






'''simple docstring'''







import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    GPTNeoXForCausalLM,
			    GPTNeoXForQuestionAnswering,
			    GPTNeoXForSequenceClassification,
			    GPTNeoXForTokenClassification,
			    GPTNeoXModel,
			)




class 		UpperCamelCase__    :




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=13   ,_lowerCAmelCase=7   ,_lowerCAmelCase=True   ,_lowerCAmelCase=True   ,_lowerCAmelCase=True   ,_lowerCAmelCase=True   ,_lowerCAmelCase=99   ,_lowerCAmelCase=64   ,_lowerCAmelCase=5   ,_lowerCAmelCase=4   ,_lowerCAmelCase=37   ,_lowerCAmelCase="gelu"   ,_lowerCAmelCase=0.1   ,_lowerCAmelCase=0.1   ,_lowerCAmelCase=5_12   ,_lowerCAmelCase=16   ,_lowerCAmelCase=2   ,_lowerCAmelCase=0.02   ,_lowerCAmelCase=3   ,_lowerCAmelCase=4   ,_lowerCAmelCase=None   ,):
								lowerCamelCase__         =  parent
								lowerCamelCase__         =  batch_size
								lowerCamelCase__         =  seq_length
								lowerCamelCase__         =  is_training
								lowerCamelCase__         =  use_input_mask
								lowerCamelCase__         =  use_token_type_ids
								lowerCamelCase__         =  use_labels
								lowerCamelCase__         =  vocab_size
								lowerCamelCase__         =  hidden_size
								lowerCamelCase__         =  num_hidden_layers
								lowerCamelCase__         =  num_attention_heads
								lowerCamelCase__         =  intermediate_size
								lowerCamelCase__         =  hidden_act
								lowerCamelCase__         =  hidden_dropout_prob
								lowerCamelCase__         =  attention_probs_dropout_prob
								lowerCamelCase__         =  max_position_embeddings
								lowerCamelCase__         =  type_vocab_size
								lowerCamelCase__         =  type_sequence_label_size
								lowerCamelCase__         =  initializer_range
								lowerCamelCase__         =  num_labels
								lowerCamelCase__         =  num_choices
								lowerCamelCase__         =  scope
								lowerCamelCase__         =  vocab_size - 1






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size       )

								lowerCamelCase__         =  None
								if self.use_input_mask:
														lowerCamelCase__         =  random_attention_mask([self.batch_size, self.seq_length]       )

								lowerCamelCase__         =  None
								if self.use_labels:
														lowerCamelCase__         =  ids_tensor([self.batch_size, self.seq_length]   ,self.num_labels       )

								lowerCamelCase__         =  self.get_config()

								return config, input_ids, input_mask, token_labels






		def 			UpperCamelCase_					(      self       ):
								return GPTNeoXConfig(
								    vocab_size=self.vocab_size   ,hidden_size=self.hidden_size   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,intermediate_size=self.intermediate_size   ,hidden_act=self.hidden_act   ,hidden_dropout_prob=self.hidden_dropout_prob   ,attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,max_position_embeddings=self.max_position_embeddings   ,type_vocab_size=self.type_vocab_size   ,is_decoder=lowerCAmelCase__   ,initializer_range=self.initializer_range   ,pad_token_id=self.pad_token_id   ,)






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.prepare_config_and_inputs()

