


'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
							import jax.numpy as jnp

if is_tf_available():
							import tensorflow as tf

if is_torch_available():
							import torch
class 						__UpperCamelCase	(						unittest.TestCase							):





							def 		a__   (  self		:int			):
									snake_case_		:							Union[str, Any]			       =						{
									    """task_specific_params""": {
									        """summarization""": {"""length_penalty""": 1.0, """max_length""": 1_2_8, """min_length""": 1_2, """num_beams""": 4},
									        """summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 1_4_2, """min_length""": 5_6, """num_beams""": 4},
									        """summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 6_2, """min_length""": 1_1, """num_beams""": 6},
									    }
									}
									snake_case_		:							Optional[Any]			       =						{
									    """task_specific_params.summarization.length_penalty""": 1.0,
									    """task_specific_params.summarization.max_length""": 1_2_8,
									    """task_specific_params.summarization.min_length""": 1_2,
									    """task_specific_params.summarization.num_beams""": 4,
									    """task_specific_params.summarization_cnn.length_penalty""": 2.0,
									    """task_specific_params.summarization_cnn.max_length""": 1_4_2,
									    """task_specific_params.summarization_cnn.min_length""": 5_6,
									    """task_specific_params.summarization_cnn.num_beams""": 4,
									    """task_specific_params.summarization_xsum.length_penalty""": 1.0,
									    """task_specific_params.summarization_xsum.max_length""": 6_2,
									    """task_specific_params.summarization_xsum.min_length""": 1_1,
									    """task_specific_params.summarization_xsum.num_beams""": 6,
									}

									self.assertEqual(flatten_dict(_UpperCamelCase			)  ,_UpperCamelCase			)





							def 		a__   (  self		:Any			):
									snake_case_		:							Optional[Any]			       =						np.random.randn(3  ,4			)
									self.assertTrue(np.allclose(transpose(_UpperCamelCase			)  ,x.transpose()			)			)

									snake_case_		:							Tuple			       =						np.random.randn(3  ,4  ,5			)
									self.assertTrue(np.allclose(transpose(_UpperCamelCase  ,axes=(1, 2, 0)			)  ,x.transpose((1, 2, 0)			)			)			)





							@require_torch
							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							Dict			       =						np.random.randn(3  ,4			)
									snake_case_		:							str			       =						torch.tensor(_UpperCamelCase			)
									self.assertTrue(np.allclose(transpose(_UpperCamelCase			)  ,transpose(_UpperCamelCase			).numpy()			)			)

									snake_case_		:							List[Any]			       =						np.random.randn(3  ,4  ,5			)
									snake_case_		:							Optional[Any]			       =						torch.tensor(_UpperCamelCase			)
									self.assertTrue(np.allclose(transpose(_UpperCamelCase  ,axes=(1, 2, 0)			)  ,transpose(_UpperCamelCase  ,axes=(1, 2, 0)			).numpy()			)			)





							@require_tf
							def 		a__   (  self		:Tuple			):
									snake_case_		:							Optional[Any]			       =						np.random.randn(3  ,4			)
									snake_case_		:							Optional[int]			       =						tf.constant(_UpperCamelCase			)
									self.assertTrue(np.allclose(transpose(_UpperCamelCase			)  ,transpose(_UpperCamelCase			).numpy()			)			)

									snake_case_		:							Dict			       =						np.random.randn(3  ,4  ,5			)
									snake_case_		:							List[str]			       =						tf.constant(_UpperCamelCase			)
									self.assertTrue(np.allclose(transpose(_UpperCamelCase  ,axes=(1, 2, 0)			)  ,transpose(_UpperCamelCase  ,axes=(1, 2, 0)			).numpy()			)			)





							@require_flax
							def 		a__   (  self		:Optional[int]			):
									snake_case_		:							Any			       =						np.random.randn(3  ,4			)
									snake_case_		:							Tuple			       =						jnp.array(_UpperCamelCase			)
									self.assertTrue(np.allclose(transpose(_UpperCamelCase			)  ,np.asarray(transpose(_UpperCamelCase			)			)			)			)

									snake_case_		:							int			       =						np.random.randn(3  ,4  ,5			)
									snake_case_		:							Union[str, Any]			       =						jnp.array(_UpperCamelCase			)
									self.assertTrue(np.allclose(transpose(_UpperCamelCase  ,axes=(1, 2, 0)			)  ,np.asarray(transpose(_UpperCamelCase  ,axes=(1, 2, 0)			)			)			)			)





							def 		a__   (  self		:str			):
									snake_case_		:							List[str]			       =						np.random.randn(3  ,4			)
									self.assertTrue(np.allclose(reshape(_UpperCamelCase  ,(4, 3)			)  ,np.reshape(_UpperCamelCase  ,(4, 3)			)			)			)

									snake_case_		:							Optional[int]			       =						np.random.randn(3  ,4  ,5			)
									self.assertTrue(np.allclose(reshape(_UpperCamelCase  ,(1_2, 5)			)  ,np.reshape(_UpperCamelCase  ,(1_2, 5)			)			)			)





