



'''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        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






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







	def   A			(		self    :  List[str]	)    ->	Union[str, Any]:
								UpperCAmelCase				: str											=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size	)

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

								UpperCAmelCase				: str											=	None
								if self.use_token_type_ids:
															UpperCAmelCase				: Optional[Any]											=	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=__snake_case	,    initializer_range=self.initializer_range	,    )

								return config, input_ids, token_type_ids, attention_mask







	def   A			(		self    :  str	)    ->	str:
								UpperCAmelCase				: Dict											=	self.prepare_config_and_inputs()
								UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: List[Any]											=	config_and_inputs
								UpperCAmelCase				: List[Any]											=	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
								return config, inputs_dict







	def   A			(		self    :  List[Any]	)    ->	Union[str, Any]:
								UpperCAmelCase				: Tuple											=	self.prepare_config_and_inputs()
								UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: Dict											=	config_and_inputs

								UpperCAmelCase				: Optional[Any]											=	True
								UpperCAmelCase				: str											=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
								UpperCAmelCase				: str											=	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        SCREAMING_SNAKE_CASE(							A__							,	unittest.TestCase    ):







	"""simple docstring"""






	lowerCamelCase__    				= True

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







	def   A			(		self    :  Optional[Any]	)    ->	Dict:
								UpperCAmelCase				: Optional[int]											=	FlaxRobertaPreLayerNormModelTester(self	)







	@slow
	def   A			(		self    :  Tuple	)    ->	List[Any]:
								for model_class_name in self.all_model_classes:
															UpperCAmelCase				: Any											=	model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''	,    from_pt=__snake_case	)
															UpperCAmelCase				: str											=	model(np.ones((1, 1)	)	)
															self.assertIsNotNone(__snake_case	)

@require_flax
class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	@slow
	def   A			(		self    :  Union[str, Any]	)    ->	str:
								UpperCAmelCase				: Any											=	FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''	,    from_pt=__snake_case	)

								UpperCAmelCase				: Optional[int]											=	np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]	,    dtype=jnp.intaa	)
								UpperCAmelCase				: str											=	model(__snake_case	)[0]
								UpperCAmelCase				: Optional[int]											=	[1, 11, 50265]
								self.assertEqual(list(output.shape	)	,    __snake_case	)
								# compare the actual values for a slice.
								UpperCAmelCase				: int											=	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]	,    __snake_case	,    atol=1E-4	)	)







	@slow
	def   A			(		self    :  Dict	)    ->	Tuple:
								UpperCAmelCase				: Any											=	FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''	,    from_pt=__snake_case	)

								UpperCAmelCase				: List[Any]											=	np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]	,    dtype=jnp.intaa	)
								UpperCAmelCase				: str											=	model(__snake_case	)[0]
								# compare the actual values for a slice.
								UpperCAmelCase				: 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]	,    __snake_case	,    atol=1E-4	)	)










'''simple docstring'''






import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


UpperCamelCase__:     List[str]										=    logging.getLogger(__name__)

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def   A			(		self    :  str	,    __snake_case    :  Tuple	,    __snake_case    :  int	,    __snake_case    :  List[Any]=None	,    __snake_case    :  List[Any]=None	)    ->	Optional[int]:
								UpperCAmelCase				: Union[str, Any]											=	self.layer[current_layer](__snake_case	,    __snake_case	,    head_mask[current_layer]	)

								UpperCAmelCase				: Optional[Any]											=	layer_outputs[0]

								return hidden_states

@add_start_docstrings(
    """The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top."""							,	A__							,	)
class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  int	,    __snake_case    :  Any	)    ->	Any:
								super().__init__(__snake_case	)

								UpperCAmelCase				: Any											=	BertEncoderWithPabee(__snake_case	)

								self.init_weights()
								UpperCAmelCase				: Optional[Any]											=	0
								UpperCAmelCase				: List[str]											=	0
								UpperCAmelCase				: Tuple											=	0

