


'''simple docstring'''

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class   __lowerCAmelCase						(unittest.TestCase     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             lowercase__      			= tempfile.mkdtemp()

             lowercase__      			= [
                 '''[UNK]''',
                 '''[CLS]''',
                 '''[SEP]''',
                 '''[PAD]''',
                 '''[MASK]''',
                 '''want''',
                 '''##want''',
                 '''##ed''',
                 '''wa''',
                 '''un''',
                 '''runn''',
                 '''##ing''',
                 ''',''',
                 '''low''',
                 '''lowest''',
             ]
             lowercase__      			= os.path.join(self.tmpdirname   ,						VOCAB_FILES_NAMES['''vocab_file'''] )
             with open(self.vocab_file   ,						'''w'''   ,						encoding='''utf-8''' ) as vocab_writer:
                    vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )

             lowercase__      			= {
                 '''do_resize''': True,
                 '''size''': 20,
                 '''do_center_crop''': True,
                 '''crop_size''': 18,
                 '''do_normalize''': True,
                 '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
                 '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
             }
             lowercase__      			= os.path.join(self.tmpdirname   ,						UpperCamelCase )
             with open(self.image_processor_file   ,						'''w'''   ,						encoding='''utf-8''' ) as fp:
                    json.dump(UpperCamelCase   ,						UpperCamelCase )



      def       UpperCamelCase__		(self    :    Tuple   ,						**UpperCamelCase    :    Optional[int] ):

             '''simple docstring'''







             return BertTokenizer.from_pretrained(self.tmpdirname   ,						**UpperCamelCase )



      def       UpperCamelCase__		(self    :    Tuple   ,						**UpperCamelCase    :    List[str] ):

             '''simple docstring'''







             return BertTokenizerFast.from_pretrained(self.tmpdirname   ,						**UpperCamelCase )



      def       UpperCamelCase__		(self    :    List[Any]   ,						**UpperCamelCase    :    Optional[int] ):

             '''simple docstring'''







             return EfficientNetImageProcessor.from_pretrained(self.tmpdirname   ,						**UpperCamelCase )



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             shutil.rmtree(self.tmpdirname )



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             lowercase__      			= [np.random.randint(255   ,						size=(3, 30, 400)   ,						dtype=np.uinta )]
             lowercase__      			= [Image.fromarray(np.moveaxis(UpperCamelCase   ,						0   ,						-1 ) ) for x in image_inputs]
             return image_inputs



      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= self.get_tokenizer()
             lowercase__      			= self.get_rust_tokenizer()
             lowercase__      			= self.get_image_processor()

             lowercase__      			= AlignProcessor(tokenizer=UpperCamelCase   ,						image_processor=UpperCamelCase )
             processor_slow.save_pretrained(self.tmpdirname )
             lowercase__      			= AlignProcessor.from_pretrained(self.tmpdirname   ,						use_fast=UpperCamelCase )

             lowercase__      			= AlignProcessor(tokenizer=UpperCamelCase   ,						image_processor=UpperCamelCase )
             processor_fast.save_pretrained(self.tmpdirname )
             lowercase__      			= AlignProcessor.from_pretrained(self.tmpdirname )

             self.assertEqual(processor_slow.tokenizer.get_vocab()   ,						tokenizer_slow.get_vocab() )
             self.assertEqual(processor_fast.tokenizer.get_vocab()   ,						tokenizer_fast.get_vocab() )
             self.assertEqual(tokenizer_slow.get_vocab()   ,						tokenizer_fast.get_vocab() )
             self.assertIsInstance(processor_slow.tokenizer   ,						UpperCamelCase )
             self.assertIsInstance(processor_fast.tokenizer   ,						UpperCamelCase )

             self.assertEqual(processor_slow.image_processor.to_json_string()   ,						image_processor.to_json_string() )
             self.assertEqual(processor_fast.image_processor.to_json_string()   ,						image_processor.to_json_string() )
             self.assertIsInstance(processor_slow.image_processor   ,						UpperCamelCase )
             self.assertIsInstance(processor_fast.image_processor   ,						UpperCamelCase )



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= AlignProcessor(tokenizer=self.get_tokenizer()   ,						image_processor=self.get_image_processor() )
             processor.save_pretrained(self.tmpdirname )

             lowercase__      			= self.get_tokenizer(bos_token='''(BOS)'''   ,						eos_token='''(EOS)''' )
             lowercase__      			= self.get_image_processor(do_normalize=UpperCamelCase   ,						padding_value=1.0 )

             lowercase__      			= AlignProcessor.from_pretrained(
                 self.tmpdirname   ,						bos_token='''(BOS)'''   ,						eos_token='''(EOS)'''   ,						do_normalize=UpperCamelCase   ,						padding_value=1.0 )

             self.assertEqual(processor.tokenizer.get_vocab()   ,						tokenizer_add_kwargs.get_vocab() )
             self.assertIsInstance(processor.tokenizer   ,						UpperCamelCase )

             self.assertEqual(processor.image_processor.to_json_string()   ,						image_processor_add_kwargs.to_json_string() )
             self.assertIsInstance(processor.image_processor   ,						UpperCamelCase )



