"""simple docstring"""






import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_A   					=    get_tests_dir('fixtures/test_sentencepiece.model')

@require_sentencepiece
@require_tokenizers
class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		XGLMTokenizer
					a   	=		XGLMTokenizerFast
					a   	=		True
					a   	=		True



					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Tuple:
									super().setUp()

									# We have a SentencePiece fixture for testing
									SCREAMING_SNAKE_CASE__		  =				XGLMTokenizer(_snake_case   , keep_accents=_snake_case		)
									tokenizer.save_pretrained(self.tmpdirname		)



					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				"<pad>"
									SCREAMING_SNAKE_CASE__		  =				1

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



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				list(self.get_tokenizer().get_vocab().keys()		)

									self.assertEqual(vocab_keys[0]   , "<s>"		)
									self.assertEqual(vocab_keys[1]   , "<pad>"		)
									self.assertEqual(len(_snake_case		)   , 1008		)



					def 			lowerCAmelCase_					(						self			:				Any		)							->       Dict:
									self.assertEqual(self.get_tokenizer().vocab_size   , 1008		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				XGLMTokenizer(_snake_case   , keep_accents=_snake_case		)

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

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(_snake_case		)   , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]   , )

									SCREAMING_SNAKE_CASE__		  =				tokenizer.tokenize("I was born in 92000, and this is falsé."		)
									self.assertListEqual(
									    _snake_case   , [
									        SPIECE_UNDERLINE + "I",
									        SPIECE_UNDERLINE + "was",
									        SPIECE_UNDERLINE + "b",
									        "or",
									        "n",
									        SPIECE_UNDERLINE + "in",
									        SPIECE_UNDERLINE + "",
									        "9",
									        "2",
									        "0",
									        "0",
									        "0",
									        ",",
									        SPIECE_UNDERLINE + "and",
									        SPIECE_UNDERLINE + "this",
									        SPIECE_UNDERLINE + "is",
									        SPIECE_UNDERLINE + "f",
									        "al",
									        "s",
									        "é",
									        ".",
									    ]   , )
									SCREAMING_SNAKE_CASE__		  =				tokenizer.convert_tokens_to_ids(_snake_case		)
									self.assertListEqual(
									    _snake_case   , [
									        value + tokenizer.fairseq_offset
									        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
									    ]   , )

									SCREAMING_SNAKE_CASE__		  =				tokenizer.convert_ids_to_tokens(_snake_case		)
									self.assertListEqual(
									    _snake_case   , [
									        SPIECE_UNDERLINE + "I",
									        SPIECE_UNDERLINE + "was",
									        SPIECE_UNDERLINE + "b",
									        "or",
									        "n",
									        SPIECE_UNDERLINE + "in",
									        SPIECE_UNDERLINE + "",
									        "<unk>",
									        "2",
									        "0",
									        "0",
									        "0",
									        ",",
									        SPIECE_UNDERLINE + "and",
									        SPIECE_UNDERLINE + "this",
									        SPIECE_UNDERLINE + "is",
									        SPIECE_UNDERLINE + "f",
									        "al",
									        "s",
									        "<unk>",
									        ".",
									    ]   , )



					@cached_property
					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Optional[int]:
									return XGLMTokenizer.from_pretrained("facebook/xglm-564M"		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       int:
									with tempfile.NamedTemporaryFile() as f:
													shutil.copyfile(_snake_case   , f.name		)
													SCREAMING_SNAKE_CASE__		  =				XGLMTokenizer(f.name   , keep_accents=_snake_case		)
													SCREAMING_SNAKE_CASE__		  =				pickle.dumps(_snake_case		)
									pickle.loads(_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Optional[Any]:
									if not self.test_rust_tokenizer:
													return

									SCREAMING_SNAKE_CASE__		  =				self.get_tokenizer()
									SCREAMING_SNAKE_CASE__		  =				self.get_rust_tokenizer()

									SCREAMING_SNAKE_CASE__		  =				"I was born in 92000, and this is falsé."