								lowerCamelCase__         =  True

								return config, input_ids, input_mask, token_labels






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  GPTNeoXModel(config=lowerCAmelCase__       )
								model.to(lowerCAmelCase__       )
								model.eval()
								lowerCamelCase__         =  model(lowerCAmelCase__   ,attention_mask=lowerCAmelCase__       )
								lowerCamelCase__         =  model(lowerCAmelCase__       )
								self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  True
								lowerCamelCase__         =  GPTNeoXModel(lowerCAmelCase__       )
								model.to(lowerCAmelCase__       )
								model.eval()
								lowerCamelCase__         =  model(lowerCAmelCase__   ,attention_mask=lowerCAmelCase__       )
								self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  GPTNeoXForCausalLM(config=lowerCAmelCase__       )
								model.to(lowerCAmelCase__       )
								model.eval()
								lowerCamelCase__         =  model(lowerCAmelCase__   ,attention_mask=lowerCAmelCase__   ,labels=lowerCAmelCase__       )
								self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length, self.vocab_size)       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  self.num_labels
								lowerCamelCase__         =  GPTNeoXForQuestionAnswering(lowerCAmelCase__       )
								model.to(lowerCAmelCase__       )
								model.eval()
								lowerCamelCase__         =  model(lowerCAmelCase__   ,attention_mask=lowerCAmelCase__       )
								self.parent.assertEqual(result.start_logits.shape   ,(self.batch_size, self.seq_length)       )
								self.parent.assertEqual(result.end_logits.shape   ,(self.batch_size, self.seq_length)       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  self.num_labels
								lowerCamelCase__         =  GPTNeoXForSequenceClassification(lowerCAmelCase__       )
								model.to(lowerCAmelCase__       )
								model.eval()
								lowerCamelCase__         =  ids_tensor([self.batch_size]   ,self.type_sequence_label_size       )
								lowerCamelCase__         =  model(lowerCAmelCase__   ,attention_mask=lowerCAmelCase__   ,labels=lowerCAmelCase__       )
								self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  self.num_labels
								lowerCamelCase__         =  GPTNeoXForTokenClassification(lowerCAmelCase__       )
								model.to(lowerCAmelCase__       )
								model.eval()
								lowerCamelCase__         =  model(lowerCAmelCase__   ,attention_mask=lowerCAmelCase__   ,labels=lowerCAmelCase__       )
								self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length, self.num_labels)       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  True
								lowerCamelCase__         =  GPTNeoXForCausalLM(config=lowerCAmelCase__       )
								model.to(lowerCAmelCase__       )
								model.eval()

								# first forward pass
								lowerCamelCase__         =  model(lowerCAmelCase__   ,attention_mask=lowerCAmelCase__   ,use_cache=lowerCAmelCase__       )
								lowerCamelCase__         =  outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								lowerCamelCase__         =  ids_tensor((self.batch_size, 3)   ,config.vocab_size       )
								lowerCamelCase__         =  ids_tensor((self.batch_size, 3)   ,vocab_size=2       )

								# append to next input_ids and
								lowerCamelCase__         =  torch.cat([input_ids, next_tokens]   ,dim=-1       )
								lowerCamelCase__         =  torch.cat([input_mask, next_mask]   ,dim=-1       )

								lowerCamelCase__         =  model(lowerCAmelCase__   ,attention_mask=lowerCAmelCase__   ,output_hidden_states=lowerCAmelCase__       )
								lowerCamelCase__         =  output_from_no_past["hidden_states"][0]
								lowerCamelCase__         =  model(
								    lowerCAmelCase__   ,attention_mask=lowerCAmelCase__   ,past_key_values=lowerCAmelCase__   ,output_hidden_states=lowerCAmelCase__   ,)["hidden_states"][0]

								# select random slice
								lowerCamelCase__         =  ids_tensor((1,)   ,output_from_past.shape[-1]       ).item()
								lowerCamelCase__         =  output_from_no_past[:, -3:, random_slice_idx].detach()
								lowerCamelCase__         =  output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]       )

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(lowerCAmelCase__   ,lowerCAmelCase__   ,atol=1E-3       )       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.prepare_config_and_inputs()
								lowerCamelCase__         =  config_and_inputs
								lowerCamelCase__         =  {"input_ids": input_ids, "attention_mask": input_mask}
								return config, inputs_dict




@require_torch
class 		UpperCamelCase__    (a__   ,a__   ,a__   ,unittest.TestCase			):




		'''simple docstring'''