      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             lowercase__      			= self.get_image_processor()
             lowercase__      			= self.get_tokenizer()

             lowercase__      			= AlignProcessor(tokenizer=UpperCamelCase   ,						image_processor=UpperCamelCase )

             lowercase__      			= self.prepare_image_inputs()

             lowercase__      			= image_processor(UpperCamelCase   ,						return_tensors='''np''' )
             lowercase__      			= processor(images=UpperCamelCase   ,						return_tensors='''np''' )

             for key in input_image_proc.keys():
                    self.assertAlmostEqual(input_image_proc[key].sum()   ,						input_processor[key].sum()   ,						delta=1E-2 )



      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= self.get_image_processor()
             lowercase__      			= self.get_tokenizer()

             lowercase__      			= AlignProcessor(tokenizer=UpperCamelCase   ,						image_processor=UpperCamelCase )

             lowercase__      			= '''lower newer'''

             lowercase__      			= processor(text=UpperCamelCase )

             lowercase__      			= tokenizer(UpperCamelCase   ,						padding='''max_length'''   ,						max_length=64 )

             for key in encoded_tok.keys():
                    self.assertListEqual(encoded_tok[key]   ,						encoded_processor[key] )



      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= self.get_image_processor()
             lowercase__      			= self.get_tokenizer()

             lowercase__      			= AlignProcessor(tokenizer=UpperCamelCase   ,						image_processor=UpperCamelCase )

             lowercase__      			= '''lower newer'''
             lowercase__      			= self.prepare_image_inputs()

             lowercase__      			= processor(text=UpperCamelCase   ,						images=UpperCamelCase )

             self.assertListEqual(list(inputs.keys() )   ,						['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )

             # test if it raises when no input is passed
             with pytest.raises(UpperCamelCase ):
                    processor()



      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= self.get_image_processor()
             lowercase__      			= self.get_tokenizer()

             lowercase__      			= AlignProcessor(tokenizer=UpperCamelCase   ,						image_processor=UpperCamelCase )

             lowercase__      			= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             lowercase__      			= processor.batch_decode(UpperCamelCase )
             lowercase__      			= tokenizer.batch_decode(UpperCamelCase )

             self.assertListEqual(UpperCamelCase   ,						UpperCamelCase )






      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             lowercase__      			= self.get_image_processor()
             lowercase__      			= self.get_tokenizer()

             lowercase__      			= AlignProcessor(tokenizer=UpperCamelCase   ,						image_processor=UpperCamelCase )

             lowercase__      			= '''lower newer'''
             lowercase__      			= self.prepare_image_inputs()

             lowercase__      			= processor(text=UpperCamelCase   ,						images=UpperCamelCase )

             self.assertListEqual(list(inputs.keys() )   ,						processor.model_input_names )










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 						lowercase							(   UpperCamelCase__		):
     _a							 =						RobertaConfig
     _a							 =						"roberta"

     def __init__(  self		,     _a				)						->							Optional[int]:
           super().__init__(_a				)

           _A							:     Union[str, Any]			=						RobertaEmbeddings(_a				)
           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 						lowercase							(   UpperCamelCase__		):
     _a							 =						RobertaConfig
     _a							 =						"roberta"

     def __init__(  self		,     _a				)						->							str:
           super().__init__(_a				)
           _A							:     Any			=						config.num_labels
           _A							:     Dict			=						config.num_hidden_layers

           _A							:     List[str]			=						DeeRobertaModel(_a				)
           _A							:     int			=						nn.Dropout(config.hidden_dropout_prob				)
           _A							:     int			=						nn.Linear(config.hidden_size		,     self.config.num_labels				)



     @add_start_docstrings_to_model_forward(_a				)
     def 					a__	(  self		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=None		,     _a=-1		,     _a=False		,     )						->							Any:
           _A							:     Optional[int]			=						self.num_layers
           try:
                 _A							:     List[str]			=						self.roberta(
                     _a		,     attention_mask=_a		,     token_type_ids=_a		,     position_ids=_a		,     head_mask=_a		,     inputs_embeds=_a		,     )

                 _A							:     List[str]			=						outputs[1]

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

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

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

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

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

