"""simple docstring"""




from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
     import torch

     from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

_a      :		Dict           =   logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE_		)
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     *a__				,     **a__  ):
       super().__init__(*a__				,     **a__  )
       requires_backends(self				,     """vision"""  )
       self.check_model_type(
           TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING  )






     def     __A  (		self				,     a__=None				,     a__=None				,     a__=None  ):
       _lowerCAmelCase						:       List[str]  			=     {}
       _lowerCAmelCase						:       Union[str, Any]  			=     {}

       if prompt is not None:
         _lowerCAmelCase						:       List[Any]  			=     prompt

       if generate_kwargs is not None:
         _lowerCAmelCase						:       List[str]  			=     generate_kwargs
       if max_new_tokens is not None:
         if "generate_kwargs" not in forward_kwargs:
           _lowerCAmelCase						:       str  			=     {}
         if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
           raise ValueError(
               """'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
               """ please use only one"""  )
         _lowerCAmelCase						:       Optional[Any]  			=     max_new_tokens
       return preprocess_params, forward_kwargs, {}






     def __call__(		self				,     a__				,     **a__  ):
       return super().__call__(a__				,     **a__  )






     def     __A  (		self				,     a__				,     a__=None  ):
       _lowerCAmelCase						:       Tuple  			=     load_image(a__  )

       if prompt is not None:
         if not isinstance(a__				,     a__  ):
           raise ValueError(
               F"Received an invalid text input, got - {type(a__  )} - but expected a single string. "
               """Note also that one single text can be provided for conditional image to text generation."""  )

         _lowerCAmelCase						:       Optional[int]  			=     self.model.config.model_type

         if model_type == "git":
           _lowerCAmelCase						:       Optional[Any]  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )
           _lowerCAmelCase						:       List[str]  			=     self.tokenizer(text=a__				,     add_special_tokens=a__  ).input_ids
           _lowerCAmelCase						:       Union[str, Any]  			=     [self.tokenizer.cls_token_id] + input_ids
           _lowerCAmelCase						:       Dict  			=     torch.tensor(a__  ).unsqueeze(0  )
           model_inputs.update({"""input_ids""": input_ids}  )

         elif model_type == "pix2struct":
           _lowerCAmelCase						:       Tuple  			=     self.image_processor(images=a__				,     header_text=a__				,     return_tensors=self.framework  )

         elif model_type != "vision-encoder-decoder":
           # vision-encoder-decoder does not support conditional generation
           _lowerCAmelCase						:       Optional[int]  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )
           _lowerCAmelCase						:       Optional[int]  			=     self.tokenizer(a__				,     return_tensors=self.framework  )
           model_inputs.update(a__  )

         else:
           raise ValueError(F"Model type {model_type} does not support conditional text generation"  )

       else:
         _lowerCAmelCase						:       Any  			=     self.image_processor(images=a__				,     return_tensors=self.framework  )

       if self.model.config.model_type == "git" and prompt is None:
         _lowerCAmelCase						:       Union[str, Any]  			=     None

       return model_inputs






     def     __A  (		self				,     a__				,     a__=None  ):
       # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
       # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
       if (
           "input_ids" in model_inputs
           and isinstance(model_inputs["""input_ids"""]				,     a__  )
           and all(x is None for x in model_inputs["""input_ids"""]  )
       ):
         _lowerCAmelCase						:       Optional[int]  			=     None

       if generate_kwargs is None:
         _lowerCAmelCase						:       List[str]  			=     {}
       # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
       #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
       #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
       #  in the `_prepare_model_inputs` method.
       _lowerCAmelCase						:       Tuple  			=     model_inputs.pop(self.model.main_input_name  )
       _lowerCAmelCase						:       Union[str, Any]  			=     self.model.generate(a__				,     **a__				,     **a__  )
       return model_outputs






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     []
       for output_ids in model_outputs:
         _lowerCAmelCase						:       Any  			=     {
             """generated_text""": self.tokenizer.decode(
                 a__				,     skip_special_tokens=a__				,     )
         }
         records.append(a__  )
       return records





"""simple docstring"""




