




import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling





def        __UpperCAmelCase						(							lowerCamelCase_ :     Tuple  ,							lowerCamelCase_ :     Optional[int]  )       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : int					  =      old_name

			if "patch_embed" in old_name:
						SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[int]					  =      old_name.split('.'  )

						if layer == "0":
									SCREAMING_SNAKE_CASE_   : Any					  =      old_name.replace('0'  ,							'convolution1'  )
						elif layer == "1":
									SCREAMING_SNAKE_CASE_   : List[str]					  =      old_name.replace('1'  ,							'batchnorm_before'  )
						elif layer == "3":
									SCREAMING_SNAKE_CASE_   : Any					  =      old_name.replace('3'  ,							'convolution2'  )
						else:
									SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      old_name.replace('4'  ,							'batchnorm_after'  )

			if "network" in old_name and re.search(R'\d\.\d'  ,							lowerCamelCase_  ):
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      R'\b\d{2}\b'
						if bool(re.search(lowerCamelCase_  ,							lowerCamelCase_  )  ):
									SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      re.search(R'\d\.\d\d.'  ,							lowerCamelCase_  ).group()
						else:
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      re.search(R'\d\.\d.'  ,							lowerCamelCase_  ).group()
						if int(match[0]  ) < 6:
									SCREAMING_SNAKE_CASE_   : int					  =      old_name.replace(lowerCamelCase_  ,							''  )
									SCREAMING_SNAKE_CASE_   : Dict					  =      trimmed_name.replace('network'  ,							match[0] + '.meta4D_layers.blocks.' + match[2:-1]  )
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      'intermediate_stages.' + trimmed_name
						else:
									SCREAMING_SNAKE_CASE_   : Any					  =      old_name.replace(lowerCamelCase_  ,							''  )
									if int(match[2]  ) < num_meta4D_last_stage:
												SCREAMING_SNAKE_CASE_   : str					  =      trimmed_name.replace('network'  ,							'meta4D_layers.blocks.' + match[2]  )
									else:
												SCREAMING_SNAKE_CASE_   : Dict					  =      str(int(match[2]  ) - num_meta4D_last_stage  )
												SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      trimmed_name.replace('network'  ,							'meta3D_layers.blocks.' + layer_index  )
												if "norm1" in old_name:
															SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      trimmed_name.replace('norm1'  ,							'layernorm1'  )
												elif "norm2" in old_name:
															SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      trimmed_name.replace('norm2'  ,							'layernorm2'  )
												elif "fc1" in old_name:
															SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      trimmed_name.replace('fc1'  ,							'linear_in'  )
												elif "fc2" in old_name:
															SCREAMING_SNAKE_CASE_   : Any					  =      trimmed_name.replace('fc2'  ,							'linear_out'  )

									SCREAMING_SNAKE_CASE_   : List[Any]					  =      'last_stage.' + trimmed_name

			elif "network" in old_name and re.search(R'.\d.'  ,							lowerCamelCase_  ):
						SCREAMING_SNAKE_CASE_   : str					  =      old_name.replace('network'  ,							'intermediate_stages'  )

			if "fc" in new_name:
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      new_name.replace('fc'  ,							'convolution'  )
			elif ("norm1" in new_name) and ("layernorm1" not in new_name):
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      new_name.replace('norm1'  ,							'batchnorm_before'  )
			elif ("norm2" in new_name) and ("layernorm2" not in new_name):
						SCREAMING_SNAKE_CASE_   : Tuple					  =      new_name.replace('norm2'  ,							'batchnorm_after'  )
			if "proj" in new_name:
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      new_name.replace('proj'  ,							'projection'  )
			if "dist_head" in new_name:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      new_name.replace('dist_head'  ,							'distillation_classifier'  )
			elif "head" in new_name:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      new_name.replace('head'  ,							'classifier'  )
			elif "patch_embed" in new_name:
						SCREAMING_SNAKE_CASE_   : Any					  =      'efficientformer.' + new_name
			elif new_name == "norm.weight" or new_name == "norm.bias":
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      new_name.replace('norm'  ,							'layernorm'  )
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      'efficientformer.' + new_name
			else:
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      'efficientformer.encoder.' + new_name

			return new_name





def        __UpperCAmelCase						(							lowerCamelCase_ :     Optional[Any]  ,							lowerCamelCase_ :     str  )       ->  Dict:







