



import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


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

__SCREAMING_SNAKE_CASE           =  {
    """b0""": efficientnet.EfficientNetBa,
    """b1""": efficientnet.EfficientNetBa,
    """b2""": efficientnet.EfficientNetBa,
    """b3""": efficientnet.EfficientNetBa,
    """b4""": efficientnet.EfficientNetBa,
    """b5""": efficientnet.EfficientNetBa,
    """b6""": efficientnet.EfficientNetBa,
    """b7""": efficientnet.EfficientNetBa,
}

__SCREAMING_SNAKE_CASE           =  {
    """b0""": {
        """hidden_dim""": 1280,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 224,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 1280,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 240,
        """dropout_rate""": 0.2,
        """dw_padding""": [16],
    },
    """b2""": {
        """hidden_dim""": 1408,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 260,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 16],
    },
    """b3""": {
        """hidden_dim""": 1536,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 300,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 18],
    },
    """b4""": {
        """hidden_dim""": 1792,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 380,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 2048,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 456,
        """dropout_rate""": 0.4,
        """dw_padding""": [13, 27],
    },
    """b6""": {
        """hidden_dim""": 2304,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 528,
        """dropout_rate""": 0.5,
        """dw_padding""": [31],
    },
    """b7""": {
        """hidden_dim""": 2560,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 600,
        """dropout_rate""": 0.5,
        """dw_padding""": [18],
    },
}




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					List[str]              =						EfficientNetConfig()
			A							:					Optional[Any]              =						CONFIG_MAP[model_name]["hidden_dim"]
			A							:					Tuple              =						CONFIG_MAP[model_name]["width_coef"]
			A							:					List[Any]              =						CONFIG_MAP[model_name]["depth_coef"]
			A							:					Optional[Any]              =						CONFIG_MAP[model_name]["image_size"]
			A							:					int              =						CONFIG_MAP[model_name]["dropout_rate"]
			A							:					Optional[Any]              =						CONFIG_MAP[model_name]["dw_padding"]

			A							:					List[Any]              =						"huggingface/label-files"
			A							:					int              =						"imagenet-1k-id2label.json"
			A							:					Dict              =						1000
			A							:					int              =						json.load(open(hf_hub_download(_lowerCamelCase ,    _lowerCamelCase ,    repo_type="dataset"    ) ,    "r"    )    )
			A							:					Dict              =						{int(_lowerCamelCase    ): v for k, v in idalabel.items()}

			A							:					Dict              =						idalabel
			A							:					Optional[Any]              =						{v: k for k, v in idalabel.items()}
			return config




def 	UpperCAmelCase   (						):
			A							:					Union[str, Any]              =						"http://images.cocodataset.org/val2017/000000039769.jpg"
			A							:					Dict              =						Image.open(requests.get(_lowerCamelCase ,    stream=_lowerCamelCase    ).raw    )
			return im




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					Optional[int]              =						CONFIG_MAP[model_name]["image_size"]
			A							:					str              =						EfficientNetImageProcessor(
			    size={"height": size, "width": size} ,    image_mean=[0.4_85, 0.4_56, 0.4_06] ,    image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63] ,    do_center_crop=_lowerCamelCase ,    )
			return preprocessor




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					Any              =						[v.split("_"    )[0].split("block"    )[1] for v in original_param_names if v.startswith("block"    )]
			A							:					List[Any]              =						sorted(set(_lowerCamelCase    )    )
			A							:					Optional[int]              =						len(_lowerCamelCase    )
			A							:					List[str]              =						{b: str(_lowerCamelCase    ) for b, i in zip(_lowerCamelCase ,    range(_lowerCamelCase    )    )}

			A							:					Tuple              =						[]
			rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight")    )
			rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight")    )
			rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias")    )
			rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean")    )
			rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var")    )

			for b in block_names:
						A							:					List[Any]              =						block_name_mapping[b]
						rename_keys.append((f"""block{b}_expand_conv/kernel:0""", f"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""")    )
						rename_keys.append((f"""block{b}_expand_bn/gamma:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""")    )
						rename_keys.append((f"""block{b}_expand_bn/beta:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""")    )
						rename_keys.append(
						    (f"""block{b}_expand_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""")    )
						rename_keys.append(
						    (f"""block{b}_expand_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""")    )
						rename_keys.append(
						    (f"""block{b}_dwconv/depthwise_kernel:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""")    )
						rename_keys.append((f"""block{b}_bn/gamma:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""")    )
						rename_keys.append((f"""block{b}_bn/beta:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""")    )
						rename_keys.append(
						    (f"""block{b}_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""")    )
						rename_keys.append(
						    (f"""block{b}_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""")    )

						rename_keys.append((f"""block{b}_se_reduce/kernel:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""")    )
						rename_keys.append((f"""block{b}_se_reduce/bias:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""")    )
						rename_keys.append((f"""block{b}_se_expand/kernel:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""")    )
						rename_keys.append((f"""block{b}_se_expand/bias:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""")    )
						rename_keys.append(
						    (f"""block{b}_project_conv/kernel:0""", f"""encoder.blocks.{hf_b}.projection.project_conv.weight""")    )
						rename_keys.append((f"""block{b}_project_bn/gamma:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.weight""")    )
						rename_keys.append((f"""block{b}_project_bn/beta:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.bias""")    )
						rename_keys.append(
						    (f"""block{b}_project_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""")    )
						rename_keys.append(
						    (f"""block{b}_project_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.running_var""")    )

			rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight")    )
			rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight")    )
			rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias")    )
			rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean")    )
			rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var")    )

			A							:					List[Any]              =						{}
			for item in rename_keys:
						if item[0] in original_param_names:
									A							:					Tuple              =						"efficientnet." + item[1]

			A							:					Any              =						"classifier.weight"
			A							:					Union[str, Any]              =						"classifier.bias"
			return key_mapping




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			for key, value in tf_params.items():
						if "normalization" in key:
									continue

						A							:					List[str]              =						key_mapping[key]
						if "_conv" in key and "kernel" in key:
									A							:					List[str]              =						torch.from_numpy(_lowerCamelCase    ).permute(3 ,    2 ,    0 ,    1    )
						elif "depthwise_kernel" in key:
									A							:					List[Any]              =						torch.from_numpy(_lowerCamelCase    ).permute(2 ,    3 ,    0 ,    1    )
						elif "kernel" in key:
									A							:					List[str]              =						torch.from_numpy(np.transpose(_lowerCamelCase    )    )
						else:
									A							:					Union[str, Any]              =						torch.from_numpy(_lowerCamelCase    )

						# Replace HF parameters with original TF model parameters
						assert hf_params[hf_key].shape == new_hf_value.shape
						hf_params[hf_key].copy_(_lowerCamelCase    )




@torch.no_grad()
def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					int              =						model_classes[model_name](
			    include_top=_lowerCamelCase ,    weights="imagenet" ,    input_tensor=_lowerCamelCase ,    input_shape=_lowerCamelCase ,    pooling=_lowerCamelCase ,    classes=1000 ,    classifier_activation="softmax" ,    )

			A							:					int              =						original_model.trainable_variables
			A							:					int              =						original_model.non_trainable_variables
			A							:					Optional[int]              =						{param.name: param.numpy() for param in tf_params}
			for param in tf_non_train_params:
						A							:					Optional[int]              =						param.numpy()
			A							:					Optional[int]              =						list(tf_params.keys()    )

			# Load HuggingFace model
			A							:					Optional[int]              =						get_efficientnet_config(_lowerCamelCase    )
			A							:					str              =						EfficientNetForImageClassification(_lowerCamelCase    ).eval()
			A							:					Optional[int]              =						hf_model.state_dict()

			# Create src-to-dst parameter name mapping dictionary
			print("Converting parameters..."    )
			A							:					Optional[Any]              =						rename_keys(_lowerCamelCase    )
			replace_params(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )

			# Initialize preprocessor and preprocess input image
			A							:					Optional[Any]              =						convert_image_processor(_lowerCamelCase    )
			A							:					Any              =						preprocessor(images=prepare_img() ,    return_tensors="pt"    )

			# HF model inference
			hf_model.eval()
			with torch.no_grad():
						A							:					List[Any]              =						hf_model(**_lowerCamelCase    )
			A							:					Optional[Any]              =						outputs.logits.detach().numpy()

			# Original model inference
			A							:					Optional[Any]              =						False
			A							:					Any              =						CONFIG_MAP[model_name]["image_size"]
			A							:					Dict              =						prepare_img().resize((image_size, image_size) ,    resample=PIL.Image.NEAREST    )
			A							:					Tuple              =						image.img_to_array(_lowerCamelCase    )
			A							:					Tuple              =						np.expand_dims(_lowerCamelCase ,    axis=0    )
			A							:					Optional[Any]              =						original_model.predict(_lowerCamelCase    )

			# Check whether original and HF model outputs match  -> np.allclose
			assert np.allclose(_lowerCamelCase ,    _lowerCamelCase ,    atol=1e-3    ), "The predicted logits are not the same."
			print("Model outputs match!"    )

			if save_model:
						# Create folder to save model
						if not os.path.isdir(_lowerCamelCase    ):
									os.mkdir(_lowerCamelCase    )
						# Save converted model and image processor
						hf_model.save_pretrained(_lowerCamelCase    )
						preprocessor.save_pretrained(_lowerCamelCase    )

			if push_to_hub:
						# Push model and image processor to hub
						print(f"""Pushing converted {model_name} to the hub..."""    )
						A							:					int              =						f"""efficientnet-{model_name}"""
						preprocessor.push_to_hub(_lowerCamelCase    )
						hf_model.push_to_hub(_lowerCamelCase    )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--model_name""",
							    default="""b0""",
							    type=str,
							    help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default="""hf_model""",
							    type=str,
							    help="""Path to the output PyTorch model directory.""",
							)
							parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
							parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")

							__SCREAMING_SNAKE_CASE           =  parser.parse_args()
							convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)




import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE           =  {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE           =  {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

__SCREAMING_SNAKE_CASE           =  {
    """allenai/led-base-16384""": 16384,
}

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  VOCAB_FILES_NAMES
						a__   =  PRETRAINED_VOCAB_FILES_MAP
						a__   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__   =  LEDTokenizer
						a__   =  ["input_ids", "attention_mask"]







						def __init__(	self      :			int	,	__lowerCamelCase      :			List[str]=None	,	__lowerCamelCase      :			Optional[int]=None	,	__lowerCamelCase      :			Optional[int]=None	,	__lowerCamelCase      :			str="replace"	,	__lowerCamelCase      :			Optional[int]="<s>"	,	__lowerCamelCase      :			str="</s>"	,	__lowerCamelCase      :			Any="</s>"	,	__lowerCamelCase      :			Dict="<s>"	,	__lowerCamelCase      :			Optional[int]="<unk>"	,	__lowerCamelCase      :			Union[str, Any]="<pad>"	,	__lowerCamelCase      :			Dict="<mask>"	,	__lowerCamelCase      :			Union[str, Any]=False	,	__lowerCamelCase      :			str=True	,	**__lowerCamelCase      :			Union[str, Any]	,	)      ->				Optional[int]:
									super().__init__(
									    __lowerCamelCase	,	__lowerCamelCase	,	tokenizer_file=__lowerCamelCase	,	errors=__lowerCamelCase	,	bos_token=__lowerCamelCase	,	eos_token=__lowerCamelCase	,	sep_token=__lowerCamelCase	,	cls_token=__lowerCamelCase	,	unk_token=__lowerCamelCase	,	pad_token=__lowerCamelCase	,	mask_token=__lowerCamelCase	,	add_prefix_space=__lowerCamelCase	,	trim_offsets=__lowerCamelCase	,	**__lowerCamelCase	,	)

									A							:					Union[str, Any]              =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
									if pre_tok_state.get("add_prefix_space"	,	__lowerCamelCase ) != add_prefix_space:
												A							:					Any              =						getattr(__lowerCamelCase	,	pre_tok_state.pop("type" ) )
												A							:					Any              =						add_prefix_space
												A							:					Tuple              =						pre_tok_class(**__lowerCamelCase )

									A							:					str              =						add_prefix_space

									# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
									A							:					List[str]              =						"post_processor"
									A							:					Union[str, Any]              =						getattr(self.backend_tokenizer	,	__lowerCamelCase	,	__lowerCamelCase )
									if tokenizer_component_instance:
												A							:					Dict              =						json.loads(tokenizer_component_instance.__getstate__() )

												# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
												if "sep" in state:
															A							:					Union[str, Any]              =						tuple(state["sep"] )
												if "cls" in state:
															A							:					str              =						tuple(state["cls"] )

												A							:					int              =						False

												if state.get("add_prefix_space"	,	__lowerCamelCase ) != add_prefix_space:
															A							:					List[Any]              =						add_prefix_space
															A							:					Dict              =						True

												if state.get("trim_offsets"	,	__lowerCamelCase ) != trim_offsets:
															A							:					Dict              =						trim_offsets
															A							:					str              =						True

												if changes_to_apply:
															A							:					int              =						getattr(__lowerCamelCase	,	state.pop("type" ) )
															A							:					Dict              =						component_class(**__lowerCamelCase )
															setattr(self.backend_tokenizer	,	__lowerCamelCase	,	__lowerCamelCase )







						@property
						# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				str:
									if self._mask_token is None:
												if self.verbose:
															logger.error("Using mask_token, but it is not set yet." )
												return None
									return str(self._mask_token )







						@mask_token.setter
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			Any )      ->				Dict:
									A							:					Tuple              =						AddedToken(__lowerCamelCase	,	lstrip=__lowerCamelCase	,	rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase	,	__lowerCamelCase ) else value
									A							:					Tuple              =						value







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	*__lowerCamelCase      :			Optional[Any]	,	**__lowerCamelCase      :			List[str] )      ->				BatchEncoding:
									A							:					List[str]              =						kwargs.get("is_split_into_words"	,	__lowerCamelCase )

									if is_split_into_words and not self.add_prefix_space:
												raise ValueError(
												    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs." )

									return super()._batch_encode_plus(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	*__lowerCamelCase      :			str	,	**__lowerCamelCase      :			Optional[Any] )      ->				BatchEncoding:
									A							:					List[str]              =						kwargs.get("is_split_into_words"	,	__lowerCamelCase )

									if is_split_into_words and not self.add_prefix_space:
												raise ValueError(
												    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs." )

									return super()._encode_plus(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			Optional[str] = None )      ->				Tuple[str]:
									A							:					Optional[Any]              =						self._tokenizer.model.save(__lowerCamelCase	,	name=__lowerCamelCase )
									return tuple(__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			List[Any]=None )      ->				List[str]:
									A							:					Optional[int]              =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
												return output

									return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			List[int]	,	__lowerCamelCase      :			Optional[List[int]] = None )      ->				List[int]:
									A							:					str              =						[self.sep_token_id]
									A							:					int              =						[self.cls_token_id]

									if token_ids_a is None:
												return len(cls + token_ids_a + sep ) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			Union[Dict[str, EncodedInput], BatchEncoding]	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			PaddingStrategy = PaddingStrategy.DO_NOT_PAD	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			Optional[bool] = None	,	)      ->				dict:
									A							:					Dict              =						super()._pad(
									    encoded_inputs=__lowerCamelCase	,	max_length=__lowerCamelCase	,	padding_strategy=__lowerCamelCase	,	pad_to_multiple_of=__lowerCamelCase	,	return_attention_mask=__lowerCamelCase	,	)

									# Load from model defaults
									if return_attention_mask is None:
												A							:					List[Any]              =						"attention_mask" in self.model_input_names

									if return_attention_mask and "global_attention_mask" in encoded_inputs:
												A							:					Optional[int]              =						encoded_inputs[self.model_input_names[0]]
												# `global_attention_mask` need to have the same length as other (sequential) inputs.
												A							:					Tuple              =						len(encoded_inputs["global_attention_mask"] ) != len(__lowerCamelCase )

												if needs_to_be_padded:
															A							:					Any              =						len(__lowerCamelCase ) - len(encoded_inputs["global_attention_mask"] )

															if self.padding_side == "right":
																		# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																		A							:					Tuple              =						(
																		    encoded_inputs["global_attention_mask"] + [-1] * difference
																		)
															elif self.padding_side == "left":
																		A							:					Tuple              =						[-1] * difference + encoded_inputs[
																		    "global_attention_mask"
																		]
															else:
																		raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )

									return encoded_inputs