"""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_	:
    def __init__(	self			:       Tuple   ,						_lowercase			:       Tuple   ,						_lowercase			:       Any=1_2   ,						_lowercase			:       str=7   ,						_lowercase			:       Dict=True   ,						_lowercase			:       Any=True   ,						_lowercase			:       str=True   ,						_lowercase			:       Dict=9_9   ,						_lowercase			:       Tuple=3_2   ,						_lowercase			:       List[Any]=3_2   ,						_lowercase			:       List[Any]=5   ,						_lowercase			:       List[str]=4   ,						_lowercase			:       List[str]=3_7   ,						_lowercase			:       str=0.1   ,						_lowercase			:       Optional[Any]=0.1   ,						_lowercase			:       int=5_1_2   ,						_lowercase			:       int=0.02   ,						_lowercase			:       List[Any]=0   ,						_lowercase			:       Optional[int]=None   ,						)						->    str:
       _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 		_lowerCamelCase      (	self			:       Union[str, Any]		)						->    int:
       _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(_lowercase		):
             _lowercase							     =       1
             _lowercase							     =       0

       _lowercase							     =       self.get_config()

       return config, input_ids, input_mask
    def 		_lowerCamelCase      (	self			:       int		)						->    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			:       Optional[int]   ,						_lowercase			:       Any   ,						_lowercase			:       int   ,						_lowercase			:       Dict		)						->    str:
       _lowercase							     =       BlipTextModel(config=_lowercase		)
       model.to(_lowercase		)
       model.eval()
       with torch.no_grad():
          _lowercase							     =       model(_lowercase   ,						attention_mask=_lowercase		)
          _lowercase							     =       model(_lowercase		)
       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			:       Tuple		)						->    str:
       _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   UpperCAmelCase_	(				lowercase__,    unittest.TestCase   ):
    snake_case_											=      (BlipTextModel,) if is_torch_available() else ()
    snake_case_											=      False
    snake_case_											=      False
    snake_case_											=      False
    def 		_lowerCamelCase      (	self			:       int		)						->    List[str]:
       _lowercase							     =       BlipTextModelTester(self		)
       _lowercase							     =       ConfigTester(self   ,						config_class=_lowercase   ,						hidden_size=3_7		)
    def 		_lowerCamelCase      (	self			:       int		)						->    Union[str, Any]:
       self.config_tester.run_common_tests()
    def 		_lowerCamelCase      (	self			:       str		)						->    Dict:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowercase		)
    def 		_lowerCamelCase      (	self			:       Any		)						->    List[str]:
       pass
    def 		_lowerCamelCase      (	self			:       Any		)						->    int:
       pass
    @unittest.skip(reason="Blip does not use inputs_embeds"		)
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    Union[str, Any]:
       pass
    @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"		)
    def 		_lowerCamelCase      (	self			:       int		)						->    Optional[int]:
       pass
    @unittest.skip(reason="BlipTextModel has no base class and is not available in MODEL_MAPPING"		)
    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Optional[Any]:
       pass
    @slow
    def 		_lowerCamelCase      (	self			:       Union[str, Any]		)						->    List[Any]:
       for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _lowercase							     =       BlipTextModel.from_pretrained(_lowercase		)
          self.assertIsNotNone(_lowercase		)




    def 		_lowerCamelCase      (	self			:       Union[str, Any]		)						->    Any:
       super().test_pt_tf_model_equivalence(allow_missing_keys=_lowercase		)
"""simple docstring"""


from typing import Literal

__UpperCamelCase       :    int         =		"ABCDEFGHIJKLMNOPQRSTUVWXYZ"


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str,				_snake_case		:       Literal["encrypt", "decrypt"]       ):
   _lowercase							     =       LETTERS if mode == "decrypt" else key
   _lowercase							     =       key if mode == "decrypt" else LETTERS
   _lowercase							     =       ""
   # loop through each symbol in the message
   for symbol in message:
      if symbol.upper() in chars_a:
         # encrypt/decrypt the symbol
         _lowercase							     =       chars_a.find(symbol.upper()       )
         if symbol.isupper():
            translated += chars_b[sym_index].upper()
         else:
            translated += chars_b[sym_index].lower()
      else:
         # symbol is not in LETTERS, just add it
         translated += symbol
   return translated


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str       ):
   return translate_message(_snake_case,				_snake_case,				"encrypt"       )


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str       ):
   return translate_message(_snake_case,				_snake_case,				"decrypt"       )


def   __UpperCAmelCase     (	):
   _lowercase							     =       "Hello World"
   _lowercase							     =       "QWERTYUIOPASDFGHJKLZXCVBNM"
   _lowercase							     =       "decrypt"  # set to 'encrypt' or 'decrypt'

   if mode == "encrypt":
      _lowercase							     =       encrypt_message(_snake_case,				_snake_case       )
   elif mode == "decrypt":
      _lowercase							     =       decrypt_message(_snake_case,				_snake_case       )
   print(f"""Using the key {key}, the {mode}ed message is: {translated}"""       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()