




from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class 		lowercase							(     nn.Module	):

     def __init__(						self	,  _a = 16	,  _a = 88	,  _a = None	,  _a = 1	,  _a = 0.0	,  _a = 32	,  _a = None	,  _a = False	,  _a = None	,  _a = None	,  _a = "geglu"	,  _a = None	,  )		->							List[Any]:
        super().__init__()
        _A							:  List[str]    =		nn.ModuleList(
            [
                TransformeraDModel(
                    num_attention_heads=_A	,  attention_head_dim=_A	,  in_channels=_A	,  num_layers=_A	,  dropout=_A	,  norm_num_groups=_A	,  cross_attention_dim=_A	,  attention_bias=_A	,  sample_size=_A	,  num_vector_embeds=_A	,  activation_fn=_A	,  num_embeds_ada_norm=_A	,  )
                for _ in range(2       )
            ]       )

        # Variables that can be set by a pipeline:

        # The ratio of transformer1 to transformer2's output states to be combined during inference
        _A							:  List[Any]    =		0.5

        # The shape of `encoder_hidden_states` is expected to be
        # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
        _A							:  Any    =		[77, 257]

        # Which transformer to use to encode which condition.
        # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
        _A							:  Any    =		[1, 0]



     def       a__			(						self	,  _a	,  _a	,  _a=None	,  _a=None	,  _a=None	,  _a = True	,  )		->							Any:
        _A							:  List[str]    =		hidden_states

        _A							:  Optional[Any]    =		[]
        _A							:  Union[str, Any]    =		0
        # attention_mask is not used yet
        for i in range(2       ):
           # for each of the two transformers, pass the corresponding condition tokens
           _A							:  Union[str, Any]    =		encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
           _A							:  int    =		self.transformer_index_for_condition[i]
           _A							:  List[Any]    =		self.transformers[transformer_index](
               _A	,  encoder_hidden_states=_A	,  timestep=_A	,  cross_attention_kwargs=_A	,  return_dict=_A	,  )[0]
           encoded_states.append(encoded_state - input_states       )
           tokens_start += self.condition_lengths[i]

        _A							:  Optional[int]    =		encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
        _A							:  int    =		output_states + input_states

        if not return_dict:
           return (output_states,)

        return TransformeraDModelOutput(sample=_A       )







from __future__ import annotations







class 		lowercase							:

     def __init__(						self	,  _a = 0       )		->							str:
           _A							:  Any    =		key

     def       a__			(						self	,  _a	,  _a       )		->							list[str]:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  Any    =		key or self.__key or 1

           # make sure key is an appropriate size
           key %= 255

           return [chr(ord(_a       ) ^ key       ) for ch in content]

     def       a__			(						self	,  _a	,  _a       )		->							list[str]:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  List[Any]    =		key or self.__key or 1

           # make sure key is an appropriate size
           key %= 255

           return [chr(ord(_a       ) ^ key       ) for ch in content]

     def       a__			(						self	,  _a	,  _a = 0       )		->							str:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  List[Any]    =		key or self.__key or 1

           # make sure key can be any size
           while key > 255:
                 key -= 255

           # This will be returned
           _A							:  List[str]    =		""""""

           for ch in content:
                 ans += chr(ord(_a       ) ^ key       )

           return ans

     def       a__			(						self	,  _a	,  _a = 0       )		->							str:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           _A							:  List[str]    =		key or self.__key or 1

           # make sure key can be any size
           while key > 255:
                 key -= 255

           # This will be returned
           _A							:  List[str]    =		""""""

           for ch in content:
                 ans += chr(ord(_a       ) ^ key       )

           return ans

     def       a__			(						self	,  _a	,  _a = 0       )		->							bool:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           try:
                 with open(_a       ) as fin, open("""encrypt.out"""	,  """w+"""       ) as fout:
                       # actual encrypt-process
                       for line in fin:
                             fout.write(self.encrypt_string(_a	,  _a       )       )

           except OSError:
                 return False

           return True



     def       a__			(						self	,  _a	,  _a       )		->							bool:
           assert isinstance(_a	,  _a       ) and isinstance(_a	,  _a       )

           try:
                 with open(_a       ) as fin, open("""decrypt.out"""	,  """w+"""       ) as fout:
                       # actual encrypt-process
                       for line in fin:
                             fout.write(self.decrypt_string(_a	,  _a       )       )

           except OSError:
                 return False

           return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")

