




from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


_lowerCamelCase							=      logging.get_logger(__name__)

_lowerCamelCase							=      {
    '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json''',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}


class        UpperCAmelCase__				(							UpperCAmelCase__				):



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					int     					=    "blenderbot"
     _SCREAMING_SNAKE_CASE      :					Optional[Any]     					=    ["past_key_values"]
     _SCREAMING_SNAKE_CASE      :					Optional[Any]     					=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
     def __init__(				self ,    _lowerCAmelCase=8_008 ,    _lowerCAmelCase=128 ,    _lowerCAmelCase=2 ,    _lowerCAmelCase=10_240 ,    _lowerCAmelCase=32 ,    _lowerCAmelCase=24 ,    _lowerCAmelCase=10_240 ,    _lowerCAmelCase=32 ,    _lowerCAmelCase=0.0 ,    _lowerCAmelCase=0.0 ,    _lowerCAmelCase=True ,    _lowerCAmelCase=True ,    _lowerCAmelCase="gelu" ,    _lowerCAmelCase=2_560 ,    _lowerCAmelCase=0.1 ,    _lowerCAmelCase=0.0 ,    _lowerCAmelCase=0.0 ,    _lowerCAmelCase=0.02 ,    _lowerCAmelCase=1 ,    _lowerCAmelCase=False ,    _lowerCAmelCase=0 ,    _lowerCAmelCase=1 ,    _lowerCAmelCase=2 ,    _lowerCAmelCase=3 ,    _lowerCAmelCase=2 ,    **_lowerCAmelCase ,    ):
            a			   =vocab_size
            a			   =max_position_embeddings
            a			   =d_model
            a			   =encoder_ffn_dim
            a			   =encoder_layers
            a			   =encoder_attention_heads
            a			   =decoder_ffn_dim
            a			   =decoder_layers
            a			   =decoder_attention_heads
            a			   =dropout
            a			   =attention_dropout
            a			   =activation_dropout
            a			   =activation_function
            a			   =init_std
            a			   =encoder_layerdrop
            a			   =decoder_layerdrop
            a			   =use_cache
            a			   =encoder_layers
            a			   =scale_embedding  # scale factor will be sqrt(d_model) if True

            super().__init__(
                pad_token_id=_lowerCAmelCase ,    bos_token_id=_lowerCAmelCase ,    eos_token_id=_lowerCAmelCase ,    is_encoder_decoder=_lowerCAmelCase ,    decoder_start_token_id=_lowerCAmelCase ,    encoder_no_repeat_ngram_size=_lowerCAmelCase ,    forced_eos_token_id=_lowerCAmelCase ,    **_lowerCAmelCase ,    )



class        UpperCAmelCase__				(							UpperCAmelCase__				):



     '''simple docstring'''
     @property
     def        lowerCAmelCase__  (				self	):
            if self.task in ["default", "seq2seq-lm"]:
                   a			   =OrderedDict(
                       [
                           ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                           ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                       ]	)
                   if self.use_past:
                          a			   ={0: """batch"""}
                          a			   ={0: """batch""", 1: """past_decoder_sequence + sequence"""}
                   else:
                          a			   ={0: """batch""", 1: """decoder_sequence"""}
                          a			   ={0: """batch""", 1: """decoder_sequence"""}
                   if self.use_past:
                          self.fill_with_past_key_values_(_lowerCAmelCase ,    direction="""inputs"""	)
            elif self.task == "causal-lm":
                   a			   =OrderedDict(
                       [
                           ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                           ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                       ]	)
                   if self.use_past:
                          a   ,      a			   =self.num_layers
                          for i in range(_lowerCAmelCase	):
                                 a			   ={0: """batch""", 2: """past_sequence + sequence"""}
                                 a			   ={0: """batch""", 2: """past_sequence + sequence"""}
            else:
                   a			   =OrderedDict(
                       [
                           ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                           ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                           ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                           ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
                       ]	)

            return common_inputs
     @property
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
     def        lowerCAmelCase__  (				self	):
            if self.task in ["default", "seq2seq-lm"]:
                   a			   =super().outputs
            else:
                   a			   =super(_lowerCAmelCase ,    self	).outputs
                   if self.use_past:
                          a   ,      a			   =self.num_layers
                          for i in range(_lowerCAmelCase	):
                                 a			   ={0: """batch""", 2: """past_sequence + sequence"""}
                                 a			   ={0: """batch""", 2: """past_sequence + sequence"""}
            return common_outputs
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase = -1 ,    _lowerCAmelCase = -1 ,    _lowerCAmelCase = False ,    _lowerCAmelCase = None ,    ):
            a			   =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	)
            # Generate decoder inputs
            a			   =seq_length if not self.use_past else 1
            a			   =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	)
            a			   ={F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
            a			   =dict(**_lowerCAmelCase ,    **_lowerCAmelCase	)

