


import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase			 =	logging.get_logger(__name__)




@dataclass
class A  :
      UpperCamelCase__							:  nn.Module
      UpperCamelCase__							:  List[nn.Module]				=field(default_factory=UpperCamelCase_					)
      UpperCamelCase__							:  list				=field(default_factory=UpperCamelCase_					)






      def 	lowerCamelCase	( self						:					Tuple      ,       lowercase_						:					List[Any]      ,       lowercase_						:					Tensor      ,       lowercase_						:					Tensor			) ->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =len(list(m.modules()			)			) == 1 or isinstance(lowercase_      ,       nn.Convad			) or isinstance(lowercase_      ,       nn.BatchNormad			)
             if has_not_submodules:
                    if not isinstance(lowercase_      ,       lowercase_			):
                           self.traced.append(lowercase_			)






      def __call__( self						:					Any      ,       lowercase_						:					Tensor			) ->						Tuple:
             """simple docstring"""




             for m in self.module.modules():
                    self.handles.append(m.register_forward_hook(self._forward_hook			)			)
             self.module(lowercase_			)
             [x.remove() for x in self.handles]
             return self






      @property
      def 	lowerCamelCase	( self						:					Any			) ->						Tuple:
             """simple docstring"""




             return list(filter(lambda lowercase_			: len(list(x.state_dict().keys()			)			) > 0      ,       self.traced			)			)




@dataclass
class A  :
      UpperCamelCase__							:  nn.Module
      UpperCamelCase__							:  nn.Module
      UpperCamelCase__							:  int				=0
      UpperCamelCase__							:  List				=field(default_factory=UpperCamelCase_					)
      UpperCamelCase__							:  List				=field(default_factory=UpperCamelCase_					)






      def __call__( self						:					Optional[int]      ,       lowercase_						:					Tensor			) ->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase	:		Any          =Tracker(self.dest			)(lowercase_			).parametrized
             _lowerCamelCase	:		int          =Tracker(self.src			)(lowercase_			).parametrized

             _lowerCamelCase	:		Union[str, Any]          =list(filter(lambda lowercase_			: type(lowercase_			) not in self.src_skip      ,       lowercase_			)			)
             _lowerCamelCase	:		Any          =list(filter(lambda lowercase_			: type(lowercase_			) not in self.dest_skip      ,       lowercase_			)			)

             if len(lowercase_			) != len(lowercase_			):
                    raise Exception(
                        F'''Numbers of operations are different. Source module has {len(lowercase_			)} operations while'''
                        F''' destination module has {len(lowercase_			)}.'''			)

             for dest_m, src_m in zip(lowercase_      ,       lowercase_			):
                    dest_m.load_state_dict(src_m.state_dict()			)
                    if self.verbose == 1:
                           print(F'''Transfered from={src_m} to={dest_m}'''			)




def      a_				(   SCREAMING_SNAKE_CASE__ :				nn.Module		,      SCREAMING_SNAKE_CASE__ :				nn.Module					):
       '''simple docstring'''





       _lowerCamelCase	:		str          =from_model.state_dict()
       _lowerCamelCase	:		List[Any]          =our_model.state_dict()
       _lowerCamelCase	:		Union[str, Any]          =our_model.config
       _lowerCamelCase	:		Any          =[]
       for stage_idx in range(len(config.hidden_sizes					)					):
              for block_id in range(config.depths[stage_idx]					):
                     _lowerCamelCase	:		str          =F'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
                     _lowerCamelCase	:		Dict          =F'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

                     all_keys.append((from_key, to_key)					)
                     _lowerCamelCase	:		int          =F'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
                     _lowerCamelCase	:		List[Any]          =F'''van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'''

                     all_keys.append((from_key, to_key)					)

       for from_key, to_key in all_keys:
              _lowerCamelCase	:		Union[str, Any]          =from_state_dict.pop(SCREAMING_SNAKE_CASE__					)

       our_model.load_state_dict(SCREAMING_SNAKE_CASE__					)
       return our_model




def      a_				(   SCREAMING_SNAKE_CASE__ :				str		,      SCREAMING_SNAKE_CASE__ :				VanConfig		,      SCREAMING_SNAKE_CASE__ :				str		,      SCREAMING_SNAKE_CASE__ :				nn.Module		,      SCREAMING_SNAKE_CASE__ :				Path		,      SCREAMING_SNAKE_CASE__ :				bool = True		,      ):
       '''simple docstring'''





