

import json
import os
import shutil
import tempfile
import unittest

import numpy as np

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


if is_vision_available():
 from PIL import Image

 from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor



@require_tokenizers
@require_vision
class   lowercase_   (  unittest.TestCase					):



      def 						UpperCamelCase_		(      self				:    str		)   ->					List[Any]:
        _snake_case						=							tempfile.mkdtemp()

        # fmt: off
        _snake_case						=							['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
        # fmt: on
        _snake_case						=							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]		)		)

        _snake_case						=							{
            '''do_resize''': True,
            '''size''': {'''height''': 18, '''width''': 18},
            '''do_normalize''': True,
            '''image_mean''': [0.5, 0.5, 0.5],
            '''image_std''': [0.5, 0.5, 0.5],
        }
        _snake_case						=							os.path.join(self.tmpdirname      , A__		)
        with open(self.image_processor_file      , '''w'''      , encoding='''utf-8'''		) as fp:
          json.dump(A__      , A__		)



      def 						UpperCamelCase_		(      self				:    Any      , **A__				:    Optional[Any]		)   ->					Dict:
        return BertTokenizer.from_pretrained(self.tmpdirname      , **A__		)



      def 						UpperCamelCase_		(      self				:    List[Any]      , **A__				:    int		)   ->					List[str]:
        return ViTImageProcessor.from_pretrained(self.tmpdirname      , **A__		)



      def 						UpperCamelCase_		(      self				:    str		)   ->					Any:
        shutil.rmtree(self.tmpdirname		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					Any:
        _snake_case						=							[np.random.randint(255      , size=(3, 30, 400)      , dtype=np.uinta		)]

        _snake_case						=							[Image.fromarray(np.moveaxis(A__      , 0      , -1		)		) for x in image_inputs]

        return image_inputs



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					Optional[Any]:
        _snake_case						=							self.get_tokenizer()
        _snake_case						=							self.get_image_processor()

        _snake_case						=							VisionTextDualEncoderProcessor(tokenizer=A__      , image_processor=A__		)

        processor.save_pretrained(self.tmpdirname		)
        _snake_case						=							VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname		)

        self.assertEqual(processor.tokenizer.get_vocab()      , tokenizer.get_vocab()		)
        self.assertIsInstance(processor.tokenizer      , (BertTokenizer, BertTokenizerFast)		)

        self.assertEqual(processor.image_processor.to_json_string()      , image_processor.to_json_string()		)
        self.assertIsInstance(processor.image_processor      , A__		)



      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					int:
        _snake_case						=							VisionTextDualEncoderProcessor(
            tokenizer=self.get_tokenizer()      , image_processor=self.get_image_processor()		)
        processor.save_pretrained(self.tmpdirname		)

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

        _snake_case						=							VisionTextDualEncoderProcessor.from_pretrained(
            self.tmpdirname      , bos_token='''(BOS)'''      , eos_token='''(EOS)'''      , do_normalize=A__      , padding_value=1.0		)

        self.assertEqual(processor.tokenizer.get_vocab()      , tokenizer_add_kwargs.get_vocab()		)
        self.assertIsInstance(processor.tokenizer      , (BertTokenizer, BertTokenizerFast)		)

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



      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Tuple:
        _snake_case						=							self.get_image_processor()
        _snake_case						=							self.get_tokenizer()

        _snake_case						=							VisionTextDualEncoderProcessor(tokenizer=A__      , image_processor=A__		)

        _snake_case						=							self.prepare_image_inputs()

        _snake_case						=							image_processor(A__      , return_tensors='''np'''		)
        _snake_case						=							processor(images=A__      , return_tensors='''np'''		)

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



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					int:
        _snake_case						=							self.get_image_processor()
        _snake_case						=							self.get_tokenizer()

        _snake_case						=							VisionTextDualEncoderProcessor(tokenizer=A__      , image_processor=A__		)

        _snake_case						=							'''lower newer'''

        _snake_case						=							processor(text=A__		)

        _snake_case						=							tokenizer(A__		)

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



      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					int:
        _snake_case						=							self.get_image_processor()
        _snake_case						=							self.get_tokenizer()

        _snake_case						=							VisionTextDualEncoderProcessor(tokenizer=A__      , image_processor=A__		)

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

        _snake_case						=							processor(text=A__      , images=A__		)

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

        # test if it raises when no input is passed
        with self.assertRaises(A__		):
          processor()



      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					List[Any]:
        _snake_case						=							self.get_image_processor()
        _snake_case						=							self.get_tokenizer()

        _snake_case						=							VisionTextDualEncoderProcessor(tokenizer=A__      , image_processor=A__		)

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

        _snake_case						=							processor.batch_decode(A__		)
        _snake_case						=							tokenizer.batch_decode(A__		)

        self.assertListEqual(A__      , A__		)







