



import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()
class 				_snake_case			(					lowercase__    ,	lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					Any     		=AutoencoderKL
				UpperCamelCase__				:					Any     		="""sample"""
				UpperCamelCase__				:					str     		=1E-2




				@property
				def 							A__      (						self					:						int						):
							lowercase__					  =				4
							lowercase__					  =				3
							lowercase__					  =				(32, 32)

							lowercase__					  =				floats_tensor((batch_size, num_channels) + sizes						).to(__lowercase						)

							return {"sample": image}




				@property
				def 							A__      (						self					:						int						):
							return (3, 32, 32)




				@property
				def 							A__      (						self					:						Optional[int]						):
							return (3, 32, 32)




				def 							A__      (						self					:						int						):
							lowercase__					  =				{
							    "block_out_channels": [32, 64],
							    "in_channels": 3,
							    "out_channels": 3,
							    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
							    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
							    "latent_channels": 4,
							}
							lowercase__					  =				self.dummy_input
							return init_dict, inputs_dict




				def 							A__      (						self					:						Union[str, Any]						):
							pass




				def 							A__      (						self					:						Dict						):
							pass




				@unittest.skipIf(torch_device == "mps",					"Gradient checkpointing skipped on MPS"						)
				def 							A__      (						self					:						List[Any]						):
							# enable deterministic behavior for gradient checkpointing
							lowercase__  ,       lowercase__					  =				self.prepare_init_args_and_inputs_for_common()
							lowercase__					  =				self.model_class(**__lowercase						)
							model.to(__lowercase						)

							assert not model.is_gradient_checkpointing and model.training

							lowercase__					  =				model(**__lowercase						).sample
							# run the backwards pass on the model. For backwards pass, for simplicity purpose,
							# we won't calculate the loss and rather backprop on out.sum()
							model.zero_grad()

							lowercase__					  =				torch.randn_like(__lowercase						)
							lowercase__					  =				(out - labels).mean()
							loss.backward()

							# re-instantiate the model now enabling gradient checkpointing
							lowercase__					  =				self.model_class(**__lowercase						)
							# clone model
							model_a.load_state_dict(model.state_dict()						)
							model_a.to(__lowercase						)
							model_a.enable_gradient_checkpointing()

							assert model_a.is_gradient_checkpointing and model_a.training

							lowercase__					  =				model_a(**__lowercase						).sample
							# run the backwards pass on the model. For backwards pass, for simplicity purpose,
							# we won't calculate the loss and rather backprop on out.sum()
							model_a.zero_grad()
							lowercase__					  =				(out_a - labels).mean()
							loss_a.backward()

							# compare the output and parameters gradients
							self.assertTrue((loss - loss_a).abs() < 1e-5						)
							lowercase__					  =				dict(model.named_parameters()						)
							lowercase__					  =				dict(model_a.named_parameters()						)
							for name, param in named_params.items():
										self.assertTrue(torch_all_close(param.grad.data,					named_params_a[name].grad.data,					atol=5e-5						)						)




				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__  ,       lowercase__					  =				AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy",					output_loading_info=__lowercase						)
							self.assertIsNotNone(__lowercase						)
							self.assertEqual(len(loading_info["missing_keys"]						),					0						)

							model.to(__lowercase						)
							lowercase__					  =				model(**self.dummy_input						)

							assert image is not None, "Make sure output is not None"




				def 							A__      (						self					:						Any						):
							lowercase__					  =				AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"						)
							lowercase__					  =				model.to(__lowercase						)
							model.eval()

							if torch_device == "mps":
										lowercase__					  =				torch.manual_seed(0						)
							else:
										lowercase__					  =				torch.Generator(device=__lowercase						).manual_seed(0						)

							lowercase__					  =				torch.randn(
							    1,					model.config.in_channels,					model.config.sample_size,					model.config.sample_size,					generator=torch.manual_seed(0						),					)
							lowercase__					  =				image.to(__lowercase						)
							with torch.no_grad():
										lowercase__					  =				model(__lowercase,					sample_posterior=__lowercase,					generator=__lowercase						).sample

							lowercase__					  =				output[0, -1, -3:, -3:].flatten().cpu()

							# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
							# the expected output slices are not the same for CPU and GPU.
							if torch_device == "mps":
										lowercase__					  =				torch.tensor(
										    [
										        -4.0_0_7_8e-0_1,
										        -3.8_3_2_3e-0_4,
										        -1.2_6_8_1e-0_1,
										        -1.1_4_6_2e-0_1,
										        2.0_0_9_5e-0_1,
										        1.0_8_9_3e-0_1,
										        -8.8_2_4_7e-0_2,
										        -3.0_3_6_1e-0_1,
										        -9.8_6_4_4e-0_3,
										    ]						)
							elif torch_device == "cpu":
										lowercase__					  =				torch.tensor(
										    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]						)
							else:
										lowercase__					  =				torch.tensor(
										    [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]						)

							self.assertTrue(torch_all_close(__lowercase,					__lowercase,					rtol=1e-2						)						)
@slow
class 				_snake_case			(					unittest.TestCase):




				def 							A__      (						self					:						Dict,					__lowercase					:						int,					__lowercase					:						str						):
							return F'''gaussian_noise_s={seed}_shape={"_".join([str(__lowercase						) for s in shape]						)}.npy'''




				def 							A__      (						self					:						Optional[Any]						):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




				def 							A__      (						self					:						Optional[Any],					__lowercase					:						Optional[Any]=0,					__lowercase					:						List[str]=(4, 3, 512, 512),					__lowercase					:						List[Any]=False						):
							lowercase__					  =				torch.floataa if fpaa else torch.floataa
							lowercase__					  =				torch.from_numpy(load_hf_numpy(self.get_file_format(__lowercase,					__lowercase						)						)						).to(__lowercase						).to(__lowercase						)
							return image




				def 							A__      (						self					:						Optional[int],					__lowercase					:						Any="CompVis/stable-diffusion-v1-4",					__lowercase					:						List[Any]=False						):
							lowercase__					  =				"fp16" if fpaa else None
							lowercase__					  =				torch.floataa if fpaa else torch.floataa

							lowercase__					  =				AutoencoderKL.from_pretrained(
							    __lowercase,					subfolder="vae",					torch_dtype=__lowercase,					revision=__lowercase,					)
							model.to(__lowercase						).eval()

							return model




				def 							A__      (						self					:						str,					__lowercase					:						Optional[int]=0						):
							if torch_device == "mps":
										return torch.manual_seed(__lowercase						)
							return torch.Generator(device=__lowercase						).manual_seed(__lowercase						)




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
				        [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
				        # fmt: on
				    ]						)
				def 							A__      (						self					:						int,					__lowercase					:						str,					__lowercase					:						Union[str, Any],					__lowercase					:						List[Any]						):
							lowercase__					  =				self.get_sd_vae_model()
							lowercase__					  =				self.get_sd_image(__lowercase						)
							lowercase__					  =				self.get_generator(__lowercase						)

							with torch.no_grad():
										lowercase__					  =				model(__lowercase,					generator=__lowercase,					sample_posterior=__lowercase						).sample

							assert sample.shape == image.shape

							lowercase__					  =				sample[-1, -2:, -2:, :2].flatten().float().cpu()
							lowercase__					  =				torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice						)

							assert torch_all_close(__lowercase,					__lowercase,					atol=3e-3						)




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
				        [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
				        # fmt: on
				    ]						)
				@require_torch_gpu
				def 							A__      (						self					:						int,					__lowercase					:						Optional[int],					__lowercase					:						Tuple						):
							lowercase__					  =				self.get_sd_vae_model(fpaa=__lowercase						)
							lowercase__					  =				self.get_sd_image(__lowercase,					fpaa=__lowercase						)
							lowercase__					  =				self.get_generator(__lowercase						)

							with torch.no_grad():
										lowercase__					  =				model(__lowercase,					generator=__lowercase,					sample_posterior=__lowercase						).sample

							assert sample.shape == image.shape

							lowercase__					  =				sample[-1, -2:, :2, -2:].flatten().float().cpu()
							lowercase__					  =				torch.tensor(__lowercase						)

							assert torch_all_close(__lowercase,					__lowercase,					atol=1e-2						)