			"""simple docstring"""





			for key in checkpoint.copy().keys():
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      checkpoint.pop(lowerCamelCase_  )
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      val

			return checkpoint





def        __UpperCAmelCase						(							)       ->  List[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Dict					  =      'http://images.cocodataset.org/val2017/000000039769.jpg'
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      Image.open(requests.get(lowerCamelCase_  ,							stream=lowerCamelCase_  ).raw  )

			return image





def        __UpperCAmelCase						(							lowerCamelCase_ :     Path  ,							lowerCamelCase_ :     Path  ,							lowerCamelCase_ :     Path  ,							lowerCamelCase_ :     bool  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Dict					  =      torch.load(lowerCamelCase_  ,							map_location='cpu'  )['model']
			SCREAMING_SNAKE_CASE_   : Any					  =      EfficientFormerConfig.from_json_file(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      EfficientFormerForImageClassificationWithTeacher(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      '_'.join(checkpoint_path.split('/'  )[-1].split('.'  )[0].split('_'  )[:-1]  )

			SCREAMING_SNAKE_CASE_   : Dict					  =      config.depths[-1] - config.num_metaad_blocks + 1
			SCREAMING_SNAKE_CASE_   : Tuple					  =      convert_torch_checkpoint(lowerCamelCase_  ,							lowerCamelCase_  )

			model.load_state_dict(lowerCamelCase_  )
			model.eval()

			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      {
			    'bilinear': PILImageResampling.BILINEAR,
			    'bicubic': PILImageResampling.BICUBIC,
			    'nearest': PILImageResampling.NEAREST,
			}

			# prepare image
			SCREAMING_SNAKE_CASE_   : List[str]					  =      prepare_img()
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      2_56
			SCREAMING_SNAKE_CASE_   : Any					  =      2_24
			SCREAMING_SNAKE_CASE_   : Any					  =      EfficientFormerImageProcessor(
			    size={'shortest_edge': image_size}  ,							crop_size={'height': crop_size, 'width': crop_size}  ,							resample=pillow_resamplings['bicubic']  ,							)
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      processor(images=lowerCamelCase_  ,							return_tensors='pt'  ).pixel_values

			# original processing pipeline
			SCREAMING_SNAKE_CASE_   : List[str]					  =      Compose(
			    [
			        Resize(lowerCamelCase_  ,							interpolation=pillow_resamplings['bicubic']  ),
			        CenterCrop(lowerCamelCase_  ),
			        ToTensor(),
			        Normalize(lowerCamelCase_  ,							lowerCamelCase_  ),
			    ]  )
			SCREAMING_SNAKE_CASE_   : Any					  =      image_transforms(lowerCamelCase_  ).unsqueeze(0  )

			assert torch.allclose(lowerCamelCase_  ,							lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : Dict					  =      model(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : str					  =      outputs.logits

			SCREAMING_SNAKE_CASE_   : Any					  =      (1, 10_00)

			if "l1" in model_name:
						SCREAMING_SNAKE_CASE_   : str					  =      torch.Tensor(
						    [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]  )
						assert torch.allclose(logits[0, :10]  ,							lowerCamelCase_  ,							atol=1E-3  )
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      torch.Tensor(
						    [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]  )
						assert torch.allclose(logits[0, :10]  ,							lowerCamelCase_  ,							atol=1E-3  )
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      torch.Tensor(
						    [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]  )
						assert logits.shape == expected_shape
			else:
						raise ValueError(
						    F'Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'  )

