
"""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       ):


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


			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_	:							Optional[int]									=ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size )

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

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

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


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

										lowerCamelCase_	:							List[str]									=True
										lowerCamelCase_	:							List[str]									=floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
										lowerCamelCase_	:							Optional[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        lowercase__      (							__SCREAMING_SNAKE_CASE,       unittest.TestCase       ):
			_UpperCAmelCase		:int             =			True

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


			def      UpperCAmelCase__						(				self					:  Tuple ):
										lowerCamelCase_	:							str									=FlaxRobertaPreLayerNormModelTester(self )


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







@require_flax
class        lowercase__      (							unittest.TestCase       ):


			@slow
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							Optional[int]									=FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"       ,	from_pt=_a )

										lowerCamelCase_	:							Optional[Any]									=np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]       ,	dtype=jnp.intaa )
										lowerCamelCase_	:							List[str]									=model(_a )[0]
										lowerCamelCase_	:							str									=[1, 11, 5_0265]
										self.assertEqual(list(output.shape )       ,	_a )
										# compare the actual values for a slice.
										lowerCamelCase_	:							Optional[Any]									=np.array(
										    [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]       ,	dtype=np.floataa )
										self.assertTrue(np.allclose(output[:, :3, :3]       ,	_a       ,	atol=1E-4 ) )


			@slow
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							Union[str, Any]									=FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"       ,	from_pt=_a )

										lowerCamelCase_	:							Any									=np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]       ,	dtype=jnp.intaa )
										lowerCamelCase_	:							Optional[int]									=model(_a )[0]
										# compare the actual values for a slice.
										lowerCamelCase_	:							List[str]									=np.array(
										    [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]       ,	dtype=np.floataa )
										self.assertTrue(np.allclose(output[:, :3, :3]       ,	_a       ,	atol=1E-4 ) )











"""simple docstring"""



def 			_snake_case    (					lowerCamelCase__     :   Optional[Any]  )    ->     Optional[int]:
							if not head:
														return True
							# split the list to two parts
							lowerCamelCase_							,						lowerCamelCase_	:							Union[str, Any]									=head.next, head
							while fast and fast.next:
														lowerCamelCase_	:							Optional[Any]									=fast.next.next
														lowerCamelCase_	:							str									=slow.next
							lowerCamelCase_	:							Tuple									=slow.next
							lowerCamelCase_	:							Any									=None  # Don't forget here! But forget still works!
							# reverse the second part
							lowerCamelCase_	:							List[str]									=None
							while second:
														lowerCamelCase_	:							Any									=second.next
														lowerCamelCase_	:							Union[str, Any]									=node
														lowerCamelCase_	:							Union[str, Any]									=second
														lowerCamelCase_	:							Optional[Any]									=nxt
							# compare two parts
							# second part has the same or one less node
							while node:
														if node.val != head.val:
																					return False
														lowerCamelCase_	:							List[str]									=node.next
														lowerCamelCase_	:							Optional[Any]									=head.next
							return True

def 			_snake_case    (					lowerCamelCase__     :   str  )    ->     Optional[int]:
							if not head or not head.next:
														return True

							# 1. Get the midpoint (slow)
							lowerCamelCase_	:							List[str]									=head
							while fast and fast.next:
														lowerCamelCase_							,						lowerCamelCase_	:							Union[str, Any]									=fast.next.next, slow.next

							# 2. Push the second half into the stack
							lowerCamelCase_	:							List[Any]									=[slow.val]
							while slow.next:
														lowerCamelCase_	:							List[Any]									=slow.next
														stack.append(slow.val  )

							# 3. Comparison
							while stack:
														if stack.pop() != cur.val:
																					return False
														lowerCamelCase_	:							Union[str, Any]									=cur.next

							return True

def 			_snake_case    (					lowerCamelCase__     :   Dict  )    ->     Optional[Any]:
							if not head or not head.next:
														return True
							lowerCamelCase_	:							Union[str, Any]									={}
							lowerCamelCase_	:							List[Any]									=0
							while head:
														if head.val in d:
																					d[head.val].append(lowerCamelCase__  )
														else:
																					lowerCamelCase_	:							List[str]									=[pos]
														lowerCamelCase_	:							Optional[int]									=head.next
														pos += 1
							lowerCamelCase_	:							Union[str, Any]									=pos - 1
							lowerCamelCase_	:							Optional[int]									=0
							for v in d.values():
														if len(lowerCamelCase__  ) % 2 != 0:
																					middle += 1
														else:
																					lowerCamelCase_	:							Optional[Any]									=0
																					for i in range(0    ,						len(lowerCamelCase__  )  ):
																												if v[i] + v[len(lowerCamelCase__  ) - 1 - step] != checksum:
																																			return False
																												step += 1
														if middle > 1:
																					return False
							return True