      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Union[str, Any]:
        _snake_case						=							self.get_image_processor()
        _snake_case						=							self.get_tokenizer()

        _snake_case						=							VisionTextDualEncoderProcessor(tokenizer=A__      , image_processor=A__		)

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

        _snake_case						=							processor(text=A__      , images=A__		)

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







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,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)



def 				snake_case_(_UpperCamelCase					)							->     List[Any]:



  """simple docstring"""






  _snake_case						=							torch.exp(_UpperCamelCase					)
  _snake_case						=							torch.sum(_UpperCamelCase			,	dim=1					)  # sum of exp(x_i)
  _snake_case						=							torch.sum(x * exp_x			,	dim=1					)  # sum of x_i * exp(x_i)
  return torch.log(_UpperCamelCase					) - B / A



class   lowercase_   (  nn.Module					):



      def __init__(      self				:    Tuple      , A__				:    int		)   ->					Tuple:
        super().__init__()
        _snake_case						=							config.output_attentions
        _snake_case						=							config.output_hidden_states
        _snake_case						=							nn.ModuleList([BertLayer(A__		) for _ in range(config.num_hidden_layers		)]		)
        _snake_case						=							nn.ModuleList([BertHighway(A__		) for _ in range(config.num_hidden_layers		)]		)

        _snake_case						=							[-1 for _ in range(config.num_hidden_layers		)]



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    Any		)   ->					Any:
        if (type(A__		) is float) or (type(A__		) is int):
          for i in range(len(self.early_exit_entropy		)		):
            _snake_case						=							x
        else:
          _snake_case						=							x



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    Tuple		)   ->					int:
        _snake_case						=							pooler.state_dict()
        for highway in self.highway:
          for name, param in highway.pooler.state_dict().items():
            param.copy_(loaded_model[name]		)







      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    Optional[int]      , A__				:    Dict=None      , A__				:    List[str]=None      , A__				:    Union[str, Any]=None      , A__				:    Dict=None      , )   ->					Dict:
        _snake_case						=							()
        _snake_case						=							()
        _snake_case						=							()
        for i, layer_module in enumerate(self.layer		):
          if self.output_hidden_states:
            _snake_case						=							all_hidden_states + (hidden_states,)

          _snake_case						=							layer_module(
              A__      , A__      , head_mask[i]      , A__      , A__		)
          _snake_case						=							layer_outputs[0]

          if self.output_attentions:
            _snake_case						=							all_attentions + (layer_outputs[1],)

          _snake_case						=							(hidden_states,)
          if self.output_hidden_states:
            _snake_case						=							current_outputs + (all_hidden_states,)
          if self.output_attentions:
            _snake_case						=							current_outputs + (all_attentions,)

          _snake_case						=							self.highway[i](A__		)
          # logits, pooled_output

          if not self.training:
            _snake_case						=							highway_exit[0]
            _snake_case						=							entropy(A__		)
            _snake_case						=							highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
            _snake_case						=							all_highway_exits + (highway_exit,)

            if highway_entropy < self.early_exit_entropy[i]:
              _snake_case						=							(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
              raise HighwayException(A__      , i + 1		)
          else:
            _snake_case						=							all_highway_exits + (highway_exit,)

        # Add last layer
        if self.output_hidden_states:
          _snake_case						=							all_hidden_states + (hidden_states,)

        _snake_case						=							(hidden_states,)
        if self.output_hidden_states:
          _snake_case						=							outputs + (all_hidden_states,)
        if self.output_attentions:
          _snake_case						=							outputs + (all_attentions,)

        _snake_case						=							outputs + (all_highway_exits,)
        return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits



@add_start_docstrings(
    "The Bert Model transformer with early exiting (DeeBERT). "       ,							__lowercase       ,							)
class   lowercase_   (  __lowercase					):



      def __init__(      self				:    Optional[Any]      , A__				:    Any		)   ->					str:
        super().__init__(A__		)
        _snake_case						=							config

        _snake_case						=							BertEmbeddings(A__		)
        _snake_case						=							DeeBertEncoder(A__		)
        _snake_case						=							BertPooler(A__		)

        self.init_weights()



      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					Optional[Any]:
        self.encoder.init_highway_pooler(self.pooler		)



      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Tuple:
        return self.embeddings.word_embeddings



      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    str		)   ->					str:
        _snake_case						=							value



      def 						UpperCamelCase_		(      self				:    Union[str, Any]      , A__				:    List[Any]		)   ->					Any:
        for layer, heads in heads_to_prune.items():
          self.encoder.layer[layer].attention.prune_heads(A__		)







