


import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()

def    lowercase_	(	_A    :					Optional[int]					,      _A    :					int					,      _A    :					Optional[int]=None		):

      """simple docstring"""




      assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
      lowerCamelCase__	:       List[Any]					=						nn.Parameter(_A		)
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
            lowerCamelCase__	:       Any					=						nn.Parameter(_A		)

def    lowercase_	(	_A    :					List[Any]					,      _A    :					Union[str, Any]					,      _A    :					str		):

      """simple docstring"""




      lowerCamelCase__	:       List[Any]					=						np.asarray(weights[0]		)
      lowerCamelCase__	:       Dict					=						np.asarray(weights[1]		)
      lowerCamelCase__	:       Optional[int]					=						np.asarray(weights[2]		)

      set_param(
          torch_layer.self_attention.query_key					,      torch.tensor(_A		).transpose(1					,      2		).contiguous().view(-1					,      _A		)					,      )
      set_param(
          torch_layer.self_attention.value					,      torch.tensor(_A		).transpose(1					,      2		).contiguous().view(-1					,      _A		)					,      )
      set_param(
          torch_layer.output.dense					,      torch.tensor(_A		).view(-1					,      _A		).contiguous().transpose(0					,      1		)					,      )

def    lowercase_	(	_A    :					Dict					,      _A    :					Tuple					,      _A    :					Optional[int]		):

      """simple docstring"""




      lowerCamelCase__	:       Tuple					=						np.asarray(weights[0]		)
      lowerCamelCase__	:       List[Any]					=						np.asarray(weights[1]		)
      lowerCamelCase__	:       str					=						np.asarray(weights[2]		)
      lowerCamelCase__	:       Optional[int]					=						np.asarray(weights[3]		)

      set_param(
          torch_layer.self_attention.query					,      torch.tensor(_A		).transpose(1					,      2		).contiguous().view(-1					,      _A		)					,      )
      set_param(
          torch_layer.self_attention.key					,      torch.tensor(_A		).transpose(1					,      2		).contiguous().view(-1					,      _A		)					,      )
      set_param(
          torch_layer.self_attention.value					,      torch.tensor(_A		).transpose(1					,      2		).contiguous().view(-1					,      _A		)					,      )
      set_param(
          torch_layer.output.dense					,      torch.tensor(_A		).view(-1					,      _A		).contiguous().transpose(0					,      1		)					,      )

def    lowercase_	(	_A    :					Tuple					,      _A    :					str					,      _A    :					Dict		):

      """simple docstring"""




      lowerCamelCase__	:       List[Any]					=						weights[0][0][0]
      lowerCamelCase__	:       str					=						np.asarray(layer_norm_a[0]		)
      lowerCamelCase__	:       Any					=						np.asarray(layer_norm_a[1]		)
      set_param(
          torch_block.attention.layer_norm					,      torch.tensor(_A		)					,      torch.tensor(_A		)					,      )

      # lsh weights + output
      lowerCamelCase__	:       Union[str, Any]					=						weights[0][1]
      if len(_A		) < 4:
            set_layer_weights_in_torch_lsh(_A					,      torch_block.attention					,      _A		)
      else:
            set_layer_weights_in_torch_local(_A					,      torch_block.attention					,      _A		)

      # intermediate weighs
      lowerCamelCase__	:       Dict					=						weights[2][0][1][2]

      # Chunked Feed Forward
      if len(_A		) == 4:
            lowerCamelCase__	:       Optional[int]					=						intermediate_weights[2]

      # layernorm 2
      lowerCamelCase__	:       List[str]					=						np.asarray(intermediate_weights[0][0]		)
      lowerCamelCase__	:       Union[str, Any]					=						np.asarray(intermediate_weights[0][1]		)
      set_param(
          torch_block.feed_forward.layer_norm					,      torch.tensor(_A		)					,      torch.tensor(_A		)					,      )

      # intermediate dense
      lowerCamelCase__	:       Optional[Any]					=						np.asarray(intermediate_weights[1][0]		)
      lowerCamelCase__	:       Optional[Any]					=						np.asarray(intermediate_weights[1][1]		)
      set_param(
          torch_block.feed_forward.dense.dense					,      torch.tensor(_A		).transpose(0					,      1		).contiguous()					,      torch.tensor(_A		)					,      )