							@require_torch
							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							List[Any]			       =						np.random.randn(3  ,4			)
									snake_case_		:							Dict			       =						torch.tensor(_UpperCamelCase			)
									self.assertTrue(np.allclose(reshape(_UpperCamelCase  ,(4, 3)			)  ,reshape(_UpperCamelCase  ,(4, 3)			).numpy()			)			)

									snake_case_		:							Optional[Any]			       =						np.random.randn(3  ,4  ,5			)
									snake_case_		:							Any			       =						torch.tensor(_UpperCamelCase			)
									self.assertTrue(np.allclose(reshape(_UpperCamelCase  ,(1_2, 5)			)  ,reshape(_UpperCamelCase  ,(1_2, 5)			).numpy()			)			)





							@require_tf
							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							Dict			       =						np.random.randn(3  ,4			)
									snake_case_		:							List[Any]			       =						tf.constant(_UpperCamelCase			)
									self.assertTrue(np.allclose(reshape(_UpperCamelCase  ,(4, 3)			)  ,reshape(_UpperCamelCase  ,(4, 3)			).numpy()			)			)

									snake_case_		:							List[str]			       =						np.random.randn(3  ,4  ,5			)
									snake_case_		:							str			       =						tf.constant(_UpperCamelCase			)
									self.assertTrue(np.allclose(reshape(_UpperCamelCase  ,(1_2, 5)			)  ,reshape(_UpperCamelCase  ,(1_2, 5)			).numpy()			)			)





							@require_flax
							def 		a__   (  self		:List[str]			):
									snake_case_		:							Union[str, Any]			       =						np.random.randn(3  ,4			)
									snake_case_		:							Any			       =						jnp.array(_UpperCamelCase			)
									self.assertTrue(np.allclose(reshape(_UpperCamelCase  ,(4, 3)			)  ,np.asarray(reshape(_UpperCamelCase  ,(4, 3)			)			)			)			)

									snake_case_		:							int			       =						np.random.randn(3  ,4  ,5			)
									snake_case_		:							Optional[Any]			       =						jnp.array(_UpperCamelCase			)
									self.assertTrue(np.allclose(reshape(_UpperCamelCase  ,(1_2, 5)			)  ,np.asarray(reshape(_UpperCamelCase  ,(1_2, 5)			)			)			)			)





							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							str			       =						np.random.randn(1  ,3  ,4			)
									self.assertTrue(np.allclose(squeeze(_UpperCamelCase			)  ,np.squeeze(_UpperCamelCase			)			)			)

									snake_case_		:							Any			       =						np.random.randn(1  ,4  ,1  ,5			)
									self.assertTrue(np.allclose(squeeze(_UpperCamelCase  ,axis=2			)  ,np.squeeze(_UpperCamelCase  ,axis=2			)			)			)





							@require_torch
							def 		a__   (  self		:int			):
									snake_case_		:							List[Any]			       =						np.random.randn(1  ,3  ,4			)
									snake_case_		:							List[str]			       =						torch.tensor(_UpperCamelCase			)
									self.assertTrue(np.allclose(squeeze(_UpperCamelCase			)  ,squeeze(_UpperCamelCase			).numpy()			)			)

									snake_case_		:							Optional[int]			       =						np.random.randn(1  ,4  ,1  ,5			)
									snake_case_		:							Union[str, Any]			       =						torch.tensor(_UpperCamelCase			)
									self.assertTrue(np.allclose(squeeze(_UpperCamelCase  ,axis=2			)  ,squeeze(_UpperCamelCase  ,axis=2			).numpy()			)			)





