


from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
						from transformers import (
						    TFAutoModel,
						    TFAutoModelForCausalLM,
						    TFAutoModelForMaskedLM,
						    TFAutoModelForPreTraining,
						    TFAutoModelForQuestionAnswering,
						    TFAutoModelForSeqaSeqLM,
						    TFAutoModelForSequenceClassification,
						    TFAutoModelForTableQuestionAnswering,
						    TFAutoModelForTokenClassification,
						    TFAutoModelWithLMHead,
						    TFBertForMaskedLM,
						    TFBertForPreTraining,
						    TFBertForQuestionAnswering,
						    TFBertForSequenceClassification,
						    TFBertModel,
						    TFFunnelBaseModel,
						    TFFunnelModel,
						    TFGPTaLMHeadModel,
						    TFRobertaForMaskedLM,
						    TFTaForConditionalGeneration,
						    TFTapasForQuestionAnswering,
						)
						from transformers.models.auto.modeling_tf_auto import (
						    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
						    TF_MODEL_FOR_MASKED_LM_MAPPING,
						    TF_MODEL_FOR_PRETRAINING_MAPPING,
						    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
						    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
						    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
						    TF_MODEL_MAPPING,
						)
						from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
						from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
						from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
						from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST







class  snake_case__(							snake_case_							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Tuple    =							'''new-model'''


if is_tf_available():







						class  snake_case__(							snake_case_							):







												'''simple docstring'''




												SCREAMING_SNAKE_CASE__					:     Optional[int]    =							NewModelConfig







@require_tf
class  snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						@slow
						def   lowercase_       ( self							)    ->			Union[str, Any]:
										lowerCAmelCase_       :					Optional[int]			 = "bert-base-cased"
										lowerCAmelCase_       :					Dict			 = AutoConfig.from_pretrained(UpperCamelCase__							)
										self.assertIsNotNone(UpperCamelCase__							)
										self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)

										lowerCAmelCase_       :					Tuple			 = TFAutoModel.from_pretrained(UpperCamelCase__							)
										self.assertIsNotNone(UpperCamelCase__							)
										self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)




						@slow
						def   lowercase_       ( self							)    ->			str:
										lowerCAmelCase_       :					List[Any]			 = "bert-base-cased"
										lowerCAmelCase_       :					int			 = AutoConfig.from_pretrained(UpperCamelCase__							)
										self.assertIsNotNone(UpperCamelCase__							)
										self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)

										lowerCAmelCase_       :					Union[str, Any]			 = TFAutoModelForPreTraining.from_pretrained(UpperCamelCase__							)
										self.assertIsNotNone(UpperCamelCase__							)
										self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)




						@slow
						def   lowercase_       ( self							)    ->			Tuple:




										for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase_       :					Optional[int]			 = AutoConfig.from_pretrained(UpperCamelCase__							)
														self.assertIsNotNone(UpperCamelCase__							)
														self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)

														lowerCAmelCase_       :					List[Any]			 = TFAutoModelForCausalLM.from_pretrained(UpperCamelCase__							)
														lowerCAmelCase_       :					Dict			 = TFAutoModelForCausalLM.from_pretrained(UpperCamelCase__      ,  output_loading_info=UpperCamelCase__							)
														self.assertIsNotNone(UpperCamelCase__							)
														self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)




						@slow
						def   lowercase_       ( self							)    ->			Any:




										for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase_       :					Tuple			 = AutoConfig.from_pretrained(UpperCamelCase__							)
														self.assertIsNotNone(UpperCamelCase__							)
														self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)

														lowerCAmelCase_       :					Any			 = TFAutoModelWithLMHead.from_pretrained(UpperCamelCase__							)
														self.assertIsNotNone(UpperCamelCase__							)
														self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)




