'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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


def     __lowerCAmelCase						(       a_       ,      a_=False		)      ->	List[str]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       []

     # fmt: off
     # stem:
     rename_keys.append(('cls_token', 'vit.embeddings.cls_token')		)
     rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')		)

     rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')		)
     rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')		)

     # backbone
     rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')		)
     rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')		)
     rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')		)

     for stage_idx in range(len(config.backbone_config.depths		)		):
          for layer_idx in range(config.backbone_config.depths[stage_idx]		):
               rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")		)
               rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")		)
               rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")		)
               rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")		)
               rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")		)
               rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")		)
               rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")		)
               rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")		)
               rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")		)

          rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")		)
          rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")		)
          rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")		)

     # transformer encoder
     for i in range(config.num_hidden_layers		):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")		)
          rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")		)
          rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")		)
          rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")		)
          rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")		)
          rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")		)
          rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")		)
          rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")		)
          rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")		)
          rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")		)

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ('norm.weight', 'layernorm.weight'),
                  ('norm.bias', 'layernorm.bias'),
                  ('pre_logits.fc.weight', 'pooler.dense.weight'),
                  ('pre_logits.fc.bias', 'pooler.dense.bias'),
              ]		)

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          SCREAMING_SNAKE_CASE					:			int    	=       [(pair[0], pair[1][4:]) if pair[1].startswith('vit'		) else pair for pair in rename_keys]
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('norm.weight', 'vit.layernorm.weight'),
                  ('norm.bias', 'vit.layernorm.bias'),
                  ('head.weight', 'classifier.weight'),
                  ('head.bias', 'classifier.bias'),
              ]		)
     # fmt: on

     return rename_keys


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_=False		)      ->	str:





     '''simple docstring'''





     for i in range(config.num_hidden_layers		):
          if base_model:
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       ''
          else:
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       'vit.'
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          SCREAMING_SNAKE_CASE					:			Any    	=       state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""		)
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""		)
          # next, add query, keys and values (in that order) to the state dict
          SCREAMING_SNAKE_CASE					:			Tuple    	=       in_proj_weight[
              : config.hidden_size, :
          ]
          SCREAMING_SNAKE_CASE					:			Dict    	=       in_proj_bias[: config.hidden_size]
          SCREAMING_SNAKE_CASE					:			int    	=       in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          SCREAMING_SNAKE_CASE					:			Tuple    	=       in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       in_proj_weight[
              -config.hidden_size :, :
          ]
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       in_proj_bias[-config.hidden_size :]


def     __lowerCAmelCase						(       a_		)      ->	List[str]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			str    	=       ['head.weight', 'head.bias']
     for k in ignore_keys:
          state_dict.pop(a_       ,      a_		)


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	Optional[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Any    	=       dct.pop(a_		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       val


def     __lowerCAmelCase						(       )      ->	str:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Any    	=       'http://images.cocodataset.org/val2017/000000039769.jpg'
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       Image.open(requests.get(a_       ,      stream=a_		).raw		)
     return im


@torch.no_grad()
def     __lowerCAmelCase						(       a_       ,      a_       ,      a_=False		)      ->	List[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       BitConfig(
         global_padding='same'       ,      layer_type='bottleneck'       ,      depths=(3, 4, 9)       ,      out_features=['stage3']       ,      embedding_dynamic_padding=a_       ,      )
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ViTHybridConfig(backbone_config=a_       ,      image_size=384       ,      num_labels=1000		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       False

     # load original model from timm
     SCREAMING_SNAKE_CASE					:			List[str]    	=       timm.create_model(a_       ,      pretrained=a_		)
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       timm_model.state_dict()
     if base_model:
          remove_classification_head_(a_		)
     SCREAMING_SNAKE_CASE					:			Tuple    	=       create_rename_keys(a_       ,      a_		)
     for src, dest in rename_keys:
          rename_key(a_       ,      a_       ,      a_		)
     read_in_q_k_v(a_       ,      a_       ,      a_		)

     SCREAMING_SNAKE_CASE					:			str    	=       'huggingface/label-files'
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       'imagenet-1k-id2label.json'
     SCREAMING_SNAKE_CASE					:			List[str]    	=       json.load(open(hf_hub_download(a_       ,      a_       ,      repo_type='dataset'		)       ,      'r'		)		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       {int(a_		): v for k, v in idalabel.items()}
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       idalabel
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {v: k for k, v in idalabel.items()}