							@require_tf
							def 		a__   (  self		:int			):
									snake_case_		:							Dict			       =						np.random.randn(1  ,3  ,4			)
									snake_case_		:							Any			       =						tf.constant(_UpperCamelCase			)
									self.assertTrue(np.allclose(squeeze(_UpperCamelCase			)  ,squeeze(_UpperCamelCase			).numpy()			)			)

									snake_case_		:							Optional[Any]			       =						np.random.randn(1  ,4  ,1  ,5			)
									snake_case_		:							List[str]			       =						tf.constant(_UpperCamelCase			)
									self.assertTrue(np.allclose(squeeze(_UpperCamelCase  ,axis=2			)  ,squeeze(_UpperCamelCase  ,axis=2			).numpy()			)			)





							@require_flax
							def 		a__   (  self		:Tuple			):
									snake_case_		:							Optional[int]			       =						np.random.randn(1  ,3  ,4			)
									snake_case_		:							Tuple			       =						jnp.array(_UpperCamelCase			)
									self.assertTrue(np.allclose(squeeze(_UpperCamelCase			)  ,np.asarray(squeeze(_UpperCamelCase			)			)			)			)

									snake_case_		:							Optional[int]			       =						np.random.randn(1  ,4  ,1  ,5			)
									snake_case_		:							Any			       =						jnp.array(_UpperCamelCase			)
									self.assertTrue(np.allclose(squeeze(_UpperCamelCase  ,axis=2			)  ,np.asarray(squeeze(_UpperCamelCase  ,axis=2			)			)			)			)





							def 		a__   (  self		:Any			):
									snake_case_		:							List[str]			       =						np.random.randn(3  ,4			)
									self.assertTrue(np.allclose(expand_dims(_UpperCamelCase  ,axis=1			)  ,np.expand_dims(_UpperCamelCase  ,axis=1			)			)			)





							@require_torch
							def 		a__   (  self		:List[Any]			):
									snake_case_		:							Dict			       =						np.random.randn(3  ,4			)
									snake_case_		:							Optional[Any]			       =						torch.tensor(_UpperCamelCase			)
									self.assertTrue(np.allclose(expand_dims(_UpperCamelCase  ,axis=1			)  ,expand_dims(_UpperCamelCase  ,axis=1			).numpy()			)			)





							@require_tf
							def 		a__   (  self		:Dict			):
									snake_case_		:							int			       =						np.random.randn(3  ,4			)
									snake_case_		:							Any			       =						tf.constant(_UpperCamelCase			)
									self.assertTrue(np.allclose(expand_dims(_UpperCamelCase  ,axis=1			)  ,expand_dims(_UpperCamelCase  ,axis=1			).numpy()			)			)





							@require_flax
							def 		a__   (  self		:int			):
									snake_case_		:							Optional[Any]			       =						np.random.randn(3  ,4			)
									snake_case_		:							List[Any]			       =						jnp.array(_UpperCamelCase			)
									self.assertTrue(np.allclose(expand_dims(_UpperCamelCase  ,axis=1			)  ,np.asarray(expand_dims(_UpperCamelCase  ,axis=1			)			)			)			)



'''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,
)


__A	:	Any     =     logging.getLogger(__name__)
class 						__UpperCamelCase	(						lowercase__							):





							def 		a__   (  self		:int  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:List[Any]=None  ,_UpperCamelCase		:Any=None			):
									snake_case_		:							List[str]			       =						self.layer[current_layer](_UpperCamelCase  ,_UpperCamelCase  ,head_mask[current_layer]			)

									snake_case_		:							Tuple			       =						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.'						,  lowercase__						,  )
class 						__UpperCamelCase	(						lowercase__							):





							def __init__(  self		:Dict  ,_UpperCamelCase		:Optional[Any]			):
									super().__init__(_UpperCamelCase			)

									snake_case_		:							List[Any]			       =						BertEncoderWithPabee(_UpperCamelCase			)

									self.init_weights()
									snake_case_		:							int			       =						0
									snake_case_		:							Optional[Any]			       =						0
									snake_case_		:							Any			       =						0

									snake_case_		:							str			       =						0





							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:int			):
									snake_case_		:							Optional[Any]			       =						threshold





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:Union[str, Any]			):
									snake_case_		:							str			       =						patience





							def 		a__   (  self		:Optional[int]			):
									snake_case_		:							List[str]			       =						0
									snake_case_		:							List[Any]			       =						0





							def 		a__   (  self		:List[Any]			):
									snake_case_		:							str			       =						self.inference_layers_num / self.inference_instances_num
									snake_case_		:							List[str]			       =						(
									    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(_UpperCamelCase			)





