


import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

		from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
		A_								=   False

if is_vision_available():
		from PIL import Image

		from transformers import PixaStructImageProcessor

class     snake_case      (							unittest.TestCase						):
			'''simple docstring'''

			def __init__(						self  :  Optional[int]				,				lowerCAmelCase_  :  Optional[int]				,				lowerCAmelCase_  :  Optional[int]=7				,				lowerCAmelCase_  :  List[str]=3				,				lowerCAmelCase_  :  Dict=18				,				lowerCAmelCase_  :  Dict=30				,				lowerCAmelCase_  :  Tuple=400				,				lowerCAmelCase_  :  Any=None				,				lowerCAmelCase_  :  Any=True				,				lowerCAmelCase_  :  int=True				,				lowerCAmelCase_  :  int=None				,				)       ->       int:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_						   =							size if size is not None else {'''height''': 20, '''width''': 20}
								SCREAMING_SNAKE_CASE_						   =							parent
								SCREAMING_SNAKE_CASE_						   =							batch_size
								SCREAMING_SNAKE_CASE_						   =							num_channels
								SCREAMING_SNAKE_CASE_						   =							image_size
								SCREAMING_SNAKE_CASE_						   =							min_resolution
								SCREAMING_SNAKE_CASE_						   =							max_resolution
								SCREAMING_SNAKE_CASE_						   =							size
								SCREAMING_SNAKE_CASE_						   =							do_normalize
								SCREAMING_SNAKE_CASE_						   =							do_convert_rgb
								SCREAMING_SNAKE_CASE_						   =							[512, 1_024, 2_048, 4_096]
								SCREAMING_SNAKE_CASE_						   =							patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}

			def _lowercase			(						self  :  Union[str, Any]       )       ->       Union[str, Any]:




								"""simple docstring"""






								return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




			def _lowercase			(						self  :  List[str]       )       ->       str:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_						   =							'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
								SCREAMING_SNAKE_CASE_						   =							Image.open(requests.get(__SCREAMING_SNAKE_CASE				,				stream=__SCREAMING_SNAKE_CASE       ).raw       ).convert('''RGB'''       )
								return raw_image

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11						,			reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""						,			)
@require_torch
@require_vision
class     snake_case      (							__lowerCAmelCase						,			unittest.TestCase						):
			'''simple docstring'''

			UpperCAmelCase							:							str							=			PixaStructImageProcessor if is_vision_available() else None

			def _lowercase			(						self  :  Optional[Any]       )       ->       Union[str, Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_						   =							PixaStructImageProcessingTester(self       )

			@property
			def _lowercase			(						self  :  List[Any]       )       ->       Dict:




								"""simple docstring"""






								return self.image_processor_tester.prepare_image_processor_dict()

			def _lowercase			(						self  :  Any       )       ->       List[Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_						   =							self.image_processing_class(**self.image_processor_dict       )
								self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,				'''do_normalize'''       )       )
								self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,				'''do_convert_rgb'''       )       )

			def _lowercase			(						self  :  List[str]       )       ->       Optional[Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_						   =							self.image_processor_tester.prepare_dummy_image()

								SCREAMING_SNAKE_CASE_						   =							self.image_processing_class(**self.image_processor_dict       )
								SCREAMING_SNAKE_CASE_						   =							2_048

								SCREAMING_SNAKE_CASE_						   =							image_processor(__SCREAMING_SNAKE_CASE				,				return_tensors='''pt'''				,				max_patches=__SCREAMING_SNAKE_CASE       )
								self.assertTrue(torch.allclose(inputs.flattened_patches.mean()				,				torch.tensor(0.0_606       )				,				atol=1e-3				,				rtol=1e-3       )       )

			def _lowercase			(						self  :  str       )       ->       int:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_						   =							self.image_processing_class(**self.image_processor_dict       )
								# create random PIL images
								SCREAMING_SNAKE_CASE_						   =							prepare_image_inputs(self.image_processor_tester				,				equal_resolution=__SCREAMING_SNAKE_CASE       )
								for image in image_inputs:
													self.assertIsInstance(__SCREAMING_SNAKE_CASE				,				Image.Image       )