       print(F'''Downloading weights for {name}...'''					)
       _lowerCamelCase	:		Any          =cached_download(SCREAMING_SNAKE_CASE__					)
       print(F'''Converting {name}...'''					)
       _lowerCamelCase	:		str          =torch.load(SCREAMING_SNAKE_CASE__					)['state_dict']
       from_model.load_state_dict(SCREAMING_SNAKE_CASE__					)
       from_model.eval()
       with torch.no_grad():
              _lowerCamelCase	:		List[str]          =VanForImageClassification(SCREAMING_SNAKE_CASE__					).eval()
              _lowerCamelCase	:		List[Any]          =ModuleTransfer(src=SCREAMING_SNAKE_CASE__		,      dest=SCREAMING_SNAKE_CASE__					)
              _lowerCamelCase	:		List[Any]          =torch.randn((1, 3, 224, 224)					)
              module_transfer(SCREAMING_SNAKE_CASE__					)
              _lowerCamelCase	:		int          =copy_parameters(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)

       if not torch.allclose(from_model(SCREAMING_SNAKE_CASE__					)		,      our_model(SCREAMING_SNAKE_CASE__					).logits					):
              raise ValueError('The model logits don\'t match the original one.'					)

       _lowerCamelCase	:		int          =name
       print(SCREAMING_SNAKE_CASE__					)

       if push_to_hub:
              our_model.push_to_hub(
                  repo_path_or_name=save_directory / checkpoint_name		,      commit_message='Add model'		,      use_temp_dir=SCREAMING_SNAKE_CASE__		,      )

              # we can use the convnext one
              _lowerCamelCase	:		int          =AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'					)
              image_processor.push_to_hub(
                  repo_path_or_name=save_directory / checkpoint_name		,      commit_message='Add image processor'		,      use_temp_dir=SCREAMING_SNAKE_CASE__		,      )

              print(F'''Pushed {checkpoint_name}'''					)




def      a_				(   SCREAMING_SNAKE_CASE__ :				Path		,      SCREAMING_SNAKE_CASE__ :				str = None		,      SCREAMING_SNAKE_CASE__ :				bool = True					):
       '''simple docstring'''





       _lowerCamelCase	:		List[str]          ='imagenet-1k-id2label.json'
       _lowerCamelCase	:		str          =1_000

       _lowerCamelCase	:		Tuple          ='huggingface/label-files'
       _lowerCamelCase	:		Union[str, Any]          =num_labels
       _lowerCamelCase	:		List[str]          =json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      repo_type='dataset'					)		,      'r'					)					)
       _lowerCamelCase	:		List[str]          ={int(SCREAMING_SNAKE_CASE__					): v for k, v in idalabel.items()}

       _lowerCamelCase	:		Optional[Any]          =idalabel
       _lowerCamelCase	:		List[str]          ={v: k for k, v in idalabel.items()}

       _lowerCamelCase	:		Optional[int]          =partial(SCREAMING_SNAKE_CASE__		,      num_labels=SCREAMING_SNAKE_CASE__		,      idalabel=SCREAMING_SNAKE_CASE__		,      labelaid=SCREAMING_SNAKE_CASE__					)

       _lowerCamelCase	:		Union[str, Any]          ={
           'van-tiny': ImageNetPreTrainedConfig(
               hidden_sizes=[32, 64, 160, 256]		,      depths=[3, 3, 5, 2]		,      mlp_ratios=[8, 8, 4, 4]		,      ),
           'van-small': ImageNetPreTrainedConfig(
               hidden_sizes=[64, 128, 320, 512]		,      depths=[2, 2, 4, 2]		,      mlp_ratios=[8, 8, 4, 4]		,      ),
           'van-base': ImageNetPreTrainedConfig(
               hidden_sizes=[64, 128, 320, 512]		,      depths=[3, 3, 12, 3]		,      mlp_ratios=[8, 8, 4, 4]		,      ),
           'van-large': ImageNetPreTrainedConfig(
               hidden_sizes=[64, 128, 320, 512]		,      depths=[3, 5, 27, 3]		,      mlp_ratios=[8, 8, 4, 4]		,      ),
       }

       _lowerCamelCase	:		Dict          ={
           'van-tiny': van_tiny,
           'van-small': van_small,
           'van-base': van_base,
           'van-large': van_large,
       }

