





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   A			(unittest.TestCase    ):
			'''simple docstring'''




			def __init__(			self   :    str							,	__lowerCAmelCase   :    Dict							,	__lowerCAmelCase   :    Dict=13							,	__lowerCAmelCase   :    int=7							,	__lowerCAmelCase   :    List[str]=True							,	__lowerCAmelCase   :    Dict=True							,	__lowerCAmelCase   :    List[Any]=True							,	__lowerCAmelCase   :    Optional[Any]=True							,	__lowerCAmelCase   :    Dict=99							,	__lowerCAmelCase   :    List[Any]=32							,	__lowerCAmelCase   :    Dict=5							,	__lowerCAmelCase   :    Union[str, Any]=4							,	__lowerCAmelCase   :    List[str]=37							,	__lowerCAmelCase   :    str="gelu"							,	__lowerCAmelCase   :    int=0.1							,	__lowerCAmelCase   :    Union[str, Any]=0.1							,	__lowerCAmelCase   :    Tuple=5_12							,	__lowerCAmelCase   :    Dict=16							,	__lowerCAmelCase   :    List[Any]=2							,	__lowerCAmelCase   :    Dict=0.0_2							,	__lowerCAmelCase   :    List[str]=4							,	)	->	Dict:







					"""simple docstring"""






					A__        =   parent
					A__        =   batch_size
					A__        =   seq_length
					A__        =   is_training
					A__        =   use_attention_mask
					A__        =   use_token_type_ids
					A__        =   use_labels
					A__        =   vocab_size
					A__        =   hidden_size
					A__        =   num_hidden_layers
					A__        =   num_attention_heads
					A__        =   intermediate_size
					A__        =   hidden_act
					A__        =   hidden_dropout_prob
					A__        =   attention_probs_dropout_prob
					A__        =   max_position_embeddings
					A__        =   type_vocab_size
					A__        =   type_sequence_label_size
					A__        =   initializer_range
					A__        =   num_choices






			def    a_       (			self   :    int   )	->	Optional[int]:







					"""simple docstring"""






					A__        =   ids_tensor([self.batch_size, self.seq_length]							,	self.vocab_size   )

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

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

					A__        =   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=lowerCAmelCase__							,	initializer_range=self.initializer_range							,	)

					return config, input_ids, token_type_ids, attention_mask







			def    a_       (			self   :    List[str]   )	->	Optional[int]:







					"""simple docstring"""






					A__        =   self.prepare_config_and_inputs()
					A__        =   config_and_inputs
					A__        =   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
					return config, inputs_dict


@require_flax
class   A			(UpperCAmelCase_   ,     unittest.TestCase    ):
			'''simple docstring'''




			__lowerCamelCase	:       List[Any]							=    True

			__lowerCamelCase	:       str							=    (
			    (
			        FlaxRoFormerModel,
			        FlaxRoFormerForMaskedLM,
			        FlaxRoFormerForSequenceClassification,
			        FlaxRoFormerForTokenClassification,
			        FlaxRoFormerForMultipleChoice,
			        FlaxRoFormerForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)






			def    a_       (			self   :    Optional[int]   )	->	List[str]:







					"""simple docstring"""






					A__        =   FlaxRoFormerModelTester(self   )







			@slow
			def    a_       (			self   :    Optional[Any]   )	->	int:







					"""simple docstring"""






					for model_class_name in self.all_model_classes:
							A__        =   model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""							,	from_pt=lowerCAmelCase__   )
							A__        =   model(np.ones((1, 1)   )   )
							self.assertIsNotNone(lowerCAmelCase__   )


@require_flax
class   A			(unittest.TestCase    ):
			'''simple docstring'''




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







					"""simple docstring"""






					A__        =   FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""   )
					A__        =   jnp.array([[0, 1, 2, 3, 4, 5]]   )
					A__        =   model(lowerCAmelCase__   )[0]

					A__        =   5_00_00

					A__        =   (1, 6, vocab_size)
					self.assertEqual(output.shape							,	lowerCAmelCase__   )

					A__        =   jnp.array(
					    [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]   )

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






from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
						from ..ta.tokenization_ta import TaTokenizer
else:
						from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCAmelCase							:  Any   	=						TaTokenizer

if is_tokenizers_available():
						from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
						from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCAmelCase							:  Dict   	=						TaTokenizerFast

lowerCAmelCase							:  Optional[int]   	=						{"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  int   	=						[
						    """MT5EncoderModel""",
						    """MT5ForConditionalGeneration""",
						    """MT5ForQuestionAnswering""",
						    """MT5Model""",
						    """MT5PreTrainedModel""",
						    """MT5Stack""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Tuple   	=						["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  List[str]   	=						["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
						from .configuration_mta import MTaConfig, MTaOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mta import (
												    MTaEncoderModel,
												    MTaForConditionalGeneration,
												    MTaForQuestionAnswering,
												    MTaModel,
												    MTaPreTrainedModel,
												    MTaStack,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
						import sys

						lowerCAmelCase							:  Optional[Any]   	=						_LazyModule(
						    __name__,
						    globals()["""__file__"""],
						    _import_structure,
						    extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
						    module_spec=__spec__,
						)






