"""simple docstring"""






import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import BeitImageProcessor
class 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					def __init__(				self   :	Union[str, Any]    ,lowerCamelCase__   :	Tuple    ,lowerCamelCase__   :	Optional[Any]=7    ,lowerCamelCase__   :	Union[str, Any]=3    ,lowerCamelCase__   :	int=18    ,lowerCamelCase__   :	Any=30    ,lowerCamelCase__   :	str=400    ,lowerCamelCase__   :	Any=True    ,lowerCamelCase__   :	Tuple=None    ,lowerCamelCase__   :	List[Any]=True    ,lowerCamelCase__   :	List[Any]=None    ,lowerCamelCase__   :	Dict=True    ,lowerCamelCase__   :	int=[0.5, 0.5, 0.5]    ,lowerCamelCase__   :	Tuple=[0.5, 0.5, 0.5]    ,lowerCamelCase__   :	Optional[Any]=False    ,):
									UpperCAmelCase__             =							size if size is not None else {'height': 20, 'width': 20}
									UpperCAmelCase__             =							crop_size if crop_size is not None else {'height': 18, 'width': 18}
									UpperCAmelCase__             =							parent
									UpperCAmelCase__             =							batch_size
									UpperCAmelCase__             =							num_channels
									UpperCAmelCase__             =							image_size
									UpperCAmelCase__             =							min_resolution
									UpperCAmelCase__             =							max_resolution
									UpperCAmelCase__             =							do_resize
									UpperCAmelCase__             =							size
									UpperCAmelCase__             =							do_center_crop
									UpperCAmelCase__             =							crop_size
									UpperCAmelCase__             =							do_normalize
									UpperCAmelCase__             =							image_mean
									UpperCAmelCase__             =							image_std
									UpperCAmelCase__             =							do_reduce_labels







					def 		__lowerCAmelCase						(				self   :	str			):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_reduce_labels": self.do_reduce_labels,
									}






def   a_						(					):
				UpperCAmelCase__             =							load_dataset('hf-internal-testing/fixtures_ade20k'					,			split='test'       )

				UpperCAmelCase__             =							Image.open(dataset[0]['file']       )
				UpperCAmelCase__             =							Image.open(dataset[1]['file']       )

				return image, map



def   a_						(					):
				UpperCAmelCase__             =							load_dataset('hf-internal-testing/fixtures_ade20k'					,			split='test'       )

				UpperCAmelCase__             =							Image.open(ds[0]['file']       )
				UpperCAmelCase__             =							Image.open(ds[1]['file']       )
				UpperCAmelCase__             =							Image.open(ds[2]['file']       )
				UpperCAmelCase__             =							Image.open(ds[3]['file']       )

				return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class 				snake_case       ( __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = BeitImageProcessor if is_vision_available() else None







					def 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							BeitImageProcessingTester(self			)







					@property
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									return self.image_processor_tester.prepare_image_processor_dict()







					def 		__lowerCAmelCase						(				self   :	Dict			):
									UpperCAmelCase__             =							self.image_processing_class(**self.image_processor_dict			)
									self.assertTrue(hasattr(lowerCamelCase__    ,'do_resize'			)			)
									self.assertTrue(hasattr(lowerCamelCase__    ,'size'			)			)
									self.assertTrue(hasattr(lowerCamelCase__    ,'do_center_crop'			)			)
									self.assertTrue(hasattr(lowerCamelCase__    ,'center_crop'			)			)
									self.assertTrue(hasattr(lowerCamelCase__    ,'do_normalize'			)			)
									self.assertTrue(hasattr(lowerCamelCase__    ,'image_mean'			)			)
									self.assertTrue(hasattr(lowerCamelCase__    ,'image_std'			)			)







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							self.image_processing_class.from_dict(self.image_processor_dict			)
									self.assertEqual(image_processor.size    ,{'height': 20, 'width': 20}			)
									self.assertEqual(image_processor.crop_size    ,{'height': 18, 'width': 18}			)
									self.assertEqual(image_processor.do_reduce_labels    ,lowerCamelCase__			)

