
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


_UpperCAmelCase    			=					logging.get_logger(__name__)

_UpperCAmelCase    			=					{
    '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__				( __A					):


  """simple docstring"""





  _UpperCamelCase						 = "blenderbot"
  _UpperCamelCase						 = ["past_key_values"]
  _UpperCamelCase						 = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


  def __init__(					self	,		a__=80_08	,		a__=1_28	,		a__=2	,		a__=1_02_40	,		a__=32	,		a__=24	,		a__=1_02_40	,		a__=32	,		a__=0.0	,		a__=0.0	,		a__=True	,		a__=True	,		a__="gelu"	,		a__=25_60	,		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__	,		):
    _lowerCamelCase      =						vocab_size
    _lowerCamelCase      =						max_position_embeddings
    _lowerCamelCase      =						d_model
    _lowerCamelCase      =						encoder_ffn_dim
    _lowerCamelCase      =						encoder_layers
    _lowerCamelCase      =						encoder_attention_heads
    _lowerCamelCase      =						decoder_ffn_dim
    _lowerCamelCase      =						decoder_layers
    _lowerCamelCase      =						decoder_attention_heads
    _lowerCamelCase      =						dropout
    _lowerCamelCase      =						attention_dropout
    _lowerCamelCase      =						activation_dropout
    _lowerCamelCase      =						activation_function
    _lowerCamelCase      =						init_std
    _lowerCamelCase      =						encoder_layerdrop
    _lowerCamelCase      =						decoder_layerdrop
    _lowerCamelCase      =						use_cache
    _lowerCamelCase      =						encoder_layers
    _lowerCamelCase      =						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 		__magic_name__				( __A					):


  """simple docstring"""





  @property
  def     _UpperCAmelCase  (					self		):
    if self.task in ["default", "seq2seq-lm"]:
      _lowerCamelCase      =						OrderedDict(
          [
              ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
              ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
          ]		)
      if self.use_past:
        _lowerCamelCase      =						{0: """batch"""}
        _lowerCamelCase      =						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
      else:
        _lowerCamelCase      =						{0: """batch""", 1: """decoder_sequence"""}
        _lowerCamelCase      =						{0: """batch""", 1: """decoder_sequence"""}
      if self.use_past:
        self.fill_with_past_key_values_(_lowerCamelCase	,		direction='''inputs'''		)
    elif self.task == "causal-lm":
      _lowerCamelCase      =						OrderedDict(
          [
              ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
              ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
          ]		)
      if self.use_past:
        _lowerCamelCase      =						self.num_layers
        for i in range(_lowerCamelCase		):
          _lowerCamelCase      =						{0: """batch""", 2: """past_sequence + sequence"""}
          _lowerCamelCase      =						{0: """batch""", 2: """past_sequence + sequence"""}
    else:
      _lowerCamelCase      =						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		):
    if self.task in ["default", "seq2seq-lm"]:
      _lowerCamelCase      =						super().outputs
    else:
      _lowerCamelCase      =						super(_lowerCamelCase	,		self		).outputs
      if self.use_past:
        _lowerCamelCase      =						self.num_layers
        for i in range(_lowerCamelCase		):
          _lowerCamelCase      =						{0: """batch""", 2: """past_sequence + sequence"""}
          _lowerCamelCase      =						{0: """batch""", 2: """past_sequence + sequence"""}
    return common_outputs


