





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,
)


snake_case_           =      logging.getLogger(__name__)

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


						def        a			(self	:				Tuple    ,      a__	:				Optional[int]    ,      a__	:				Dict    ,      a__	:				Optional[Any]=None    ,      a__	:				int=None				):





									"""simple docstring"""



									__snake_case								=       self.layer[current_layer](a__    ,      a__    ,      head_mask[current_layer]				)

									__snake_case								=       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 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


						def __init__(self	:				Optional[int]    ,      a__	:				int				):





									"""simple docstring"""



									super().__init__(a__				)

									__snake_case								=       BertEncoderWithPabee(a__				)

									self.init_weights()
									__snake_case								=       0
									__snake_case								=       0
									__snake_case								=       0

									__snake_case								=       0


						def        a			(self	:				List[str]    ,      a__	:				Optional[Any]				):





									"""simple docstring"""



									__snake_case								=       threshold


						def        a			(self	:				Tuple    ,      a__	:				Union[str, Any]				):





									"""simple docstring"""



									__snake_case								=       patience


						def        a			(self	:				Any				):





									"""simple docstring"""



									__snake_case								=       0
									__snake_case								=       0


						def        a			(self	:				Tuple				):





									"""simple docstring"""



									__snake_case								=       self.inference_layers_num / self.inference_instances_num
									__snake_case								=       (
									    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(a__				)


						@add_start_docstrings_to_model_forward(a__				)
						def        a			(self	:				Optional[Any]    ,      a__	:				Dict=None    ,      a__	:				int=None    ,      a__	:				str=None    ,      a__	:				List[Any]=None    ,      a__	:				List[Any]=None    ,      a__	:				Dict=None    ,      a__	:				Tuple=None    ,      a__	:				str=None    ,      a__	:				Dict=None    ,      a__	:				Any=None    ,      a__	:				Optional[Any]=False    ,      ):





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

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

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

									# 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								=       self.get_extended_attention_mask(a__    ,      a__    ,      a__				)

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

									__snake_case								=       self.embeddings(
									    input_ids=a__    ,      position_ids=a__    ,      token_type_ids=a__    ,      inputs_embeds=a__				)
									__snake_case								=       embedding_output

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

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

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

															__snake_case								=       logits
															if patient_counter == self.patience:
																		break
												__snake_case								=       [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. '						,				_UpperCAmelCase						,				)
class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


						def __init__(self	:				Any    ,      a__	:				Union[str, Any]				):





									"""simple docstring"""



									super().__init__(a__				)
									__snake_case								=       config.num_labels

									__snake_case								=       BertModelWithPabee(a__				)
									__snake_case								=       nn.Dropout(config.hidden_dropout_prob				)
									__snake_case								=       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(a__				)
						def        a			(self	:				List[Any]    ,      a__	:				Dict=None    ,      a__	:				Any=None    ,      a__	:				List[str]=None    ,      a__	:				Tuple=None    ,      a__	:				Optional[Any]=None    ,      a__	:				Union[str, Any]=None    ,      a__	:				str=None    ,      ):





									"""simple docstring"""



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

									__snake_case								=       (logits[-1],)

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

									return outputs








from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

snake_case_           =      logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def __init__(self	:				int    ,      *a__	:				List[Any]    ,      **a__	:				Dict				):





         """simple docstring"""



         super().__init__(*a__    ,      **a__				)
         requires_backends(self    ,      '''vision'''				)
         self.check_model_type(a__				)


      def __call__(self	:				Optional[Any]    ,      a__	:				Union[str, List[str], "Image.Image", List["Image.Image"]]    ,      **a__	:				List[str]				):





         """simple docstring"""



         return super().__call__(a__    ,      **a__				)


      def        a			(self	:				int    ,      **a__	:				int				):





         """simple docstring"""



         return {}, {}, {}


      def        a			(self	:				Optional[int]    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       load_image(a__				)
         __snake_case								=       image.size
         __snake_case								=       self.image_processor(images=a__    ,      return_tensors=self.framework				)
         return model_inputs


      def        a			(self	:				List[Any]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.model(**a__				)
         return model_outputs


      def        a			(self	:				int    ,      a__	:				str				):





         """simple docstring"""



         __snake_case								=       model_outputs.predicted_depth
         __snake_case								=       torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1				)    ,      size=self.image_size[::-1]    ,      mode='''bicubic'''    ,      align_corners=a__				)
         __snake_case								=       prediction.squeeze().cpu().numpy()
         __snake_case								=       (output * 255 / np.max(a__				)).astype('''uint8'''				)
         __snake_case								=       Image.fromarray(a__				)
         __snake_case								=       {}
         __snake_case								=       predicted_depth
         __snake_case								=       depth
         return output_dict