									UpperCAmelCase__             =							self.image_processing_class.from_dict(
									    self.image_processor_dict    ,size=42    ,crop_size=84    ,reduce_labels=lowerCamelCase__			)
									self.assertEqual(image_processor.size    ,{'height': 42, 'width': 42}			)
									self.assertEqual(image_processor.crop_size    ,{'height': 84, 'width': 84}			)
									self.assertEqual(image_processor.do_reduce_labels    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									pass







					def 		__lowerCAmelCase						(				self   :	str			):
									# Initialize image_processing
									UpperCAmelCase__             =							self.image_processing_class(**self.image_processor_dict			)
									# create random PIL images
									UpperCAmelCase__             =							prepare_image_inputs(self.image_processor_tester    ,equal_resolution=lowerCamelCase__			)
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__    ,Image.Image			)

									# Test not batched input
									UpperCAmelCase__             =							image_processing(image_inputs[0]    ,return_tensors='pt'			).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )    ,)

									# Test batched
									UpperCAmelCase__             =							image_processing(lowerCamelCase__    ,return_tensors='pt'			).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )    ,)







					def 		__lowerCAmelCase						(				self   :	Dict			):
									# Initialize image_processing
									UpperCAmelCase__             =							self.image_processing_class(**self.image_processor_dict			)
									# create random numpy tensors
									UpperCAmelCase__             =							prepare_image_inputs(self.image_processor_tester    ,equal_resolution=lowerCamelCase__    ,numpify=lowerCamelCase__			)
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__    ,np.ndarray			)

									# Test not batched input
									UpperCAmelCase__             =							image_processing(image_inputs[0]    ,return_tensors='pt'			).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )    ,)

									# Test batched
									UpperCAmelCase__             =							image_processing(lowerCamelCase__    ,return_tensors='pt'			).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )    ,)







					def 		__lowerCAmelCase						(				self   :	Any			):
									# Initialize image_processing
									UpperCAmelCase__             =							self.image_processing_class(**self.image_processor_dict			)
									# create random PyTorch tensors
									UpperCAmelCase__             =							prepare_image_inputs(self.image_processor_tester    ,equal_resolution=lowerCamelCase__    ,torchify=lowerCamelCase__			)
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__    ,torch.Tensor			)

									# Test not batched input
									UpperCAmelCase__             =							image_processing(image_inputs[0]    ,return_tensors='pt'			).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )    ,)

									# Test batched
									UpperCAmelCase__             =							image_processing(lowerCamelCase__    ,return_tensors='pt'			).pixel_values
									self.assertEqual(
									    encoded_images.shape    ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )    ,)







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									# Initialize image_processing
									UpperCAmelCase__             =							self.image_processing_class(**self.image_processor_dict			)
									# create random PyTorch tensors
									UpperCAmelCase__             =							prepare_image_inputs(self.image_processor_tester    ,equal_resolution=lowerCamelCase__    ,torchify=lowerCamelCase__			)
									UpperCAmelCase__             =							[]
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__    ,torch.Tensor			)
													maps.append(torch.zeros(image.shape[-2:]			).long()			)

									# Test not batched input
									UpperCAmelCase__             =							image_processing(image_inputs[0]    ,maps[0]    ,return_tensors='pt'			)
									self.assertEqual(
									    encoding['pixel_values'].shape    ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )    ,)
									self.assertEqual(
									    encoding['labels'].shape    ,(
									        1,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )    ,)
									self.assertEqual(encoding['labels'].dtype    ,torch.long			)
									self.assertTrue(encoding['labels'].min().item() >= 0			)
									self.assertTrue(encoding['labels'].max().item() <= 255			)

									# Test batched
									UpperCAmelCase__             =							image_processing(lowerCamelCase__    ,lowerCamelCase__    ,return_tensors='pt'			)
									self.assertEqual(
									    encoding['pixel_values'].shape    ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )    ,)
									self.assertEqual(
									    encoding['labels'].shape    ,(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )    ,)
									self.assertEqual(encoding['labels'].dtype    ,torch.long			)
									self.assertTrue(encoding['labels'].min().item() >= 0			)
									self.assertTrue(encoding['labels'].max().item() <= 255			)