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
				        [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
				        # fmt: on
				    ]						)
				def 							A__      (						self					:						Any,					__lowercase					:						Optional[Any],					__lowercase					:						Tuple,					__lowercase					:						List[Any]						):
							lowercase__					  =				self.get_sd_vae_model()
							lowercase__					  =				self.get_sd_image(__lowercase						)

							with torch.no_grad():
										lowercase__					  =				model(__lowercase						).sample

							assert sample.shape == image.shape

							lowercase__					  =				sample[-1, -2:, -2:, :2].flatten().float().cpu()
							lowercase__					  =				torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice						)

							assert torch_all_close(__lowercase,					__lowercase,					atol=3e-3						)




				@parameterized.expand(
				    [
				        # fmt: off
				        [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
				        [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
				        # fmt: on
				    ]						)
				@require_torch_gpu
				def 							A__      (						self					:						Optional[Any],					__lowercase					:						Union[str, Any],					__lowercase					:						Tuple						):
							lowercase__					  =				self.get_sd_vae_model()
							lowercase__					  =				self.get_sd_image(__lowercase,					shape=(3, 4, 64, 64)						)

							with torch.no_grad():
										lowercase__					  =				model.decode(__lowercase						).sample

							assert list(sample.shape						) == [3, 3, 512, 512]

							lowercase__					  =				sample[-1, -2:, :2, -2:].flatten().cpu()
							lowercase__					  =				torch.tensor(__lowercase						)

							assert torch_all_close(__lowercase,					__lowercase,					atol=1e-3						)




				@parameterized.expand(
				    [
				        # fmt: off
				        [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
				        [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
				        # fmt: on
				    ]						)
				@require_torch_gpu
				def 							A__      (						self					:						Optional[int],					__lowercase					:						List[Any],					__lowercase					:						Any						):
							lowercase__					  =				self.get_sd_vae_model(fpaa=__lowercase						)
							lowercase__					  =				self.get_sd_image(__lowercase,					shape=(3, 4, 64, 64),					fpaa=__lowercase						)

							with torch.no_grad():
										lowercase__					  =				model.decode(__lowercase						).sample

							assert list(sample.shape						) == [3, 3, 512, 512]

							lowercase__					  =				sample[-1, -2:, :2, -2:].flatten().float().cpu()
							lowercase__					  =				torch.tensor(__lowercase						)

							assert torch_all_close(__lowercase,					__lowercase,					atol=5e-3						)




				@parameterized.expand([(13,), (16,), (27,)]						)
				@require_torch_gpu
				@unittest.skipIf(not is_xformers_available(),					reason="xformers is not required when using PyTorch 2.0."						)
				def 							A__      (						self					:						Tuple,					__lowercase					:						Union[str, Any]						):
							lowercase__					  =				self.get_sd_vae_model(fpaa=__lowercase						)
							lowercase__					  =				self.get_sd_image(__lowercase,					shape=(3, 4, 64, 64),					fpaa=__lowercase						)

							with torch.no_grad():
										lowercase__					  =				model.decode(__lowercase						).sample

							model.enable_xformers_memory_efficient_attention()
							with torch.no_grad():
										lowercase__					  =				model.decode(__lowercase						).sample

							assert list(sample.shape						) == [3, 3, 512, 512]

							assert torch_all_close(__lowercase,					__lowercase,					atol=1e-1						)




				@parameterized.expand([(13,), (16,), (37,)]						)
				@require_torch_gpu
				@unittest.skipIf(not is_xformers_available(),					reason="xformers is not required when using PyTorch 2.0."						)
				def 							A__      (						self					:						int,					__lowercase					:						Any						):
							lowercase__					  =				self.get_sd_vae_model()
							lowercase__					  =				self.get_sd_image(__lowercase,					shape=(3, 4, 64, 64)						)

							with torch.no_grad():
										lowercase__					  =				model.decode(__lowercase						).sample

							model.enable_xformers_memory_efficient_attention()
							with torch.no_grad():
										lowercase__					  =				model.decode(__lowercase						).sample

							assert list(sample.shape						) == [3, 3, 512, 512]

							assert torch_all_close(__lowercase,					__lowercase,					atol=1e-2						)




