"""simple docstring"""






import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax.numpy as jnp

						from transformers.models.albert.modeling_flax_albert import (
						    FlaxAlbertForMaskedLM,
						    FlaxAlbertForMultipleChoice,
						    FlaxAlbertForPreTraining,
						    FlaxAlbertForQuestionAnswering,
						    FlaxAlbertForSequenceClassification,
						    FlaxAlbertForTokenClassification,
						    FlaxAlbertModel,
						)

class lowerCamelCase		(unittest.TestCase   ):






					'''simple docstring'''


					def __init__(    self      :					Tuple ,			_snake_case      :					List[Any] ,			_snake_case      :					Optional[int]=13 ,			_snake_case      :					List[Any]=7 ,			_snake_case      :					Tuple=True ,			_snake_case      :					Union[str, Any]=True ,			_snake_case      :					List[str]=True ,			_snake_case      :					int=True ,			_snake_case      :					int=99 ,			_snake_case      :					Tuple=32 ,			_snake_case      :					str=5 ,			_snake_case      :					List[Any]=4 ,			_snake_case      :					Dict=37 ,			_snake_case      :					Tuple="gelu" ,			_snake_case      :					Dict=0.1 ,			_snake_case      :					str=0.1 ,			_snake_case      :					Tuple=512 ,			_snake_case      :					Any=16 ,			_snake_case      :					List[str]=2 ,			_snake_case      :					Optional[Any]=0.02 ,			_snake_case      :					Optional[Any]=4 ,			)				->							Union[str, Any]:
									SCREAMING_SNAKE_CASE__			=					parent
									SCREAMING_SNAKE_CASE__			=					batch_size
									SCREAMING_SNAKE_CASE__			=					seq_length
									SCREAMING_SNAKE_CASE__			=					is_training
									SCREAMING_SNAKE_CASE__			=					use_attention_mask
									SCREAMING_SNAKE_CASE__			=					use_token_type_ids
									SCREAMING_SNAKE_CASE__			=					use_labels
									SCREAMING_SNAKE_CASE__			=					vocab_size
									SCREAMING_SNAKE_CASE__			=					hidden_size
									SCREAMING_SNAKE_CASE__			=					num_hidden_layers
									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_choices



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

									SCREAMING_SNAKE_CASE__			=					None
									if self.use_attention_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__			=					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 ,			is_decoder=_snake_case ,			initializer_range=self.initializer_range ,			)

									return config, input_ids, token_type_ids, attention_mask



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



@require_flax
class lowerCamelCase		(__snake_case     ,     unittest.TestCase   ):






					'''simple docstring'''


					a 						=		(
					    (
					        FlaxAlbertModel,
					        FlaxAlbertForPreTraining,
					        FlaxAlbertForMaskedLM,
					        FlaxAlbertForMultipleChoice,
					        FlaxAlbertForQuestionAnswering,
					        FlaxAlbertForSequenceClassification,
					        FlaxAlbertForTokenClassification,
					        FlaxAlbertForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)



					def       lowerCAmelCase_       (    self      :					Tuple		)				->							Dict:
									SCREAMING_SNAKE_CASE__			=					FlaxAlbertModelTester(self		)



					@slow
					def       lowerCAmelCase_       (    self      :					Dict		)				->							Optional[Any]:
									for model_class_name in self.all_model_classes:
													SCREAMING_SNAKE_CASE__			=					model_class_name.from_pretrained("albert-base-v2"		)
													SCREAMING_SNAKE_CASE__			=					model(np.ones((1, 1)		)		)
													self.assertIsNotNone(_snake_case		)



@require_flax
class lowerCamelCase		(unittest.TestCase   ):






					'''simple docstring'''


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

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






import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union




@dataclass
class   __lowercase						:
	_A       =   None
	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   None
	_A       =   None
	_A       =   False
	_A       =   False
	_A       =   False
	_A       =   True
	_A       =   None
	_A       =   1
	_A       =   None
	_A       =   False
	_A       =   None
	_A       =   None






	def       _a(self				: str    ) ->			"DownloadConfig":
							return self.__class__(**{k: copy.deepcopy(snake_case    ) for k, v in self.__dict__.items()}    )