						@slow
						def   lowercase_       ( self							)    ->			Any:




										for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase_       :					Optional[int]			 = AutoConfig.from_pretrained(UpperCamelCase__							)
														self.assertIsNotNone(UpperCamelCase__							)
														self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)

														lowerCAmelCase_       :					Dict			 = TFAutoModelForMaskedLM.from_pretrained(UpperCamelCase__							)
														lowerCAmelCase_       :					str			 = TFAutoModelForMaskedLM.from_pretrained(UpperCamelCase__      ,  output_loading_info=UpperCamelCase__							)
														self.assertIsNotNone(UpperCamelCase__							)
														self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)




						@slow
						def   lowercase_       ( self							)    ->			List[Any]:




										for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase_       :					List[Any]			 = AutoConfig.from_pretrained(UpperCamelCase__							)
														self.assertIsNotNone(UpperCamelCase__							)
														self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)

														lowerCAmelCase_       :					str			 = TFAutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__							)
														lowerCAmelCase_       :					Optional[Any]			 = TFAutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__      ,  output_loading_info=UpperCamelCase__							)
														self.assertIsNotNone(UpperCamelCase__							)
														self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)




						@slow
						def   lowercase_       ( self							)    ->			List[Any]:




										for model_name in ["bert-base-uncased"]:
														lowerCAmelCase_       :					str			 = AutoConfig.from_pretrained(UpperCamelCase__							)
														self.assertIsNotNone(UpperCamelCase__							)
														self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)

														lowerCAmelCase_       :					Any			 = TFAutoModelForSequenceClassification.from_pretrained(UpperCamelCase__							)
														self.assertIsNotNone(UpperCamelCase__							)
														self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)




						@slow
						def   lowercase_       ( self							)    ->			int:




										for model_name in ["bert-base-uncased"]:
														lowerCAmelCase_       :					List[Any]			 = AutoConfig.from_pretrained(UpperCamelCase__							)
														self.assertIsNotNone(UpperCamelCase__							)
														self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)

														lowerCAmelCase_       :					str			 = TFAutoModelForQuestionAnswering.from_pretrained(UpperCamelCase__							)
														self.assertIsNotNone(UpperCamelCase__							)
														self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)




						@slow
						@require_tensorflow_probability
						def   lowercase_       ( self							)    ->			Optional[int]:




										for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
														lowerCAmelCase_       :					Any			 = AutoConfig.from_pretrained(UpperCamelCase__							)
														self.assertIsNotNone(UpperCamelCase__							)
														self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)

														lowerCAmelCase_       :					Optional[Any]			 = TFAutoModelForTableQuestionAnswering.from_pretrained(UpperCamelCase__							)
														lowerCAmelCase_       :					Any			 = TFAutoModelForTableQuestionAnswering.from_pretrained(
														    UpperCamelCase__      ,  output_loading_info=UpperCamelCase__							)
														self.assertIsNotNone(UpperCamelCase__							)
														self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)




						def   lowercase_       ( self							)    ->			Union[str, Any]:
										lowerCAmelCase_       :					List[Any]			 = TFAutoModelWithLMHead.from_pretrained(UpperCamelCase__							)
										self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)
										self.assertEqual(model.num_parameters()      ,  1_4_4_1_0							)
										self.assertEqual(model.num_parameters(only_trainable=UpperCamelCase__							)      ,  1_4_4_1_0							)




						def   lowercase_       ( self							)    ->			int:
										lowerCAmelCase_       :					List[str]			 = TFAutoModelWithLMHead.from_pretrained(UpperCamelCase__							)
										self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)
										self.assertEqual(model.num_parameters()      ,  1_4_4_1_0							)
										self.assertEqual(model.num_parameters(only_trainable=UpperCamelCase__							)      ,  1_4_4_1_0							)




						def   lowercase_       ( self							)    ->			Union[str, Any]:
										lowerCAmelCase_       :					Optional[int]			 = TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny'''							)
										self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)