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     (			_lowerCamelCase						:       Union[str, Any]	)       ->      Dict:
  _lowerCAmelCase						:       List[Any]  			=     torch.exp(_lowerCamelCase	)
  _lowerCAmelCase						:       List[Any]  			=     torch.sum(_lowerCamelCase			,dim=1	)  # sum of exp(x_i)
  _lowerCAmelCase						:       Dict  			=     torch.sum(x * exp_x			,dim=1	)  # sum of x_i * exp(x_i)
  return torch.log(_lowerCamelCase	) - B / A
class 						__A							( nn.Module		):






     def __init__(		self				,     a__  ):
       super().__init__()
       _lowerCAmelCase						:       int  			=     config.output_attentions
       _lowerCAmelCase						:       Any  			=     config.output_hidden_states
       _lowerCAmelCase						:       List[Any]  			=     nn.ModuleList([BertLayer(a__  ) for _ in range(config.num_hidden_layers  )]  )
       _lowerCAmelCase						:       Any  			=     nn.ModuleList([BertHighway(a__  ) for _ in range(config.num_hidden_layers  )]  )

       _lowerCAmelCase						:       str  			=     [-1 for _ in range(config.num_hidden_layers  )]






     def     __A  (		self				,     a__  ):
       if (type(a__  ) is float) or (type(a__  ) is int):
         for i in range(len(self.early_exit_entropy  )  ):
           _lowerCAmelCase						:       Tuple  			=     x
       else:
         _lowerCAmelCase						:       Optional[int]  			=     x






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     pooler.state_dict()
       for highway in self.highway:
         for name, param in highway.pooler.state_dict().items():
           param.copy_(loaded_model[name]  )






     def     __A  (		self				,     a__				,     a__=None				,     a__=None				,     a__=None				,     a__=None				,     ):
       _lowerCAmelCase						:       Any  			=     ()
       _lowerCAmelCase						:       Optional[int]  			=     ()
       _lowerCAmelCase						:       List[Any]  			=     ()
       for i, layer_module in enumerate(self.layer  ):
         if self.output_hidden_states:
           _lowerCAmelCase						:       str  			=     all_hidden_states + (hidden_states,)

         _lowerCAmelCase						:       List[str]  			=     layer_module(
             a__				,     a__				,     head_mask[i]				,     a__				,     a__  )
         _lowerCAmelCase						:       Union[str, Any]  			=     layer_outputs[0]

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

         _lowerCAmelCase						:       Optional[int]  			=     (hidden_states,)
         if self.output_hidden_states:
           _lowerCAmelCase						:       Union[str, Any]  			=     current_outputs + (all_hidden_states,)
         if self.output_attentions:
           _lowerCAmelCase						:       Optional[int]  			=     current_outputs + (all_attentions,)

         _lowerCAmelCase						:       Optional[Any]  			=     self.highway[i](a__  )
         # logits, pooled_output

         if not self.training:
           _lowerCAmelCase						:       Tuple  			=     highway_exit[0]
           _lowerCAmelCase						:       Any  			=     entropy(a__  )
           _lowerCAmelCase						:       Optional[Any]  			=     highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
           _lowerCAmelCase						:       Union[str, Any]  			=     all_highway_exits + (highway_exit,)

           if highway_entropy < self.early_exit_entropy[i]:
             _lowerCAmelCase						:       List[str]  			=     (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
             raise HighwayException(a__				,     i + 1  )
         else:
           _lowerCAmelCase						:       Dict  			=     all_highway_exits + (highway_exit,)

        # Add last layer
       if self.output_hidden_states:
         _lowerCAmelCase						:       List[Any]  			=     all_hidden_states + (hidden_states,)

       _lowerCAmelCase						:       List[Any]  			=     (hidden_states,)
       if self.output_hidden_states:
         _lowerCAmelCase						:       List[str]  			=     outputs + (all_hidden_states,)
       if self.output_attentions:
         _lowerCAmelCase						:       Any  			=     outputs + (all_attentions,)

       _lowerCAmelCase						:       Optional[int]  			=     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). "    ,	SCREAMING_SNAKE_CASE_    ,	)
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:       Any  			=     config

       _lowerCAmelCase						:       Tuple  			=     BertEmbeddings(a__  )
       _lowerCAmelCase						:       Tuple  			=     DeeBertEncoder(a__  )
       _lowerCAmelCase						:       List[str]  			=     BertPooler(a__  )

       self.init_weights()






     def     __A  (		self  ):
       self.encoder.init_highway_pooler(self.pooler  )






     def     __A  (		self  ):
       return self.embeddings.word_embeddings






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Dict  			=     value






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






     @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__=None				,     ):
       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:
         _lowerCAmelCase						:       Any  			=     input_ids.size()
       elif inputs_embeds is not None:
         _lowerCAmelCase						:       List[str]  			=     inputs_embeds.size()[:-1]
       else:
         raise ValueError("""You have to specify either input_ids or inputs_embeds"""  )