      # intermediate out
      lowerCamelCase__	:       str					=						np.asarray(intermediate_weights[4][0]		)
      lowerCamelCase__	:       Tuple					=						np.asarray(intermediate_weights[4][1]		)
      set_param(
          torch_block.feed_forward.output.dense					,      torch.tensor(_A		).transpose(0					,      1		).contiguous()					,      torch.tensor(_A		)					,      )

def    lowercase_	(	_A    :					List[Any]					,      _A    :					List[str]					,      _A    :					List[str]		):

      """simple docstring"""




      lowerCamelCase__	:       Optional[int]					=						torch_model.reformer

      # word embeds
      lowerCamelCase__	:       List[Any]					=						np.asarray(weights[1]		)
      set_param(
          torch_model_reformer.embeddings.word_embeddings					,      torch.tensor(_A		)					,      )

      if isinstance(weights[3]					,      _A		):
            lowerCamelCase__	:       Union[str, Any]					=						torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights		)		):
                  lowerCamelCase__	:       Optional[int]					=						np.asarray(weights[3][emb_idx][0]		)
                  assert (
                      position_embeddings.weights[emb_idx].shape == emb_weights.shape
                  ), F"{position_embeddings[emb_idx]} emb does not match"
                  lowerCamelCase__	:       str					=						nn.Parameter(torch.tensor(_A		)		)

      lowerCamelCase__	:       Optional[int]					=						weights[5]
      assert len(torch_model_reformer.encoder.layers		) * 4 == len(
          _A		), "HF and trax model do not have the same number of layers"
      for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers		):
            lowerCamelCase__	:       Optional[int]					=						trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(_A					,      _A					,      _A		)

      # output layer norm
      lowerCamelCase__	:       Dict					=						np.asarray(weights[7][0]		)
      lowerCamelCase__	:       Any					=						np.asarray(weights[7][1]		)
      set_param(
          torch_model_reformer.encoder.layer_norm					,      torch.tensor(_A		)					,      torch.tensor(_A		)					,      )

      # output embeddings
      lowerCamelCase__	:       List[Any]					=						np.asarray(weights[9][0]		)
      lowerCamelCase__	:       Union[str, Any]					=						np.asarray(weights[9][1]		)
      set_param(
          torch_model.lm_head.decoder					,      torch.tensor(_A		).transpose(0					,      1		).contiguous()					,      torch.tensor(_A		)					,      )

def    lowercase_	(	_A    :					Any					,      _A    :					Union[str, Any]					,      _A    :					Optional[int]		):

      """simple docstring"""




      lowerCamelCase__	:       Any					=						ReformerConfig.from_json_file(_A		)
      print(F"Building PyTorch model from configuration: {config}"		)
      lowerCamelCase__	:       List[Any]					=						ReformerModelWithLMHead(_A		)

      with open(_A					,      "rb"		) as f:
            lowerCamelCase__	:       Tuple					=						pickle.load(_A		)["weights"]

      set_model_weights_in_torch(_A					,      _A					,      config.hidden_size		)

      # Save pytorch-model
      print(F"Save PyTorch model to {pytorch_dump_path}"		)
      torch.save(model.state_dict()					,      _A		)


if __name__ == "__main__":
    A   :					Tuple	=      argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
    )
    parser.add_argument(
        "--config_file",
        default=None,
        type=str,
        required=True,
        help=(
            "The config json file corresponding to the pre-trained Reformer model. \n"
            "This specifies the model architecture."
        ),
    )
    parser.add_argument(
        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    A   :					Union[str, Any]	=      parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)






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


A   :					List[Any]	=      logging.get_logger(__name__)

A   :					Any	=      {
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "blenderbot-small"
       A__      		=    ["past_key_values"]
       A__      		=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





