





'''simple docstring'''







from __future__ import absolute_import, division, print_function, unicode_literals

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

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy


@add_start_docstrings(
    "The RoBERTa Model transformer with early exiting (DeeRoBERTa). "       ,		__UpperCAmelCase       ,		)
class 					__a      (__UpperCAmelCase     ):
							__a					:   Dict									=       RobertaConfig
							__a					:   Optional[Any]									=       '''roberta'''

							def __init__(							self							:     Optional[int]       ,     __magic_name__							:     str   )					->       List[Any]:







													"""simple docstring"""
													super().__init__(__SCREAMING_SNAKE_CASE   )

													UpperCAmelCase_			:      List[Any]     						=  RobertaEmbeddings(__SCREAMING_SNAKE_CASE   )
													self.init_weights()





@add_start_docstrings(
    "RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n    also takes care of multi-layer training. "       ,		__UpperCAmelCase       ,		)
class 					__a      (__UpperCAmelCase     ):
							__a					:   Dict									=       RobertaConfig
							__a					:   Tuple									=       '''roberta'''

							def __init__(							self							:     Dict       ,     __magic_name__							:     Any   )					->       int:







													"""simple docstring"""
													super().__init__(__SCREAMING_SNAKE_CASE   )
													UpperCAmelCase_			:      Optional[Any]     						=  config.num_labels
													UpperCAmelCase_			:      Any     						=  config.num_hidden_layers

													UpperCAmelCase_			:      Dict     						=  DeeRobertaModel(__SCREAMING_SNAKE_CASE   )
													UpperCAmelCase_			:      Any     						=  nn.Dropout(config.hidden_dropout_prob   )
													UpperCAmelCase_			:      str     						=  nn.Linear(config.hidden_size       ,     self.config.num_labels   )



							@add_start_docstrings_to_model_forward(__SCREAMING_SNAKE_CASE   )
							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     Any=None       ,     __magic_name__							:     Optional[int]=None       ,     __magic_name__							:     Dict=None       ,     __magic_name__							:     Dict=None       ,     __magic_name__							:     List[str]=None       ,     __magic_name__							:     Dict=None       ,     __magic_name__							:     List[Any]=None       ,     __magic_name__							:     List[Any]=-1       ,     __magic_name__							:     List[Any]=False       ,     )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  self.num_layers
													try:
																			UpperCAmelCase_			:      Dict     						=  self.roberta(
																			    __SCREAMING_SNAKE_CASE       ,     attention_mask=__SCREAMING_SNAKE_CASE       ,     token_type_ids=__SCREAMING_SNAKE_CASE       ,     position_ids=__SCREAMING_SNAKE_CASE       ,     head_mask=__SCREAMING_SNAKE_CASE       ,     inputs_embeds=__SCREAMING_SNAKE_CASE       ,     )

																			UpperCAmelCase_			:      Optional[Any]     						=  outputs[1]

																			UpperCAmelCase_			:      Dict     						=  self.dropout(__SCREAMING_SNAKE_CASE   )
																			UpperCAmelCase_			:      Dict     						=  self.classifier(__SCREAMING_SNAKE_CASE   )
																			UpperCAmelCase_			:      Dict     						=  (logits,) + outputs[2:]  # add hidden states and attention if they are here
													except HighwayException as e:
																			UpperCAmelCase_			:      str     						=  e.message
																			UpperCAmelCase_			:      Tuple     						=  e.exit_layer
																			UpperCAmelCase_			:      Optional[Any]     						=  outputs[0]

													if not self.training:
																			UpperCAmelCase_			:      Dict     						=  entropy(__SCREAMING_SNAKE_CASE   )
																			UpperCAmelCase_			:      Any     						=  []
																			UpperCAmelCase_			:      Optional[Any]     						=  []
													if labels is not None:
																			if self.num_labels == 1:
																									#  We are doing regression
																									UpperCAmelCase_			:      int     						=  MSELoss()
																									UpperCAmelCase_			:      Any     						=  loss_fct(logits.view(-1   )       ,     labels.view(-1   )   )
																			else:
																									UpperCAmelCase_			:      int     						=  CrossEntropyLoss()
																									UpperCAmelCase_			:      List[Any]     						=  loss_fct(logits.view(-1       ,     self.num_labels   )       ,     labels.view(-1   )   )