			# Save Checkpoints
			Path(lowerCamelCase_  ).mkdir(exist_ok=lowerCamelCase_  )
			model.save_pretrained(lowerCamelCase_  )
			print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'  )
			processor.save_pretrained(lowerCamelCase_  )
			print(F'Processor successfuly saved at {pytorch_dump_path}'  )

			if push_to_hub:
						print('Pushing model to the hub...'  )

						model.push_to_hub(
						    repo_id=F'Bearnardd/{pytorch_dump_path}'  ,							commit_message='Add model'  ,							use_temp_dir=lowerCamelCase_  ,							)
						processor.push_to_hub(
						    repo_id=F'Bearnardd/{pytorch_dump_path}'  ,							commit_message='Add image processor'  ,							use_temp_dir=lowerCamelCase_  ,							)


if __name__ == "__main__":
						UpperCamelCase__ :   Optional[int]						=							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--pytorch_model_path''',
						    default=None,
						    type=str,
						    required=True,
						    help='''Path to EfficientFormer pytorch checkpoint.''',
						)
						parser.add_argument(
						    '''--config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help='''The json file for EfficientFormer model config.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)

						parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
						parser.add_argument(
						    '''--no-push_to_hub''',
						    dest='''push_to_hub''',
						    action='''store_false''',
						    help='''Do not push model and image processor to the hub''',
						)
						parser.set_defaults(push_to_hub=True)

						UpperCamelCase__ :   List[str]						=							parser.parse_args()
						convert_efficientformer_checkpoint(
						    checkpoint_path=args.pytorch_model_path,
						    efficientformer_config_file=args.config_file,
						    pytorch_dump_path=args.pytorch_dump_path,
						    push_to_hub=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 ...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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "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  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Union[str, Any]								=       "blenderbot-small"
 __lowerCamelCase    :				Optional[Any]								=       ["past_key_values"]
 __lowerCamelCase    :				str								=       {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




 def __init__(						self      ,  _lowerCAmelCase=50265      ,  _lowerCAmelCase=512      ,  _lowerCAmelCase=8      ,  _lowerCAmelCase=2048      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=8      ,  _lowerCAmelCase=2048      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=512      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase=1      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=2      ,  **_lowerCAmelCase      ,  )     ->				Dict:
     _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      ,  forced_eos_token_id=_lowerCAmelCase      ,  **_lowerCAmelCase      ,  )







class  lowerCAmelCase_		(		__magic_name__		):




 @property
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     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":
         # TODO: figure this case out.
         _lowerCAmelCase						      =   OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ]		)
         if self.use_past:
             _lowerCAmelCase      ,      _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
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     if self.task in ["default", "seq2seq-lm"]:
         _lowerCAmelCase						      =   super().outputs
     else:
         _lowerCAmelCase						      =   super(_lowerCAmelCase      ,  self		).outputs
         if self.use_past:
             _lowerCAmelCase      ,      _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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  )     ->				Mapping[str, Any]:
     _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      ,      _lowerCAmelCase						      =   common_inputs["input_ids"].shape
         _lowerCAmelCase						      =   common_inputs["decoder_input_ids"].shape[1]
         _lowerCAmelCase      ,      _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 + 3
         _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						      =   []
         # If the number of encoder and decoder layers are present in the model configuration, both are considered
         _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.num_layers
         _lowerCAmelCase						      =   min(_lowerCAmelCase      ,  _lowerCAmelCase		)
         _lowerCAmelCase						      =   max(_lowerCAmelCase      ,  _lowerCAmelCase		) - min_num_layers
         _lowerCAmelCase						      =   "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						      =   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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  )     ->				Mapping[str, Any]:
     _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      ,      _lowerCAmelCase						      =   common_inputs["input_ids"].shape
         # Not using the same length for past_key_values
         _lowerCAmelCase						      =   seqlen + 2
         _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.num_layers
         _lowerCAmelCase      ,      _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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  )     ->				Mapping[str, Any]:
     # 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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = -1      ,  _lowerCAmelCase = False      ,  _lowerCAmelCase = None      ,  )     ->				Mapping[str, Any]:
     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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Dict:
     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		)


