



import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase      :      Optional[int]   					=					logging.get_logger()

@dataclass
class 	__lowerCAmelCase							:


					'''simple docstring'''


					a__        =     42
					a__        =     field(default_factory=__lowerCamelCase  )
					a__        =     field(default_factory=__lowerCamelCase  )
					a__        =     field(default_factory=__lowerCamelCase  )







					def 		_a (      self    ,       a    ,       a    ,       a    ,       a   ):




										"""simple docstring"""





										snake_case_						:List[str]								=						len(list(m.modules()   )   ) == 1 or isinstance(_lowerCAmelCase    ,       nn.Convad   ) or isinstance(_lowerCAmelCase    ,       nn.BatchNormad   )
										if has_not_submodules:
															self.traced.append(_lowerCAmelCase   )
															snake_case_						:Optional[int]								=						m







					def __call__(      self    ,       a   ):




										"""simple docstring"""





										for name, m in self.module.named_modules():
															self.handles.append(m.register_forward_hook(partial(self._forward_hook    ,       name=_lowerCAmelCase   )   )   )
										self.module(_lowerCAmelCase   )
										[x.remove() for x in self.handles]
										return self







					@property
					def 		_a (      self   ):




										"""simple docstring"""





										return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()   )   ) > 0}



class 	__lowerCAmelCase							(nn.Module  ):


					'''simple docstring'''


					def __init__(      self    ,       a   ):




										"""simple docstring"""





										super().__init__()

										snake_case_						:List[Tuple[str, nn.Module]]								=						[]
										# - get the stem
										feature_blocks.append(("conv1", model.stem)   )
										# - get all the feature blocks
										for k, v in model.trunk_output.named_children():
															assert k.startswith("block"   ), F'''Unexpected layer name {k}'''
															snake_case_						:Dict								=						len(_lowerCAmelCase   ) + 1
															feature_blocks.append((F'''res{block_index}''', v)   )

										snake_case_						:Optional[int]								=						nn.ModuleDict(_lowerCAmelCase   )







					def 		_a (      self    ,       a   ):




										"""simple docstring"""





										return get_trunk_forward_outputs(
										    _lowerCAmelCase    ,       out_feat_keys=_lowerCAmelCase    ,       feature_blocks=self._feature_blocks    ,       )



class 	__lowerCAmelCase							(__lowerCamelCase  ):


					'''simple docstring'''


					def 		_a (      self   ):




										"""simple docstring"""





										return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]



def      A      (			_A							):




					"""simple docstring"""
					snake_case_						:Dict								=						RegNetConfig(depths=[2, 7, 17, 1],     hidden_sizes=[8, 8, 8, 8],     groups_width=8							)
					if "in1k" in model_name:
										snake_case_						:Optional[Any]								=						RegNetForImageClassification(__lowerCamelCase							)
					else:
										snake_case_						:Optional[Any]								=						RegNetModel(__lowerCamelCase							)
					# create from model (with small weights)
					snake_case_						:str								=						FakeRegNetVisslWrapper(
					    RegNet(FakeRegNetParams(depth=27,     group_width=1_010,     w_a=1_744,     w_a=620.83,     w_m=2.52							)							)							)

					with torch.no_grad():
										snake_case_						:List[Any]								=						from_model.eval()
										snake_case_						:Dict								=						our_model.eval()

										snake_case_						:Any								=						torch.randn((1, 3, 32, 32)							)
										# trace both
										snake_case_						:str								=						Tracker(__lowerCamelCase							)
										snake_case_						:Tuple								=						dest_tracker(__lowerCamelCase							).parametrized

										pprint(dest_tracker.nameamodule							)
										snake_case_						:List[str]								=						Tracker(__lowerCamelCase							)
										snake_case_						:List[str]								=						src_tracker(__lowerCamelCase							).parametrized

					# convert the keys -> module dict to keys -> params
					def to_params_dict(_A							):
										snake_case_						:Optional[Any]								=						OrderedDict()
										for name, module in dict_with_modules.items():
															for param_name, param in module.state_dict().items():
																				snake_case_						:Optional[int]								=						param
										return params_dict

					snake_case_						:Optional[int]								=						{}

					snake_case_						:int								=						to_params_dict(__lowerCamelCase							)
					snake_case_						:Optional[int]								=						to_params_dict(__lowerCamelCase							)

					for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items(),     dst_state_dict.items()							):
										snake_case_						:Optional[int]								=						dest_key
										logger.info(F'''{src_key} -> {dest_key}'''							)
					# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
					if "in1k" in model_name:
										snake_case_						:str								=						"""classifier.1.weight"""
										snake_case_						:Any								=						"""classifier.1.bias"""

					return from_to_ours_keys
def      A      (			_A,     _A = None,     _A = True							):