									# Test not batched input (PIL images)
									UpperCAmelCase__      ,	UpperCAmelCase__             =							prepare_semantic_single_inputs()

									UpperCAmelCase__             =							image_processing(lowerCamelCase__    ,lowerCamelCase__    ,return_tensors='pt'			)
									self.assertEqual(
									    encoding['pixel_values'].shape    ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )    ,)
									self.assertEqual(
									    encoding['labels'].shape    ,(
									        1,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )    ,)
									self.assertEqual(encoding['labels'].dtype    ,torch.long			)
									self.assertTrue(encoding['labels'].min().item() >= 0			)
									self.assertTrue(encoding['labels'].max().item() <= 255			)

									# Test batched input (PIL images)
									UpperCAmelCase__      ,	UpperCAmelCase__             =							prepare_semantic_batch_inputs()

									UpperCAmelCase__             =							image_processing(lowerCamelCase__    ,lowerCamelCase__    ,return_tensors='pt'			)
									self.assertEqual(
									    encoding['pixel_values'].shape    ,(
									        2,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )    ,)
									self.assertEqual(
									    encoding['labels'].shape    ,(
									        2,
									        self.image_processor_tester.crop_size['height'],
									        self.image_processor_tester.crop_size['width'],
									    )    ,)
									self.assertEqual(encoding['labels'].dtype    ,torch.long			)
									self.assertTrue(encoding['labels'].min().item() >= 0			)
									self.assertTrue(encoding['labels'].max().item() <= 255			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									# Initialize image_processing
									UpperCAmelCase__             =							self.image_processing_class(**self.image_processor_dict			)

									# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
									UpperCAmelCase__      ,	UpperCAmelCase__             =							prepare_semantic_single_inputs()
									UpperCAmelCase__             =							image_processing(lowerCamelCase__    ,lowerCamelCase__    ,return_tensors='pt'			)
									self.assertTrue(encoding['labels'].min().item() >= 0			)
									self.assertTrue(encoding['labels'].max().item() <= 150			)

									UpperCAmelCase__             =							True
									UpperCAmelCase__             =							image_processing(lowerCamelCase__    ,lowerCamelCase__    ,return_tensors='pt'			)
									self.assertTrue(encoding['labels'].min().item() >= 0			)
									self.assertTrue(encoding['labels'].max().item() <= 255			)






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,
)


lowercase_									=   logging.getLogger(__name__)





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase=None							,   lowerCAmelCase=None					)  -> List[str]:
        '''simple docstring'''






        _lowercase	       =self.layer[current_layer](lowerCAmelCase							,   lowerCAmelCase							,   head_mask[current_layer]					)

        _lowercase	       =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."""			,			SCREAMING_SNAKE_CASE			,			)
class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def __init__(				self							,   lowerCAmelCase					)  -> Union[str, Any]:
        '''simple docstring'''






        super().__init__(lowerCAmelCase					)

        _lowercase	       =BertEncoderWithPabee(lowerCAmelCase					)

        self.init_weights()
        _lowercase	       =0
        _lowercase	       =0
        _lowercase	       =0

        _lowercase	       =0




  def   A__     (				self							,   lowerCAmelCase					)  -> Optional[Any]:
        '''simple docstring'''






        _lowercase	       =threshold




  def   A__     (				self							,   lowerCAmelCase					)  -> List[Any]:
        '''simple docstring'''






        _lowercase	       =patience




  def   A__     (				self					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =0
        _lowercase	       =0




  def   A__     (				self					)  -> int:
        '''simple docstring'''






        _lowercase	       =self.inference_layers_num / self.inference_instances_num
        _lowercase	       =(
            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(lowerCAmelCase					)




  @add_start_docstrings_to_model_forward(lowerCAmelCase					)
  def   A__     (				self							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=False							,   )  -> str:
        '''simple docstring'''






        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:
              _lowercase	       =input_ids.size()
        elif inputs_embeds is not None:
              _lowercase	       =inputs_embeds.size()[:-1]
        else:
              raise ValueError('You have to specify either input_ids or inputs_embeds'					)

