

import numpy as np




def       UpperCAmelCase__    (      _A				:			List[Any]     ,				_A				:			Dict="utf-8"     ,				_A				:			Optional[int]="surrogatepass"							):





  '''simple docstring'''







  a__  =bin(int.from_bytes(text.encode(lowercase_     ,				lowercase_							)     ,				'''big'''							)							)[2:]
  return bits.zfill(8 * ((len(lowercase_							) + 7) // 8)							)




def       UpperCAmelCase__    (      _A				:			Optional[int]     ,				_A				:			Optional[int]="utf-8"     ,				_A				:			Optional[int]="surrogatepass"							):





  '''simple docstring'''







  a__  =int(lowercase_     ,				2							)
  return n.to_bytes((n.bit_length() + 7) // 8     ,				'''big'''							).decode(lowercase_     ,				lowercase_							) or "\0"




def       UpperCAmelCase__    (      _A				:			str     ,				_A				:			Union[str, Any]							):





  '''simple docstring'''







  if size_par + len(lowercase_							) <= 2**size_par - (len(lowercase_							) - 1):
    raise ValueError('''size of parity don\'t match with size of data'''							)

  a__  =[]
  a__  =[]
  a__  =[bin(lowercase_							)[2:] for x in range(1     ,				size_par + len(lowercase_							) + 1							)]

  # sorted information data for the size of the output data
  a__  =[]
  # data position template + parity
  a__  =[]
  # parity bit counter
  a__  =0
  # counter position of data bits
  a__  =0

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

    # Calculates parity
  a__  =0  # parity bit counter
  for bp in range(1     ,				size_par + 1							):
    # Bit counter one for a given parity
    a__  =0
    # counter to control the loop reading
    a__  =0
    for x in data_ord:
      if x is not None:
        try:
          a__  =(bin_pos[cont_loop])[-1 * (bp)]
        except IndexError:
          a__  ='''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__  =0  # parity bit counter
  for x in range(0     ,				size_par + len(lowercase_							)							):
    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__    (      _A				:			List[Any]     ,				_A				:			Optional[int]							):





  '''simple docstring'''







  a__  =[]
  # Parity bit counter
  a__  =0
  # Counter p data bit reading
  a__  =0
  # list of parity received
  a__  =[]
  a__  =[]

  for x in range(1     ,				len(lowercase_							) + 1							):
    # Performs a template of bit positions - who should be given,
    #  and who should be parity
    if qtd_bp < size_par and (np.log(lowercase_							) / np.log(2							)).is_integer():
      data_out_gab.append('''P'''							)
      a__  =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__  =[]
  a__  =[]
  a__  =[bin(lowercase_							)[2:] for x in range(1     ,				size_par + len(lowercase_							) + 1							)]

  #  sorted information data for the size of the output data
  a__  =[]
  # Data position feedback + parity
  a__  =[]
  # Parity bit counter
  a__  =0
  # Counter p data bit reading
  a__  =0

  for x in range(1     ,				size_par + len(lowercase_							) + 1							):
    # Performs a template position of bits - who should be given,
    # and who should be parity
    if qtd_bp < size_par and (np.log(lowercase_							) / np.log(2							)).is_integer():
      data_out_gab.append('''P'''							)
      a__  =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(lowercase_							)

    # Calculates parity
  a__  =0  # parity bit counter
  for bp in range(1     ,				size_par + 1							):
    # Bit counter one for a certain parity
    a__  =0
    # Counter to control loop reading
    a__  =0
    for x in data_ord:
      if x is not None:
        try:
          a__  =(bin_pos[cont_loop])[-1 * (bp)]
        except IndexError:
          a__  ='''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__  =0  # Parity bit counter
  for x in range(0     ,				size_par + len(lowercase_							)							):
    if data_ord[x] is None:
      data_out.append(str(parity[cont_bp]							)							)
      cont_bp += 1
    else:
      data_out.append(data_ord[x]							)

  a__  =parity_received == parity
  return data_output, ack



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 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__			:     List[Any]    =       'blenderbot'
   lowerCamelCase__			:     int    =       ['past_key_values']
   lowerCamelCase__			:     Optional[Any]    =       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



   def __init__(    self,   lowercase_=8008,   lowercase_=128,   lowercase_=2,   lowercase_=10240,   lowercase_=32,   lowercase_=24,   lowercase_=10240,   lowercase_=32,   lowercase_=0.0,   lowercase_=0.0,   lowercase_=True,   lowercase_=True,   lowercase_="gelu",   lowercase_=2560,   lowercase_=0.1,   lowercase_=0.0,   lowercase_=0.0,   lowercase_=0.02,   lowercase_=1,   lowercase_=False,   lowercase_=0,   lowercase_=1,   lowercase_=2,   lowercase_=3,   lowercase_=2,   **lowercase_,   )							->   Any:






     """simple docstring"""






     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=lowercase_,   bos_token_id=lowercase_,   eos_token_id=lowercase_,   is_encoder_decoder=lowercase_,   decoder_start_token_id=lowercase_,   encoder_no_repeat_ngram_size=lowercase_,   forced_eos_token_id=lowercase_,   **lowercase_,   )







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   @property
   def 			_UpperCAmelCase			(    self	)							->   Mapping[str, Mapping[int, str]]:






     """simple docstring"""






     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_(lowercase_,   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(lowercase_	):
           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 			_UpperCAmelCase			(    self	)							->   Mapping[str, Mapping[int, str]]:






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = -1,   lowercase_ = -1,   lowercase_ = False,   lowercase_ = None,   )							->   Mapping[str, Any]:






     """simple docstring"""






     a__  =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)
     # Generate decoder inputs
     a__  =seq_length if not self.use_past else 1
     a__  =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)
     a__  ={F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
     a__  =dict(**lowercase_,   **lowercase_	)

     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(lowercase_,   lowercase_	)],   dim=1	)
       a__  =[]
       a__,				a__  =self.num_layers

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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = -1,   lowercase_ = -1,   lowercase_ = False,   lowercase_ = None,   )							->   Mapping[str, Any]:






     """simple docstring"""






     a__  =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)

     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(lowercase_,   lowercase_,   dtype=lowercase_	)],   dim=1	)
       a__  =[
           (torch.zeros(lowercase_	), torch.zeros(lowercase_	)) for _ in range(lowercase_	)
       ]
     return common_inputs



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = -1,   lowercase_ = -1,   lowercase_ = False,   lowercase_ = None,   )							->   Mapping[str, Any]:






     """simple docstring"""






     a__  =compute_effective_axis_dimension(
         lowercase_,   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(lowercase_	)
     a__  =compute_effective_axis_dimension(
         lowercase_,   fixed_dimension=OnnxConfig.default_fixed_sequence,   num_token_to_add=lowercase_	)

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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = -1,   lowercase_ = -1,   lowercase_ = False,   lowercase_ = None,   )							->   Mapping[str, Any]:






     """simple docstring"""






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

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

     return common_inputs



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   Optional[int]:






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Tuple:






     """simple docstring"""






     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(lowercase_	):
       a__  ={0: '''batch''', 2: decoder_sequence}
       a__  ={0: '''batch''', 2: decoder_sequence}
       a__  ={0: '''batch''', 2: encoder_sequence}
       a__  ={0: '''batch''', 2: encoder_sequence}