     # load HuggingFace model
     if vit_name[-5:] == "in21k":
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       ViTHybridModel(a_		).eval()
     else:
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       ViTHybridForImageClassification(a_		).eval()
     model.load_state_dict(a_		)

     # create image processor
     SCREAMING_SNAKE_CASE					:			Tuple    	=       create_transform(**resolve_data_config({}       ,      model=a_		)		)
     SCREAMING_SNAKE_CASE					:			str    	=       transform.transforms

     SCREAMING_SNAKE_CASE					:			Tuple    	=       {
         'bilinear': PILImageResampling.BILINEAR,
         'bicubic': PILImageResampling.BICUBIC,
         'nearest': PILImageResampling.NEAREST,
     }

     SCREAMING_SNAKE_CASE					:			Any    	=       ViTHybridImageProcessor(
         do_resize=a_       ,      size={'shortest_edge': timm_transforms[0].size}       ,      resample=pillow_resamplings[timm_transforms[0].interpolation.value]       ,      do_center_crop=a_       ,      crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}       ,      do_normalize=a_       ,      image_mean=timm_transforms[-1].mean.tolist()       ,      image_std=timm_transforms[-1].std.tolist()       ,      )

     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       prepare_img()
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       transform(a_		).unsqueeze(0		)
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       processor(a_       ,      return_tensors='pt'		).pixel_values

     # verify pixel values
     assert torch.allclose(a_       ,      a_		)

     # verify logits
     with torch.no_grad():
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model(a_		)
          SCREAMING_SNAKE_CASE					:			int    	=       outputs.logits

     print('Predicted class:'       ,      logits.argmax(-1		).item()		)
     if base_model:
          SCREAMING_SNAKE_CASE					:			Dict    	=       timm_model.forward_features(a_		)
          assert timm_pooled_output.shape == outputs.pooler_output.shape
          assert torch.allclose(a_       ,      outputs.pooler_output       ,      atol=1e-3		)
     else:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       timm_model(a_		)
          assert timm_logits.shape == outputs.logits.shape
          assert torch.allclose(a_       ,      outputs.logits       ,      atol=1e-3		)
     print('Looks ok!'		)

     if pytorch_dump_folder_path is not None:
          Path(a_		).mkdir(exist_ok=a_		)
          print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""		)
          model.save_pretrained(a_		)
          print(f"""Saving processor to {pytorch_dump_folder_path}"""		)
          processor.save_pretrained(a_		)

     if push_to_hub:
          print(f"""Pushing model and processor to the hub {vit_name}"""		)
          model.push_to_hub(f"""ybelkada/{vit_name}"""		)
          processor.push_to_hub(f"""ybelkada/{vit_name}"""		)


if __name__ == "__main__":
 _lowerCAmelCase  :Any  				=    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--vit_name""",
     default="""vit_base_r50_s16_384""",
     type=str,
     help="""Name of the hybrid ViT timm model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
 )
 parser.add_argument(
     """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
 )

 _lowerCAmelCase  :str  				=    parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''





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  :int  				=    logging.get_logger(__name__)


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

_lowerCAmelCase  :int  				=    {
    """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  :str  				=    {
    """allenai/led-base-16384""": 16_384,
}






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Optional[Any]							=						VOCAB_FILES_NAMES
    snake_case__							:   Union[str, Any]							=						PRETRAINED_VOCAB_FILES_MAP
    snake_case__							:   Tuple							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case__							:   int							=						LEDTokenizer
    snake_case__							:   Optional[int]							=						["input_ids", "attention_mask"]






    def __init__(       self    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__=None    ,							lowercase__="replace"    ,							lowercase__="<s>"    ,							lowercase__="</s>"    ,							lowercase__="</s>"    ,							lowercase__="<s>"    ,							lowercase__="<unk>"    ,							lowercase__="<pad>"    ,							lowercase__="<mask>"    ,							lowercase__=False    ,							lowercase__=True    ,							**lowercase__    ,							)							-> str:
         super().__init__(
             lowercase__    ,							lowercase__    ,							tokenizer_file=lowercase__    ,							errors=lowercase__    ,							bos_token=lowercase__    ,							eos_token=lowercase__    ,							sep_token=lowercase__    ,							cls_token=lowercase__    ,							unk_token=lowercase__    ,							pad_token=lowercase__    ,							mask_token=lowercase__    ,							add_prefix_space=lowercase__    ,							trim_offsets=lowercase__    ,							**lowercase__    ,							)

