
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 		UpperCamelCase__  (       unittest.TestCase    ):
   def __init__(self :					Any					,				snake_case_ :					List[Any]					,				snake_case_ :					Any=1_3					,				snake_case_ :					Tuple=7					,				snake_case_ :					str=True					,				snake_case_ :					Optional[int]=True					,				snake_case_ :					str=True					,				snake_case_ :					List[str]=True					,				snake_case_ :					Tuple=9_9					,				snake_case_ :					Union[str, Any]=3_2					,				snake_case_ :					int=5					,				snake_case_ :					Optional[Any]=4					,				snake_case_ :					Union[str, Any]=3_7					,				snake_case_ :					Any="gelu"					,				snake_case_ :					Optional[Any]=0.1					,				snake_case_ :					List[Any]=0.1					,				snake_case_ :					List[str]=5_1_2					,				snake_case_ :					List[Any]=1_6					,				snake_case_ :					List[Any]=2					,				snake_case_ :					List[Any]=0.02					,				snake_case_ :					Union[str, Any]=4					,				):
    __a :  int						=  parent
    __a :  Optional[Any]						=  batch_size
    __a :  Optional[int]						=  seq_length
    __a :  Dict						=  is_training
    __a :  Optional[int]						=  use_attention_mask
    __a :  List[str]						=  use_token_type_ids
    __a :  str						=  use_labels
    __a :  Optional[int]						=  vocab_size
    __a :  Union[str, Any]						=  hidden_size
    __a :  str						=  num_hidden_layers
    __a :  List[str]						=  num_attention_heads
    __a :  str						=  intermediate_size
    __a :  Dict						=  hidden_act
    __a :  Union[str, Any]						=  hidden_dropout_prob
    __a :  List[Any]						=  attention_probs_dropout_prob
    __a :  Optional[int]						=  max_position_embeddings
    __a :  List[str]						=  type_vocab_size
    __a :  Dict						=  type_sequence_label_size
    __a :  Optional[int]						=  initializer_range
    __a :  int						=  num_choices
   def       lowerCAmelCase			(self :					Dict   ):
    __a :  int						=  ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size   )

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

    __a :  Optional[Any]						=  None
    if self.use_token_type_ids:
     __a :  Optional[int]						=  ids_tensor([self.batch_size, self.seq_length]					,				self.type_vocab_size   )

    __a :  Dict						=  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       lowerCAmelCase			(self :					int   ):
    __a :  int						=  self.prepare_config_and_inputs()
    __a				,       __a				,       __a				,       __a :  Tuple						=  config_and_inputs
    __a :  Tuple						=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
    return config, inputs_dict







   def       lowerCAmelCase			(self :					Dict   ):
    __a :  Union[str, Any]						=  self.prepare_config_and_inputs()
    __a				,       __a				,       __a				,       __a :  List[Any]						=  config_and_inputs

    __a :  List[str]						=  True
    __a :  Dict						=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
    __a :  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 		UpperCamelCase__  (       __lowercase    ,unittest.TestCase    ):
   _SCREAMING_SNAKE_CASE      :							Tuple	       =      True

   _SCREAMING_SNAKE_CASE      :							Optional[int]	       =      (
       (
           FlaxRobertaPreLayerNormModel,
           FlaxRobertaPreLayerNormForCausalLM,
           FlaxRobertaPreLayerNormForMaskedLM,
           FlaxRobertaPreLayerNormForSequenceClassification,
           FlaxRobertaPreLayerNormForTokenClassification,
           FlaxRobertaPreLayerNormForMultipleChoice,
           FlaxRobertaPreLayerNormForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )
   def       lowerCAmelCase			(self :					Optional[int]   ):
    __a :  Any						=  FlaxRobertaPreLayerNormModelTester(self   )







   @slow
   def       lowerCAmelCase			(self :					List[str]   ):
    for model_class_name in self.all_model_classes:
     __a :  str						=  model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''					,				from_pt=snake_case_   )
     __a :  Union[str, Any]						=  model(np.ones((1, 1)   )   )
     self.assertIsNotNone(snake_case_   )





@require_flax
class 		UpperCamelCase__  (       unittest.TestCase    ):
   @slow
   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  Tuple						=  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''					,				from_pt=snake_case_   )

    __a :  Optional[int]						=  np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]					,				dtype=jnp.intaa   )
    __a :  str						=  model(snake_case_   )[0]
    __a :  Optional[int]						=  [1, 1_1, 5_0_2_6_5]
    self.assertEqual(list(output.shape   )					,				snake_case_   )
    # compare the actual values for a slice.
    __a :  int						=  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       lowerCAmelCase			(self :					Dict   ):
    __a :  Optional[int]						=  FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''					,				from_pt=snake_case_   )

    __a :  Optional[Any]						=  np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]					,				dtype=jnp.intaa   )
    __a :  str						=  model(snake_case_   )[0]
    # compare the actual values for a slice.
    __a :  int						=  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   )   )


def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 if n_term == "":
  return []
 __a :  list						=  []
 for temp in range(int(lowerCAmelCase__  )  ):
  series.append(f"1/{temp + 1}" if series else '''1'''  )
 return series


if __name__ == "__main__":
      lowercase__       =input('Enter the last number (nth term) of the Harmonic Series')
      print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
      print(harmonic_series(nth_term))
