



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       __lowerCAmelCase   (      unittest.TestCase    ):



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



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  str)       ->   Union[str, Any]:



						"""simple docstring"""






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

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

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

						_UpperCAmelCase			=       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=A			,		initializer_range=self.initializer_range			,		)

						return config, input_ids, token_type_ids, attention_mask



			def    _lowerCamelCase	(						self       :  int)       ->   Optional[Any]:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   List[str]:



						"""simple docstring"""






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

						_UpperCAmelCase			=       True
						_UpperCAmelCase			=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
						_UpperCAmelCase			=       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       __lowerCAmelCase   (      A    ,						unittest.TestCase    ):
			UpperCamelCase        =					True

			UpperCamelCase        =					(
			    (
			        FlaxRobertaPreLayerNormModel,
			        FlaxRobertaPreLayerNormForCausalLM,
			        FlaxRobertaPreLayerNormForMaskedLM,
			        FlaxRobertaPreLayerNormForSequenceClassification,
			        FlaxRobertaPreLayerNormForTokenClassification,
			        FlaxRobertaPreLayerNormForMultipleChoice,
			        FlaxRobertaPreLayerNormForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       FlaxRobertaPreLayerNormModelTester(self)



			@slow
			def    _lowerCamelCase	(						self       :  List[Any])       ->   Tuple:



						"""simple docstring"""






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







@require_flax
class       __lowerCAmelCase   (      unittest.TestCase    ):



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



						"""simple docstring"""






						_UpperCAmelCase			=       FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'			,		from_pt=A)

						_UpperCAmelCase			=       np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]			,		dtype=jnp.intaa)
						_UpperCAmelCase			=       model(A)[0]
						_UpperCAmelCase			=       [1, 11, 5_02_65]
						self.assertEqual(list(output.shape)			,		A)
						# compare the actual values for a slice.
						_UpperCAmelCase			=       np.array(
						    [[[40.48_80, 18.01_99, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 10.70_85], [-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]			,		A			,		atol=1E-4))



			@slow
			def    _lowerCamelCase	(						self       :  Dict)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'			,		from_pt=A)

						_UpperCAmelCase			=       np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]			,		dtype=jnp.intaa)
						_UpperCAmelCase			=       model(A)[0]
						# compare the actual values for a slice.
						_UpperCAmelCase			=       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]			,		A			,		atol=1E-4))





def 							A     (					_UpperCAmelCase    :       int   )    ->			"list[int]":







			'''simple docstring'''
			if upper_limit < 0:
						raise ValueError('Limit for the Catalan sequence must be ≥ 0'   )

			_UpperCAmelCase			=       [0] * (upper_limit + 1)

			# Base case: C(0) = C(1) = 1
			_UpperCAmelCase			=       1
			if upper_limit > 0:
						_UpperCAmelCase			=       1

			# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
			for i in range(2							,       upper_limit + 1   ):
						for j in range(_UpperCAmelCase   ):
									catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

			return catalan_list


if __name__ == "__main__":
							print("\n********* Catalan Numbers Using Dynamic Programming ************\n")
							print("\n*** Enter -1 at any time to quit ***")
							print("\nEnter the upper limit (≥ 0) for the Catalan number sequence: ", end="")
							try:
														while True:
																					UpperCAmelCase__       =	int(input().strip())
																					if N < 0:
																												print("\n********* Goodbye!! ************")
																												break
																					else:
																												print(f"""The Catalan numbers from 0 through {N} are:""")
																												print(catalan_numbers(N))
																												print("Try another upper limit for the sequence: ", end="")
							except (NameError, ValueError):
														print("\n********* Invalid input, goodbye! ************\n")

							import doctest

							doctest.testmod()