		_UpperCamelCase				  =					(
		    (
		        GPTNeoXModel,
		        GPTNeoXForCausalLM,
		        GPTNeoXForQuestionAnswering,
		        GPTNeoXForSequenceClassification,
		        GPTNeoXForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		_UpperCamelCase				  =					(GPTNeoXForCausalLM,) if is_torch_available() else ()
		_UpperCamelCase				  =					(
		    {
		        """feature-extraction""": GPTNeoXModel,
		        """question-answering""": GPTNeoXForQuestionAnswering,
		        """text-classification""": GPTNeoXForSequenceClassification,
		        """text-generation""": GPTNeoXForCausalLM,
		        """token-classification""": GPTNeoXForTokenClassification,
		        """zero-shot""": GPTNeoXForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		_UpperCamelCase				  =					False
		_UpperCamelCase				  =					False
		_UpperCamelCase				  =					False
		_UpperCamelCase				  =					False






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  GPTNeoXModelTester(self       )
								lowerCamelCase__         =  ConfigTester(self   ,config_class=lowerCAmelCase__   ,hidden_size=64   ,num_attention_heads=8       )






		def 			UpperCamelCase_					(      self       ):
								self.config_tester.run_common_tests()






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(lowerCAmelCase__   ,lowerCAmelCase__   ,lowerCAmelCase__       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(lowerCAmelCase__   ,lowerCAmelCase__   ,lowerCAmelCase__       )






		def 			UpperCamelCase_					(      self       ):
								# This regression test was failing with PyTorch < 1.3
								lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_decoder()

								lowerCamelCase__         =  None

								self.model_tester.create_and_check_model_as_decoder(lowerCAmelCase__   ,lowerCAmelCase__   ,lowerCAmelCase__       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCAmelCase__   ,lowerCAmelCase__   ,lowerCAmelCase__       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*lowerCAmelCase__       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__       )






		@unittest.skip(reason="""Feed forward chunking is not implemented"""       )
		def 			UpperCamelCase_					(      self       ):
								pass






		@parameterized.expand([("""linear""",), ("""dynamic""",)]       )
		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase__         =  ids_tensor([1, 10]   ,config.vocab_size       )
								lowerCamelCase__         =  ids_tensor([1, int(config.max_position_embeddings * 1.5       )]   ,config.vocab_size       )

								set_seed(42       )  # Fixed seed at init time so the two models get the same random weights
								lowerCamelCase__         =  GPTNeoXModel(lowerCAmelCase__       )
								original_model.to(lowerCAmelCase__       )
								original_model.eval()
								lowerCamelCase__         =  original_model(lowerCAmelCase__       ).last_hidden_state
								lowerCamelCase__         =  original_model(lowerCAmelCase__       ).last_hidden_state

								set_seed(42       )  # Fixed seed at init time so the two models get the same random weights
								lowerCamelCase__         =  {"type": scaling_type, "factor": 10.0}
								lowerCamelCase__         =  GPTNeoXModel(lowerCAmelCase__       )
								scaled_model.to(lowerCAmelCase__       )
								scaled_model.eval()
								lowerCamelCase__         =  scaled_model(lowerCAmelCase__       ).last_hidden_state
								lowerCamelCase__         =  scaled_model(lowerCAmelCase__       ).last_hidden_state

								# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
								# maximum sequence length, so the outputs for the short input should match.
								if scaling_type == "dynamic":
														self.assertTrue(torch.allclose(lowerCAmelCase__   ,lowerCAmelCase__   ,atol=1E-5       )       )
								else:
														self.assertFalse(torch.allclose(lowerCAmelCase__   ,lowerCAmelCase__   ,atol=1E-5       )       )

								# The output should be different for long inputs
								self.assertFalse(torch.allclose(lowerCAmelCase__   ,lowerCAmelCase__   ,atol=1E-5       )       )




@require_torch
class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		@slow
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped"""       )
								for checkpointing in [True, False]:
														lowerCamelCase__         =  GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped"""       )

														if checkpointing:
																				model.gradient_checkpointing_enable()
														else:
																				model.gradient_checkpointing_disable()
														model.to(lowerCAmelCase__       )

														lowerCamelCase__         =  tokenizer("""My favorite food is"""   ,return_tensors="""pt"""       ).to(lowerCAmelCase__       )
														# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
														# See: https://github.com/huggingface/transformers/pull/24193
														lowerCamelCase__         =  "My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"

														lowerCamelCase__         =  model.generate(**lowerCAmelCase__   ,do_sample=lowerCAmelCase__   ,max_new_tokens=20       )
														lowerCamelCase__         =  tokenizer.batch_decode(lowerCAmelCase__       )[0]

														self.assertEqual(lowerCAmelCase__   ,lowerCAmelCase__       )