								# Test not batched input
								SCREAMING_SNAKE_CASE_						   =							(
								    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
								    * self.image_processor_tester.num_channels
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
													# Test not batched input
													SCREAMING_SNAKE_CASE_						   =							image_processor(
													    image_inputs[0]				,				return_tensors='''pt'''				,				max_patches=__SCREAMING_SNAKE_CASE       ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,				(1, max_patch, expected_hidden_dim)				,				)

													# Test batched
													SCREAMING_SNAKE_CASE_						   =							image_processor(
													    __SCREAMING_SNAKE_CASE				,				return_tensors='''pt'''				,				max_patches=__SCREAMING_SNAKE_CASE       ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,				)

			def _lowercase			(						self  :  List[Any]       )       ->       Optional[int]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_						   =							self.image_processing_class(**self.image_processor_dict       )
								# create random PIL images
								SCREAMING_SNAKE_CASE_						   =							prepare_image_inputs(self.image_processor_tester				,				equal_resolution=__SCREAMING_SNAKE_CASE       )
								for image in image_inputs:
													self.assertIsInstance(__SCREAMING_SNAKE_CASE				,				Image.Image       )

								# Test not batched input
								SCREAMING_SNAKE_CASE_						   =							(
								    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
								    * self.image_processor_tester.num_channels
								) + 2

								SCREAMING_SNAKE_CASE_						   =							True

								for max_patch in self.image_processor_tester.max_patches:
													# Test not batched input
													with self.assertRaises(__SCREAMING_SNAKE_CASE       ):
																		SCREAMING_SNAKE_CASE_						   =							image_processor(
																		    image_inputs[0]				,				return_tensors='''pt'''				,				max_patches=__SCREAMING_SNAKE_CASE       ).flattened_patches

													SCREAMING_SNAKE_CASE_						   =							'''Hello'''

													SCREAMING_SNAKE_CASE_						   =							image_processor(
													    image_inputs[0]				,				return_tensors='''pt'''				,				max_patches=__SCREAMING_SNAKE_CASE				,				header_text=__SCREAMING_SNAKE_CASE       ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,				(1, max_patch, expected_hidden_dim)				,				)

													# Test batched
													SCREAMING_SNAKE_CASE_						   =							image_processor(
													    __SCREAMING_SNAKE_CASE				,				return_tensors='''pt'''				,				max_patches=__SCREAMING_SNAKE_CASE				,				header_text=__SCREAMING_SNAKE_CASE       ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,				)

			def _lowercase			(						self  :  str       )       ->       int:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_						   =							self.image_processing_class(**self.image_processor_dict       )
								# create random numpy tensors
								SCREAMING_SNAKE_CASE_						   =							prepare_image_inputs(self.image_processor_tester				,				equal_resolution=__SCREAMING_SNAKE_CASE				,				numpify=__SCREAMING_SNAKE_CASE       )
								for image in image_inputs:
													self.assertIsInstance(__SCREAMING_SNAKE_CASE				,				np.ndarray       )

								SCREAMING_SNAKE_CASE_						   =							(
								    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
								    * self.image_processor_tester.num_channels
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
													# Test not batched input
													SCREAMING_SNAKE_CASE_						   =							image_processor(
													    image_inputs[0]				,				return_tensors='''pt'''				,				max_patches=__SCREAMING_SNAKE_CASE       ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,				(1, max_patch, expected_hidden_dim)				,				)

													# Test batched
													SCREAMING_SNAKE_CASE_						   =							image_processor(
													    __SCREAMING_SNAKE_CASE				,				return_tensors='''pt'''				,				max_patches=__SCREAMING_SNAKE_CASE       ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,				)




			def _lowercase			(						self  :  Dict       )       ->       Any:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_						   =							self.image_processing_class(**self.image_processor_dict       )
								# create random PyTorch tensors
								SCREAMING_SNAKE_CASE_						   =							prepare_image_inputs(self.image_processor_tester				,				equal_resolution=__SCREAMING_SNAKE_CASE				,				torchify=__SCREAMING_SNAKE_CASE       )
								for image in image_inputs:
													self.assertIsInstance(__SCREAMING_SNAKE_CASE				,				torch.Tensor       )