										lowerCAmelCase_       :					Tuple			 = copy.deepcopy(model.config							)
										lowerCAmelCase_       :					str			 = ["FunnelBaseModel"]
										lowerCAmelCase_       :					int			 = TFAutoModel.from_config(UpperCamelCase__							)
										self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)

										with tempfile.TemporaryDirectory() as tmp_dir:
														model.save_pretrained(UpperCamelCase__							)
														lowerCAmelCase_       :					Union[str, Any]			 = TFAutoModel.from_pretrained(UpperCamelCase__							)
														self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)




						def   lowercase_       ( self							)    ->			Tuple:




										try:
														AutoConfig.register('''new-model'''      ,  UpperCamelCase__							)

														lowerCAmelCase_       :					int			 = [
														    TFAutoModel,
														    TFAutoModelForCausalLM,
														    TFAutoModelForMaskedLM,
														    TFAutoModelForPreTraining,
														    TFAutoModelForQuestionAnswering,
														    TFAutoModelForSequenceClassification,
														    TFAutoModelForTokenClassification,
														]

														for auto_class in auto_classes:
																		with self.subTest(auto_class.__name__							):
																						# Wrong config class will raise an error
																						with self.assertRaises(UpperCamelCase__							):
																										auto_class.register(UpperCamelCase__      ,  UpperCamelCase__							)
																						auto_class.register(UpperCamelCase__      ,  UpperCamelCase__							)
																						# Trying to register something existing in the Transformers library will raise an error
																						with self.assertRaises(UpperCamelCase__							):
																										auto_class.register(UpperCamelCase__      ,  UpperCamelCase__							)

																						# Now that the config is registered, it can be used as any other config with the auto-API
																						lowerCAmelCase_       :					List[str]			 = BertModelTester(self							).get_config()
																						lowerCAmelCase_       :					List[Any]			 = NewModelConfig(**tiny_config.to_dict()							)
																						lowerCAmelCase_       :					Optional[Any]			 = auto_class.from_config(UpperCamelCase__							)
																						self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)

																						with tempfile.TemporaryDirectory() as tmp_dir:
																										model.save_pretrained(UpperCamelCase__							)
																										lowerCAmelCase_       :					str			 = auto_class.from_pretrained(UpperCamelCase__							)
																										self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__							)

										finally:
														if "new-model" in CONFIG_MAPPING._extra_content:
																		del CONFIG_MAPPING._extra_content["new-model"]
														for mapping in (
														    TF_MODEL_MAPPING,
														    TF_MODEL_FOR_PRETRAINING_MAPPING,
														    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
														    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
														    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
														    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
														    TF_MODEL_FOR_MASKED_LM_MAPPING,
														):
																		if NewModelConfig in mapping._extra_content:
																						del mapping._extra_content[NewModelConfig]




						def   lowercase_       ( self							)    ->			str:




										with self.assertRaisesRegex(
										    UpperCamelCase__      ,  '''bert-base is not a local folder and is not a valid model identifier'''							):
														lowerCAmelCase_       :					Union[str, Any]			 = TFAutoModel.from_pretrained('''bert-base'''							)




						def   lowercase_       ( self							)    ->			int:




										with self.assertRaisesRegex(
										    UpperCamelCase__      ,  R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''							):
														lowerCAmelCase_       :					Union[str, Any]			 = TFAutoModel.from_pretrained(UpperCamelCase__      ,  revision='''aaaaaa'''							)




						def   lowercase_       ( self							)    ->			Tuple:




										with self.assertRaisesRegex(
										    UpperCamelCase__      ,  '''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'''      ,  ):
														lowerCAmelCase_       :					List[Any]			 = TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''							)




						def   lowercase_       ( self							)    ->			Optional[Any]:




										with self.assertRaisesRegex(UpperCamelCase__      ,  '''Use `from_pt=True` to load this model'''							):
														lowerCAmelCase_       :					Optional[Any]			 = TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''							)




						def   lowercase_       ( self							)    ->			int:
										lowerCAmelCase_       :					int			 = TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''							)
										with RequestCounter() as counter:
														lowerCAmelCase_       :					Optional[int]			 = TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''							)
														self.assertEqual(counter.get_request_count      ,  0							)
														self.assertEqual(counter.head_request_count      ,  1							)
														self.assertEqual(counter.other_request_count      ,  0							)

