase    (UpperCamelCase__					:  Tuple    ,							UpperCamelCase__					:  int="utf-8"    ,							UpperCamelCase__					:  List[str]="surrogatepass"						):
       _A				:	str       =			int(UpperCamelCase__    ,							2						)
       return n.to_bytes((n.bit_length() + 7) // 8    ,							"big"						).decode(UpperCamelCase__    ,							UpperCamelCase__						) or "\0"
def 							_UpperCAmelCase    (UpperCamelCase__					:  Optional[int]    ,							UpperCamelCase__					:  Optional[Any]						):
       if size_par + len(UpperCamelCase__						) <= 2**size_par - (len(UpperCamelCase__						) - 1):
              raise ValueError("size of parity don't match with size of data"						)

       _A				:	Optional[Any]       =			[]
       _A				:	List[Any]       =			[]
       _A				:	List[Any]       =			[bin(UpperCamelCase__						)[2:] for x in range(1    ,							size_par + len(UpperCamelCase__						) + 1						)]

       # sorted information data for the size of the output data
       _A				:	List[Any]       =			[]
       # data position template + parity
       _A				:	str       =			[]
       # parity bit counter
       _A				:	Any       =			0
       # counter position of data bits
       _A				:	Any       =			0

       for x in range(1    ,							size_par + len(UpperCamelCase__						) + 1						):
              # Performs a template of bit positions - who should be given,
              # and who should be parity
              if qtd_bp < size_par:
                     if (np.log(UpperCamelCase__						) / np.log(2						)).is_integer():
                            data_out_gab.append("P"						)
                            _A				:	Optional[Any]       =			qtd_bp + 1
                     else:
                            data_out_gab.append("D"						)
              else:
                     data_out_gab.append("D"						)

              # Sorts the data to the new output size
              if data_out_gab[-1] == "D":
                     data_ord.append(data[cont_data]						)
                     cont_data += 1
              else:
                     data_ord.append(UpperCamelCase__						)

    # Calculates parity
       _A				:	Optional[int]       =			0  # parity bit counter
       for bp in range(1    ,							size_par + 1						):
              # Bit counter one for a given parity
              _A				:	Dict       =			0
              # counter to control the loop reading
              _A				:	List[Any]       =			0
              for x in data_ord:
                     if x is not None:
                            try:
                                   _A				:	Union[str, Any]       =			(bin_pos[cont_loop])[-1 * (bp)]
                            except IndexError:
                                   _A				:	Dict       =			"0"
                            if aux == "1" and x == "1":
                                   cont_bo += 1
                     cont_loop += 1
              parity.append(cont_bo % 2						)

              qtd_bp += 1

       # Mount the message
       _A				:	str       =			0  # parity bit counter
       for x in range(0    ,							size_par + len(UpperCamelCase__						)						):
              if data_ord[x] is None:
                     data_out.append(str(parity[cont_bp]						)						)
                     cont_bp += 1
              else:
                     data_out.append(data_ord[x]						)

       return data_out
def 							_UpperCAmelCase    (UpperCamelCase__					:  List[Any]    ,							UpperCamelCase__					:  Dict						):
       _A				:	Union[str, Any]       =			[]
       # Parity bit counter
       _A				:	Optional[Any]       =			0
       # Counter p data bit reading
       _A				:	Union[str, Any]       =			0
       # list of parity received
       _A				:	str       =			[]
       _A				:	Tuple       =			[]

       for x in range(1    ,							len(UpperCamelCase__						) + 1						):
              # Performs a template of bit positions - who should be given,
              #  and who should be parity
              if qtd_bp < size_par and (np.log(UpperCamelCase__						) / np.log(2						)).is_integer():
                     data_out_gab.append("P"						)
                     _A				:	Any       =			qtd_bp + 1
              else:
                     data_out_gab.append("D"						)

              # Sorts the data to the new output size
              if data_out_gab[-1] == "D":
                     data_output.append(data[cont_data]						)
              else:
                     parity_received.append(data[cont_data]						)
              cont_data += 1

       # -----------calculates the parity with the data
       _A				:	Any       =			[]
       _A				:	List[str]       =			[]
       _A				:	Tuple       =			[bin(UpperCamelCase__						)[2:] for x in range(1    ,							size_par + len(UpperCamelCase__						) + 1						)]

       #  sorted information data for the size of the output data
       _A				:	Optional[Any]       =			[]
       # Data position feedback + parity
       _A				:	int       =			[]
       # Parity bit counter
       _A				:	List[Any]       =			0
       # Counter p data bit reading
       _A				:	Tuple       =			0

       for x in range(1    ,							size_par + len(UpperCamelCase__						) + 1						):
              # Performs a template position of bits - who should be given,
              # and who should be parity
              if qtd_bp < size_par and (np.log(UpperCamelCase__						) / np.log(2						)).is_integer():
                     data_out_gab.append("P"						)
                     _A				:	Optional[Any]       =			qtd_bp + 1
              else:
                     data_out_gab.append("D"						)

              # Sorts the data to the new output size
              if data_out_gab[-1] == "D":
                     data_ord.append(data_output[cont_data]						)
                     cont_data += 1
              else:
                     data_ord.append(UpperCamelCase__						)