       def __init__(     self							:    Dict      ,   __lowerCamelCase							:    List[str]=50265      ,   __lowerCamelCase							:    str=512      ,   __lowerCamelCase							:    Tuple=8      ,   __lowerCamelCase							:    str=2048      ,   __lowerCamelCase							:    str=16      ,   __lowerCamelCase							:    List[Any]=8      ,   __lowerCamelCase							:    Any=2048      ,   __lowerCamelCase							:    List[str]=16      ,   __lowerCamelCase							:    Dict=0.0      ,   __lowerCamelCase							:    List[Any]=0.0      ,   __lowerCamelCase							:    Optional[int]=True      ,   __lowerCamelCase							:    Union[str, Any]=True      ,   __lowerCamelCase							:    Tuple="gelu"      ,   __lowerCamelCase							:    Tuple=512      ,   __lowerCamelCase							:    Dict=0.1      ,   __lowerCamelCase							:    int=0.0      ,   __lowerCamelCase							:    Union[str, Any]=0.0      ,   __lowerCamelCase							:    Any=0.0_2      ,   __lowerCamelCase							:    str=1      ,   __lowerCamelCase							:    Dict=False      ,   __lowerCamelCase							:    int=0      ,   __lowerCamelCase							:    Optional[Any]=1      ,   __lowerCamelCase							:    str=2      ,   __lowerCamelCase							:    Any=2      ,   **__lowerCamelCase							:    int      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						vocab_size
             lowerCamelCase__	:       Union[str, Any]					=						max_position_embeddings
             lowerCamelCase__	:       Union[str, Any]					=						d_model
             lowerCamelCase__	:       Optional[int]					=						encoder_ffn_dim
             lowerCamelCase__	:       Dict					=						encoder_layers
             lowerCamelCase__	:       Any					=						encoder_attention_heads
             lowerCamelCase__	:       Union[str, Any]					=						decoder_ffn_dim
             lowerCamelCase__	:       str					=						decoder_layers
             lowerCamelCase__	:       Optional[Any]					=						decoder_attention_heads
             lowerCamelCase__	:       List[str]					=						dropout
             lowerCamelCase__	:       List[Any]					=						attention_dropout
             lowerCamelCase__	:       Dict					=						activation_dropout
             lowerCamelCase__	:       Optional[Any]					=						activation_function
             lowerCamelCase__	:       Dict					=						init_std
             lowerCamelCase__	:       List[str]					=						encoder_layerdrop
             lowerCamelCase__	:       Dict					=						decoder_layerdrop
             lowerCamelCase__	:       int					=						use_cache
             lowerCamelCase__	:       List[Any]					=						encoder_layers
             lowerCamelCase__	:       Tuple					=						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      ,   forced_eos_token_id=__lowerCamelCase      ,   **__lowerCamelCase      ,   )







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             if self.task in ["default", "seq2seq-lm"]:
                   lowerCamelCase__	:       int					=						OrderedDict(
                       [
                           ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                           ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                       ] )

                   if self.use_past:
                         lowerCamelCase__	:       Union[str, Any]					=						{0: "batch"}
                         lowerCamelCase__	:       int					=						{0: "batch", 1: "past_decoder_sequence + sequence"}
                   else:
                         lowerCamelCase__	:       Tuple					=						{0: "batch", 1: "decoder_sequence"}
                         lowerCamelCase__	:       str					=						{0: "batch", 1: "decoder_sequence"}

                   if self.use_past:
                         self.fill_with_past_key_values_(__lowerCamelCase      ,   direction="inputs" )
             elif self.task == "causal-lm":
                   # TODO: figure this case out.
                   lowerCamelCase__	:       Tuple					=						OrderedDict(
                       [
                           ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                           ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                       ] )
                   if self.use_past:
                         lowerCamelCase__					,     lowerCamelCase__	:       Tuple					=						self.num_layers
                         for i in range(__lowerCamelCase ):
                               lowerCamelCase__	:       Union[str, Any]					=						{0: "batch", 2: "past_sequence + sequence"}
                               lowerCamelCase__	:       Optional[int]					=						{0: "batch", 2: "past_sequence + sequence"}
             else:
                   lowerCamelCase__	:       Any					=						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
       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             if self.task in ["default", "seq2seq-lm"]:
                   lowerCamelCase__	:       Union[str, Any]					=						super().outputs
             else:
                   lowerCamelCase__	:       int					=						super(__lowerCamelCase      ,   self ).outputs
                   if self.use_past:
                         lowerCamelCase__					,     lowerCamelCase__	:       Tuple					=						self.num_layers
                         for i in range(__lowerCamelCase ):
                               lowerCamelCase__	:       Tuple					=						{0: "batch", 2: "past_sequence + sequence"}
                               lowerCamelCase__	:       Any					=						{0: "batch", 2: "past_sequence + sequence"}
             return common_outputs





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )

             # Generate decoder inputs
             lowerCamelCase__	:       List[str]					=						seq_length if not self.use_past else 1
             lowerCamelCase__	:       List[Any]					=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )
             lowerCamelCase__	:       Optional[Any]					=						{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
             lowerCamelCase__	:       Optional[Any]					=						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__					,     lowerCamelCase__	:       Tuple					=						common_inputs["input_ids"].shape
                   lowerCamelCase__	:       int					=						common_inputs["decoder_input_ids"].shape[1]
                   lowerCamelCase__					,     lowerCamelCase__	:       Optional[Any]					=						self.num_attention_heads
                   lowerCamelCase__	:       str					=						(
                       batch,
                       num_encoder_attention_heads,
                       encoder_seq_length,
                       self._config.hidden_size // num_encoder_attention_heads,
                   )
                   lowerCamelCase__	:       Optional[int]					=						decoder_seq_length + 3
                   lowerCamelCase__	:       Dict					=						(
                       batch,
                       num_decoder_attention_heads,
                       decoder_past_length,
                       self._config.hidden_size // num_decoder_attention_heads,
                   )

                   lowerCamelCase__	:       List[Any]					=						torch.cat(
                       [common_inputs["decoder_attention_mask"], torch.ones(__lowerCamelCase      ,   __lowerCamelCase )]      ,   dim=1 )

                   lowerCamelCase__	:       Optional[Any]					=						[]
                   # If the number of encoder and decoder layers are present in the model configuration, both are considered
                   lowerCamelCase__					,     lowerCamelCase__	:       str					=						self.num_layers
                   lowerCamelCase__	:       Union[str, Any]					=						min(__lowerCamelCase      ,   __lowerCamelCase )
                   lowerCamelCase__	:       Union[str, Any]					=						max(__lowerCamelCase      ,   __lowerCamelCase ) - min_num_layers
                   lowerCamelCase__	:       str					=						"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

                   for _ in range(__lowerCamelCase ):
                         common_inputs["past_key_values"].append(
                             (
                                 torch.zeros(__lowerCamelCase ),
                                 torch.zeros(__lowerCamelCase ),
                                 torch.zeros(__lowerCamelCase ),
                                 torch.zeros(__lowerCamelCase ),
                             ) )
                   # TODO: test this.
                   lowerCamelCase__	:       Optional[int]					=						encoder_shape if remaining_side_name == "encoder" else decoder_shape
                   for _ in range(__lowerCamelCase      ,   __lowerCamelCase ):
                         common_inputs["past_key_values"].append((torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) )
             return common_inputs





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						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__					,     lowerCamelCase__	:       int					=						common_inputs["input_ids"].shape
                   # Not using the same length for past_key_values
                   lowerCamelCase__	:       str					=						seqlen + 2
                   lowerCamelCase__					,     lowerCamelCase__	:       Optional[int]					=						self.num_layers
                   lowerCamelCase__					,     lowerCamelCase__	:       int					=						self.num_attention_heads
                   lowerCamelCase__	:       Tuple					=						(
                       batch,
                       num_encoder_attention_heads,
                       past_key_values_length,
                       self._config.hidden_size // num_encoder_attention_heads,
                   )

                   lowerCamelCase__	:       Union[str, Any]					=						common_inputs["attention_mask"].dtype
                   lowerCamelCase__	:       List[str]					=						torch.cat(
                       [common_inputs["attention_mask"], torch.ones(__lowerCamelCase      ,   __lowerCamelCase      ,   dtype=__lowerCamelCase )]      ,   dim=1 )
                   lowerCamelCase__	:       Tuple					=						[
                       (torch.zeros(__lowerCamelCase ), torch.zeros(__lowerCamelCase )) for _ in range(__lowerCamelCase )
                   ]
             return common_inputs





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						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__	:       List[str]					=						tokenizer.num_special_tokens_to_add(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						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__	:       Optional[int]					=						[" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
             lowerCamelCase__	:       Optional[Any]					=						dict(tokenizer(__lowerCamelCase      ,   return_tensors=__lowerCamelCase ) )
             return common_inputs





       def 		lowerCAmelCase  (     self							:    Any      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             if self.task in ["default", "seq2seq-lm"]:
                   lowerCamelCase__	:       Optional[int]					=						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__	:       Any					=						self._generate_dummy_inputs_for_causal_lm(
                       __lowerCamelCase      ,   batch_size=__lowerCamelCase      ,   seq_length=__lowerCamelCase      ,   is_pair=__lowerCamelCase      ,   framework=__lowerCamelCase )
             else:
                   lowerCamelCase__	:       Any					=						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							:    Any      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



             if self.task in ["default", "seq2seq-lm"]:
                   lowerCamelCase__	:       Dict					=						super()._flatten_past_key_values_(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )
             else:
                   lowerCamelCase__	:       int					=						super(__lowerCamelCase      ,   self )._flatten_past_key_values_(
                       __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )


