




import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase						:   Dict		=   logging.get_logger(__name__)



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[int]	):




 """simple docstring"""







 print("Loading config file..."	)

 def flatten_yaml_as_dict(_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   str=""						,							_lowerCAmelCase							:   Any="."	):
  UpperCAmelCase__							 =						[]
  for k, v in d.items():
   UpperCAmelCase__							 =						parent_key + sep + k if parent_key else k
   if isinstance(_lowerCAmelCase						,							collections.abc.MutableMapping	):
    items.extend(flatten_yaml_as_dict(_lowerCAmelCase						,							_lowerCAmelCase						,							sep=_lowerCAmelCase	).items()	)
   else:
    items.append((new_key, v)	)
  return dict(_lowerCAmelCase	)

 UpperCAmelCase__							 =						argparse.Namespace()
 with open(_lowerCAmelCase						,							"r"	) as yaml_file:
  try:
   UpperCAmelCase__							 =						yaml.load(_lowerCAmelCase						,							Loader=yaml.FullLoader	)

   UpperCAmelCase__							 =						flatten_yaml_as_dict(_lowerCAmelCase	)
   for k, v in flat_cfg.items():
    setattr(_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase	)
  except yaml.YAMLError as exc:
   logger.error("Error while loading config file: {}. Error message: {}".format(_lowerCAmelCase						,							str(_lowerCAmelCase	)	)	)
 return config



def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[Any]						,							_lowerCAmelCase							:   List[str]	):




 """simple docstring"""







 UpperCAmelCase__							 =						MobileViTVaConfig()

 UpperCAmelCase__							 =						False

 # dataset
 if task_name.startswith("imagenet1k_"	):
  UpperCAmelCase__							 =						1000
  if int(task_name.strip().split("_"	)[-1]	) == 384:
   UpperCAmelCase__							 =						384
  else:
   UpperCAmelCase__							 =						256
  UpperCAmelCase__							 =						"imagenet-1k-id2label.json"
 elif task_name.startswith("imagenet21k_to_1k_"	):
  UpperCAmelCase__							 =						2_1000
  if int(task_name.strip().split("_"	)[-1]	) == 384:
   UpperCAmelCase__							 =						384
  else:
   UpperCAmelCase__							 =						256
  UpperCAmelCase__							 =						"imagenet-22k-id2label.json"
 elif task_name.startswith("ade20k_"	):
  UpperCAmelCase__							 =						151
  UpperCAmelCase__							 =						512
  UpperCAmelCase__							 =						"ade20k-id2label.json"
  UpperCAmelCase__							 =						True
 elif task_name.startswith("voc_"	):
  UpperCAmelCase__							 =						21
  UpperCAmelCase__							 =						512
  UpperCAmelCase__							 =						"pascal-voc-id2label.json"
  UpperCAmelCase__							 =						True

 # orig_config
 UpperCAmelCase__							 =						load_orig_config_file(_lowerCAmelCase	)
 assert getattr(_lowerCAmelCase						,							"model.classification.name"						,							-1	) == "mobilevit_v2", "Invalid model"
 UpperCAmelCase__							 =						getattr(_lowerCAmelCase						,							"model.classification.mitv2.width_multiplier"						,							1.0	)
 assert (
     getattr(_lowerCAmelCase						,							"model.classification.mitv2.attn_norm_layer"						,							-1	) == "layer_norm_2d"
 ), "Norm layers other than layer_norm_2d is not supported"
 UpperCAmelCase__							 =						getattr(_lowerCAmelCase						,							"model.classification.activation.name"						,							"swish"	)
 # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

 if is_segmentation_model:
  UpperCAmelCase__							 =						getattr(_lowerCAmelCase						,							"model.segmentation.output_stride"						,							16	)
  if "_deeplabv3" in task_name:
   UpperCAmelCase__							 =						getattr(_lowerCAmelCase						,							"model.segmentation.deeplabv3.aspp_rates"						,							[12, 24, 36]	)
   UpperCAmelCase__							 =						getattr(_lowerCAmelCase						,							"model.segmentation.deeplabv3.aspp_out_channels"						,							512	)
   UpperCAmelCase__							 =						getattr(_lowerCAmelCase						,							"model.segmentation.deeplabv3.aspp_dropout"						,							0.1	)

    # id2label
 UpperCAmelCase__							 =						"huggingface/label-files"
 UpperCAmelCase__							 =						json.load(open(hf_hub_download(_lowerCAmelCase						,							_lowerCAmelCase						,							repo_type="dataset"	)						,							"r"	)	)
 UpperCAmelCase__							 =						{int(_lowerCAmelCase	): v for k, v in idalabel.items()}
 UpperCAmelCase__							 =						idalabel
 UpperCAmelCase__							 =						{v: k for k, v in idalabel.items()}

 return config



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Union[str, Any]						,							_lowerCAmelCase							:   Union[str, Any]						,							_lowerCAmelCase							:   Optional[int]	):




 """simple docstring"""







 UpperCAmelCase__							 =						dct.pop(_lowerCAmelCase	)
 UpperCAmelCase__							 =						val



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   Tuple=False	):




 """simple docstring"""







 if base_model:
  UpperCAmelCase__							 =						""
 else:
  UpperCAmelCase__							 =						"mobilevitv2."

 UpperCAmelCase__							 =						[]
 for k in state_dict.keys():
  if k[:8] == "encoder.":
   UpperCAmelCase__							 =						k[8:]
  else:
   UpperCAmelCase__							 =						k

  if ".block." in k:
   UpperCAmelCase__							 =						k_new.replace(".block."						,							"."	)
  if ".conv." in k:
   UpperCAmelCase__							 =						k_new.replace(".conv."						,							".convolution."	)
  if ".norm." in k:
   UpperCAmelCase__							 =						k_new.replace(".norm."						,							".normalization."	)

  if "conv_1." in k:
   UpperCAmelCase__							 =						k_new.replace("conv_1."						,							F'''{model_prefix}conv_stem.'''	)
  for i in [1, 2]:
   if F'''layer_{i}.''' in k:
    UpperCAmelCase__							 =						k_new.replace(F'''layer_{i}.'''						,							F'''{model_prefix}encoder.layer.{i-1}.layer.'''	)
  if ".exp_1x1." in k:
   UpperCAmelCase__							 =						k_new.replace(".exp_1x1."						,							".expand_1x1."	)
  if ".red_1x1." in k:
   UpperCAmelCase__							 =						k_new.replace(".red_1x1."						,							".reduce_1x1."	)

  for i in [3, 4, 5]:
   if F'''layer_{i}.0.''' in k:
    UpperCAmelCase__							 =						k_new.replace(F'''layer_{i}.0.'''						,							F'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.'''	)
   if F'''layer_{i}.1.local_rep.0.''' in k:
    UpperCAmelCase__							 =						k_new.replace(F'''layer_{i}.1.local_rep.0.'''						,							F'''{model_prefix}encoder.layer.{i-1}.conv_kxk.'''	)
   if F'''layer_{i}.1.local_rep.1.''' in k:
    UpperCAmelCase__							 =						k_new.replace(F'''layer_{i}.1.local_rep.1.'''						,							F'''{model_prefix}encoder.layer.{i-1}.conv_1x1.'''	)

  for i in [3, 4, 5]:
   if i == 3:
    UpperCAmelCase__							 =						[0, 1]
   elif i == 4:
    UpperCAmelCase__							 =						[0, 1, 2, 3]
   elif i == 5:
    UpperCAmelCase__							 =						[0, 1, 2]

   for j in j_in:
    if F'''layer_{i}.1.global_rep.{j}.''' in k:
     UpperCAmelCase__							 =						k_new.replace(
         F'''layer_{i}.1.global_rep.{j}.'''						,							F'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'''	)
   if F'''layer_{i}.1.global_rep.{j+1}.''' in k:
    UpperCAmelCase__							 =						k_new.replace(
        F'''layer_{i}.1.global_rep.{j+1}.'''						,							F'''{model_prefix}encoder.layer.{i-1}.layernorm.'''	)

   if F'''layer_{i}.1.conv_proj.''' in k:
    UpperCAmelCase__							 =						k_new.replace(F'''layer_{i}.1.conv_proj.'''						,							F'''{model_prefix}encoder.layer.{i-1}.conv_projection.'''	)

  if "pre_norm_attn.0." in k:
   UpperCAmelCase__							 =						k_new.replace("pre_norm_attn.0."						,							"layernorm_before."	)
  if "pre_norm_attn.1." in k:
   UpperCAmelCase__							 =						k_new.replace("pre_norm_attn.1."						,							"attention."	)
  if "pre_norm_ffn.0." in k:
   UpperCAmelCase__							 =						k_new.replace("pre_norm_ffn.0."						,							"layernorm_after."	)
  if "pre_norm_ffn.1." in k:
   UpperCAmelCase__							 =						k_new.replace("pre_norm_ffn.1."						,							"ffn.conv1."	)
  if "pre_norm_ffn.3." in k:
   UpperCAmelCase__							 =						k_new.replace("pre_norm_ffn.3."						,							"ffn.conv2."	)

  if "classifier.1." in k:
   UpperCAmelCase__							 =						k_new.replace("classifier.1."						,							"classifier."	)

  if "seg_head." in k:
   UpperCAmelCase__							 =						k_new.replace("seg_head."						,							"segmentation_head."	)
  if ".aspp_layer." in k:
   UpperCAmelCase__							 =						k_new.replace(".aspp_layer."						,							"."	)
  if ".aspp_pool." in k:
   UpperCAmelCase__							 =						k_new.replace(".aspp_pool."						,							"."	)

  rename_keys.append((k, k_new)	)
 return rename_keys



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						[]
 for k in state_dict.keys():
  if k.startswith("seg_head.aux_head."	):
   keys_to_ignore.append(_lowerCAmelCase	)
 for k in keys_to_ignore:
  state_dict.pop(_lowerCAmelCase						,							_lowerCAmelCase	)



def 						lowerCAmelCase		(		):




 """simple docstring"""







 UpperCAmelCase__							 =						"http://images.cocodataset.org/val2017/000000039769.jpg"
 # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
 UpperCAmelCase__							 =						Image.open(requests.get(_lowerCAmelCase						,							stream=_lowerCAmelCase	).raw	)
 return im



@torch.no_grad()
def 						lowerCAmelCase		(		_lowerCAmelCase							:   Optional[int]						,							_lowerCAmelCase							:   Tuple						,							_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   Tuple	):




 """simple docstring"""







 UpperCAmelCase__							 =						get_mobilevitva_config(_lowerCAmelCase						,							_lowerCAmelCase	)

 # load original state_dict
 UpperCAmelCase__							 =						torch.load(_lowerCAmelCase						,							map_location="cpu"	)

 # load huggingface model
 if task_name.startswith("ade20k_"	) or task_name.startswith("voc_"	):
  UpperCAmelCase__							 =						MobileViTVaForSemanticSegmentation(_lowerCAmelCase	).eval()
  UpperCAmelCase__							 =						False
 else:
  UpperCAmelCase__							 =						MobileViTVaForImageClassification(_lowerCAmelCase	).eval()
  UpperCAmelCase__							 =						False

 # remove and rename some keys of load the original model
 UpperCAmelCase__							 =						checkpoint
 remove_unused_keys(_lowerCAmelCase	)
 UpperCAmelCase__							 =						create_rename_keys(_lowerCAmelCase						,							base_model=_lowerCAmelCase	)
 for rename_key_src, rename_key_dest in rename_keys:
  rename_key(_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase	)

 # load modified state_dict
 model.load_state_dict(_lowerCAmelCase	)

 # Check outputs on an image, prepared by MobileViTImageProcessor
 UpperCAmelCase__							 =						MobileViTImageProcessor(crop_size=config.image_size						,							size=config.image_size + 32	)
 UpperCAmelCase__							 =						image_processor(images=prepare_img()						,							return_tensors="pt"	)
 UpperCAmelCase__							 =						model(**_lowerCAmelCase	)

 # verify classification model
 if task_name.startswith("imagenet"	):
  UpperCAmelCase__							 =						outputs.logits
  UpperCAmelCase__							 =						logits.argmax(-1	).item()
  print("Predicted class:"						,							model.config.idalabel[predicted_class_idx]	)
  if task_name.startswith("imagenet1k_256"	) and config.width_multiplier == 1.0:
   # expected_logits for base variant
   UpperCAmelCase__							 =						torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-01]	)
   assert torch.allclose(logits[0, :3]						,							_lowerCAmelCase						,							atol=1E-4	)

 Path(_lowerCAmelCase	).mkdir(exist_ok=_lowerCAmelCase	)
 print(F'''Saving model {task_name} to {pytorch_dump_folder_path}'''	)
 model.save_pretrained(_lowerCAmelCase	)
 print(F'''Saving image processor to {pytorch_dump_folder_path}'''	)
 image_processor.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
  _lowerCAmelCase						:   Tuple		=   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--task",
      default="imagenet1k_256",
      type=str,
      help=(
          "Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
          "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
      ),
      choices=[
          "imagenet1k_256",
          "imagenet1k_384",
          "imagenet21k_to_1k_256",
          "imagenet21k_to_1k_384",
          "ade20k_deeplabv3",
          "voc_deeplabv3",
      ],
  )

  parser.add_argument(
      "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
  )
  parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
  )

  _lowerCAmelCase						:   Optional[Any]		=   parser.parse_args()
  convert_mobilevitva_checkpoint(
      args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
  )











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


_lowerCAmelCase						:   Tuple		=   logging.get_logger(__name__)


_lowerCAmelCase						:   Union[str, Any]		=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase						:   str		=   {
    "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",
    },
}

_lowerCAmelCase						:   List[Any]		=   {
    "allenai/led-base-16384": 1_6_3_8_4,
}
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		VOCAB_FILES_NAMES
      UpperCAmelCase_        =		PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_        =		LEDTokenizer
      UpperCAmelCase_        =		["""input_ids""", """attention_mask"""]
      def __init__(			self   :Dict ,      lowerCamelCase   :Any=None ,      lowerCamelCase   :Dict=None ,      lowerCamelCase   :Dict=None ,      lowerCamelCase   :int="replace" ,      lowerCamelCase   :List[Any]="<s>" ,      lowerCamelCase   :Optional[Any]="</s>" ,      lowerCamelCase   :Optional[Any]="</s>" ,      lowerCamelCase   :int="<s>" ,      lowerCamelCase   :Optional[Any]="<unk>" ,      lowerCamelCase   :Dict="<pad>" ,      lowerCamelCase   :Tuple="<mask>" ,      lowerCamelCase   :str=False ,      lowerCamelCase   :Union[str, Any]=True ,      **lowerCamelCase   :List[str] ,      )     ->			Tuple:
       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 ,      )

       UpperCAmelCase__							 =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
       if pre_tok_state.get("add_prefix_space" ,      lowerCamelCase      ) != add_prefix_space:
        UpperCAmelCase__							 =						getattr(lowerCamelCase ,      pre_tok_state.pop("type"      )      )
        UpperCAmelCase__							 =						add_prefix_space
        UpperCAmelCase__							 =						pre_tok_class(**lowerCamelCase      )

       UpperCAmelCase__							 =						add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       UpperCAmelCase__							 =						"post_processor"
       UpperCAmelCase__							 =						getattr(self.backend_tokenizer ,      lowerCamelCase ,      lowerCamelCase      )
       if tokenizer_component_instance:
        UpperCAmelCase__							 =						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:
         UpperCAmelCase__							 =						tuple(state["sep"]      )
        if "cls" in state:
         UpperCAmelCase__							 =						tuple(state["cls"]      )

        UpperCAmelCase__							 =						False

        if state.get("add_prefix_space" ,      lowerCamelCase      ) != add_prefix_space:
         UpperCAmelCase__							 =						add_prefix_space
         UpperCAmelCase__							 =						True

        if state.get("trim_offsets" ,      lowerCamelCase      ) != trim_offsets:
         UpperCAmelCase__							 =						trim_offsets
         UpperCAmelCase__							 =						True

        if changes_to_apply:
         UpperCAmelCase__							 =						getattr(lowerCamelCase ,      state.pop("type"      )      )
         UpperCAmelCase__							 =						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 	UpperCAmelCase_						(			self   :List[str]      )     ->			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 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :List[str]      )     ->			Dict:
       UpperCAmelCase__							 =						AddedToken(lowerCamelCase ,      lstrip=lowerCamelCase ,      rstrip=lowerCamelCase      ) if isinstance(lowerCamelCase ,      lowerCamelCase      ) else value
       UpperCAmelCase__							 =						value
      def 	UpperCAmelCase_						(			self   :Optional[Any] ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :int      )     ->			BatchEncoding:
       UpperCAmelCase__							 =						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 	UpperCAmelCase_						(			self   :int ,      *lowerCamelCase   :Optional[Any] ,      **lowerCamelCase   :Optional[int]      )     ->			BatchEncoding:
       UpperCAmelCase__							 =						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 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :str ,      lowerCamelCase   :Optional[str] = None      )     ->			Tuple[str]:
       UpperCAmelCase__							 =						self._tokenizer.model.save(lowerCamelCase ,      name=lowerCamelCase      )
       return tuple(lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Optional[Any] ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :Optional[Any]=None      )     ->			Dict:
       UpperCAmelCase__							 =						[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 	UpperCAmelCase_						(			self   :Optional[int] ,      lowerCamelCase   :List[int] ,      lowerCamelCase   :Optional[List[int]] = None      )     ->			List[int]:
       UpperCAmelCase__							 =						[self.sep_token_id]
       UpperCAmelCase__							 =						[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 	UpperCAmelCase_						(			self   :List[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:
       UpperCAmelCase__							 =						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:
        UpperCAmelCase__							 =						"attention_mask" in self.model_input_names

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

        if needs_to_be_padded:
         UpperCAmelCase__							 =						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`
          UpperCAmelCase__							 =						(
              encoded_inputs["global_attention_mask"] + [-1] * difference
          )
         elif self.padding_side == "left":
          UpperCAmelCase__							 =						[-1] * difference + encoded_inputs[
              "global_attention_mask"
          ]
         else:
          raise ValueError("Invalid padding strategy:" + str(self.padding_side      )      )

       return encoded_inputs





