





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  SCREAMING_SNAKE_CASE				(      snake_case							)		->    str:
		__lowercase											=     torch.exp(snake_case							)
		__lowercase											=     torch.sum(snake_case						,   dim=1							)  # sum of exp(x_i)
		__lowercase											=     torch.sum(x * exp_x						,   dim=1							)  # sum of x_i * exp(x_i)
		return torch.log(snake_case							) - B / A

class 		snake_case_   (							nn.Module						):







	'''simple docstring'''




	def __init__(						self							:     List[str]					,      __lowerCamelCase							:     Union[str, Any]   )       -> int:

			'''simple docstring'''




			super().__init__()
			__lowercase											=     config.output_attentions
			__lowercase											=     config.output_hidden_states
			__lowercase											=     nn.ModuleList([BertLayer(__lowerCamelCase   ) for _ in range(config.num_hidden_layers   )]   )
			__lowercase											=     nn.ModuleList([BertHighway(__lowerCamelCase   ) for _ in range(config.num_hidden_layers   )]   )

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




	def    UpperCAmelCase			(						self							:     Dict					,      __lowerCamelCase							:     List[Any]   )       -> int:

			'''simple docstring'''




			if (type(__lowerCamelCase   ) is float) or (type(__lowerCamelCase   ) is int):
					for i in range(len(self.early_exit_entropy   )   ):
							__lowercase											=     x
			else:
					__lowercase											=     x




	def    UpperCAmelCase			(						self							:     List[str]					,      __lowerCamelCase							:     List[Any]   )       -> Any:

			'''simple docstring'''




			__lowercase											=     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							:     Any					,      __lowerCamelCase							:     Union[str, Any]					,      __lowerCamelCase							:     Optional[Any]=None					,      __lowerCamelCase							:     Tuple=None					,      __lowerCamelCase							:     Tuple=None					,      __lowerCamelCase							:     Tuple=None					,      )       -> Optional[Any]:

			'''simple docstring'''




			__lowercase											=     ()
			__lowercase											=     ()
			__lowercase											=     ()
			for i, layer_module in enumerate(self.layer   ):
					if self.output_hidden_states:
							__lowercase											=     all_hidden_states + (hidden_states,)

					__lowercase											=     layer_module(
					    __lowerCamelCase					,      __lowerCamelCase					,      head_mask[i]					,      __lowerCamelCase					,      __lowerCamelCase   )
					__lowercase											=     layer_outputs[0]

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

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

					__lowercase											=     self.highway[i](__lowerCamelCase   )
					# logits, pooled_output

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

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

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

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

			__lowercase											=     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). '''     ,     UpperCAmelCase_     ,     )
class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''




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

			'''simple docstring'''




			super().__init__(__lowerCamelCase   )
			__lowercase											=     config

			__lowercase											=     BertEmbeddings(__lowerCamelCase   )
			__lowercase											=     DeeBertEncoder(__lowerCamelCase   )
			__lowercase											=     BertPooler(__lowerCamelCase   )

			self.init_weights()




	def    UpperCAmelCase			(						self							:     Tuple   )       -> str:

			'''simple docstring'''




			self.encoder.init_highway_pooler(self.pooler   )




	def    UpperCAmelCase			(						self							:     int   )       -> List[Any]:

			'''simple docstring'''




			return self.embeddings.word_embeddings




	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     Optional[Any]   )       -> Tuple:

			'''simple docstring'''




			__lowercase											=     value




	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     Dict   )       -> List[Any]:

			'''simple docstring'''




			for layer, heads in heads_to_prune.items():
					self.encoder.layer[layer].attention.prune_heads(__lowerCamelCase   )






	@add_start_docstrings_to_model_forward(__lowerCamelCase   )
	def    UpperCAmelCase			(						self							:     Dict					,      __lowerCamelCase							:     Dict=None					,      __lowerCamelCase							:     Dict=None					,      __lowerCamelCase							:     List[str]=None					,      __lowerCamelCase							:     Optional[int]=None					,      __lowerCamelCase							:     Optional[int]=None					,      __lowerCamelCase							:     Tuple=None					,      __lowerCamelCase							:     Dict=None					,      __lowerCamelCase							:     List[str]=None					,      )       -> Union[str, Any]:

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

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

			if attention_mask is None:
					__lowercase											=     torch.ones(__lowerCamelCase					,      device=__lowerCamelCase   )
			if encoder_attention_mask is None:
					__lowercase											=     torch.ones(__lowerCamelCase					,      device=__lowerCamelCase   )
			if token_type_ids is None:
					__lowercase											=     torch.zeros(__lowerCamelCase					,      dtype=torch.long					,      device=__lowerCamelCase   )

			# 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.
			__lowercase											=     self.get_extended_attention_mask(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase   )

			# 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:
					__lowercase											=     encoder_attention_mask[:, None, :, :]
			if encoder_attention_mask.dim() == 2:
					__lowercase											=     encoder_attention_mask[:, None, None, :]

			__lowercase											=     encoder_extended_attention_mask.to(
			    dtype=next(self.parameters()   ).dtype   )  # fp16 compatibility
			__lowercase											=     (1.0 - encoder_extended_attention_mask) * -10000.0

			# 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]
			__lowercase											=     self.get_head_mask(__lowerCamelCase					,      self.config.num_hidden_layers   )

			__lowercase											=     self.embeddings(
			    input_ids=__lowerCamelCase					,      position_ids=__lowerCamelCase					,      token_type_ids=__lowerCamelCase					,      inputs_embeds=__lowerCamelCase   )
			__lowercase											=     self.encoder(
			    __lowerCamelCase					,      attention_mask=__lowerCamelCase					,      head_mask=__lowerCamelCase					,      encoder_hidden_states=__lowerCamelCase					,      encoder_attention_mask=__lowerCamelCase					,      )
			__lowercase											=     encoder_outputs[0]
			__lowercase											=     self.pooler(__lowerCamelCase   )

			__lowercase											=     (
			    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 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''




	def __init__(						self							:     List[str]					,      __lowerCamelCase							:     Union[str, Any]					,      __lowerCamelCase							:     List[Any]   )       -> List[Any]:

			'''simple docstring'''




			__lowercase											=     message
			__lowercase											=     exit_layer  # start from 1!





class 		snake_case_   (							nn.Module						):







	'''simple docstring'''




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

			'''simple docstring'''




			super().__init__()
			__lowercase											=     BertPooler(__lowerCamelCase   )
			__lowercase											=     nn.Dropout(config.hidden_dropout_prob   )
			__lowercase											=     nn.Linear(config.hidden_size					,      config.num_labels   )






	def    UpperCAmelCase			(						self							:     Dict					,      __lowerCamelCase							:     Any   )       -> int:

			'''simple docstring'''




			__lowercase											=     encoder_outputs[0]
			__lowercase											=     self.pooler(__lowerCamelCase   )
			# "return" pooler_output

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

			# Dropout and classification
			__lowercase											=     bmodel_output[1]

			__lowercase											=     self.dropout(__lowerCamelCase   )
			__lowercase											=     self.classifier(__lowerCamelCase   )

			return logits, pooled_output





@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''     ,     UpperCAmelCase_     ,     )
class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''




	def __init__(						self							:     List[str]					,      __lowerCamelCase							:     str   )       -> Any:

			'''simple docstring'''




			super().__init__(__lowerCamelCase   )
			__lowercase											=     config.num_labels
			__lowercase											=     config.num_hidden_layers

			__lowercase											=     DeeBertModel(__lowerCamelCase   )
			__lowercase											=     nn.Dropout(config.hidden_dropout_prob   )
			__lowercase											=     nn.Linear(config.hidden_size					,      self.config.num_labels   )

			self.init_weights()






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

			'''simple docstring'''




			__lowercase											=     self.num_layers
			try:
					__lowercase											=     self.bert(
					    __lowerCamelCase					,      attention_mask=__lowerCamelCase					,      token_type_ids=__lowerCamelCase					,      position_ids=__lowerCamelCase					,      head_mask=__lowerCamelCase					,      inputs_embeds=__lowerCamelCase					,      )
					# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

					__lowercase											=     outputs[1]

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

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

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

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











from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
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():
		from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   logging.get_logger(__name__)

@add_end_docstrings(UpperCAmelCase_						)
class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''




	def __init__(						self							:     Optional[int]					,      *__lowerCamelCase							:     Optional[int]					,      **__lowerCamelCase							:     List[Any]   )       -> Optional[Any]:

			'''simple docstring'''




			super().__init__(*__lowerCamelCase					,      **__lowerCamelCase   )
			self.check_model_type(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     int=None					,      __lowerCamelCase							:     List[Any]=None					,      __lowerCamelCase							:     Any=None					,      **__lowerCamelCase							:     Optional[int]   )       -> Any:

			'''simple docstring'''




			__lowercase						,      __lowercase											=     {}, {}
			if padding is not None:
					__lowercase											=     padding
			if truncation is not None:
					__lowercase											=     truncation
			if top_k is not None:
					__lowercase											=     top_k
			return preprocess_params, {}, postprocess_params




	def __call__(						self							:     str					,      __lowerCamelCase							:     Union["Image.Image", str]					,      __lowerCamelCase							:     str = None					,      **__lowerCamelCase							:     Dict   )       -> Tuple:

			'''simple docstring'''




			if isinstance(__lowerCamelCase					,      (Image.Image, str)   ) and isinstance(__lowerCamelCase					,      __lowerCamelCase   ):
					__lowercase											=     {'image': image, 'question': question}
			else:
					__lowercase											=     image
			__lowercase											=     super().__call__(__lowerCamelCase					,      **__lowerCamelCase   )
			return results




	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     List[str]					,      __lowerCamelCase							:     Optional[Any]=False					,      __lowerCamelCase							:     Optional[Any]=False   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     load_image(inputs['image']   )
			__lowercase											=     self.tokenizer(
			    inputs['question']					,      return_tensors=self.framework					,      padding=__lowerCamelCase					,      truncation=__lowerCamelCase   )
			__lowercase											=     self.image_processor(images=__lowerCamelCase					,      return_tensors=self.framework   )
			model_inputs.update(__lowerCamelCase   )
			return model_inputs




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     Tuple   )       -> Any:

			'''simple docstring'''




			__lowercase											=     self.model(**__lowerCamelCase   )
			return model_outputs






	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     Any					,      __lowerCamelCase							:     int=5   )       -> Tuple:

			'''simple docstring'''




			if top_k > self.model.config.num_labels:
					__lowercase											=     self.model.config.num_labels

			if self.framework == "pt":
					__lowercase											=     model_outputs.logits.sigmoid()[0]
					__lowercase						,      __lowercase											=     probs.topk(__lowerCamelCase   )
			else:
					raise ValueError(F"Unsupported framework: {self.framework}"   )

			__lowercase											=     scores.tolist()
			__lowercase											=     ids.tolist()
			return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__lowerCamelCase					,      __lowerCamelCase   )]