        _lowercase	       =input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
              _lowercase	       =torch.ones(lowerCAmelCase							,   device=lowerCAmelCase					)
        if token_type_ids is None:
              _lowercase	       =torch.zeros(lowerCAmelCase							,   dtype=torch.long							,   device=lowerCAmelCase					)

        # 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.
        _lowercase	       =self.get_extended_attention_mask(lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					)

        # 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:
              _lowercase       ,       _lowercase       ,       _lowercase	       =encoder_hidden_states.size()
              _lowercase	       =(encoder_batch_size, encoder_sequence_length)
              if encoder_attention_mask is None:
                    _lowercase	       =torch.ones(lowerCAmelCase							,   device=lowerCAmelCase					)
              _lowercase	       =self.invert_attention_mask(lowerCAmelCase					)
        else:
              _lowercase	       =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]
        _lowercase	       =self.get_head_mask(lowerCAmelCase							,   self.config.num_hidden_layers					)

        _lowercase	       =self.embeddings(
            input_ids=lowerCAmelCase							,   position_ids=lowerCAmelCase							,   token_type_ids=lowerCAmelCase							,   inputs_embeds=lowerCAmelCase					)
        _lowercase	       =embedding_output

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

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

                    _lowercase	       =self.pooler(lowerCAmelCase					)
                    _lowercase	       =output_layers[i](lowerCAmelCase					)
                    if regression:
                          _lowercase	       =logits.detach()
                          if patient_result is not None:
                                _lowercase	       =patient_result.detach()
                          if (patient_result is not None) and torch.abs(patient_result - labels					) < self.regression_threshold:
                                patient_counter += 1
                          else:
                                _lowercase	       =0
                    else:
                          _lowercase	       =logits.detach().argmax(dim=1					)
                          if patient_result is not None:
                                _lowercase	       =patient_result.detach().argmax(dim=1					)
                          if (patient_result is not None) and torch.all(labels.eq(lowerCAmelCase					)					):
                                patient_counter += 1
                          else:
                                _lowercase	       =0

                    _lowercase	       =logits
                    if patient_counter == self.patience:
                          break
              _lowercase	       =[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. """			,			SCREAMING_SNAKE_CASE			,			)
class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def __init__(				self							,   lowerCAmelCase					)  -> List[Any]:
        '''simple docstring'''






        super().__init__(lowerCAmelCase					)
        _lowercase	       =config.num_labels

        _lowercase	       =BertModelWithPabee(lowerCAmelCase					)
        _lowercase	       =nn.Dropout(config.hidden_dropout_prob					)
        _lowercase	       =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(lowerCAmelCase					)
  def   A__     (				self							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   lowerCAmelCase=None							,   )  -> Union[str, Any]:
        '''simple docstring'''






        _lowercase	       =self.bert(
            input_ids=lowerCAmelCase							,   attention_mask=lowerCAmelCase							,   token_type_ids=lowerCAmelCase							,   position_ids=lowerCAmelCase							,   head_mask=lowerCAmelCase							,   inputs_embeds=lowerCAmelCase							,   output_dropout=self.dropout							,   output_layers=self.classifiers							,   regression=self.num_labels == 1							,   )

        _lowercase	       =(logits[-1],)

        if labels is not None:
              _lowercase	       =None
              _lowercase	       =0
              for ix, logits_item in enumerate(lowerCAmelCase					):
                    if self.num_labels == 1:
                          #  We are doing regression
                          _lowercase	       =MSELoss()
                          _lowercase	       =loss_fct(logits_item.view(-1					)							,   labels.view(-1					)					)
                    else:
                          _lowercase	       =CrossEntropyLoss()
                          _lowercase	       =loss_fct(logits_item.view(-1							,   self.num_labels					)							,   labels.view(-1					)					)
                    if total_loss is None:
                          _lowercase	       =loss
                    else:
                          total_loss += loss * (ix + 1)
                    total_weights += ix + 1
              _lowercase	       =(total_loss / total_weights,) + outputs

        return outputs