					"""simple docstring"""
					snake_case_						:Dict								=						"""imagenet-1k-id2label.json"""
					snake_case_						:Optional[int]								=						1_000

					snake_case_						:Tuple								=						"""huggingface/label-files"""
					snake_case_						:Union[str, Any]								=						num_labels
					snake_case_						:Tuple								=						json.load(open(cached_download(hf_hub_url(__lowerCamelCase,     __lowerCamelCase,     repo_type="dataset"							)							),     "r"							)							)
					snake_case_						:int								=						{int(__lowerCamelCase							): v for k, v in idalabel.items()}

					snake_case_						:Optional[int]								=						idalabel
					snake_case_						:str								=						{v: k for k, v in idalabel.items()}

					snake_case_						:Optional[int]								=						partial(__lowerCamelCase,     num_labels=__lowerCamelCase,     idalabel=__lowerCamelCase,     labelaid=__lowerCamelCase							)

					snake_case_						:List[str]								=						{
					    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1],     hidden_sizes=[2_020, 4_040, 11_110, 28_280],     groups_width=1_010							),
					    # finetuned on imagenet
					    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1],     hidden_sizes=[2_020, 4_040, 11_110, 28_280],     groups_width=1_010							),
					}

					# add seer weights logic
					def load_using_classy_vision(_A							) -> Tuple[Dict, Dict]:
										snake_case_						:Optional[int]								=						torch.hub.load_state_dict_from_url(__lowerCamelCase,     model_dir=str(__lowerCamelCase							),     map_location="cpu"							)
										# check if we have a head, if yes add it
										snake_case_						:Optional[int]								=						files["""classy_state_dict"""]["""base_model"""]["""model"""]
										return model_state_dict["trunk"], model_state_dict["heads"]

					snake_case_						:int								=						{
					    """regnet-y-10b-seer""": partial(
					        __lowerCamelCase,     "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch",     ),
					    """regnet-y-10b-seer-in1k""": partial(
					        __lowerCamelCase,     "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch",     ),
					}

					snake_case_						:Union[str, Any]								=						get_from_to_our_keys(__lowerCamelCase							)

					if not (save_directory / F'''{model_name}.pth''').exists():
										logger.info("Loading original state_dict."							)
										snake_case_						:List[str]								=						names_to_from_model[model_name]()
										snake_case_						:Dict								=						from_state_dict_trunk
										if "in1k" in model_name:
															# add the head
															snake_case_						:Tuple								=						{**from_state_dict_trunk, **from_state_dict_head}
										logger.info("Done!"							)

										snake_case_						:int								=						{}

										snake_case_						:Dict								=						list(from_state_dict.keys()							)
										snake_case_						:Any								=						r"""\.block.-part."""
										# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
										for key in from_state_dict.keys():
															# remove the weird "block[0,1]-part" from the key
															snake_case_						:int								=						re.sub(__lowerCamelCase,     "",     __lowerCamelCase							)
															# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
															snake_case_						:Optional[Any]								=						from_to_ours_keys[src_key]
															# store the parameter with our key
															snake_case_						:Union[str, Any]								=						from_state_dict[key]
															not_used_keys.remove(__lowerCamelCase							)
										# check that all keys have been updated
										assert len(__lowerCamelCase							) == 0, F'''Some keys where not used {','.join(__lowerCamelCase							)}'''

										logger.info(F'''The following keys were not used: {','.join(__lowerCamelCase							)}'''							)

										# save our state dict to disk
										torch.save(__lowerCamelCase,     save_directory / F'''{model_name}.pth'''							)

										del converted_state_dict
					else:
										logger.info("The state_dict was already stored on disk."							)
					if push_to_hub:
										logger.info(F'''Token is {os.environ['HF_TOKEN']}'''							)
										logger.info("Loading our model."							)
										# create our model
										snake_case_						:Union[str, Any]								=						names_to_config[model_name]
										snake_case_						:Tuple								=						RegNetModel
										if "in1k" in model_name:
															snake_case_						:Optional[Any]								=						RegNetForImageClassification
										snake_case_						:Optional[int]								=						our_model_func(__lowerCamelCase							)
										# place our model to the meta device (so remove all the weights)
										our_model.to(torch.device("meta"							)							)
										logger.info("Loading state_dict in our model."							)
										# load state dict
										snake_case_						:int								=						our_model.state_dict().keys()
										PreTrainedModel._load_pretrained_model_low_mem(
										    __lowerCamelCase,     __lowerCamelCase,     [save_directory / F'''{model_name}.pth''']							)
										logger.info("Finally, pushing!"							)
										# push it to hub
										our_model.push_to_hub(
										    repo_path_or_name=save_directory / model_name,     commit_message="Add model",     output_dir=save_directory / model_name,     )
										snake_case_						:Optional[int]								=						384
										# we can use the convnext one
										snake_case_						:Tuple								=						AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k",     size=__lowerCamelCase							)
										image_processor.push_to_hub(
										    repo_path_or_name=save_directory / model_name,     commit_message="Add image processor",     output_dir=save_directory / model_name,     )


