


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,
)


lowerCAmelCase							=						logging.getLogger(__name__)




class        lowerCamelCase      (     _UpperCamelCase   ):


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__=None							,    lowercase__=None):
							__UpperCAmelCase :       Optional[int]  						=  self.layer[current_layer](lowercase__							,    lowercase__							,    head_mask[current_layer])

							__UpperCAmelCase :       List[str]  						=  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.'''       ,	_UpperCamelCase       ,	)
class        lowerCamelCase      (     _UpperCamelCase   ):


	def __init__(						self							,    lowercase__):
							super().__init__(lowercase__)

							__UpperCAmelCase :       Dict  						=  BertEncoderWithPabee(lowercase__)

							self.init_weights()
							__UpperCAmelCase :       Dict  						=  0
							__UpperCAmelCase :       str  						=  0
							__UpperCAmelCase :       Any  						=  0

							__UpperCAmelCase :       List[str]  						=  0


	def    A(						self							,    lowercase__):
							__UpperCAmelCase :       str  						=  threshold


	def    A(						self							,    lowercase__):
							__UpperCAmelCase :       str  						=  patience


	def    A(						self):
							__UpperCAmelCase :       List[str]  						=  0
							__UpperCAmelCase :       Optional[int]  						=  0


	def    A(						self):
							__UpperCAmelCase :       List[Any]  						=  self.inference_layers_num / self.inference_instances_num
							__UpperCAmelCase :       Optional[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(lowercase__)


	@add_start_docstrings_to_model_forward(lowercase__)
	def    A(						self							,    lowercase__=None							,    lowercase__=None							,    lowercase__=None							,    lowercase__=None							,    lowercase__=None							,    lowercase__=None							,    lowercase__=None							,    lowercase__=None							,    lowercase__=None							,    lowercase__=None							,    lowercase__=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:
													__UpperCAmelCase :       str  						=  input_ids.size()
							elif inputs_embeds is not None:
													__UpperCAmelCase :       List[str]  						=  inputs_embeds.size()[:-1]
							else:
													raise ValueError('''You have to specify either input_ids or inputs_embeds''')

							__UpperCAmelCase :       Any  						=  input_ids.device if input_ids is not None else inputs_embeds.device

							if attention_mask is None:
													__UpperCAmelCase :       Any  						=  torch.ones(lowercase__							,    device=lowercase__)
							if token_type_ids is None:
													__UpperCAmelCase :       List[Any]  						=  torch.zeros(lowercase__							,    dtype=torch.long							,    device=lowercase__)

							# 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(lowercase__							,    lowercase__							,    lowercase__)

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

							__UpperCAmelCase :       Union[str, Any]  						=  self.embeddings(
							    input_ids=lowercase__							,    position_ids=lowercase__							,    token_type_ids=lowercase__							,    inputs_embeds=lowercase__)
							__UpperCAmelCase :       Any  						=  embedding_output

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

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

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

																			__UpperCAmelCase :       str  						=  logits
																			if patient_counter == self.patience:
																									break
													__UpperCAmelCase :       Optional[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
    the pooled output) e.g. for GLUE tasks. '''       ,	_UpperCamelCase       ,	)
class        lowerCamelCase      (     _UpperCamelCase   ):


	def __init__(						self							,    lowercase__):
							super().__init__(lowercase__)
							__UpperCAmelCase :       Optional[int]  						=  config.num_labels

							__UpperCAmelCase :       Any  						=  BertModelWithPabee(lowercase__)
							__UpperCAmelCase :       List[str]  						=  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(lowercase__)
	def    A(						self							,    lowercase__=None							,    lowercase__=None							,    lowercase__=None							,    lowercase__=None							,    lowercase__=None							,    lowercase__=None							,    lowercase__=None							,    ):
							__UpperCAmelCase :       str  						=  self.bert(
							    input_ids=lowercase__							,    attention_mask=lowercase__							,    token_type_ids=lowercase__							,    position_ids=lowercase__							,    head_mask=lowercase__							,    inputs_embeds=lowercase__							,    output_dropout=self.dropout							,    output_layers=self.classifiers							,    regression=self.num_labels == 1							,    )

							__UpperCAmelCase :       Dict  						=  (logits[-1],)

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

							return outputs





from typing import Dict, Optional

import numpy as np

import datasets


lowerCAmelCase							=						"""
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
"""

lowerCAmelCase							=						"""
Args:
    predictions (`List[ndarray]`):
        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    references (`List[ndarray]`):
        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    num_labels (`int`):
        Number of classes (categories).
    ignore_index (`int`):
        Index that will be ignored during evaluation.
    nan_to_num (`int`, *optional*):
        If specified, NaN values will be replaced by the number defined by the user.
    label_map (`dict`, *optional*):
        If specified, dictionary mapping old label indices to new label indices.
    reduce_labels (`bool`, *optional*, defaults to `False`):
        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.

Returns:
    `Dict[str, float | ndarray]` comprising various elements:
    - *mean_iou* (`float`):
        Mean Intersection-over-Union (IoU averaged over all categories).
    - *mean_accuracy* (`float`):
        Mean accuracy (averaged over all categories).
    - *overall_accuracy* (`float`):
        Overall accuracy on all images.
    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
        Per category accuracy.
    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):
        Per category IoU.