         SCREAMING_SNAKE_CASE					:			Dict    	=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
         if pre_tok_state.get('add_prefix_space'    ,							lowercase__ ) != add_prefix_space:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       getattr(lowercase__    ,							pre_tok_state.pop('type' ) )
              SCREAMING_SNAKE_CASE					:			Dict    	=       add_prefix_space
              SCREAMING_SNAKE_CASE					:			str    	=       pre_tok_class(**lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         SCREAMING_SNAKE_CASE					:			Dict    	=       'post_processor'
         SCREAMING_SNAKE_CASE					:			Dict    	=       getattr(self.backend_tokenizer    ,							lowercase__    ,							lowercase__ )
         if tokenizer_component_instance:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       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:
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       tuple(state['sep'] )
              if "cls" in state:
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       tuple(state['cls'] )

              SCREAMING_SNAKE_CASE					:			str    	=       False

              if state.get('add_prefix_space'    ,							lowercase__ ) != add_prefix_space:
                   SCREAMING_SNAKE_CASE					:			Dict    	=       add_prefix_space
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       True

              if state.get('trim_offsets'    ,							lowercase__ ) != trim_offsets:
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       trim_offsets
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       True

              if changes_to_apply:
                   SCREAMING_SNAKE_CASE					:			str    	=       getattr(lowercase__    ,							state.pop('type' ) )
                   SCREAMING_SNAKE_CASE					:			Dict    	=       component_class(**lowercase__ )
                   setattr(self.backend_tokenizer    ,							lowercase__    ,							lowercase__ )






    @property
    # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
    def       _UpperCamelCase    (       self )							-> 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    ,							lowercase__ )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       AddedToken(lowercase__    ,							lstrip=lowercase__    ,							rstrip=lowercase__ ) if isinstance(lowercase__    ,							lowercase__ ) else value
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       value






    def       _UpperCamelCase    (       self    ,							*lowercase__    ,							**lowercase__ )							-> BatchEncoding:
         SCREAMING_SNAKE_CASE					:			int    	=       kwargs.get('is_split_into_words'    ,							lowercase__ )

         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(*lowercase__    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							*lowercase__    ,							**lowercase__ )							-> BatchEncoding:
         SCREAMING_SNAKE_CASE					:			Dict    	=       kwargs.get('is_split_into_words'    ,							lowercase__ )

         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(*lowercase__    ,							**lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None )							-> Tuple[str]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self._tokenizer.model.save(lowercase__    ,							name=lowercase__ )
         return tuple(lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__=None )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			str    	=       [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    ,							lowercase__    ,							lowercase__ = None )							-> List[int]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       [self.sep_token_id]
         SCREAMING_SNAKE_CASE					:			str    	=       [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    ,							lowercase__    ,							lowercase__ = None    ,							lowercase__ = PaddingStrategy.DO_NOT_PAD    ,							lowercase__ = None    ,							lowercase__ = None    ,							)							-> dict:
         SCREAMING_SNAKE_CASE					:			int    	=       super()._pad(
             encoded_inputs=lowercase__    ,							max_length=lowercase__    ,							padding_strategy=lowercase__    ,							pad_to_multiple_of=lowercase__    ,							return_attention_mask=lowercase__    ,							)

         # Load from model defaults
         if return_attention_mask is None:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       'attention_mask' in self.model_input_names

         if return_attention_mask and "global_attention_mask" in encoded_inputs:
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       encoded_inputs[self.model_input_names[0]]
              # `global_attention_mask` need to have the same length as other (sequential) inputs.
              SCREAMING_SNAKE_CASE					:			str    	=       len(encoded_inputs['global_attention_mask'] ) != len(lowercase__ )

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

         return encoded_inputs