       _lowerCAmelCase						:       str  			=     input_ids.device if input_ids is not None else inputs_embeds.device

       if attention_mask is None:
         _lowerCAmelCase						:       List[Any]  			=     torch.ones(a__				,     device=a__  )
       if encoder_attention_mask is None:
         _lowerCAmelCase						:       Optional[Any]  			=     torch.ones(a__				,     device=a__  )
       if token_type_ids is None:
         _lowerCAmelCase						:       Dict  			=     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.
       _lowerCAmelCase						:       torch.Tensor  			=     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:
         _lowerCAmelCase						:       Dict  			=     encoder_attention_mask[:, None, :, :]
       if encoder_attention_mask.dim() == 2:
         _lowerCAmelCase						:       Tuple  			=     encoder_attention_mask[:, None, None, :]

       _lowerCAmelCase						:       Union[str, Any]  			=     encoder_extended_attention_mask.to(
           dtype=next(self.parameters()  ).dtype  )  # fp16 compatibility
       _lowerCAmelCase						:       Optional[Any]  			=     (1.0 - encoder_extended_attention_mask) * -1_0_0_0_0.0

       # 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]
       _lowerCAmelCase						:       Optional[int]  			=     self.get_head_mask(a__				,     self.config.num_hidden_layers  )

       _lowerCAmelCase						:       Dict  			=     self.embeddings(
           input_ids=a__				,     position_ids=a__				,     token_type_ids=a__				,     inputs_embeds=a__  )
       _lowerCAmelCase						:       Union[str, Any]  			=     self.encoder(
           a__				,     attention_mask=a__				,     head_mask=a__				,     encoder_hidden_states=a__				,     encoder_attention_mask=a__				,     )
       _lowerCAmelCase						:       Dict  			=     encoder_outputs[0]
       _lowerCAmelCase						:       Union[str, Any]  			=     self.pooler(a__  )

       _lowerCAmelCase						:       Dict  			=     (
           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 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__				,     a__  ):
       _lowerCAmelCase						:       str  			=     message
       _lowerCAmelCase						:       str  			=     exit_layer  # start from 1!







class 						__A							( nn.Module		):






     def __init__(		self				,     a__  ):
       super().__init__()
       _lowerCAmelCase						:       Any  			=     BertPooler(a__  )
       _lowerCAmelCase						:       str  			=     nn.Dropout(config.hidden_dropout_prob  )
       _lowerCAmelCase						:       Union[str, Any]  			=     nn.Linear(config.hidden_size				,     config.num_labels  )






     def     __A  (		self				,     a__  ):
       # Pooler
       _lowerCAmelCase						:       Tuple  			=     encoder_outputs[0]
       _lowerCAmelCase						:       int  			=     self.pooler(a__  )
       # "return" pooler_output

       # BertModel
       _lowerCAmelCase						:       Union[str, Any]  			=     (pooler_input, pooler_output) + encoder_outputs[1:]
       # "return" bmodel_output

       # Dropout and classification
       _lowerCAmelCase						:       Optional[int]  			=     bmodel_output[1]

       _lowerCAmelCase						:       Tuple  			=     self.dropout(a__  )
       _lowerCAmelCase						:       Dict  			=     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. "    ,	SCREAMING_SNAKE_CASE_    ,	)
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:       List[str]  			=     config.num_labels
       _lowerCAmelCase						:       Optional[Any]  			=     config.num_hidden_layers

       _lowerCAmelCase						:       str  			=     DeeBertModel(a__  )
       _lowerCAmelCase						:       Tuple  			=     nn.Dropout(config.hidden_dropout_prob  )
       _lowerCAmelCase						:       List[Any]  			=     nn.Linear(config.hidden_size				,     self.config.num_labels  )

       self.init_weights()






     @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				,     ):
       _lowerCAmelCase						:       Dict  			=     self.num_layers
       try:
         _lowerCAmelCase						:       str  			=     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

         _lowerCAmelCase						:       Any  			=     outputs[1]

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

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

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

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