       _lowerCamelCase	:		Union[str, Any]          ={
           'van-tiny': (
               'https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'
           ),
           'van-small': (
               'https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'
           ),
           'van-base': (
               'https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'
           ),
           'van-large': (
               'https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'
           ),
       }

       if model_name:
              convert_weight_and_push(
                  SCREAMING_SNAKE_CASE__		,      names_to_config[model_name]		,      checkpoint=names_to_original_checkpoints[model_name]		,      from_model=names_to_original_models[model_name]()		,      save_directory=SCREAMING_SNAKE_CASE__		,      push_to_hub=SCREAMING_SNAKE_CASE__		,      )
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(
                         SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      checkpoint=names_to_original_checkpoints[model_name]		,      from_model=names_to_original_models[model_name]()		,      save_directory=SCREAMING_SNAKE_CASE__		,      push_to_hub=SCREAMING_SNAKE_CASE__		,      )


if __name__ == "__main__":
 lowerCamelCase			 =	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 resnet* architecture,'
         ' currently: van-tiny/small/base/large. 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(
     '--van_dir',
     required=True,
     type=Path,
     help=(
         'A path to VAN\'s original implementation directory. You can download from here:'
         ' https://github.com/Visual-Attention-Network/VAN-Classification'
     ),
 )
 parser.add_argument(
     '--push_to_hub',
     default=True,
     type=bool,
     required=False,
     help='If True, push model and image processor to the hub.',
 )

 lowerCamelCase			 =	parser.parse_args()
 lowerCamelCase			 =	args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 lowerCamelCase			 =	args.van_dir
 # append the path to the parents to maskformer dir
 sys.path.append(str(van_dir.parent))
 from van.models.van import van_base, van_large, van_small, van_tiny

 convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)






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


lowerCamelCase						 =	logging.get_logger(__name__)

lowerCamelCase						 =	{
    '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 A  (       UpperCamelCase_					):
      UpperCamelCase__							:  int				='bigbird_pegasus'
      UpperCamelCase__							:  Tuple				=['past_key_values']
      UpperCamelCase__							:  str				={
          'num_attention_heads': 'encoder_attention_heads',
          'hidden_size': 'd_model',
          'attention_probs_dropout_prob': 'attention_dropout',
      }






      def __init__( self						:					Tuple      ,       lowercase_						:					int=9_6103      ,       lowercase_						:					Optional[Any]=4096      ,       lowercase_						:					Tuple=16      ,       lowercase_						:					int=4096      ,       lowercase_						:					Any=16      ,       lowercase_						:					str=16      ,       lowercase_						:					List[str]=4096      ,       lowercase_						:					Optional[Any]=16      ,       lowercase_						:					Any=0.0      ,       lowercase_						:					List[Any]=0.0      ,       lowercase_						:					Optional[int]=True      ,       lowercase_						:					Tuple=True      ,       lowercase_						:					Dict="gelu_new"      ,       lowercase_						:					List[str]=1024      ,       lowercase_						:					Dict=0.1      ,       lowercase_						:					Union[str, Any]=0.0      ,       lowercase_						:					Any=0.0      ,       lowercase_						:					str=0.02      ,       lowercase_						:					List[str]=2      ,       lowercase_						:					Any=0.0      ,       lowercase_						:					int=True      ,       lowercase_						:					Union[str, Any]=0      ,       lowercase_						:					List[str]=2      ,       lowercase_						:					Optional[Any]=1      ,       lowercase_						:					Any="block_sparse"      ,       lowercase_						:					str=64      ,       lowercase_						:					Dict=3      ,       lowercase_						:					List[str]=False      ,       **lowercase_						:					List[str]      ,       ) ->						str:
             """simple docstring"""




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

             # extra config
             _lowerCamelCase	:		Tuple          =attention_type
             _lowerCamelCase	:		str          =block_size
             _lowerCamelCase	:		Optional[int]          =num_random_blocks
             _lowerCamelCase	:		Optional[int]          =use_bias

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




class A  (       UpperCamelCase_					):






      @property
      def 	lowerCamelCase	( self						:					Dict			) ->						Mapping[str, Mapping[int, str]]:
             """simple docstring"""




