





"""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        __UpperCamelCase				(						unittest.TestCase						):






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__=13      ,					lowerCAmelCase__=7      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=99      ,					lowerCAmelCase__=32      ,					lowerCAmelCase__=5      ,					lowerCAmelCase__=4      ,					lowerCAmelCase__=37      ,					lowerCAmelCase__="gelu"      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=512      ,					lowerCAmelCase__=16      ,					lowerCAmelCase__=2      ,					lowerCAmelCase__=0.02      ,					lowerCAmelCase__=4      ,					)	-> Optional[int]:
								a      :		Any	=				parent
								a      :		Any	=				batch_size
								a      :		List[Any]	=				seq_length
								a      :		Union[str, Any]	=				is_training
								a      :		Any	=				use_attention_mask
								a      :		Dict	=				use_token_type_ids
								a      :		Tuple	=				use_labels
								a      :		Tuple	=				vocab_size
								a      :		int	=				hidden_size
								a      :		str	=				num_hidden_layers
								a      :		List[Any]	=				num_attention_heads
								a      :		List[Any]	=				intermediate_size
								a      :		Optional[Any]	=				hidden_act
								a      :		List[Any]	=				hidden_dropout_prob
								a      :		int	=				attention_probs_dropout_prob
								a      :		Union[str, Any]	=				max_position_embeddings
								a      :		Optional[Any]	=				type_vocab_size
								a      :		List[str]	=				type_sequence_label_size
								a      :		Any	=				initializer_range
								a      :		Any	=				num_choices






							def     __a   (   self      )	-> Dict:
								a      :		List[str]	=				ids_tensor([self.batch_size, self.seq_length]      ,					self.vocab_size      )

								a      :		Dict	=				None
								if self.use_attention_mask:
									a      :		Optional[Any]	=				random_attention_mask([self.batch_size, self.seq_length]      )

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

								a      :		Union[str, Any]	=				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=lowerCAmelCase__      ,					initializer_range=self.initializer_range      ,					)

								return config, input_ids, token_type_ids, attention_mask






							def     __a   (   self      )	-> Any:
								a      :		str	=				self.prepare_config_and_inputs()
								a,       a,       a,       a      :		Optional[Any]	=				config_and_inputs
								a      :		int	=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
								return config, inputs_dict


@require_flax
class        __UpperCamelCase				(						a__		,       unittest.TestCase						):
							lowerCamelCase   :					int      					=(
							    (
							        FlaxAlbertModel,
							        FlaxAlbertForPreTraining,
							        FlaxAlbertForMaskedLM,
							        FlaxAlbertForMultipleChoice,
							        FlaxAlbertForQuestionAnswering,
							        FlaxAlbertForSequenceClassification,
							        FlaxAlbertForTokenClassification,
							        FlaxAlbertForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)






							def     __a   (   self      )	-> Tuple:
								a      :		Optional[int]	=				FlaxAlbertModelTester(self      )






							@slow
							def     __a   (   self      )	-> Any:
								for model_class_name in self.all_model_classes:
									a      :		Optional[int]	=				model_class_name.from_pretrained("albert-base-v2"      )
									a      :		Tuple	=				model(np.ones((1, 1)      )      )
									self.assertIsNotNone(lowerCAmelCase__      )


@require_flax
class        __UpperCamelCase				(						unittest.TestCase						):






							@slow
							def     __a   (   self      )	-> Optional[int]:
								a      :		int	=				FlaxAlbertModel.from_pretrained("albert-base-v2"      )
								a      :		int	=				np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]      )
								a      :		Union[str, Any]	=				np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
								a      :		Optional[int]	=				model(lowerCAmelCase__      ,					attention_mask=lowerCAmelCase__      )[0]
								a      :		str	=				(1, 11, 768)
								self.assertEqual(output.shape      ,					lowerCAmelCase__      )
								a      :		Any	=				np.array(
								    [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]]      )

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






"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__				:							str						=     {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							int						=     ['''RoFormerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							int						=     [
		    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RoFormerForCausalLM''',
		    '''RoFormerForMaskedLM''',
		    '''RoFormerForMultipleChoice''',
		    '''RoFormerForQuestionAnswering''',
		    '''RoFormerForSequenceClassification''',
		    '''RoFormerForTokenClassification''',
		    '''RoFormerLayer''',
		    '''RoFormerModel''',
		    '''RoFormerPreTrainedModel''',
		    '''load_tf_weights_in_roformer''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							List[Any]						=     [
		    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFRoFormerForCausalLM''',
		    '''TFRoFormerForMaskedLM''',
		    '''TFRoFormerForMultipleChoice''',
		    '''TFRoFormerForQuestionAnswering''',
		    '''TFRoFormerForSequenceClassification''',
		    '''TFRoFormerForTokenClassification''',
		    '''TFRoFormerLayer''',
		    '''TFRoFormerModel''',
		    '''TFRoFormerPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							Tuple						=     [
		    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''FlaxRoFormerForMaskedLM''',
		    '''FlaxRoFormerForMultipleChoice''',
		    '''FlaxRoFormerForQuestionAnswering''',
		    '''FlaxRoFormerForSequenceClassification''',
		    '''FlaxRoFormerForTokenClassification''',
		    '''FlaxRoFormerModel''',
		    '''FlaxRoFormerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

		a__				:							int						=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