				@parameterized.expand(
				    [
				        # fmt: off
				        [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
				        [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
				        # fmt: on
				    ]						)
				def 							A__      (						self					:						Optional[Any],					__lowercase					:						Union[str, Any],					__lowercase					:						Optional[Any]						):
							lowercase__					  =				self.get_sd_vae_model()
							lowercase__					  =				self.get_sd_image(__lowercase						)
							lowercase__					  =				self.get_generator(__lowercase						)

							with torch.no_grad():
										lowercase__					  =				model.encode(__lowercase						).latent_dist
										lowercase__					  =				dist.sample(generator=__lowercase						)

							assert list(sample.shape						) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

							lowercase__					  =				sample[0, -1, -3:, -3:].flatten().cpu()
							lowercase__					  =				torch.tensor(__lowercase						)

							lowercase__					  =				3e-3 if torch_device != "mps" else 1e-2
							assert torch_all_close(__lowercase,					__lowercase,					atol=__lowercase						)









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


lowercase_								=     logging.get_logger(__name__)


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

lowercase_								=     {
    """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""",
    },
}

lowercase_								=     {
    """allenai/led-base-16384""": 1_6384,
}
class 				_snake_case			(					lowercase__):
				UpperCamelCase__				:					int     		=VOCAB_FILES_NAMES
				UpperCamelCase__				:					Any     		=PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase__				:					Dict     		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase__				:					List[Any]     		=LEDTokenizer
				UpperCamelCase__				:					Tuple     		=["""input_ids""", """attention_mask"""]




				def __init__(						self					:						Optional[Any],					__lowercase					:						Optional[Any]=None,					__lowercase					:						Dict=None,					__lowercase					:						Tuple=None,					__lowercase					:						Union[str, Any]="replace",					__lowercase					:						Tuple="<s>",					__lowercase					:						Optional[Any]="</s>",					__lowercase					:						Tuple="</s>",					__lowercase					:						List[str]="<s>",					__lowercase					:						Tuple="<unk>",					__lowercase					:						Dict="<pad>",					__lowercase					:						Dict="<mask>",					__lowercase					:						Any=False,					__lowercase					:						Any=True,					**__lowercase					:						List[Any],					):
							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,					)

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

							lowercase__					  =				add_prefix_space

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

										lowercase__					  =				False

										if state.get("add_prefix_space",					__lowercase						) != add_prefix_space:
													lowercase__					  =				add_prefix_space
													lowercase__					  =				True

										if state.get("trim_offsets",					__lowercase						) != trim_offsets:
													lowercase__					  =				trim_offsets
													lowercase__					  =				True

										if changes_to_apply:
													lowercase__					  =				getattr(__lowercase,					state.pop("type"						)						)
													lowercase__					  =				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 							A__      (						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 							A__      (						self					:						Optional[int],					__lowercase					:						Dict						):
							lowercase__					  =				AddedToken(__lowercase,					lstrip=__lowercase,					rstrip=__lowercase						) if isinstance(__lowercase,					__lowercase						) else value
							lowercase__					  =				value




				def 							A__      (						self					:						Any,					*__lowercase					:						List[Any],					**__lowercase					:						Optional[Any]						):
							lowercase__					  =				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 							A__      (						self					:						int,					*__lowercase					:						Union[str, Any],					**__lowercase					:						List[str]						):
							lowercase__					  =				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 							A__      (						self					:						Optional[Any],					__lowercase					:						str,					__lowercase					:						Optional[str] = None						):
							lowercase__					  =				self._tokenizer.model.save(__lowercase,					name=__lowercase						)
							return tuple(__lowercase						)




				def 							A__      (						self					:						List[str],					__lowercase					:						int,					__lowercase					:						Optional[int]=None						):
							lowercase__					  =				[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 							A__      (						self					:						int,					__lowercase					:						List[int],					__lowercase					:						Optional[List[int]] = None						):
							lowercase__					  =				[self.sep_token_id]
							lowercase__					  =				[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 							A__      (						self					:						Union[str, Any],					__lowercase					:						Union[Dict[str, EncodedInput], BatchEncoding],					__lowercase					:						Optional[int] = None,					__lowercase					:						PaddingStrategy = PaddingStrategy.DO_NOT_PAD,					__lowercase					:						Optional[int] = None,					__lowercase					:						Optional[bool] = None,					):
							lowercase__					  =				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:
										lowercase__					  =				"attention_mask" in self.model_input_names

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

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

							return encoded_inputs