								UpperCAmelCase				: List[str]											=	0







	def   A			(		self    :  List[str]	,    __snake_case    :  List[str]	)    ->	List[Any]:
								UpperCAmelCase				: Union[str, Any]											=	threshold







	def   A			(		self    :  str	,    __snake_case    :  List[str]	)    ->	Optional[Any]:
								UpperCAmelCase				: List[str]											=	patience







	def   A			(		self    :  Dict	)    ->	str:
								UpperCAmelCase				: List[Any]											=	0
								UpperCAmelCase				: Dict											=	0







	def   A			(		self    :  Optional[Any]	)    ->	Optional[int]:
								UpperCAmelCase				: Dict											=	self.inference_layers_num / self.inference_instances_num
								UpperCAmelCase				: int											=	(
								    F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
								    F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
								)
								print(__snake_case	)







	@add_start_docstrings_to_model_forward(__snake_case	)
	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  Optional[Any]=None	,    __snake_case    :  str=None	,    __snake_case    :  int=None	,    __snake_case    :  Optional[Any]=None	,    __snake_case    :  Tuple=None	,    __snake_case    :  Tuple=None	,    __snake_case    :  Tuple=None	,    __snake_case    :  Optional[Any]=None	,    __snake_case    :  Union[str, Any]=None	,    __snake_case    :  Optional[int]=None	,    __snake_case    :  Optional[int]=False	,    )    ->	List[str]:

								if input_ids is not None and inputs_embeds is not None:
															raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''	)
								elif input_ids is not None:
															UpperCAmelCase				: Union[str, Any]											=	input_ids.size()
								elif inputs_embeds is not None:
															UpperCAmelCase				: List[Any]											=	inputs_embeds.size()[:-1]
								else:
															raise ValueError('''You have to specify either input_ids or inputs_embeds'''	)

								UpperCAmelCase				: Optional[Any]											=	input_ids.device if input_ids is not None else inputs_embeds.device

								if attention_mask is None:
															UpperCAmelCase				: Optional[Any]											=	torch.ones(__snake_case	,    device=__snake_case	)
								if token_type_ids is None:
															UpperCAmelCase				: Tuple											=	torch.zeros(__snake_case	,    dtype=torch.long	,    device=__snake_case	)

								# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
								# ourselves in which case we just need to make it broadcastable to all heads.
								UpperCAmelCase				: torch.Tensor											=	self.get_extended_attention_mask(__snake_case	,    __snake_case	,    __snake_case	)

								# If a 2D ou 3D attention mask is provided for the cross-attention
								# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
								if self.config.is_decoder and encoder_hidden_states is not None:
															UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase				: Any											=	encoder_hidden_states.size()
															UpperCAmelCase				: List[Any]											=	(encoder_batch_size, encoder_sequence_length)
															if encoder_attention_mask is None:
																						UpperCAmelCase				: str											=	torch.ones(__snake_case	,    device=__snake_case	)
															UpperCAmelCase				: Tuple											=	self.invert_attention_mask(__snake_case	)
								else:
															UpperCAmelCase				: List[str]											=	None

								# Prepare head mask if needed
								# 1.0 in head_mask indicate we keep the head
								# attention_probs has shape bsz x n_heads x N x N
								# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
								# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
								UpperCAmelCase				: List[Any]											=	self.get_head_mask(__snake_case	,    self.config.num_hidden_layers	)

								UpperCAmelCase				: Any											=	self.embeddings(
								    input_ids=__snake_case	,    position_ids=__snake_case	,    token_type_ids=__snake_case	,    inputs_embeds=__snake_case	)
								UpperCAmelCase				: Optional[int]											=	embedding_output

								if self.training:
															UpperCAmelCase				: str											=	[]
															for i in range(self.config.num_hidden_layers	):
																						UpperCAmelCase				: Optional[int]											=	self.encoder.adaptive_forward(
																						    __snake_case	,    current_layer=__snake_case	,    attention_mask=__snake_case	,    head_mask=__snake_case	)

																						UpperCAmelCase				: List[str]											=	self.pooler(__snake_case	)
																						UpperCAmelCase				: Dict											=	output_layers[i](output_dropout(__snake_case	)	)
																						res.append(__snake_case	)
								elif self.patience == 0:  # Use all layers for inference
															UpperCAmelCase				: Any											=	self.encoder(
															    __snake_case	,    attention_mask=__snake_case	,    head_mask=__snake_case	,    encoder_hidden_states=__snake_case	,    encoder_attention_mask=__snake_case	,    )
															UpperCAmelCase				: Dict											=	self.pooler(encoder_outputs[0]	)
															UpperCAmelCase				: List[Any]											=	[output_layers[self.config.num_hidden_layers - 1](__snake_case	)]
								else:
															UpperCAmelCase				: Tuple											=	0
															UpperCAmelCase				: List[str]											=	None
															UpperCAmelCase				: Optional[Any]											=	0
															for i in range(self.config.num_hidden_layers	):
																						calculated_layer_num += 1
																						UpperCAmelCase				: Optional[Any]											=	self.encoder.adaptive_forward(
																						    __snake_case	,    current_layer=__snake_case	,    attention_mask=__snake_case	,    head_mask=__snake_case	)