    # Calculates parity
       _A				:	List[str]       =			0  # parity bit counter
       for bp in range(1    ,							size_par + 1						):
              # Bit counter one for a certain parity
              _A				:	str       =			0
              # Counter to control loop reading
              _A				:	str       =			0
              for x in data_ord:
                     if x is not None:
                            try:
                                   _A				:	Optional[Any]       =			(bin_pos[cont_loop])[-1 * (bp)]
                            except IndexError:
                                   _A				:	Tuple       =			"0"
                            if aux == "1" and x == "1":
                                   cont_bo += 1
                     cont_loop += 1
              parity.append(str(cont_bo % 2						)						)

              qtd_bp += 1

       # Mount the message
       _A				:	List[str]       =			0  # Parity bit counter
       for x in range(0    ,							size_par + len(UpperCamelCase__						)						):
              if data_ord[x] is None:
                     data_out.append(str(parity[cont_bp]						)						)
                     cont_bp += 1
              else:
                     data_out.append(data_ord[x]						)

       _A				:	Optional[int]       =			parity_received == parity
       return data_output, ack




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			,						__lowerCamelCase			,						__lowerCamelCase=1_2			,						__lowerCamelCase=7			,						__lowerCamelCase=True			,						__lowerCamelCase=True			,						__lowerCamelCase=True			,						__lowerCamelCase=9_9			,						__lowerCamelCase=3_2			,						__lowerCamelCase=3_2			,						__lowerCamelCase=5			,						__lowerCamelCase=4			,						__lowerCamelCase=3_7			,						__lowerCamelCase=0.1			,						__lowerCamelCase=0.1			,						__lowerCamelCase=5_1_2			,						__lowerCamelCase=0.0_2			,						__lowerCamelCase=0			,						__lowerCamelCase=None			,						)      ->    str:
            _A				:	Optional[int]       =			parent
            _A				:	Optional[int]       =			batch_size
            _A				:	Optional[int]       =			seq_length
            _A				:	Dict       =			is_training
            _A				:	Union[str, Any]       =			use_input_mask
            _A				:	Optional[int]       =			use_labels
            _A				:	Any       =			vocab_size
            _A				:	Dict       =			hidden_size
            _A				:	Optional[Any]       =			projection_dim
            _A				:	Union[str, Any]       =			num_hidden_layers
            _A				:	Tuple       =			num_attention_heads
            _A				:	List[str]       =			intermediate_size
            _A				:	Any       =			dropout
            _A				:	List[str]       =			attention_dropout
            _A				:	str       =			max_position_embeddings
            _A				:	List[Any]       =			initializer_range
            _A				:	int       =			scope
            _A				:	List[str]       =			bos_token_id


     def 			_lowerCamelCase			(						self)      ->    List[str]:
            _A				:	Tuple       =			ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size)

            _A				:	str       =			None
            if self.use_input_mask:
                   _A				:	Union[str, Any]       =			random_attention_mask([self.batch_size, self.seq_length])

            if input_mask is not None:
                   _A   ,      _A				:	int       =			input_mask.shape
                   _A				:	Optional[Any]       =			np.random.randint(1			,						seq_length - 1			,						size=(batch_size,))
                   for batch_idx, start_index in enumerate(__lowerCamelCase):
                          _A				:	Tuple       =			1
                          _A				:	List[str]       =			0

            _A				:	str       =			self.get_config()

            return config, input_ids, input_mask


     def 			_lowerCamelCase			(						self)      ->    Optional[int]:
            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			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase)      ->    List[str]:
            _A				:	int       =			BlipTextModel(config=__lowerCamelCase)
            model.to(__lowerCamelCase)
            model.eval()
            with torch.no_grad():
                   _A				:	int       =			model(__lowerCamelCase			,						attention_mask=__lowerCamelCase)
                   _A				:	List[str]       =			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 			_lowerCamelCase			(						self)      ->    Any:
            _A				:	Dict       =			self.prepare_config_and_inputs()
            _A   ,      _A   ,      _A				:	str       =			config_and_inputs
            _A				:	List[str]       =			{"input_ids": input_ids, "attention_mask": input_mask}
            return config, inputs_dict







@require_torch
class 					lowerCAmelCase__	(    a					,   unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			(BlipTextModel,) if is_torch_available() else ()
     __SCREAMING_SNAKE_CASE        =			False
     __SCREAMING_SNAKE_CASE        =			False
     __SCREAMING_SNAKE_CASE        =			False


     def 			_lowerCamelCase			(						self)      ->    Dict:
            _A				:	Optional[Any]       =			BlipTextModelTester(self)
            _A				:	List[str]       =			ConfigTester(self			,						config_class=__lowerCamelCase			,						hidden_size=3_7)


     def 			_lowerCamelCase			(						self)      ->    List[Any]:
            self.config_tester.run_common_tests()


     def 			_lowerCamelCase			(						self)      ->    Union[str, Any]:
            _A				:	Optional[Any]       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            pass


     def 			_lowerCamelCase			(						self)      ->    Dict:
            pass


     @unittest.skip(reason="Blip does not use inputs_embeds")
     def 			_lowerCamelCase			(						self)      ->    str:
            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)      ->    Optional[Any]:
            for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _A				:	Union[str, Any]       =			BlipTextModel.from_pretrained(__lowerCamelCase)
                   self.assertIsNotNone(__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    Optional[int]:
            super().test_pt_tf_model_equivalence(allow_missing_keys=__lowerCamelCase)