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

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

                    if self.use_past:
                           self.fill_with_past_key_values_(lowercase_      ,       direction='inputs'			)
             elif self.task == "causal-lm":
                    # TODO: figure this case out.
                    _lowerCamelCase	:		Union[str, Any]          =OrderedDict(
                        [
                            ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                            ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                        ]			)
                    if self.use_past:
                           _lowerCamelCase     ,					_lowerCamelCase	:		Union[str, Any]          =self.num_layers
                           for i in range(lowercase_			):
                                  _lowerCamelCase	:		Dict          ={0: 'batch', 2: 'past_sequence + sequence'}
                                  _lowerCamelCase	:		List[str]          ={0: 'batch', 2: 'past_sequence + sequence'}
             else:
                    _lowerCamelCase	:		Optional[int]          =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						:					Dict			) ->						Mapping[str, Mapping[int, str]]:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					Optional[Any]      ,       lowercase_						:					PreTrainedTokenizer      ,       lowercase_						:					int = -1      ,       lowercase_						:					int = -1      ,       lowercase_						:					bool = False      ,       lowercase_						:					Optional[TensorType] = None      ,       ) ->						Mapping[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		int          =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_			)

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

                    _lowerCamelCase	:		Union[str, Any]          =torch.cat(
                        [common_inputs['decoder_attention_mask'], torch.ones(lowercase_      ,       lowercase_			)]      ,       dim=1			)

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

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






      def 	lowerCamelCase	( self						:					Optional[Any]      ,       lowercase_						:					PreTrainedTokenizer      ,       lowercase_						:					int = -1      ,       lowercase_						:					int = -1      ,       lowercase_						:					bool = False      ,       lowercase_						:					Optional[TensorType] = None      ,       ) ->						Mapping[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		str          =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
                    _lowerCamelCase     ,					_lowerCamelCase	:		Optional[int]          =common_inputs['input_ids'].shape
                    # Not using the same length for past_key_values
                    _lowerCamelCase	:		Union[str, Any]          =seqlen + 2
                    _lowerCamelCase     ,					_lowerCamelCase	:		List[Any]          =self.num_layers
                    _lowerCamelCase     ,					_lowerCamelCase	:		Any          =self.num_attention_heads
                    _lowerCamelCase	:		List[str]          =(
                        batch,
                        num_encoder_attention_heads,
                        past_key_values_length,
                        self._config.hidden_size // num_encoder_attention_heads,
                    )

                    _lowerCamelCase	:		Tuple          =common_inputs['attention_mask'].dtype
                    _lowerCamelCase	:		Tuple          =torch.cat(
                        [common_inputs['attention_mask'], torch.ones(lowercase_      ,       lowercase_      ,       dtype=lowercase_			)]      ,       dim=1			)
                    _lowerCamelCase	:		Dict          =[
                        (torch.zeros(lowercase_			), torch.zeros(lowercase_			)) for _ in range(lowercase_			)
                    ]
             return common_inputs






      def 	lowerCamelCase	( self						:					List[str]      ,       lowercase_						:					PreTrainedTokenizer      ,       lowercase_						:					int = -1      ,       lowercase_						:					int = -1      ,       lowercase_						:					bool = False      ,       lowercase_						:					Optional[TensorType] = None      ,       ) ->						Mapping[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		Optional[Any]          =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
             _lowerCamelCase	:		Tuple          =tokenizer.num_special_tokens_to_add(lowercase_			)
             _lowerCamelCase	:		Any          =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
             _lowerCamelCase	:		Any          =[' '.join([tokenizer.unk_token]			) * seq_length] * batch_size
             _lowerCamelCase	:		int          =dict(tokenizer(lowercase_      ,       return_tensors=lowercase_			)			)
             return common_inputs






      def 	lowerCamelCase	( self						:					Any      ,       lowercase_						:					PreTrainedTokenizer      ,       lowercase_						:					int = -1      ,       lowercase_						:					int = -1      ,       lowercase_						:					bool = False      ,       lowercase_						:					Optional[TensorType] = None      ,       ) ->						Mapping[str, Any]:
             """simple docstring"""




             if self.task in ["default", "seq2seq-lm"]:
                    _lowerCamelCase	:		Union[str, Any]          =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":
                    _lowerCamelCase	:		List[str]          =self._generate_dummy_inputs_for_causal_lm(
                        lowercase_      ,       batch_size=lowercase_      ,       seq_length=lowercase_      ,       is_pair=lowercase_      ,       framework=lowercase_			)
             else:
                    _lowerCamelCase	:		List[Any]          =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 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					Union[str, Any]      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					Dict      ,       lowercase_						:					Tuple			) ->						Dict:
             """simple docstring"""




             if self.task in ["default", "seq2seq-lm"]:
                    _lowerCamelCase	:		Union[str, Any]          =super()._flatten_past_key_values_(lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_			)
             else:
                    _lowerCamelCase	:		List[str]          =super(lowercase_      ,       self			)._flatten_past_key_values_(
                        lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_			)