  def     _UpperCAmelCase  (					self	,		a__	,		a__ = -1	,		a__ = -1	,		a__ = False	,		a__ = None	,		):
    _lowerCamelCase      =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        _lowerCamelCase	,		_lowerCamelCase	,		_lowerCamelCase	,		_lowerCamelCase	,		_lowerCamelCase		)
    # Generate decoder inputs
    _lowerCamelCase      =						seq_length if not self.use_past else 1
    _lowerCamelCase      =						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
        _lowerCamelCase	,		_lowerCamelCase	,		_lowerCamelCase	,		_lowerCamelCase	,		_lowerCamelCase		)
    _lowerCamelCase      =						{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
    _lowerCamelCase      =						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
      _lowerCamelCase      =						common_inputs["""input_ids"""].shape
      _lowerCamelCase      =						common_inputs["""decoder_input_ids"""].shape[1]
      _lowerCamelCase      =						self.num_attention_heads
      _lowerCamelCase      =						(
          batch,
          num_encoder_attention_heads,
          encoder_seq_length,
          self._config.hidden_size // num_encoder_attention_heads,
      )
      _lowerCamelCase      =						decoder_seq_length
      _lowerCamelCase      =						(
          batch,
          num_decoder_attention_heads,
          decoder_past_length,
          self._config.hidden_size // num_decoder_attention_heads,
      )
      _lowerCamelCase      =						torch.cat(
          [common_inputs['''decoder_attention_mask'''], torch.ones(_lowerCamelCase	,		_lowerCamelCase		)]	,		dim=1		)
      _lowerCamelCase      =						[]
      _lowerCamelCase      =						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     _UpperCAmelCase  (					self	,		a__	,		a__ = -1	,		a__ = -1	,		a__ = False	,		a__ = None	,		):
    _lowerCamelCase      =						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
      _lowerCamelCase      =						common_inputs["""input_ids"""].shape
      _lowerCamelCase      =						seqlen
      _lowerCamelCase      =						self.num_layers
      _lowerCamelCase      =						self.num_attention_heads
      _lowerCamelCase      =						(
          batch,
          num_encoder_attention_heads,
          past_key_values_length,
          self._config.hidden_size // num_encoder_attention_heads,
      )
      _lowerCamelCase      =						common_inputs["""attention_mask"""].dtype
      _lowerCamelCase      =						torch.cat(
          [common_inputs['''attention_mask'''], torch.ones(_lowerCamelCase	,		_lowerCamelCase	,		dtype=_lowerCamelCase		)]	,		dim=1		)
      _lowerCamelCase      =						[
          (torch.zeros(_lowerCamelCase		), torch.zeros(_lowerCamelCase		)) for _ in range(_lowerCamelCase		)
      ]
    return common_inputs


  def     _UpperCAmelCase  (					self	,		a__	,		a__ = -1	,		a__ = -1	,		a__ = False	,		a__ = 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
    _lowerCamelCase      =						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
    _lowerCamelCase      =						tokenizer.num_special_tokens_to_add(_lowerCamelCase		)
    _lowerCamelCase      =						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
    _lowerCamelCase      =						[""" """.join([tokenizer.unk_token]		) * seq_length] * batch_size
    _lowerCamelCase      =						dict(tokenizer(_lowerCamelCase	,		return_tensors=_lowerCamelCase		)		)
    return common_inputs


  def     _UpperCAmelCase  (					self	,		a__	,		a__ = -1	,		a__ = -1	,		a__ = False	,		a__ = None	,		):
    if self.task in ["default", "seq2seq-lm"]:
      _lowerCamelCase      =						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":
      _lowerCamelCase      =						self._generate_dummy_inputs_for_causal_lm(
          _lowerCamelCase	,		batch_size=_lowerCamelCase	,		seq_length=_lowerCamelCase	,		is_pair=_lowerCamelCase	,		framework=_lowerCamelCase		)
    else:
      _lowerCamelCase      =						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     _UpperCAmelCase  (					self	,		a__	,		a__	,		a__	,		a__		):
    if self.task in ["default", "seq2seq-lm"]:
      _lowerCamelCase      =						super()._flatten_past_key_values_(_lowerCamelCase	,		_lowerCamelCase	,		_lowerCamelCase	,		_lowerCamelCase		)
    else:
      _lowerCamelCase      =						super(_lowerCamelCase	,		self		)._flatten_past_key_values_(
          _lowerCamelCase	,		_lowerCamelCase	,		_lowerCamelCase	,		_lowerCamelCase		)


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

    _lowerCamelCase      =						"""past_key_values""" if direction == """inputs""" else """present"""
    _lowerCamelCase      =						self.num_layers

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

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









'''simple docstring'''





def     UpperCAmelCase     (    a_      ,		a_		)   ->       set:



   """simple docstring"""






   assert isinstance(a_      ,		a_		), F"The input value of [set_a={set_a}] is not a set"
   assert isinstance(a_      ,		a_		), F"The input value of [set_b={set_b}] is not a set"

   return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
  from doctest import testmod

  testmod()
