





'''simple docstring'''

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


lowercase_         =			logging.get_logger(__name__)

lowercase_         =			{
    """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  a_  (    a_	):

    '''simple docstring'''
    UpperCamelCase			   =			'''blenderbot'''
    UpperCamelCase			   =			['''past_key_values''']
    UpperCamelCase			   =			{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


    def __init__(		self			,		A=8008			,		A=128			,		A=2			,		A=1_0240			,		A=32			,		A=24			,		A=1_0240			,		A=32			,		A=0.0			,		A=0.0			,		A=True			,		A=True			,		A="gelu"			,		A=2560			,		A=0.1			,		A=0.0			,		A=0.0			,		A=0.02			,		A=1			,		A=False			,		A=0			,		A=1			,		A=2			,		A=3			,		A=2			,		**A			,		)		->       Any:
        _SCREAMING_SNAKE_CASE     =      vocab_size
        _SCREAMING_SNAKE_CASE     =      max_position_embeddings
        _SCREAMING_SNAKE_CASE     =      d_model
        _SCREAMING_SNAKE_CASE     =      encoder_ffn_dim
        _SCREAMING_SNAKE_CASE     =      encoder_layers
        _SCREAMING_SNAKE_CASE     =      encoder_attention_heads
        _SCREAMING_SNAKE_CASE     =      decoder_ffn_dim
        _SCREAMING_SNAKE_CASE     =      decoder_layers
        _SCREAMING_SNAKE_CASE     =      decoder_attention_heads
        _SCREAMING_SNAKE_CASE     =      dropout
        _SCREAMING_SNAKE_CASE     =      attention_dropout
        _SCREAMING_SNAKE_CASE     =      activation_dropout
        _SCREAMING_SNAKE_CASE     =      activation_function
        _SCREAMING_SNAKE_CASE     =      init_std
        _SCREAMING_SNAKE_CASE     =      encoder_layerdrop
        _SCREAMING_SNAKE_CASE     =      decoder_layerdrop
        _SCREAMING_SNAKE_CASE     =      use_cache
        _SCREAMING_SNAKE_CASE     =      encoder_layers
        _SCREAMING_SNAKE_CASE     =      scale_embedding  # scale factor will be sqrt(d_model) if True

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




class  a_  (    a_	):

    '''simple docstring'''


    @property
    def       snake_case_(		self	)		->       Dict:


        if self.task in ["default", "seq2seq-lm"]:
            _SCREAMING_SNAKE_CASE     =      OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                    ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                ]	)
            if self.use_past:
                _SCREAMING_SNAKE_CASE     =      {0: """batch"""}
                _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """past_decoder_sequence + sequence"""}
            else:
                _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """decoder_sequence"""}
                _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """decoder_sequence"""}
            if self.use_past:
                self.fill_with_past_key_values_(snake_case__			,		direction="""inputs"""	)
        elif self.task == "causal-lm":
            _SCREAMING_SNAKE_CASE     =      OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                    ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                ]	)
            if self.use_past:
                _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      self.num_layers
                for i in range(snake_case__	):
                    _SCREAMING_SNAKE_CASE     =      {0: """batch""", 2: """past_sequence + sequence"""}
                    _SCREAMING_SNAKE_CASE     =      {0: """batch""", 2: """past_sequence + sequence"""}
        else:
            _SCREAMING_SNAKE_CASE     =      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       snake_case_(		self	)		->       List[str]:


        if self.task in ["default", "seq2seq-lm"]:
            _SCREAMING_SNAKE_CASE     =      super().outputs
        else:
            _SCREAMING_SNAKE_CASE     =      super(snake_case__			,		self	).outputs
            if self.use_past:
                _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      self.num_layers
                for i in range(snake_case__	):
                    _SCREAMING_SNAKE_CASE     =      {0: """batch""", 2: """past_sequence + sequence"""}
                    _SCREAMING_SNAKE_CASE     =      {0: """batch""", 2: """past_sequence + sequence"""}
        return common_outputs


    def       snake_case_(		self			,		A			,		A = -1			,		A = -1			,		A = False			,		A = None			,		)		->       Dict:
        _SCREAMING_SNAKE_CASE     =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            snake_case__			,		snake_case__			,		snake_case__			,		snake_case__			,		snake_case__	)
        # Generate decoder inputs
        _SCREAMING_SNAKE_CASE     =      seq_length if not self.use_past else 1
        _SCREAMING_SNAKE_CASE     =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            snake_case__			,		snake_case__			,		snake_case__			,		snake_case__			,		snake_case__	)
        _SCREAMING_SNAKE_CASE     =      {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
        _SCREAMING_SNAKE_CASE     =      dict(**snake_case__			,		**snake_case__	)