Examples:

    >>> import numpy as np

    >>> mean_iou = datasets.load_metric(\"mean_iou\")

    >>> # suppose one has 3 different segmentation maps predicted
    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])

    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])

    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])

    >>> predicted = [predicted_1, predicted_2, predicted_3]
    >>> ground_truth = [actual_1, actual_2, actual_3]

    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}
"""

lowerCAmelCase							=						"""\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}"""




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_ = None			,					lowercase_ = False			,					)		->    Optional[Any]:




						'''simple docstring'''







						if label_map is not None:
												for old_id, new_id in label_map.items():
																		__UpperCAmelCase :       List[str]  						=  new_id

    # turn into Numpy arrays
						__UpperCAmelCase :       Tuple  						=  np.array(lowercase_			)
						__UpperCAmelCase :       str  						=  np.array(lowercase_			)

						if reduce_labels:
												__UpperCAmelCase :       List[Any]  						=  255
												__UpperCAmelCase :       str  						=  label - 1
												__UpperCAmelCase :       Dict  						=  255

						__UpperCAmelCase :       str  						=  label != ignore_index
						__UpperCAmelCase :       Optional[int]  						=  np.not_equal(lowercase_			,					lowercase_			)
						__UpperCAmelCase :       List[str]  						=  pred_label[mask]
						__UpperCAmelCase :       Any  						=  np.array(lowercase_			)[mask]

						__UpperCAmelCase :       Optional[Any]  						=  pred_label[pred_label == label]

						__UpperCAmelCase :       Optional[Any]  						=  np.histogram(lowercase_			,					bins=lowercase_			,					range=(0, num_labels - 1)			)[0]
						__UpperCAmelCase :       Any  						=  np.histogram(lowercase_			,					bins=lowercase_			,					range=(0, num_labels - 1)			)[0]
						__UpperCAmelCase :       List[str]  						=  np.histogram(lowercase_			,					bins=lowercase_			,					range=(0, num_labels - 1)			)[0]

						__UpperCAmelCase :       List[Any]  						=  area_pred_label + area_label - area_intersect

						return area_intersect, area_union, area_pred_label, area_label




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_ = None			,					lowercase_ = False			,					)		->    Any:




						'''simple docstring'''







						__UpperCAmelCase :       List[Any]  						=  np.zeros((num_labels,)			,					dtype=np.floataa			)
						__UpperCAmelCase :       List[Any]  						=  np.zeros((num_labels,)			,					dtype=np.floataa			)
						__UpperCAmelCase :       str  						=  np.zeros((num_labels,)			,					dtype=np.floataa			)
						__UpperCAmelCase :       str  						=  np.zeros((num_labels,)			,					dtype=np.floataa			)
						for result, gt_seg_map in zip(lowercase_			,					lowercase_			):
												__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase :       Tuple  						=  intersect_and_union(
												    lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			)
												total_area_intersect += area_intersect
												total_area_union += area_union
												total_area_pred_label += area_pred_label
												total_area_label += area_label
						return total_area_intersect, total_area_union, total_area_pred_label, total_area_label




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_ = None			,					lowercase_ = None			,					lowercase_ = False			,					)		->    str:




						'''simple docstring'''







						__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase						,			__UpperCAmelCase :       Any  						=  total_intersect_and_union(
						    lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			)

						# compute metrics
						__UpperCAmelCase :       Any  						=  {}

						__UpperCAmelCase :       Union[str, Any]  						=  total_area_intersect.sum() / total_area_label.sum()
						__UpperCAmelCase :       Optional[Any]  						=  total_area_intersect / total_area_union
						__UpperCAmelCase :       List[str]  						=  total_area_intersect / total_area_label

						__UpperCAmelCase :       Optional[int]  						=  np.nanmean(lowercase_			)
						__UpperCAmelCase :       int  						=  np.nanmean(lowercase_			)
						__UpperCAmelCase :       List[str]  						=  all_acc
						__UpperCAmelCase :       Any  						=  iou
						__UpperCAmelCase :       str  						=  acc

						if nan_to_num is not None:
												__UpperCAmelCase :       Any  						=  {metric: np.nan_to_num(lowercase_			,					nan=lowercase_			) for metric, metric_value in metrics.items()}

						return metrics





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,	_KWARGS_DESCRIPTION   )
class        lowerCamelCase      (     datasets.Metric   ):


	def    A(						self):
							return datasets.MetricInfo(
							    description=_DESCRIPTION							,    citation=_CITATION							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(
							        # 1st Seq - height dim, 2nd - width dim
							        {
							            '''predictions''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16'''))),
							            '''references''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16'''))),
							        })							,    reference_urls=[
							        '''https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'''
							    ]							,    )


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = False							,    ):
							__UpperCAmelCase :       str  						=  mean_iou(
							    results=lowercase__							,    gt_seg_maps=lowercase__							,    num_labels=lowercase__							,    ignore_index=lowercase__							,    nan_to_num=lowercase__							,    label_map=lowercase__							,    reduce_labels=lowercase__							,    )
							return iou_result