if __name__ == "__main__":
				__UpperCAmelCase      :      Tuple   					=					argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default=None,
				    type=str,
				    help=(
				        'The name of the model you wish to convert, it must be one of the supported regnet* architecture,'
				        ' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'
				    ),
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default=None,
				    type=Path,
				    required=True,
				    help='Path to the output PyTorch model directory.',
				)
				parser.add_argument(
				    '--push_to_hub',
				    default=True,
				    type=bool,
				    required=False,
				    help='If True, push model and image processor to the hub.',
				)

				__UpperCAmelCase      :      str   					=					parser.parse_args()

				__UpperCAmelCase      :      Path   					=					args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)






"""simple docstring"""






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

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


__UpperCAmelCase      :      Union[str, Any]   					=					logging.get_logger(__name__)

__UpperCAmelCase      :      str   					=					{
    'google/bigbird-pegasus-large-arxiv': (
        'https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'
    ),
    'google/bigbird-pegasus-large-pubmed': (
        'https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'
    ),
    'google/bigbird-pegasus-large-bigpatent': (
        'https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}

class 	__lowerCAmelCase							(__UpperCamelCase  ):


					'''simple docstring'''


					a__        =     'bigbird_pegasus'
					a__        =     ['past_key_values']
					a__        =     {
					    'num_attention_heads': 'encoder_attention_heads',
					    'hidden_size': 'd_model',
					    'attention_probs_dropout_prob': 'attention_dropout',
					}







					def __init__(      self    ,       a=9_61_03    ,       a=40_96    ,       a=16    ,       a=40_96    ,       a=16    ,       a=16    ,       a=40_96    ,       a=16    ,       a=0.0    ,       a=0.0    ,       a=True    ,       a=True    ,       a="gelu_new"    ,       a=10_24    ,       a=0.1    ,       a=0.0    ,       a=0.0    ,       a=0.02    ,       a=2    ,       a=0.0    ,       a=True    ,       a=0    ,       a=2    ,       a=1    ,       a="block_sparse"    ,       a=64    ,       a=3    ,       a=False    ,       **a    ,       ):




										"""simple docstring"""





										snake_case_						:Optional[int]								=						vocab_size
										snake_case_						:Any								=						max_position_embeddings
										snake_case_						:List[Any]								=						d_model
										snake_case_						:Any								=						encoder_ffn_dim
										snake_case_						:str								=						encoder_layers
										snake_case_						:Union[str, Any]								=						encoder_attention_heads
										snake_case_						:Any								=						decoder_ffn_dim
										snake_case_						:Dict								=						decoder_layers
										snake_case_						:List[Any]								=						decoder_attention_heads
										snake_case_						:Any								=						dropout
										snake_case_						:Union[str, Any]								=						attention_dropout
										snake_case_						:Optional[int]								=						activation_dropout
										snake_case_						:int								=						activation_function
										snake_case_						:int								=						init_std
										snake_case_						:Union[str, Any]								=						encoder_layerdrop
										snake_case_						:Dict								=						decoder_layerdrop
										snake_case_						:Any								=						classifier_dropout
										snake_case_						:Any								=						use_cache
										snake_case_						:Any								=						encoder_layers
										snake_case_						:int								=						scale_embedding  # scale factor will be sqrt(d_model) if True

										# extra config
										snake_case_						:Optional[int]								=						attention_type
										snake_case_						:int								=						block_size
										snake_case_						:Optional[int]								=						num_random_blocks
										snake_case_						:List[str]								=						use_bias

										super().__init__(
										    pad_token_id=a    ,       bos_token_id=a    ,       eos_token_id=a    ,       is_encoder_decoder=a    ,       decoder_start_token_id=a    ,       **a    ,       )



class 	__lowerCAmelCase							(__UpperCamelCase  ):


					'''simple docstring'''


					@property
					def 		_a (      self   ):




										"""simple docstring"""





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

															if self.use_past:
																				snake_case_						:int								=						{0: "batch"}
																				snake_case_						:List[str]								=						{0: "batch", 1: "past_decoder_sequence + sequence"}
															else:
																				snake_case_						:Optional[int]								=						{0: "batch", 1: "decoder_sequence"}
																				snake_case_						:Optional[Any]								=						{0: "batch", 1: "decoder_sequence"}