								# Test not batched input
								SCREAMING_SNAKE_CASE_						   =							(
								    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
								    * self.image_processor_tester.num_channels
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
													# Test not batched input
													SCREAMING_SNAKE_CASE_						   =							image_processor(
													    image_inputs[0]				,				return_tensors='''pt'''				,				max_patches=__SCREAMING_SNAKE_CASE       ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,				(1, max_patch, expected_hidden_dim)				,				)

													# Test batched
													SCREAMING_SNAKE_CASE_						   =							image_processor(
													    __SCREAMING_SNAKE_CASE				,				return_tensors='''pt'''				,				max_patches=__SCREAMING_SNAKE_CASE       ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,				)

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11						,			reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""						,			)
@require_torch
@require_vision
class     snake_case      (							__lowerCAmelCase						,			unittest.TestCase						):
			'''simple docstring'''

			UpperCAmelCase							:							int							=			PixaStructImageProcessor if is_vision_available() else None

			def _lowercase			(						self  :  int       )       ->       Optional[Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_						   =							PixaStructImageProcessingTester(self				,				num_channels=4       )
								SCREAMING_SNAKE_CASE_						   =							3

			@property
			def _lowercase			(						self  :  int       )       ->       Tuple:




								"""simple docstring"""






								return self.image_processor_tester.prepare_image_processor_dict()

			def _lowercase			(						self  :  List[Any]       )       ->       Union[str, Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_						   =							self.image_processing_class(**self.image_processor_dict       )
								self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,				'''do_normalize'''       )       )
								self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,				'''do_convert_rgb'''       )       )




			def _lowercase			(						self  :  Dict       )       ->       Optional[int]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_						   =							self.image_processing_class(**self.image_processor_dict       )
								# create random PIL images
								SCREAMING_SNAKE_CASE_						   =							prepare_image_inputs(self.image_processor_tester				,				equal_resolution=__SCREAMING_SNAKE_CASE       )
								for image in image_inputs:
													self.assertIsInstance(__SCREAMING_SNAKE_CASE				,				Image.Image       )

								# Test not batched input
								SCREAMING_SNAKE_CASE_						   =							(
								    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
								    * (self.image_processor_tester.num_channels - 1)
								) + 2

								for max_patch in self.image_processor_tester.max_patches:
													# Test not batched input
													SCREAMING_SNAKE_CASE_						   =							image_processor(
													    image_inputs[0]				,				return_tensors='''pt'''				,				max_patches=__SCREAMING_SNAKE_CASE       ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,				(1, max_patch, expected_hidden_dim)				,				)

													# Test batched
													SCREAMING_SNAKE_CASE_						   =							image_processor(
													    __SCREAMING_SNAKE_CASE				,				return_tensors='''pt'''				,				max_patches=__SCREAMING_SNAKE_CASE       ).flattened_patches
													self.assertEqual(
													    encoded_images.shape				,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,				)








'''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


UpperCAmelCase_							:	Any	     =		logging.get_logger(__name__)


UpperCAmelCase_							:	str	     =		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_							:	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''',
    },
}

UpperCAmelCase_							:	Union[str, Any]	     =		{
    '''allenai/led-base-16384''': 1_6_3_8_4,
}





class      lowerCAmelCase (			__lowerCAmelCase):
       __lowercase	:   Dict										=      VOCAB_FILES_NAMES
       __lowercase	:   Dict										=      PRETRAINED_VOCAB_FILES_MAP
       __lowercase	:   Tuple										=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowercase	:   Union[str, Any]										=      LEDTokenizer
       __lowercase	:   int										=      ['''input_ids''', '''attention_mask''']


       def __init__( self     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE="replace"     ,	__SCREAMING_SNAKE_CASE="<s>"     ,	__SCREAMING_SNAKE_CASE="</s>"     ,	__SCREAMING_SNAKE_CASE="</s>"     ,	__SCREAMING_SNAKE_CASE="<s>"     ,	__SCREAMING_SNAKE_CASE="<unk>"     ,	__SCREAMING_SNAKE_CASE="<pad>"     ,	__SCREAMING_SNAKE_CASE="<mask>"     ,	__SCREAMING_SNAKE_CASE=False     ,	__SCREAMING_SNAKE_CASE=True     ,	**__SCREAMING_SNAKE_CASE     ,	)   ->	List[Any]:



            '''simple docstring'''




            super().__init__(
                __SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	tokenizer_file=__SCREAMING_SNAKE_CASE     ,	errors=__SCREAMING_SNAKE_CASE     ,	bos_token=__SCREAMING_SNAKE_CASE     ,	eos_token=__SCREAMING_SNAKE_CASE     ,	sep_token=__SCREAMING_SNAKE_CASE     ,	cls_token=__SCREAMING_SNAKE_CASE     ,	unk_token=__SCREAMING_SNAKE_CASE     ,	pad_token=__SCREAMING_SNAKE_CASE     ,	mask_token=__SCREAMING_SNAKE_CASE     ,	add_prefix_space=__SCREAMING_SNAKE_CASE     ,	trim_offsets=__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE     ,	)

            __snake_case            =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
            if pre_tok_state.get('''add_prefix_space'''     ,	__SCREAMING_SNAKE_CASE	) != add_prefix_space:
                 __snake_case            =	getattr(__SCREAMING_SNAKE_CASE     ,	pre_tok_state.pop('''type'''	)	)
                 __snake_case            =	add_prefix_space
                 __snake_case            =	pre_tok_class(**__SCREAMING_SNAKE_CASE	)

            __snake_case            =	add_prefix_space

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

                 __snake_case            =	False

                 if state.get('''add_prefix_space'''     ,	__SCREAMING_SNAKE_CASE	) != add_prefix_space:
                      __snake_case            =	add_prefix_space
                      __snake_case            =	True

                 if state.get('''trim_offsets'''     ,	__SCREAMING_SNAKE_CASE	) != trim_offsets:
                      __snake_case            =	trim_offsets
                      __snake_case            =	True

                 if changes_to_apply:
                      __snake_case            =	getattr(__SCREAMING_SNAKE_CASE     ,	state.pop('''type'''	)	)
                      __snake_case            =	component_class(**__SCREAMING_SNAKE_CASE	)
                      setattr(self.backend_tokenizer     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)


       @property
       # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            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    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	AddedToken(__SCREAMING_SNAKE_CASE     ,	lstrip=__SCREAMING_SNAKE_CASE     ,	rstrip=__SCREAMING_SNAKE_CASE	) if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	) else value
            __snake_case            =	value


       def    lowerCAmelCase   ( self     ,	*__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	BatchEncoding:



            '''simple docstring'''




            __snake_case            =	kwargs.get('''is_split_into_words'''     ,	__SCREAMING_SNAKE_CASE	)

            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(*__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	*__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)   ->	BatchEncoding:



            '''simple docstring'''




            __snake_case            =	kwargs.get('''is_split_into_words'''     ,	__SCREAMING_SNAKE_CASE	)

            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(*__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None	)   ->	Tuple[str]:



            '''simple docstring'''




            __snake_case            =	self._tokenizer.model.save(__SCREAMING_SNAKE_CASE     ,	name=__SCREAMING_SNAKE_CASE	)
            return tuple(__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	[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    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None	)   ->	List[int]:



            '''simple docstring'''




            __snake_case            =	[self.sep_token_id]
            __snake_case            =	[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    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = PaddingStrategy.DO_NOT_PAD     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE = None     ,	)   ->	dict:



            '''simple docstring'''




            __snake_case            =	super()._pad(
                encoded_inputs=__SCREAMING_SNAKE_CASE     ,	max_length=__SCREAMING_SNAKE_CASE     ,	padding_strategy=__SCREAMING_SNAKE_CASE     ,	pad_to_multiple_of=__SCREAMING_SNAKE_CASE     ,	return_attention_mask=__SCREAMING_SNAKE_CASE     ,	)

            # Load from model defaults
            if return_attention_mask is None:
                 __snake_case            =	'''attention_mask''' in self.model_input_names

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

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

            return encoded_inputs