									SCREAMING_SNAKE_CASE__		  =				tokenizer.tokenize(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				rust_tokenizer.tokenize(_snake_case		)
									self.assertListEqual(_snake_case   , _snake_case		)

									SCREAMING_SNAKE_CASE__		  =				tokenizer.encode(_snake_case   , add_special_tokens=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				rust_tokenizer.encode(_snake_case   , add_special_tokens=_snake_case		)
									self.assertListEqual(_snake_case   , _snake_case		)

									SCREAMING_SNAKE_CASE__		  =				self.get_rust_tokenizer()
									SCREAMING_SNAKE_CASE__		  =				tokenizer.encode(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				rust_tokenizer.encode(_snake_case		)
									self.assertListEqual(_snake_case   , _snake_case		)



					@slow
					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				"Hello World!"
									SCREAMING_SNAKE_CASE__		  =				[2, 31227, 4447, 35]

									self.assertListEqual(_snake_case   , self.big_tokenizer.encode(_snake_case		)		)



					@slow
					def 			lowerCAmelCase_					(						self			:				Any		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				(
									    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
									    " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
									)
									# fmt: off
									SCREAMING_SNAKE_CASE__		  =				[2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 71630, 28085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 13675, 377, 652, 7580, 10341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 202277, 17892, 33, 60, 87, 4, 3234, 157, 61, 2667, 52376, 19, 88, 23, 735]
									# fmt: on

									self.assertListEqual(_snake_case   , self.big_tokenizer.encode(_snake_case		)		)



					@slow
					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       int:
									# fmt: off
									SCREAMING_SNAKE_CASE__		  =				{
									    "input_ids": [[2, 108825, 1163, 15, 88010, 473, 15898, 157, 13672, 1857, 312, 8, 238021, 1163, 53, 13672, 1857, 312, 8, 53283, 182396, 8, 18566, 16, 36733, 4101, 8, 230, 244017, 122553, 7, 15, 132597, 4, 293, 12511, 7610, 4, 3414, 132597, 9, 4, 32361, 362, 4, 734, 28512, 32569, 18, 4, 32361, 26096, 14982, 73, 18715, 21433, 235261, 15, 492, 12427, 16, 53, 18715, 21433, 65454, 15, 23659, 563, 16, 278, 597, 2843, 595, 7931, 182396, 64186, 22, 886, 595, 132981, 53, 25540, 3449, 43982, 39901, 5951, 878, 330, 4, 27694, 80269, 312, 53, 6517, 11780, 611, 20408, 5], [2, 6, 132597, 67, 42897, 33, 592, 8, 163729, 25540, 361, 136997, 109514, 173230, 7, 501, 60, 102913, 196, 5631, 235, 63243, 473, 6, 231757, 74, 5277, 7905, 53, 3095, 37317, 22, 454, 183874, 5], [2, 268, 31298, 46530, 6, 132935, 43831, 7, 597, 32, 24, 3688, 9865, 5]],
									    "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, 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, 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, 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]]
									}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=_snake_case   , model_name="facebook/xglm-564M"   , padding=_snake_case   , )





"""simple docstring"""






import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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 (
						    MODEL_FOR_PRETRAINING_MAPPING,
						    AlbertForMaskedLM,
						    AlbertForMultipleChoice,
						    AlbertForPreTraining,
						    AlbertForQuestionAnswering,
						    AlbertForSequenceClassification,
						    AlbertForTokenClassification,
						    AlbertModel,
						)
						from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class lowerCamelCase		:






					'''simple docstring'''


					def __init__(						self			:				str   , _snake_case			:				List[str]   , _snake_case			:				List[Any]=13   , _snake_case			:				List[str]=7   , _snake_case			:				Dict=True   , _snake_case			:				str=True   , _snake_case			:				Optional[Any]=True   , _snake_case			:				Tuple=True   , _snake_case			:				List[Any]=99   , _snake_case			:				Dict=16   , _snake_case			:				Tuple=36   , _snake_case			:				Optional[int]=6   , _snake_case			:				Optional[int]=6   , _snake_case			:				Tuple=6   , _snake_case			:				Optional[int]=37   , _snake_case			:				Dict="gelu"   , _snake_case			:				str=0.1   , _snake_case			:				Tuple=0.1   , _snake_case			:				List[str]=512   , _snake_case			:				Any=16   , _snake_case			:				Optional[int]=2   , _snake_case			:				Optional[int]=0.02   , _snake_case			:				Union[str, Any]=3   , _snake_case			:				int=4   , _snake_case			:				Optional[int]=None   , )							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				parent
									SCREAMING_SNAKE_CASE__		  =				batch_size
									SCREAMING_SNAKE_CASE__		  =				seq_length
									SCREAMING_SNAKE_CASE__		  =				is_training
									SCREAMING_SNAKE_CASE__		  =				use_input_mask
									SCREAMING_SNAKE_CASE__		  =				use_token_type_ids
									SCREAMING_SNAKE_CASE__		  =				use_labels
									SCREAMING_SNAKE_CASE__		  =				vocab_size
									SCREAMING_SNAKE_CASE__		  =				embedding_size
									SCREAMING_SNAKE_CASE__		  =				hidden_size
									SCREAMING_SNAKE_CASE__		  =				num_hidden_layers
									SCREAMING_SNAKE_CASE__		  =				num_hidden_groups
									SCREAMING_SNAKE_CASE__		  =				num_attention_heads
									SCREAMING_SNAKE_CASE__		  =				intermediate_size
									SCREAMING_SNAKE_CASE__		  =				hidden_act
									SCREAMING_SNAKE_CASE__		  =				hidden_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				max_position_embeddings
									SCREAMING_SNAKE_CASE__		  =				type_vocab_size
									SCREAMING_SNAKE_CASE__		  =				type_sequence_label_size
									SCREAMING_SNAKE_CASE__		  =				initializer_range
									SCREAMING_SNAKE_CASE__		  =				num_labels
									SCREAMING_SNAKE_CASE__		  =				num_choices
									SCREAMING_SNAKE_CASE__		  =				scope



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size		)

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

									SCREAMING_SNAKE_CASE__		  =				None
									if self.use_token_type_ids:
													SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size, self.seq_length]   , self.type_vocab_size		)

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

									SCREAMING_SNAKE_CASE__		  =				self.get_config()

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Union[str, Any]:
									return AlbertConfig(
									    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   , initializer_range=self.initializer_range   , num_hidden_groups=self.num_hidden_groups   , )



