
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):




	'''simple docstring'''




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







	def      _lowerCamelCase       (      self	:Dict		)	->				Optional[int]:
								__UpperCamelCase			:     Union[str, Any]         =      ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size		)

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

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

								__UpperCamelCase			:     Optional[int]         =      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	:Union[str, Any]		)	->				Optional[Any]:
								__UpperCamelCase			:     List[str]         =      self.prepare_config_and_inputs()
								__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      config_and_inputs
								__UpperCamelCase			:     Optional[Any]         =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
								return config, inputs_dict







	def      _lowerCamelCase       (      self	:Dict		)	->				Union[str, Any]:
								__UpperCamelCase			:     Optional[int]         =      self.prepare_config_and_inputs()
								__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     int         =      config_and_inputs

								__UpperCamelCase			:     List[Any]         =      True
								__UpperCamelCase			:     List[str]         =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
								__UpperCamelCase			:     Optional[int]         =      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__							(       __lowercase  ,		unittest.TestCase):




	'''simple docstring'''




	_A			=  True

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







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







	@slow
	def      _lowerCamelCase       (      self	:List[str]		)	->				Dict:
								for model_class_name in self.all_model_classes:
															__UpperCamelCase			:     Any         =      model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"  ,     from_pt=a		)
															__UpperCamelCase			:     Any         =      model(np.ones((1, 1)		)		)
															self.assertIsNotNone(a		)




@require_flax
class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	@slow
	def      _lowerCamelCase       (      self	:str		)	->				Optional[Any]:
								__UpperCamelCase			:     Optional[Any]         =      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"  ,     from_pt=a		)

								__UpperCamelCase			:     str         =      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		)
								__UpperCamelCase			:     List[Any]         =      model(a		)[0]
								__UpperCamelCase			:     str         =      [1, 1_1, 5_0_2_6_5]
								self.assertEqual(list(output.shape		)  ,     a		)
								# compare the actual values for a slice.
								__UpperCamelCase			:     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]  ,     a  ,     atol=1E-4		)		)







	@slow
	def      _lowerCamelCase       (      self	:Dict		)	->				Union[str, Any]:
								__UpperCamelCase			:     int         =      FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"  ,     from_pt=a		)

								__UpperCamelCase			:     Union[str, 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		)
								__UpperCamelCase			:     Tuple         =      model(a		)[0]
								# compare the actual values for a slice.
								__UpperCamelCase			:     Union[str, Any]         =      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]  ,     a  ,     atol=1E-4		)		)

from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Sequence[float]		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    int)      ->					tuple[int | None, int | None, float]:






							'''simple docstring'''





							if not arr:
														return None, None, 0
							if low == high:
														return low, high, arr[low]

							__UpperCamelCase			:     Tuple         =      (low + high) // 2
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      max_subarray(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      max_subarray(_lowerCamelCase		,						mid + 1		,						_lowerCamelCase)
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Dict         =      max_cross_sum(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)
							if left_sum >= right_sum and left_sum >= cross_sum:
														return left_low, left_high, left_sum
							elif right_sum >= left_sum and right_sum >= cross_sum:
														return right_low, right_high, right_sum
							return cross_left, cross_right, cross_sum




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Sequence[float]		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    int)      ->					tuple[int, int, float]:






							'''simple docstring'''





							__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      float("-inf"), -1
							__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      float("-inf"), -1

							__UpperCamelCase			:     int | float         =      0
							for i in range(_lowerCamelCase		,						low - 1		,						-1):
														summ += arr[i]
														if summ > left_sum:
																					__UpperCamelCase			:     Tuple         =      summ
																					__UpperCamelCase			:     Optional[int]         =      i

							__UpperCamelCase			:     Union[str, Any]         =      0
							for i in range(mid + 1		,						high + 1):
														summ += arr[i]
														if summ > right_sum:
																					__UpperCamelCase			:     Union[str, Any]         =      summ
																					__UpperCamelCase			:     Optional[int]         =      i

							return max_left, max_right, (left_sum + right_sum)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int)      ->					float:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      [randint(1		,						_lowerCamelCase) for _ in range(_lowerCamelCase)]
							__UpperCamelCase			:     Optional[Any]         =      time.time()
							max_subarray(_lowerCamelCase		,						0		,						input_size - 1)
							__UpperCamelCase			:     Any         =      time.time()
							return end - start




def 			_SCREAMING_SNAKE_CASE      (	)      ->					None:






							'''simple docstring'''





							__UpperCamelCase			:     Dict         =      [10, 100, 1_000, 10_000, 50_000, 100_000, 200_000, 300_000, 400_000, 500_000]
							__UpperCamelCase			:     Union[str, Any]         =      [time_max_subarray(_lowerCamelCase) for input_size in input_sizes]
							print("No of Inputs\t\tTime Taken")
							for input_size, runtime in zip(_lowerCamelCase		,						_lowerCamelCase):
														print(_lowerCamelCase		,						"\t\t"		,						_lowerCamelCase)
							plt.plot(_lowerCamelCase		,						_lowerCamelCase)
							plt.xlabel("Number of Inputs")
							plt.ylabel("Time taken in seconds")
							plt.show()


if __name__ == "__main__":
		from doctest import testmod

		testmod()