            if self.use_past:
                   if not is_torch_available():
                          raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""	)
                   else:
                          import torch
                   a   ,      a			   =common_inputs["""input_ids"""].shape
                   a			   =common_inputs["""decoder_input_ids"""].shape[1]
                   a   ,      a			   =self.num_attention_heads
                   a			   =(
                       batch,
                       num_encoder_attention_heads,
                       encoder_seq_length,
                       self._config.hidden_size // num_encoder_attention_heads,
                   )
                   a			   =decoder_seq_length
                   a			   =(
                       batch,
                       num_decoder_attention_heads,
                       decoder_past_length,
                       self._config.hidden_size // num_decoder_attention_heads,
                   )
                   a			   =torch.cat(
                       [common_inputs["""decoder_attention_mask"""], torch.ones(_lowerCAmelCase ,    _lowerCAmelCase	)] ,    dim=1	)
                   a			   =[]
                   a   ,      a			   =self.num_layers

                   for _ in range(_lowerCAmelCase	):
                          common_inputs["past_key_values"].append(
                              (
                                  torch.zeros(_lowerCAmelCase	),
                                  torch.zeros(_lowerCAmelCase	),
                                  torch.zeros(_lowerCAmelCase	),
                                  torch.zeros(_lowerCAmelCase	),
                              )	)
            return common_inputs
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase = -1 ,    _lowerCAmelCase = -1 ,    _lowerCAmelCase = False ,    _lowerCAmelCase = None ,    ):
            a			   =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	)

            if self.use_past:
                   if not is_torch_available():
                          raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""	)
                   else:
                          import torch
                   a   ,      a			   =common_inputs["""input_ids"""].shape
                   a			   =seqlen
                   a   ,      a			   =self.num_layers
                   a   ,      a			   =self.num_attention_heads
                   a			   =(
                       batch,
                       num_encoder_attention_heads,
                       past_key_values_length,
                       self._config.hidden_size // num_encoder_attention_heads,
                   )
                   a			   =common_inputs["""attention_mask"""].dtype
                   a			   =torch.cat(
                       [common_inputs["""attention_mask"""], torch.ones(_lowerCAmelCase ,    _lowerCAmelCase ,    dtype=_lowerCAmelCase	)] ,    dim=1	)
                   a			   =[
                       (torch.zeros(_lowerCAmelCase	), torch.zeros(_lowerCAmelCase	)) for _ in range(_lowerCAmelCase	)
                   ]
            return common_inputs
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase = -1 ,    _lowerCAmelCase = -1 ,    _lowerCAmelCase = False ,    _lowerCAmelCase = None ,    ):
            # Copied from OnnxConfig.generate_dummy_inputs
            # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            a			   =compute_effective_axis_dimension(
                _lowerCAmelCase ,    fixed_dimension=OnnxConfig.default_fixed_batch ,    num_token_to_add=0	)

            # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
            a			   =tokenizer.num_special_tokens_to_add(_lowerCAmelCase	)
            a			   =compute_effective_axis_dimension(
                _lowerCAmelCase ,    fixed_dimension=OnnxConfig.default_fixed_sequence ,    num_token_to_add=_lowerCAmelCase	)

            # Generate dummy inputs according to compute batch and sequence
            a			   =[""" """.join([tokenizer.unk_token]	) * seq_length] * batch_size
            a			   =dict(tokenizer(_lowerCAmelCase ,    return_tensors=_lowerCAmelCase	)	)
            return common_inputs
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase = -1 ,    _lowerCAmelCase = -1 ,    _lowerCAmelCase = False ,    _lowerCAmelCase = None ,    ):
            if self.task in ["default", "seq2seq-lm"]:
                   a			   =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                       _lowerCAmelCase ,    batch_size=_lowerCAmelCase ,    seq_length=_lowerCAmelCase ,    is_pair=_lowerCAmelCase ,    framework=_lowerCAmelCase	)

            elif self.task == "causal-lm":
                   a			   =self._generate_dummy_inputs_for_causal_lm(
                       _lowerCAmelCase ,    batch_size=_lowerCAmelCase ,    seq_length=_lowerCAmelCase ,    is_pair=_lowerCAmelCase ,    framework=_lowerCAmelCase	)
            else:
                   a			   =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                       _lowerCAmelCase ,    batch_size=_lowerCAmelCase ,    seq_length=_lowerCAmelCase ,    is_pair=_lowerCAmelCase ,    framework=_lowerCAmelCase	)

            return common_inputs
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            if self.task in ["default", "seq2seq-lm"]:
                   a			   =super()._flatten_past_key_values_(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	)
            else:
                   a			   =super(_lowerCAmelCase ,    self	)._flatten_past_key_values_(
                       _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	)