							@add_start_docstrings_to_model_forward(_UpperCamelCase			)
							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:Any=None  ,_UpperCamelCase		:List[str]=None  ,_UpperCamelCase		:Any=None  ,_UpperCamelCase		:Optional[int]=None  ,_UpperCamelCase		:Optional[int]=None  ,_UpperCamelCase		:int=None  ,_UpperCamelCase		:Optional[int]=None  ,_UpperCamelCase		:Any=None  ,_UpperCamelCase		:int=None  ,_UpperCamelCase		:Union[str, Any]=None  ,_UpperCamelCase		:int=False  ,):

									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:
											snake_case_		:							Any			       =						input_ids.size()
									elif inputs_embeds is not None:
											snake_case_		:							Union[str, Any]			       =						inputs_embeds.size()[:-1]
									else:
											raise ValueError("""You have to specify either input_ids or inputs_embeds"""			)

									snake_case_		:							Tuple			       =						input_ids.device if input_ids is not None else inputs_embeds.device

									if attention_mask is None:
											snake_case_		:							Any			       =						torch.ones(_UpperCamelCase  ,device=_UpperCamelCase			)
									if token_type_ids is None:
											snake_case_		:							str			       =						torch.zeros(_UpperCamelCase  ,dtype=torch.long  ,device=_UpperCamelCase			)

									# 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.
									snake_case_		:							torch.Tensor			       =						self.get_extended_attention_mask(_UpperCamelCase  ,_UpperCamelCase  ,_UpperCamelCase			)

									# 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:
											snake_case_       ,      snake_case_       ,      snake_case_		:							List[Any]			       =						encoder_hidden_states.size()
											snake_case_		:							str			       =						(encoder_batch_size, encoder_sequence_length)
											if encoder_attention_mask is None:
													snake_case_		:							List[str]			       =						torch.ones(_UpperCamelCase  ,device=_UpperCamelCase			)
											snake_case_		:							Optional[Any]			       =						self.invert_attention_mask(_UpperCamelCase			)
									else:
											snake_case_		:							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]
									snake_case_		:							List[Any]			       =						self.get_head_mask(_UpperCamelCase  ,self.config.num_hidden_layers			)

									snake_case_		:							Dict			       =						self.embeddings(
									    input_ids=_UpperCamelCase  ,position_ids=_UpperCamelCase  ,token_type_ids=_UpperCamelCase  ,inputs_embeds=_UpperCamelCase			)
									snake_case_		:							Optional[int]			       =						embedding_output

									if self.training:
											snake_case_		:							Any			       =						[]
											for i in range(self.config.num_hidden_layers			):
													snake_case_		:							Any			       =						self.encoder.adaptive_forward(
													    _UpperCamelCase  ,current_layer=_UpperCamelCase  ,attention_mask=_UpperCamelCase  ,head_mask=_UpperCamelCase			)

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

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

													snake_case_		:							List[Any]			       =						logits
													if patient_counter == self.patience:
															break
											snake_case_		:							int			       =						[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\n    the pooled output) e.g. for GLUE tasks. '						,  lowercase__						,  )
class 						__UpperCamelCase	(						lowercase__							):





							def __init__(  self		:Tuple  ,_UpperCamelCase		:List[str]			):
									super().__init__(_UpperCamelCase			)
									snake_case_		:							List[str]			       =						config.num_labels

									snake_case_		:							Dict			       =						BertModelWithPabee(_UpperCamelCase			)
									snake_case_		:							List[str]			       =						nn.Dropout(config.hidden_dropout_prob			)
									snake_case_		:							str			       =						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(_UpperCamelCase			)
							def 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:Tuple=None  ,_UpperCamelCase		:Dict=None  ,_UpperCamelCase		:Union[str, Any]=None  ,_UpperCamelCase		:Union[str, Any]=None  ,_UpperCamelCase		:Tuple=None  ,_UpperCamelCase		:int=None  ,_UpperCamelCase		:Union[str, Any]=None  ,):
									snake_case_		:							int			       =						self.bert(
									    input_ids=_UpperCamelCase  ,attention_mask=_UpperCamelCase  ,token_type_ids=_UpperCamelCase  ,position_ids=_UpperCamelCase  ,head_mask=_UpperCamelCase  ,inputs_embeds=_UpperCamelCase  ,output_dropout=self.dropout  ,output_layers=self.classifiers  ,regression=self.num_labels == 1  ,)

									snake_case_		:							Optional[int]			       =						(logits[-1],)

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

									return outputs