


'''simple docstring'''


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     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							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    ,					):
											_SCREAMING_SNAKE_CASE :       int     		=      parent
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      batch_size
											_SCREAMING_SNAKE_CASE :       Tuple     		=      seq_length
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      is_training
											_SCREAMING_SNAKE_CASE :       Dict     		=      use_attention_mask
											_SCREAMING_SNAKE_CASE :       int     		=      use_token_type_ids
											_SCREAMING_SNAKE_CASE :       int     		=      use_labels
											_SCREAMING_SNAKE_CASE :       str     		=      vocab_size
											_SCREAMING_SNAKE_CASE :       List[str]     		=      hidden_size
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      num_hidden_layers
											_SCREAMING_SNAKE_CASE :       Any     		=      num_attention_heads
											_SCREAMING_SNAKE_CASE :       int     		=      intermediate_size
											_SCREAMING_SNAKE_CASE :       Any     		=      hidden_act
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      hidden_dropout_prob
											_SCREAMING_SNAKE_CASE :       int     		=      attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE :       Tuple     		=      max_position_embeddings
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      type_vocab_size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      type_sequence_label_size
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      initializer_range
											_SCREAMING_SNAKE_CASE :       Dict     		=      num_choices






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      ids_tensor([self.batch_size, self.seq_length]    ,					self.vocab_size  )

											_SCREAMING_SNAKE_CASE :       int     		=      None
											if self.use_attention_mask:
															_SCREAMING_SNAKE_CASE :       Dict     		=      random_attention_mask([self.batch_size, self.seq_length]  )

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      None
											if self.use_token_type_ids:
															_SCREAMING_SNAKE_CASE :       str     		=      ids_tensor([self.batch_size, self.seq_length]    ,					self.type_vocab_size  )

											_SCREAMING_SNAKE_CASE :       Tuple     		=      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=__snake_case    ,					initializer_range=self.initializer_range    ,					)

											return config, input_ids, token_type_ids, attention_mask






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Tuple     		=      config_and_inputs
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
											return config, inputs_dict






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       int     		=      self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[str]     		=      config_and_inputs

											_SCREAMING_SNAKE_CASE :       Any     		=      True
											_SCREAMING_SNAKE_CASE :       Dict     		=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]  )
											_SCREAMING_SNAKE_CASE :       Tuple     		=      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     lowercase__  (						_snake_case     ,     unittest.TestCase					):





							'''simple docstring'''


							A_							:      Tuple      	=  True

							A_							:      Any      	=  (
							    (
							        FlaxRobertaPreLayerNormModel,
							        FlaxRobertaPreLayerNormForCausalLM,
							        FlaxRobertaPreLayerNormForMaskedLM,
							        FlaxRobertaPreLayerNormForSequenceClassification,
							        FlaxRobertaPreLayerNormForTokenClassification,
							        FlaxRobertaPreLayerNormForMultipleChoice,
							        FlaxRobertaPreLayerNormForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      FlaxRobertaPreLayerNormModelTester(self  )






							@slow
							def UpperCAmelCase_	(    self  ):
											for model_class_name in self.all_model_classes:
															_SCREAMING_SNAKE_CASE :       str     		=      model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""    ,					from_pt=__snake_case  )
															_SCREAMING_SNAKE_CASE :       Any     		=      model(np.ones((1, 1)  )  )
															self.assertIsNotNone(__snake_case  )





@require_flax
class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							@slow
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""    ,					from_pt=__snake_case  )

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]    ,					dtype=jnp.intaa  )
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      model(__snake_case  )[0]
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      [1, 11, 5_0265]
											self.assertEqual(list(output.shape  )    ,					__snake_case  )
											# compare the actual values for a slice.
											_SCREAMING_SNAKE_CASE :       List[str]     		=      np.array(
											    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]    ,					dtype=np.floataa  )
											self.assertTrue(np.allclose(output[:, :3, :3]    ,					__snake_case    ,					atol=1e-4  )  )






							@slow
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Any     		=      FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""    ,					from_pt=__snake_case  )

											_SCREAMING_SNAKE_CASE :       Dict     		=      np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]    ,					dtype=jnp.intaa  )
											_SCREAMING_SNAKE_CASE :       int     		=      model(__snake_case  )[0]
											# compare the actual values for a slice.
											_SCREAMING_SNAKE_CASE :       Dict     		=      np.array(
											    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]    ,					dtype=np.floataa  )
											self.assertTrue(np.allclose(output[:, :3, :3]    ,					__snake_case    ,					atol=1e-4  )  )








'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_		:   Dict							   =		logging.get_logger(__name__)

UpperCAmelCase_		:   Optional[int]							   =		{
    'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      Optional[int]      	=  """canine"""






							def __init__(    self    ,					__snake_case=768    ,					__snake_case=12    ,					__snake_case=12    ,					__snake_case=3072    ,					__snake_case="gelu"    ,					__snake_case=0.1    ,					__snake_case=0.1    ,					__snake_case=1_6384    ,					__snake_case=16    ,					__snake_case=0.02    ,					__snake_case=1e-12    ,					__snake_case=0    ,					__snake_case=0XE000    ,					__snake_case=0XE001    ,					__snake_case=4    ,					__snake_case=4    ,					__snake_case=8    ,					__snake_case=1_6384    ,					__snake_case=128    ,					**__snake_case    ,					):
											super().__init__(pad_token_id=__snake_case    ,					bos_token_id=__snake_case    ,					eos_token_id=__snake_case    ,					**__snake_case  )

											_SCREAMING_SNAKE_CASE :       str     		=      max_position_embeddings
											_SCREAMING_SNAKE_CASE :       Any     		=      hidden_size
											_SCREAMING_SNAKE_CASE :       Dict     		=      num_hidden_layers
											_SCREAMING_SNAKE_CASE :       Tuple     		=      num_attention_heads
											_SCREAMING_SNAKE_CASE :       List[str]     		=      intermediate_size
											_SCREAMING_SNAKE_CASE :       Tuple     		=      hidden_act
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      hidden_dropout_prob
											_SCREAMING_SNAKE_CASE :       str     		=      attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE :       Dict     		=      initializer_range
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      type_vocab_size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      layer_norm_eps

											# Character config:
											_SCREAMING_SNAKE_CASE :       Any     		=      downsampling_rate
											_SCREAMING_SNAKE_CASE :       List[str]     		=      upsampling_kernel_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      num_hash_functions
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      num_hash_buckets
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      local_transformer_stride