      @add_start_docstrings_to_model_forward(A__		)
      def 						UpperCamelCase_		(      self				:    int      , A__				:    Tuple=None      , A__				:    Union[str, Any]=None      , A__				:    Union[str, Any]=None      , A__				:    Optional[Any]=None      , A__				:    Dict=None      , A__				:    Any=None      , A__				:    str=None      , A__				:    Optional[int]=None      , )   ->					Dict:
        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 encoder_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 encoder_attention_mask.dim() == 3:
          _snake_case						=							encoder_attention_mask[:, None, :, :]
        if encoder_attention_mask.dim() == 2:
          _snake_case						=							encoder_attention_mask[:, None, None, :]

        _snake_case						=							encoder_extended_attention_mask.to(
            dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
        _snake_case						=							(1.0 - encoder_extended_attention_mask) * -1_0000.0

        # 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						=							self.encoder(
            A__      , attention_mask=A__      , head_mask=A__      , encoder_hidden_states=A__      , encoder_attention_mask=A__      , )
        _snake_case						=							encoder_outputs[0]
        _snake_case						=							self.pooler(A__		)

        _snake_case						=							(
            sequence_output,
            pooled_output,
        ) + encoder_outputs[
            1:
        ]  # add hidden_states and attentions if they are here
        return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits



class   lowercase_   (  __lowercase					):



      def __init__(      self				:    Union[str, Any]      , A__				:    Dict      , A__				:    Optional[Any]		)   ->					List[str]:
        _snake_case						=							message
        _snake_case						=							exit_layer  # start from 1!



class   lowercase_   (  nn.Module					):



      def __init__(      self				:    Any      , A__				:    int		)   ->					Optional[Any]:
        super().__init__()
        _snake_case						=							BertPooler(A__		)
        _snake_case						=							nn.Dropout(config.hidden_dropout_prob		)
        _snake_case						=							nn.Linear(config.hidden_size      , config.num_labels		)







      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    str		)   ->					Optional[int]:
        # Pooler
        _snake_case						=							encoder_outputs[0]
        _snake_case						=							self.pooler(A__		)
        # "return" pooler_output

        # BertModel
        _snake_case						=							(pooler_input, pooler_output) + encoder_outputs[1:]
        # "return" bmodel_output

        # Dropout and classification
        _snake_case						=							bmodel_output[1]

        _snake_case						=							self.dropout(A__		)
        _snake_case						=							self.classifier(A__		)

        return logits, pooled_output



@add_start_docstrings(
    "Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. "       ,							__lowercase       ,							)
class   lowercase_   (  __lowercase					):



      def __init__(      self				:    List[str]      , A__				:    Optional[int]		)   ->					int:
        super().__init__(A__		)
        _snake_case						=							config.num_labels
        _snake_case						=							config.num_hidden_layers

        _snake_case						=							DeeBertModel(A__		)
        _snake_case						=							nn.Dropout(config.hidden_dropout_prob		)
        _snake_case						=							nn.Linear(config.hidden_size      , self.config.num_labels		)

        self.init_weights()







      @add_start_docstrings_to_model_forward(A__		)
      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    Optional[Any]=None      , A__				:    List[Any]=None      , A__				:    Optional[int]=None      , A__				:    List[Any]=None      , A__				:    List[Any]=None      , A__				:    Union[str, Any]=None      , A__				:    Union[str, Any]=None      , A__				:    List[Any]=-1      , A__				:    str=False      , )   ->					Dict:
        _snake_case						=							self.num_layers
        try:
          _snake_case						=							self.bert(
              A__      , attention_mask=A__      , token_type_ids=A__      , position_ids=A__      , head_mask=A__      , inputs_embeds=A__      , )
          # sequence_output, pooled_output, (hidden_states), (attentions), highway exits

          _snake_case						=							outputs[1]

          _snake_case						=							self.dropout(A__		)
          _snake_case						=							self.classifier(A__		)
          _snake_case						=							(logits,) + outputs[2:]  # add hidden states and attention if they are here
        except HighwayException as e:
          _snake_case						=							e.message
          _snake_case						=							e.exit_layer
          _snake_case						=							outputs[0]

        if not self.training:
          _snake_case						=							entropy(A__		)
          _snake_case						=							[]
          _snake_case						=							[]
        if labels is not None:
          if self.num_labels == 1:
            #  We are doing regression
            _snake_case						=							MSELoss()
            _snake_case						=							loss_fct(logits.view(-1		)      , labels.view(-1		)		)
          else:
            _snake_case						=							CrossEntropyLoss()
            _snake_case						=							loss_fct(logits.view(-1      , self.num_labels		)      , labels.view(-1		)		)

          # work with highway exits
          _snake_case						=							[]
          for highway_exit in outputs[-1]:
            _snake_case						=							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
              _snake_case						=							MSELoss()
              _snake_case						=							loss_fct(highway_logits.view(-1		)      , labels.view(-1		)		)
            else:
              _snake_case						=							CrossEntropyLoss()
              _snake_case						=							loss_fct(highway_logits.view(-1      , self.num_labels		)      , labels.view(-1		)		)
            highway_losses.append(A__		)

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

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

