



'''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       __magic_name__		(       unittest.TestCase  ):







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







							def 				__lowercase     (				self   :			Any						):
									_a	: List[str]								=				ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size						)

									_a	: List[Any]								=				None
									if self.use_attention_mask:
											_a	: Union[str, Any]								=				random_attention_mask([self.batch_size, self.seq_length]						)

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

									_a	: 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=_UpperCAmelCase      ,initializer_range=self.initializer_range      ,)

									return config, input_ids, token_type_ids, attention_mask







							def 				__lowercase     (				self   :			List[str]						):
									_a	: List[Any]								=				self.prepare_config_and_inputs()
									_a			,   _a			,   _a			,   _a	: List[Any]								=				config_and_inputs
									_a	: Tuple								=				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
									return config, inputs_dict







							def 				__lowercase     (				self   :			List[Any]						):
									_a	: Tuple								=				self.prepare_config_and_inputs()
									_a			,   _a			,   _a			,   _a	: Optional[Any]								=				config_and_inputs

									_a	: Union[str, Any]								=				True
									_a	: List[Any]								=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
									_a	: List[Any]								=				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       __magic_name__		(       _UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Optional[Any]  							=   True

							lowerCAmelCase			:       List[str]  							=   (
							    (
							        FlaxRobertaPreLayerNormModel,
							        FlaxRobertaPreLayerNormForCausalLM,
							        FlaxRobertaPreLayerNormForMaskedLM,
							        FlaxRobertaPreLayerNormForSequenceClassification,
							        FlaxRobertaPreLayerNormForTokenClassification,
							        FlaxRobertaPreLayerNormForMultipleChoice,
							        FlaxRobertaPreLayerNormForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)







							def 				__lowercase     (				self   :			List[str]						):
									_a	: List[str]								=				FlaxRobertaPreLayerNormModelTester(self						)







							@slow
							def 				__lowercase     (				self   :			int						):
									for model_class_name in self.all_model_classes:
											_a	: Optional[int]								=				model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'      ,from_pt=_UpperCAmelCase						)
											_a	: Any								=				model(np.ones((1, 1)						)						)
											self.assertIsNotNone(_UpperCAmelCase						)

@require_flax
class       __magic_name__		(       unittest.TestCase  ):







							@slow
							def 				__lowercase     (				self   :			List[str]						):
									_a	: List[str]								=				FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'      ,from_pt=_UpperCAmelCase						)

									_a	: Union[str, Any]								=				np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]      ,dtype=jnp.intaa						)
									_a	: Optional[int]								=				model(_UpperCAmelCase						)[0]
									_a	: Dict								=				[1, 11, 50265]
									self.assertEqual(list(output.shape						)      ,_UpperCAmelCase						)
									# compare the actual values for a slice.
									_a	: List[str]								=				np.array(
									    [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]      ,dtype=np.floataa						)
									self.assertTrue(np.allclose(output[:, :3, :3]      ,_UpperCAmelCase      ,atol=1E-4						)						)







							@slow
							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: Optional[int]								=				FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'      ,from_pt=_UpperCAmelCase						)

									_a	: Dict								=				np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]      ,dtype=jnp.intaa						)
									_a	: Union[str, Any]								=				model(_UpperCAmelCase						)[0]
									# compare the actual values for a slice.
									_a	: Tuple								=				np.array(
									    [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]      ,dtype=np.floataa						)
									self.assertTrue(np.allclose(output[:, :3, :3]      ,_UpperCAmelCase      ,atol=1E-4						)						)









'''simple docstring'''

def 	__lowerCamelCase	( lowerCAmelCase_    )     -> bool:
		if not all(x.isalpha() for x in string    ):
				raise ValueError('String must only contain alphabetic characters.'    )

		_a	: Tuple								=				sorted(string.lower()    )
		return len(lowerCAmelCase_    ) == len(set(lowerCAmelCase_    )    )


if __name__ == "__main__":
							__lowerCAmelCase		     =  input('''Enter a string ''').strip()

							__lowerCAmelCase		     =  is_isogram(input_str)
							print(f"""{input_str} is {'an' if isogram else 'not an'} isogram.""")