																			# work with highway exits
																			UpperCAmelCase_			:      Dict     						=  []
																			for highway_exit in outputs[-1]:
																									UpperCAmelCase_			:      Tuple     						=  highway_exit[0]
																									if not self.training:
																															highway_logits_all.append(__SCREAMING_SNAKE_CASE   )
																															highway_entropy.append(highway_exit[2]   )
																									if self.num_labels == 1:
																															#  We are doing regression
																															UpperCAmelCase_			:      List[Any]     						=  MSELoss()
																															UpperCAmelCase_			:      int     						=  loss_fct(highway_logits.view(-1   )       ,     labels.view(-1   )   )
																									else:
																															UpperCAmelCase_			:      Union[str, Any]     						=  CrossEntropyLoss()
																															UpperCAmelCase_			:      List[Any]     						=  loss_fct(highway_logits.view(-1       ,     self.num_labels   )       ,     labels.view(-1   )   )
																									highway_losses.append(__SCREAMING_SNAKE_CASE   )

																			if train_highway:
																									UpperCAmelCase_			:      Dict     						=  (sum(highway_losses[:-1]   ),) + outputs
																									# exclude the final highway, of course
																			else:
																									UpperCAmelCase_			:      str     						=  (loss,) + outputs
													if not self.training:
																			UpperCAmelCase_			:      Optional[int]     						=  outputs + ((original_entropy, highway_entropy), exit_layer)
																			if output_layer >= 0:
																									UpperCAmelCase_			:      str     						=  (
																									    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
																									)  # use the highway of the last layer

													return outputs  # (loss), logits, (hidden_states), (attentions), entropy












'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class 					__a      (unittest.TestCase     ):

							@property
							def     UpperCAmelCase__	(							self							:     Dict   )					->       str:







													"""simple docstring"""
													torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[Any]     						=  UNetaDModel(
													    block_out_channels=(32, 64)       ,     layers_per_block=2       ,     sample_size=32       ,     in_channels=3       ,     out_channels=3       ,     down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')       ,     up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')       ,     )
													return model



							def     UpperCAmelCase__	(							self							:     Dict   )					->       Tuple:







													"""simple docstring"""
													UpperCAmelCase_			:      List[Any]     						=  self.dummy_uncond_unet
													UpperCAmelCase_			:      Dict     						=  KarrasVeScheduler()

													UpperCAmelCase_			:      Union[str, Any]     						=  KarrasVePipeline(unet=__magic_name__       ,     scheduler=__magic_name__   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Dict     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[int]     						=  pipe(num_inference_steps=2       ,     generator=__magic_name__       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Tuple     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      str     						=  pipe(num_inference_steps=2       ,     generator=__magic_name__       ,     output_type='''numpy'''       ,     return_dict=__magic_name__   )[0]

													UpperCAmelCase_			:      Union[str, Any]     						=  image[0, -3:, -3:, -1]
													UpperCAmelCase_			:      Optional[Any]     						=  image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													UpperCAmelCase_			:      Dict     						=  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]   )

													assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2





@slow
@require_torch
class 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     int   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      List[str]     						=  '''google/ncsnpp-celebahq-256'''
													UpperCAmelCase_			:      List[str]     						=  UNetaDModel.from_pretrained(__magic_name__   )
													UpperCAmelCase_			:      List[Any]     						=  KarrasVeScheduler()

													UpperCAmelCase_			:      Any     						=  KarrasVePipeline(unet=__magic_name__       ,     scheduler=__magic_name__   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Dict     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[Any]     						=  pipe(num_inference_steps=20       ,     generator=__magic_name__       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Any     						=  image[0, -3:, -3:, -1]
													assert image.shape == (1, 2_56, 2_56, 3)
													UpperCAmelCase_			:      Optional[Any]     						=  np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]   )

													assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2





