





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). '       ,   __snake_case       ,   )
class 					A__   (       __snake_case			):
      _UpperCAmelCase	:str						    =   RobertaConfig
      _UpperCAmelCase	:Any						    =   'roberta'





      def __init__( self       ,							A_   ):
       '''simple docstring'''




       super().__init__(A_   )

       UpperCamelCase	:					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. '       ,   __snake_case       ,   )
class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Optional[int]						    =   RobertaConfig
      _UpperCAmelCase	:List[Any]						    =   'roberta'





      def __init__( self       ,							A_   ):
       '''simple docstring'''




       super().__init__(A_   )
       UpperCamelCase	:					Any       	=   config.num_labels
       UpperCamelCase	:					int       	=   config.num_hidden_layers

       UpperCamelCase	:					Union[str, Any]       	=   DeeRobertaModel(A_   )
       UpperCamelCase	:					str       	=   nn.Dropout(config.hidden_dropout_prob   )
       UpperCamelCase	:					List[str]       	=   nn.Linear(config.hidden_size       ,							self.config.num_labels   )





      @add_start_docstrings_to_model_forward(A_   )
      def        __UpperCamelCase( self       ,							A_=None       ,							A_=None       ,							A_=None       ,							A_=None       ,							A_=None       ,							A_=None       ,							A_=None       ,							A_=-1       ,							A_=False       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   self.num_layers
       try:
        UpperCamelCase	:					Any       	=   self.roberta(
            A_       ,							attention_mask=A_       ,							token_type_ids=A_       ,							position_ids=A_       ,							head_mask=A_       ,							inputs_embeds=A_       ,							)

        UpperCamelCase	:					Optional[int]       	=   outputs[1]

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

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

        # work with highway exits
        UpperCamelCase	:					Optional[int]       	=   []
        for highway_exit in outputs[-1]:
         UpperCamelCase	:					Optional[Any]       	=   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
          UpperCamelCase	:					Dict       	=   MSELoss()
          UpperCamelCase	:					int       	=   loss_fct(highway_logits.view(-1   )       ,							labels.view(-1   )   )
         else:
          UpperCamelCase	:					Union[str, Any]       	=   CrossEntropyLoss()
          UpperCamelCase	:					str       	=   loss_fct(highway_logits.view(-1       ,							self.num_labels   )       ,							labels.view(-1   )   )
         highway_losses.append(A_   )

        if train_highway:
         UpperCamelCase	:					Union[str, Any]       	=   (sum(highway_losses[:-1]   ),) + outputs
         # exclude the final highway, of course
        else:
         UpperCamelCase	:					int       	=   (loss,) + outputs
       if not self.training:
        UpperCamelCase	:					List[str]       	=   outputs + ((original_entropy, highway_entropy), exit_layer)
        if output_layer >= 0:
         UpperCamelCase	:					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






"""simple docstring"""






import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			["""image_processor""", """tokenizer"""]
			lowerCamelCase__      =			"""BlipImageProcessor"""
			lowerCamelCase__      =			"""AutoTokenizer"""
			def __init__(  self  ,      lowercase  ,      lowercase  ,      lowercase  ):
							super().__init__(lowercase  ,      lowercase  )

							# add QFormer tokenizer
							_lowerCamelCase     :			int    =    qformer_tokenizer
			def __call__(  self  ,      lowercase = None  ,      lowercase = None  ,      lowercase = True  ,      lowercase = False  ,      lowercase = None  ,      lowercase = None  ,      lowercase = 0  ,      lowercase = None  ,      lowercase = None  ,      lowercase = False  ,      lowercase = False  ,      lowercase = False  ,      lowercase = False  ,      lowercase = False  ,      lowercase = True  ,      lowercase = None  ,      **lowercase  ,      ):
							if images is None and text is None:
											raise ValueError('You have to specify at least images or text.'  )

							_lowerCamelCase     :			int    =    BatchFeature()

							if text is not None:
											_lowerCamelCase     :			List[str]    =    self.tokenizer(
											    text=lowercase  ,      add_special_tokens=lowercase  ,      padding=lowercase  ,      truncation=lowercase  ,      max_length=lowercase  ,      stride=lowercase  ,      pad_to_multiple_of=lowercase  ,      return_attention_mask=lowercase  ,      return_overflowing_tokens=lowercase  ,      return_special_tokens_mask=lowercase  ,      return_offsets_mapping=lowercase  ,      return_token_type_ids=lowercase  ,      return_length=lowercase  ,      verbose=lowercase  ,      return_tensors=lowercase  ,      **lowercase  ,      )
											encoding.update(lowercase  )
											_lowerCamelCase     :			List[str]    =    self.qformer_tokenizer(
											    text=lowercase  ,      add_special_tokens=lowercase  ,      padding=lowercase  ,      truncation=lowercase  ,      max_length=lowercase  ,      stride=lowercase  ,      pad_to_multiple_of=lowercase  ,      return_attention_mask=lowercase  ,      return_overflowing_tokens=lowercase  ,      return_special_tokens_mask=lowercase  ,      return_offsets_mapping=lowercase  ,      return_token_type_ids=lowercase  ,      return_length=lowercase  ,      verbose=lowercase  ,      return_tensors=lowercase  ,      **lowercase  ,      )
											_lowerCamelCase     :			List[Any]    =    qformer_text_encoding.pop('input_ids'  )
											_lowerCamelCase     :			Tuple    =    qformer_text_encoding.pop('attention_mask'  )

							if images is not None:
											_lowerCamelCase     :			int    =    self.image_processor(lowercase  ,      return_tensors=lowercase  )
											encoding.update(lowercase  )

							return encoding
			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							return self.tokenizer.batch_decode(*lowercase  ,      **lowercase  )
			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							return self.tokenizer.decode(*lowercase  ,      **lowercase  )
			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.tokenizer.model_input_names
							_lowerCamelCase     :			Any    =    self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )
			def 					A_    (  self  ,      lowercase  ,      **lowercase  ):
							if os.path.isfile(lowercase  ):
											raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''  )
							os.makedirs(lowercase  ,      exist_ok=lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    os.path.join(lowercase  ,      'qformer_tokenizer'  )
							self.qformer_tokenizer.save_pretrained(lowercase  )
							return super().save_pretrained(lowercase  ,      **lowercase  )






			@classmethod
			def 					A_    (  cls  ,      lowercase  ,      **lowercase  ):
							_lowerCamelCase     :			Union[str, Any]    =    AutoTokenizer.from_pretrained(lowercase  ,      subfolder='qformer_tokenizer'  )
							_lowerCamelCase     :			Dict    =    cls._get_arguments_from_pretrained(lowercase  ,      **lowercase  )
							args.append(lowercase  )
							return cls(*lowercase  )