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

            for _ in range(snake_case__	):
                common_inputs["past_key_values"].append(
                    (
                        torch.zeros(snake_case__	),
                        torch.zeros(snake_case__	),
                        torch.zeros(snake_case__	),
                        torch.zeros(snake_case__	),
                    )	)
        return common_inputs


    def       snake_case_(		self			,		A			,		A = -1			,		A = -1			,		A = False			,		A = None			,		)		->       Union[str, Any]:
        _SCREAMING_SNAKE_CASE     =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            snake_case__			,		snake_case__			,		snake_case__			,		snake_case__			,		snake_case__	)

        if self.use_past:
            if not is_torch_available():
                raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""	)
            else:
                import torch
            _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      common_inputs["""input_ids"""].shape
            _SCREAMING_SNAKE_CASE     =      seqlen
            _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      self.num_layers
            _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      self.num_attention_heads
            _SCREAMING_SNAKE_CASE     =      (
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            _SCREAMING_SNAKE_CASE     =      common_inputs["""attention_mask"""].dtype
            _SCREAMING_SNAKE_CASE     =      torch.cat(
                [common_inputs["""attention_mask"""], torch.ones(snake_case__			,		snake_case__			,		dtype=snake_case__	)]			,		dim=1	)
            _SCREAMING_SNAKE_CASE     =      [
                (torch.zeros(snake_case__	), torch.zeros(snake_case__	)) for _ in range(snake_case__	)
            ]
        return common_inputs


    def       snake_case_(		self			,		A			,		A = -1			,		A = -1			,		A = False			,		A = None			,		)		->       int:
        _SCREAMING_SNAKE_CASE     =      compute_effective_axis_dimension(
            snake_case__			,		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
        _SCREAMING_SNAKE_CASE     =      tokenizer.num_special_tokens_to_add(snake_case__	)
        _SCREAMING_SNAKE_CASE     =      compute_effective_axis_dimension(
            snake_case__			,		fixed_dimension=OnnxConfig.default_fixed_sequence			,		num_token_to_add=snake_case__	)

        # Generate dummy inputs according to compute batch and sequence
        _SCREAMING_SNAKE_CASE     =      [""" """.join([tokenizer.unk_token]	) * seq_length] * batch_size
        _SCREAMING_SNAKE_CASE     =      dict(tokenizer(snake_case__			,		return_tensors=snake_case__	)	)
        return common_inputs


    def       snake_case_(		self			,		A			,		A = -1			,		A = -1			,		A = False			,		A = None			,		)		->       int:


        if self.task in ["default", "seq2seq-lm"]:
            _SCREAMING_SNAKE_CASE     =      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                snake_case__			,		batch_size=snake_case__			,		seq_length=snake_case__			,		is_pair=snake_case__			,		framework=snake_case__	)

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

        return common_inputs


    def       snake_case_(		self			,		A			,		A			,		A			,		A	)		->       List[Any]:


        if self.task in ["default", "seq2seq-lm"]:
            _SCREAMING_SNAKE_CASE     =      super()._flatten_past_key_values_(snake_case__			,		snake_case__			,		snake_case__			,		snake_case__	)
        else:
            _SCREAMING_SNAKE_CASE     =      super(snake_case__			,		self	)._flatten_past_key_values_(
                snake_case__			,		snake_case__			,		snake_case__			,		snake_case__	)





    def       snake_case_(		self			,		A			,		A	)		->       List[Any]:


        if direction not in ["inputs", "outputs"]:
            raise ValueError(f'direction must either be \"inputs\" or \"outputs\", but {direction} was given'	)

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

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

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










'''simple docstring'''

import os
import sys
import time

from . import transposition_cipher as trans_cipher





def lowerCamelCase						(					)						->None:
    _SCREAMING_SNAKE_CASE     =      """Prehistoric Men.txt"""
    _SCREAMING_SNAKE_CASE     =      """Output.txt"""
    _SCREAMING_SNAKE_CASE     =      int(input("""Enter key: """   )   )
    _SCREAMING_SNAKE_CASE     =      input("""Encrypt/Decrypt [e/d]: """   )

    if not os.path.exists(__lowerCamelCase   ):
        print(F'File {input_file} does not exist. Quitting...'   )
        sys.exit()
    if os.path.exists(__lowerCamelCase   ):
        print(F'Overwrite {output_file}? [y/n]'   )
        _SCREAMING_SNAKE_CASE     =      input("""> """   )
        if not response.lower().startswith("""y"""   ):
            sys.exit()

    _SCREAMING_SNAKE_CASE     =      time.time()
    if mode.lower().startswith("""e"""   ):
        with open(__lowerCamelCase   ) as f:
            _SCREAMING_SNAKE_CASE     =      f.read()
        _SCREAMING_SNAKE_CASE     =      trans_cipher.encrypt_message(__lowerCamelCase ,	__lowerCamelCase   )
    elif mode.lower().startswith("""d"""   ):
        with open(__lowerCamelCase   ) as f:
            _SCREAMING_SNAKE_CASE     =      f.read()
        _SCREAMING_SNAKE_CASE     =      trans_cipher.decrypt_message(__lowerCamelCase ,	__lowerCamelCase   )

    with open(__lowerCamelCase ,	"""w"""   ) as output_obj:
        output_obj.write(__lowerCamelCase   )

    _SCREAMING_SNAKE_CASE     =      round(time.time() - start_time ,	2   )
    print(("""Done (""", total_time, """seconds )""")   )


if __name__ == "__main__":
      main()