     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            if direction not in ["inputs", "outputs"]:
                   raise ValueError(F'''direction must either be "inputs" or "outputs", but {direction} was given'''	)

            a			   ="""past_key_values""" if direction == """inputs""" else """present"""
            a   ,      a			   =self.num_layers

            a			   ="""past_encoder_sequence"""
            a			   ="""past_decoder_sequence""" if direction == """inputs""" else """past_decoder_sequence + sequence"""

            for i in range(_lowerCAmelCase	):
                   a			   ={0: """batch""", 2: decoder_sequence}
                   a			   ={0: """batch""", 2: decoder_sequence}
                   a			   ={0: """batch""", 2: encoder_sequence}
                   a			   ={0: """batch""", 2: encoder_sequence}






from __future__ import annotations





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      dict[str, str]      )->			str:

       """simple docstring"""




       a   ,      a   ,      a			   ="""""", """""", """"""
       a			   =[]

       for character in message_part:
              tmp.append(character_to_number[character]      )

       for each in tmp:
              one += each[0]
              two += each[1]
              three += each[2]

       return one + two + three





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      dict[str, str]      )->			tuple[str, str, str]:

       """simple docstring"""




       a   ,      a			   ="""""", """"""
       a			   =[]

       for character in message_part:
              this_part += character_to_number[character]

       for digit in this_part:
              tmp += digit
              if len(UpperCAmelCase_      ) == len(UpperCAmelCase_      ):
                     result.append(UpperCAmelCase_      )
                     a			   =""""""

       return result[0], result[1], result[2]





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      str      )->			tuple[str, str, dict[str, str], dict[str, str]]:

       """simple docstring"""




       a			   =alphabet.replace(""" """   ,       """"""      ).upper()
       a			   =message.replace(""" """   ,       """"""      ).upper()

       # Check length and characters
       if len(UpperCAmelCase_      ) != 27:
              raise KeyError("""Length of alphabet has to be 27."""      )
       for each in message:
              if each not in alphabet:
                     raise ValueError("""Each message character has to be included in alphabet!"""      )

    # Generate dictionares
       a			   =(
           """111""",
           """112""",
           """113""",
           """121""",
           """122""",
           """123""",
           """131""",
           """132""",
           """133""",
           """211""",
           """212""",
           """213""",
           """221""",
           """222""",
           """223""",
           """231""",
           """232""",
           """233""",
           """311""",
           """312""",
           """313""",
           """321""",
           """322""",
           """323""",
           """331""",
           """332""",
           """333""",
       )
       a			   ={}
       a			   ={}
       for letter, number in zip(UpperCAmelCase_   ,       UpperCAmelCase_      ):
              a			   =number
              a			   =letter

       return message, alphabet, character_to_number, number_to_character





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."   ,       UpperCAmelCase_	:      int = 5      )->			str:

       """simple docstring"""




       a   ,      a   ,      a   ,      a			   =__prepare(
           UpperCAmelCase_   ,       UpperCAmelCase_      )
       a   ,      a			   ="""""", """"""

       for i in range(0   ,       len(UpperCAmelCase_      ) + 1   ,       UpperCAmelCase_      ):
              encrypted_numeric += __encrypt_part(
                  message[i : i + period]   ,       UpperCAmelCase_      )

       for i in range(0   ,       len(UpperCAmelCase_      )   ,       3      ):
              encrypted += number_to_character[encrypted_numeric[i : i + 3]]

       return encrypted





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."   ,       UpperCAmelCase_	:      int = 5      )->			str:

       """simple docstring"""




       a   ,      a   ,      a   ,      a			   =__prepare(
           UpperCAmelCase_   ,       UpperCAmelCase_      )
       a			   =[]
       a			   =""""""

       for i in range(0   ,       len(UpperCAmelCase_      ) + 1   ,       UpperCAmelCase_      ):
              a   ,      a   ,      a			   =__decrypt_part(message[i : i + period]   ,       UpperCAmelCase_      )

              for j in range(0   ,       len(UpperCAmelCase_      )      ):
                     decrypted_numeric.append(a[j] + b[j] + c[j]      )

       for each in decrypted_numeric:
              decrypted += number_to_character[each]

       return decrypted


if __name__ == "__main__":
    _lowerCamelCase							=      '''DEFEND THE EAST WALL OF THE CASTLE.'''
    _lowerCamelCase							=      encrypt_message(msg, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
    _lowerCamelCase							=      decrypt_message(encrypted, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
    print(f"""Encrypted: {encrypted}\nDecrypted: {decrypted}""")