'''simple docstring'''







import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase    :      List[Any]  			=		get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')




@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__    (a   ,unittest.TestCase			):




		'''simple docstring'''





		_UpperCamelCase				  =					GPTSwaTokenizer
		_UpperCamelCase				  =					False
		_UpperCamelCase				  =					True
		_UpperCamelCase				  =					False






		def 			UpperCamelCase_					(      self       ):
								super().setUp()

								# We have a SentencePiece fixture for testing
								lowerCamelCase__         =  GPTSwaTokenizer(_lowerCAmelCase   ,eos_token="""<unk>"""   ,bos_token="""<unk>"""   ,pad_token="""<unk>"""       )

								tokenizer.save_pretrained(self.tmpdirname       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  """This is a test"""
								lowerCamelCase__         =  """This is a test"""
								return input_text, output_text






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  """<s>"""
								lowerCamelCase__         =  1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCAmelCase       )   ,_lowerCAmelCase       )
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCAmelCase       )   ,_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  list(self.get_tokenizer().get_vocab().keys()       )

								self.assertEqual(vocab_keys[0]   ,"""<unk>"""       )
								self.assertEqual(vocab_keys[1]   ,"""<s>"""       )
								self.assertEqual(vocab_keys[-1]   ,"""j"""       )
								self.assertEqual(len(_lowerCAmelCase       )   ,20_00       )






		def 			UpperCamelCase_					(      self       ):
								self.assertEqual(self.get_tokenizer().vocab_size   ,20_00       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  GPTSwaTokenizer(_lowerCAmelCase       )

								lowerCamelCase__         =  tokenizer.tokenize("""This is a test"""       )
								self.assertListEqual(_lowerCAmelCase   ,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]       )

								self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase       )   ,[4_65, 2_87, 2_65, 6_31, 8_42]       )

								lowerCamelCase__         =  tokenizer.tokenize("""I was born in 92000, and this is falsé."""       )
								# fmt: off
								self.assertListEqual(
								    _lowerCAmelCase   ,["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""]   ,)
								# fmt: on

								lowerCamelCase__         =  tokenizer.convert_tokens_to_ids(_lowerCAmelCase       )
								self.assertListEqual(
								    _lowerCAmelCase   ,[2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60]   ,)

								lowerCamelCase__         =  tokenizer.convert_ids_to_tokens(_lowerCAmelCase       )
								# fmt: off
								self.assertListEqual(
								    _lowerCAmelCase   ,["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""]       )
								# fmt: on






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  GPTSwaTokenizer(_lowerCAmelCase       )
								lowerCamelCase__         =  ["""This is a test""", """I was born in 92000, and this is falsé."""]
								lowerCamelCase__         =  [
								    [4_65, 2_87, 2_65, 6_31, 8_42],
								    [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
								]

								# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
								for text, expected_ids in zip(_lowerCAmelCase   ,_lowerCAmelCase       ):
														self.assertListEqual(tokenizer.encode_fast(_lowerCAmelCase       )   ,_lowerCAmelCase       )

								# Test that decode_fast returns the input text
								for text, token_ids in zip(_lowerCAmelCase   ,_lowerCAmelCase       ):
														self.assertEqual(tokenizer.decode_fast(_lowerCAmelCase       )   ,_lowerCAmelCase       )






		@slow
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  [
								    """<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')""",
								    """Hey there, how are you doing this fine day?""",
								    """This is a text with a trailing spaces followed by a dot     .""",
								    """Häj sväjs lillebrör! =)""",
								    """Det är inget fel på Mr. Cool""",
								]

								# fmt: off
								lowerCamelCase__         =  {"""input_ids""": [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
								# fmt: on
								self.tokenizer_integration_test_util(
								    expected_encoding=_lowerCAmelCase   ,model_name="""AI-Sweden/gpt-sw3-126m"""   ,sequences=_lowerCAmelCase   ,)