										# With a sharded checkpoint
										lowerCAmelCase_       :					Dict			 = TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''							)
										with RequestCounter() as counter:
														lowerCAmelCase_       :					Dict			 = TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''							)
														self.assertEqual(counter.get_request_count      ,  0							)
														self.assertEqual(counter.head_request_count      ,  1							)
														self.assertEqual(counter.other_request_count      ,  0							)



"""simple docstring"""



import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch







class _lowerCAmelCase				(      unittest.TestCase	):
   __UpperCAmelCase :       Union[str, Any]							  =  JukeboxTokenizer
   __UpperCAmelCase :       Union[str, Any]							  =  {
       '''artist''': '''Zac Brown Band''',
       '''genres''': '''Country''',
       '''lyrics''': '''I met a traveller from an antique land,
        Who said "Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        ''',
   }







   @require_torch
   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       import torch

       snake_case    :      Any      					=    JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics"						)
       snake_case    :      Optional[Any]      					=    tokenizer(**self.metas						)["input_ids"]
       # fmt: off
       snake_case    :      Optional[int]      					=    [
           torch.tensor([[
               0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
               76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
               44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
               47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
               76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
               30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
               27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
               45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
               41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
               19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
               76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
               76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
               64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
               30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
               27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
               34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
               27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
               41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
               76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
               44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
               76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
               32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
               40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
               20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
               45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
               31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
               45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
               76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
               34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
               31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
               40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
               38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
               76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
               41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
               27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
               46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
               76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
               41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
               46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
               41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
               40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
               27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
               76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
               76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
               41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
               76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
               27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
               34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
               44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
               40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
               78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
               46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
               38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
               40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
               27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
               20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
               76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
               76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
               76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
               76, 76]]						),
           torch.tensor([[0, 0, 0, 1069, 11]]						),
           torch.tensor([[0, 0, 0, 1069, 11]]						),
       ]
       # fmt: on
       self.assertTrue(torch.allclose(tokens[0]						,	EXPECTED_OUTPUT[0]						)						)
       self.assertTrue(torch.allclose(tokens[1]						,	EXPECTED_OUTPUT[1]						)						)
       self.assertTrue(torch.allclose(tokens[2]						,	EXPECTED_OUTPUT[2]						)						)







   @require_torch
   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       import torch

       snake_case    :      Tuple      					=    JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics"						)
       snake_case    :      Optional[Any]      					=    tokenizer(**self.metas						)["input_ids"]
       # fmt: off
       snake_case    :      List[Any]      					=    [
           torch.tensor([[
               0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
               31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
               31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
               40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
               79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
               77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
               27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
               37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
               32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
               77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
               77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
               77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
               46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
               77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
               77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
               77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
               77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
               64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
               40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
               40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
               38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
               31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
               41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
               77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
               46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
               41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
               31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
               31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
               23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
               44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
               31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
               38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
               40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
               27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
               31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
               34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
               31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
               1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
               31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
               45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
               31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
               15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
               11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
               45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
               41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
               44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
               46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
               27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
               35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
               77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
               31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
               77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
               41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
               77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
               40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
               77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
               77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
               27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
               77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
               77, 77, 77, 77, 77, 77]]						),
           torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]						),
           torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]						),
       ]
       # fmt: on
       self.assertTrue(torch.allclose(tokens[0]						,	EXPECTED_OUTPUT[0]						)						)
       self.assertTrue(torch.allclose(tokens[1]						,	EXPECTED_OUTPUT[1]						)						)
       self.assertTrue(torch.allclose(tokens[2]						,	EXPECTED_OUTPUT[2]						)						)





