




import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			import jax.numpy as jnp

			from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
			    FlaxRobertaPreLayerNormForCausalLM,
			    FlaxRobertaPreLayerNormForMaskedLM,
			    FlaxRobertaPreLayerNormForMultipleChoice,
			    FlaxRobertaPreLayerNormForQuestionAnswering,
			    FlaxRobertaPreLayerNormForSequenceClassification,
			    FlaxRobertaPreLayerNormForTokenClassification,
			    FlaxRobertaPreLayerNormModel,
			)







class a (     unittest.TestCase  ):
						"""simple docstring"""

						def __init__( self :     Tuple				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     Optional[int]=13				,							lowerCamelCase__ :     Union[str, Any]=7				,							lowerCamelCase__ :     Union[str, Any]=True				,							lowerCamelCase__ :     List[Any]=True				,							lowerCamelCase__ :     List[str]=True				,							lowerCamelCase__ :     Optional[int]=True				,							lowerCamelCase__ :     List[Any]=99				,							lowerCamelCase__ :     int=32				,							lowerCamelCase__ :     int=5				,							lowerCamelCase__ :     Union[str, Any]=4				,							lowerCamelCase__ :     Tuple=37				,							lowerCamelCase__ :     Optional[Any]="gelu"				,							lowerCamelCase__ :     Any=0.1				,							lowerCamelCase__ :     Dict=0.1				,							lowerCamelCase__ :     Any=512				,							lowerCamelCase__ :     Tuple=16				,							lowerCamelCase__ :     str=2				,							lowerCamelCase__ :     Optional[Any]=0.0_2				,							lowerCamelCase__ :     Tuple=4				,							)    ->						Tuple:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Union[str, Any]:
													"""simple docstring"""


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

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

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

													__lowercase      =    RobertaPreLayerNormConfig(
													    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     UpperCAmelCase_      ( self :     Dict				)    ->						Optional[Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    self.prepare_config_and_inputs()
													__lowercase     ,     __lowercase     ,     __lowercase     ,     __lowercase      =    config_and_inputs

													__lowercase      =    True
													__lowercase      =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
													__lowercase      =    ids_tensor([self.batch_size, self.seq_length]				,							vocab_size=2				)

													return (
													    config,
													    input_ids,
													    token_type_ids,
													    encoder_hidden_states,
													    encoder_attention_mask,
													)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class a (     __SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       Optional[Any]       	=		True

						UpperCamelCase_	:       Union[str, Any]       	=		(
						    (
						        FlaxRobertaPreLayerNormModel,
						        FlaxRobertaPreLayerNormForCausalLM,
						        FlaxRobertaPreLayerNormForMaskedLM,
						        FlaxRobertaPreLayerNormForSequenceClassification,
						        FlaxRobertaPreLayerNormForTokenClassification,
						        FlaxRobertaPreLayerNormForMultipleChoice,
						        FlaxRobertaPreLayerNormForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)






						def     UpperCAmelCase_      ( self :     int				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    FlaxRobertaPreLayerNormModelTester(self				)






						@slow
						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Optional[int]:
													"""simple docstring"""


													for model_class_name in self.all_model_classes:
																				__lowercase      =    model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,							from_pt=lowerCamelCase__				)
																				__lowercase      =    model(np.ones((1, 1)				)				)
																				self.assertIsNotNone(lowerCamelCase__				)







@require_flax
class a (     unittest.TestCase  ):
						"""simple docstring"""

						@slow
						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,							from_pt=lowerCamelCase__				)

													__lowercase      =    np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]				,							dtype=jnp.intaa				)
													__lowercase      =    model(lowerCamelCase__				)[0]
													__lowercase      =    [1, 11, 50_265]
													self.assertEqual(list(output.shape				)				,							lowerCamelCase__				)
													# compare the actual values for a slice.
													__lowercase      =    np.array(
													    [[[40.4_880, 18.0_199, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 10.7_085], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]				,							dtype=np.floataa				)
													self.assertTrue(np.allclose(output[:, :3, :3]				,							lowerCamelCase__				,							atol=1e-4				)				)






						@slow
						def     UpperCAmelCase_      ( self :     Any				)    ->						str:
													"""simple docstring"""


													__lowercase      =    FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,							from_pt=lowerCamelCase__				)

													__lowercase      =    np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]				,							dtype=jnp.intaa				)
													__lowercase      =    model(lowerCamelCase__				)[0]
													# compare the actual values for a slice.
													__lowercase      =    np.array(
													    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]				,							dtype=np.floataa				)
													self.assertTrue(np.allclose(output[:, :3, :3]				,							lowerCamelCase__				,							atol=1e-4				)				)











from typing import TYPE_CHECKING

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


UpperCAmelCase__				     = {
    "configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
    "tokenization_m2m_100": ["M2M100Tokenizer"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__				     = [
			    "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "M2M100ForConditionalGeneration",
			    "M2M100Model",
			    "M2M100PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
			from .tokenization_mam_aaa import MaMaaaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mam_aaa import (
						    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MaMaaaForConditionalGeneration,
						    MaMaaaModel,
						    MaMaaaPreTrainedModel,
						)


else:
			import sys

			UpperCAmelCase__				     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