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				Tuple   , _snake_case			:				Optional[Any]   , _snake_case			:				Union[str, Any]   , _snake_case			:				int   , _snake_case			:				Union[str, Any]   , _snake_case			:				str   , _snake_case			:				int		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				AlbertModel(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , token_type_ids=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)
									self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, self.seq_length, self.hidden_size)		)
									self.parent.assertEqual(result.pooler_output.shape   , (self.batch_size, self.hidden_size)		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Optional[Any]   , _snake_case			:				int   , _snake_case			:				str   , _snake_case			:				List[Any]   , _snake_case			:				Dict   , _snake_case			:				Union[str, Any]   , _snake_case			:				str		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				AlbertForPreTraining(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case   , sentence_order_label=_snake_case   , )
									self.parent.assertEqual(result.prediction_logits.shape   , (self.batch_size, self.seq_length, self.vocab_size)		)
									self.parent.assertEqual(result.sop_logits.shape   , (self.batch_size, config.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				int   , _snake_case			:				Any   , _snake_case			:				List[Any]   , _snake_case			:				Any   , _snake_case			:				str   , _snake_case			:				Tuple   , _snake_case			:				List[Any]		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				AlbertForMaskedLM(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.vocab_size)		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Any   , _snake_case			:				Optional[int]   , _snake_case			:				Optional[Any]   , _snake_case			:				Tuple		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				AlbertForQuestionAnswering(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , start_positions=_snake_case   , end_positions=_snake_case   , )
									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 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				str   , _snake_case			:				Optional[int]   , _snake_case			:				int   , _snake_case			:				List[Any]   , _snake_case			:				List[Any]   , _snake_case			:				str		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				AlbertForSequenceClassification(_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				Any   , _snake_case			:				str   , _snake_case			:				List[Any]   , _snake_case			:				Any   , _snake_case			:				Union[str, Any]   , _snake_case			:				Dict   , _snake_case			:				Tuple		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				AlbertForTokenClassification(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				str   , _snake_case			:				Any   , _snake_case			:				Any   , _snake_case			:				int   , _snake_case			:				List[Any]   , _snake_case			:				List[str]   , _snake_case			:				int		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				self.num_choices
									SCREAMING_SNAKE_CASE__		  =				AlbertForMultipleChoice(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				input_ids.unsqueeze(1		).expand(-1   , self.num_choices   , -1		).contiguous()
									SCREAMING_SNAKE_CASE__		  =				token_type_ids.unsqueeze(1		).expand(-1   , self.num_choices   , -1		).contiguous()
									SCREAMING_SNAKE_CASE__		  =				input_mask.unsqueeze(1		).expand(-1   , self.num_choices   , -1		).contiguous()
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case   , )
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_choices)		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.prepare_config_and_inputs()
									(
									    (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       
									)		  =				config_and_inputs
									SCREAMING_SNAKE_CASE__		  =				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
									return config, inputs_dict



@require_torch
class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		(
					    (
					        AlbertModel,
					        AlbertForPreTraining,
					        AlbertForMaskedLM,
					        AlbertForMultipleChoice,
					        AlbertForSequenceClassification,
					        AlbertForTokenClassification,
					        AlbertForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					a   	=		(
					    {
					        "feature-extraction": AlbertModel,
					        "fill-mask": AlbertForMaskedLM,
					        "question-answering": AlbertForQuestionAnswering,
					        "text-classification": AlbertForSequenceClassification,
					        "token-classification": AlbertForTokenClassification,
					        "zero-shot": AlbertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					a   	=		True



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				Dict   , _snake_case			:				str   , _snake_case			:				str=False		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				super()._prepare_for_class(_snake_case   , _snake_case   , return_labels=_snake_case		)

									if return_labels:
													if model_class in get_values(_snake_case		):
																	SCREAMING_SNAKE_CASE__		  =				torch.zeros(
																	    (self.model_tester.batch_size, self.model_tester.seq_length)   , dtype=torch.long   , device=_snake_case		)
																	SCREAMING_SNAKE_CASE__		  =				torch.zeros(
																	    self.model_tester.batch_size   , dtype=torch.long   , device=_snake_case		)
									return inputs_dict



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				AlbertModelTester(self		)
									SCREAMING_SNAKE_CASE__		  =				ConfigTester(self   , config_class=_snake_case   , hidden_size=37		)



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       List[str]:
									self.config_tester.run_common_tests()



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Any		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_multiple_choice(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
													SCREAMING_SNAKE_CASE__		  =				type
													self.model_tester.create_and_check_model(*_snake_case		)



					@slow
					def 			lowerCAmelCase_					(						self			:				Any		)							->       str:
									for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE__		  =				AlbertModel.from_pretrained(_snake_case		)
													self.assertIsNotNone(_snake_case		)



@require_torch
class lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					@slow
					def 			lowerCAmelCase_					(						self			:				Any		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				AlbertModel.from_pretrained("albert-base-v2"		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case		)[0]
									SCREAMING_SNAKE_CASE__		  =				torch.Size((1, 11, 768)		)
									self.assertEqual(output.shape   , _snake_case		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor(
									    [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]		)

									self.assertTrue(torch.allclose(output[:, 1:4, 1:4]   , _snake_case   , atol=1e-4		)		)