																						UpperCAmelCase				: List[str]											=	self.pooler(__snake_case	)
																						UpperCAmelCase				: Optional[int]											=	output_layers[i](__snake_case	)
																						if regression:
																													UpperCAmelCase				: Union[str, Any]											=	logits.detach()
																													if patient_result is not None:
																																				UpperCAmelCase				: List[str]											=	patient_result.detach()
																													if (patient_result is not None) and torch.abs(patient_result - labels	) < self.regression_threshold:
																																				patient_counter += 1
																													else:
																																				UpperCAmelCase				: Dict											=	0
																						else:
																													UpperCAmelCase				: Union[str, Any]											=	logits.detach().argmax(dim=1	)
																													if patient_result is not None:
																																				UpperCAmelCase				: str											=	patient_result.detach().argmax(dim=1	)
																													if (patient_result is not None) and torch.all(labels.eq(__snake_case	)	):
																																				patient_counter += 1
																													else:
																																				UpperCAmelCase				: Union[str, Any]											=	0

																						UpperCAmelCase				: List[str]											=	logits
																						if patient_counter == self.patience:
																													break
															UpperCAmelCase				: List[Any]											=	[patient_result]
															self.inference_layers_num += calculated_layer_num
															self.inference_instances_num += 1

								return res

@add_start_docstrings(
    """Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. """							,	A__							,	)
class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  str	,    __snake_case    :  str	)    ->	Optional[Any]:
								super().__init__(__snake_case	)
								UpperCAmelCase				: Optional[int]											=	config.num_labels

								UpperCAmelCase				: Optional[Any]											=	BertModelWithPabee(__snake_case	)
								UpperCAmelCase				: Tuple											=	nn.Dropout(config.hidden_dropout_prob	)
								UpperCAmelCase				: Dict											=	nn.ModuleList(
								    [nn.Linear(config.hidden_size	,    self.config.num_labels	) for _ in range(config.num_hidden_layers	)]	)

								self.init_weights()







	@add_start_docstrings_to_model_forward(__snake_case	)
	def   A			(		self    :  Any	,    __snake_case    :  Dict=None	,    __snake_case    :  str=None	,    __snake_case    :  List[Any]=None	,    __snake_case    :  List[Any]=None	,    __snake_case    :  Union[str, Any]=None	,    __snake_case    :  int=None	,    __snake_case    :  str=None	,    )    ->	Union[str, Any]:
								UpperCAmelCase				: Optional[int]											=	self.bert(
								    input_ids=__snake_case	,    attention_mask=__snake_case	,    token_type_ids=__snake_case	,    position_ids=__snake_case	,    head_mask=__snake_case	,    inputs_embeds=__snake_case	,    output_dropout=self.dropout	,    output_layers=self.classifiers	,    regression=self.num_labels == 1	,    )

								UpperCAmelCase				: List[str]											=	(logits[-1],)

								if labels is not None:
															UpperCAmelCase				: Tuple											=	None
															UpperCAmelCase				: Any											=	0
															for ix, logits_item in enumerate(__snake_case	):
																						if self.num_labels == 1:
																													#  We are doing regression
																													UpperCAmelCase				: Optional[Any]											=	MSELoss()
																													UpperCAmelCase				: Optional[Any]											=	loss_fct(logits_item.view(-1	)	,    labels.view(-1	)	)
																						else:
																													UpperCAmelCase				: Dict											=	CrossEntropyLoss()
																													UpperCAmelCase				: Dict											=	loss_fct(logits_item.view(-1	,    self.num_labels	)	,    labels.view(-1	)	)
																						if total_loss is None:
																													UpperCAmelCase				: Dict											=	loss
																						else:
																													total_loss += loss * (ix + 1)
																						total_weights += ix + 1
															UpperCAmelCase				: Any											=	(total_loss / total_weights,) + outputs

								return outputs