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




										"""simple docstring"""





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







					def 		_a (      self    ,       a    ,       a = -1    ,       a = -1    ,       a = False    ,       a = None    ,       ):




										"""simple docstring"""





										snake_case_						:int								=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    a    ,       a    ,       a    ,       a    ,       a   )

										# Generate decoder inputs
										snake_case_						:List[str]								=						seq_length if not self.use_past else 1
										snake_case_						:str								=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    a    ,       a    ,       a    ,       a    ,       a   )
										snake_case_						:Union[str, Any]								=						{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
										snake_case_						:Tuple								=						dict(**a    ,       **a   )

										if self.use_past:
															if not is_torch_available():
																				raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
															else:
																				import torch
															snake_case_	,  snake_case_						:Optional[int]								=						common_inputs["input_ids"].shape
															snake_case_						:Optional[int]								=						common_inputs["decoder_input_ids"].shape[1]
															snake_case_	,  snake_case_						:Optional[int]								=						self.num_attention_heads
															snake_case_						:str								=						(
															    batch,
															    num_encoder_attention_heads,
															    encoder_seq_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															snake_case_						:Any								=						decoder_seq_length + 3
															snake_case_						:int								=						(
															    batch,
															    num_decoder_attention_heads,
															    decoder_past_length,
															    self._config.hidden_size // num_decoder_attention_heads,
															)

															snake_case_						:Union[str, Any]								=						torch.cat(
															    [common_inputs["decoder_attention_mask"], torch.ones(a    ,       a   )]    ,       dim=1   )

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

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







					def 		_a (      self    ,       a    ,       a = -1    ,       a = -1    ,       a = False    ,       a = None    ,       ):




										"""simple docstring"""





										snake_case_						:List[str]								=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    a    ,       a    ,       a    ,       a    ,       a   )

										if self.use_past:
															if not is_torch_available():
																				raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
															else:
																				import torch
															snake_case_	,  snake_case_						:Union[str, Any]								=						common_inputs["input_ids"].shape
															# Not using the same length for past_key_values
															snake_case_						:str								=						seqlen + 2
															snake_case_	,  snake_case_						:Union[str, Any]								=						self.num_layers
															snake_case_	,  snake_case_						:Any								=						self.num_attention_heads
															snake_case_						:List[str]								=						(
															    batch,
															    num_encoder_attention_heads,
															    past_key_values_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)

															snake_case_						:Optional[Any]								=						common_inputs["attention_mask"].dtype
															snake_case_						:Tuple								=						torch.cat(
															    [common_inputs["attention_mask"], torch.ones(a    ,       a    ,       dtype=a   )]    ,       dim=1   )
															snake_case_						:Optional[int]								=						[
															    (torch.zeros(a   ), torch.zeros(a   )) for _ in range(a   )
															]
										return common_inputs







					def 		_a (      self    ,       a    ,       a = -1    ,       a = -1    ,       a = False    ,       a = None    ,       ):




										"""simple docstring"""





										snake_case_						:Optional[int]								=						compute_effective_axis_dimension(
										    a    ,       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
										snake_case_						:List[Any]								=						tokenizer.num_special_tokens_to_add(a   )
										snake_case_						:Tuple								=						compute_effective_axis_dimension(
										    a    ,       fixed_dimension=OnnxConfig.default_fixed_sequence    ,       num_token_to_add=a   )

										# Generate dummy inputs according to compute batch and sequence
										snake_case_						:Any								=						[" ".join([tokenizer.unk_token]   ) * seq_length] * batch_size
										snake_case_						:List[str]								=						dict(tokenizer(a    ,       return_tensors=a   )   )
										return common_inputs







					def 		_a (      self    ,       a    ,       a = -1    ,       a = -1    ,       a = False    ,       a = None    ,       ):




										"""simple docstring"""





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

										elif self.task == "causal-lm":
															snake_case_						:Optional[Any]								=						self._generate_dummy_inputs_for_causal_lm(
															    a    ,       batch_size=a    ,       seq_length=a    ,       is_pair=a    ,       framework=a   )
										else:
															snake_case_						:List[Any]								=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
															    a    ,       batch_size=a    ,       seq_length=a    ,       is_pair=a    ,       framework=a   )

										return common_inputs







					def 		_a (      self    ,       a    ,       a    ,       a    ,       a   ):




										"""simple docstring"""





										if self.task in ["default", "seq2seq-lm"]:
															snake_case_						:str								=						super()._flatten_past_key_values_(a    ,       a    ,       a    ,       a   )
										else:
															snake_case_						:Any								=						super(a    ,       self   )._flatten_past_key_values_(
															    a    ,       a    ,       a    ,       a   )

