

'''simple docstring'''





import unittest

import numpy as np

from transformers import RoFormerConfig, 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.roformer.modeling_flax_roformer import (
					    FlaxRoFormerForMaskedLM,
					    FlaxRoFormerForMultipleChoice,
					    FlaxRoFormerForQuestionAnswering,
					    FlaxRoFormerForSequenceClassification,
					    FlaxRoFormerForTokenClassification,
					    FlaxRoFormerModel,
					)







class 				lowercase       ( unittest.TestCase    ):




				def __init__(      self   ,     snake_case   ,     snake_case=13   ,     snake_case=7   ,     snake_case=True   ,     snake_case=True   ,     snake_case=True   ,     snake_case=True   ,     snake_case=99   ,     snake_case=32   ,     snake_case=5   ,     snake_case=4   ,     snake_case=37   ,     snake_case="gelu"   ,     snake_case=0.1   ,     snake_case=0.1   ,     snake_case=512   ,     snake_case=16   ,     snake_case=2   ,     snake_case=0.02   ,     snake_case=4   ,     ):
											snake_case_           =					parent
											snake_case_           =					batch_size
											snake_case_           =					seq_length
											snake_case_           =					is_training
											snake_case_           =					use_attention_mask
											snake_case_           =					use_token_type_ids
											snake_case_           =					use_labels
											snake_case_           =					vocab_size
											snake_case_           =					hidden_size
											snake_case_           =					num_hidden_layers
											snake_case_           =					num_attention_heads
											snake_case_           =					intermediate_size
											snake_case_           =					hidden_act
											snake_case_           =					hidden_dropout_prob
											snake_case_           =					attention_probs_dropout_prob
											snake_case_           =					max_position_embeddings
											snake_case_           =					type_vocab_size
											snake_case_           =					type_sequence_label_size
											snake_case_           =					initializer_range
											snake_case_           =					num_choices




				def 					a							(      self ):
											snake_case_           =					ids_tensor([self.batch_size, self.seq_length]   ,     self.vocab_size )

											snake_case_           =					None
											if self.use_attention_mask:
																		snake_case_           =					random_attention_mask([self.batch_size, self.seq_length] )

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

											snake_case_           =					RoFormerConfig(
											    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=UpperCamelCase__   ,     initializer_range=self.initializer_range   ,     )

											return config, input_ids, token_type_ids, attention_mask




				def 					a							(      self ):
											snake_case_           =					self.prepare_config_and_inputs()
											snake_case_       ,					snake_case_       ,					snake_case_       ,					snake_case_           =					config_and_inputs
											snake_case_           =					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
											return config, inputs_dict







@require_flax
class 				lowercase       ( _A			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				str     =    True

				__SCREAMING_SNAKE_CASE      :				Tuple     =    (
				    (
				        FlaxRoFormerModel,
				        FlaxRoFormerForMaskedLM,
				        FlaxRoFormerForSequenceClassification,
				        FlaxRoFormerForTokenClassification,
				        FlaxRoFormerForMultipleChoice,
				        FlaxRoFormerForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)




				def 					a							(      self ):
											snake_case_           =					FlaxRoFormerModelTester(self )




				@slow
				def 					a							(      self ):






											for model_class_name in self.all_model_classes:
																		snake_case_           =					model_class_name.from_pretrained('junnyu/roformer_chinese_small'   ,     from_pt=UpperCamelCase__ )
																		snake_case_           =					model(np.ones((1, 1) ) )
																		self.assertIsNotNone(UpperCamelCase__ )







@require_flax
class 				lowercase       ( unittest.TestCase    ):




				@slow
				def 					a							(      self ):
											snake_case_           =					FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base' )
											snake_case_           =					jnp.array([[0, 1, 2, 3, 4, 5]] )
											snake_case_           =					model(UpperCamelCase__ )[0]

											snake_case_           =					5_0000

											snake_case_           =					(1, 6, vocab_size)
											self.assertEqual(output.shape   ,     UpperCamelCase__ )

											snake_case_           =					jnp.array(
											    [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]] )

											self.assertTrue(jnp.allclose(output[:, :3, :3]   ,     UpperCamelCase__   ,     atol=1e-4 ) )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_UpperCAmelCase	:     Any		       = {
    """configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase	:     Any		       = [
					    """LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LongT5EncoderModel""",
					    """LongT5ForConditionalGeneration""",
					    """LongT5Model""",
					    """LongT5PreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase	:     Optional[int]		       = [
					    """FlaxLongT5ForConditionalGeneration""",
					    """FlaxLongT5Model""",
					    """FlaxLongT5PreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_longta import (
										    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LongTaEncoderModel,
										    LongTaForConditionalGeneration,
										    LongTaModel,
										    LongTaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_longta import (
										    FlaxLongTaForConditionalGeneration,
										    FlaxLongTaModel,
										    FlaxLongTaPreTrainedModel,
										)


else:
					import sys

					_UpperCAmelCase	:     Optional[Any]		       = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




