
"""simple docstring"""




import logging

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,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


_A     :			List[Any]  						=			logging.getLogger(__name__)

class 						a__		( a_		):
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a=None      ,       _a=None							):
          lowercase       :							Dict									=				self.layer[current_layer](_a      ,       _a      ,       head_mask[current_layer]							)

          lowercase       :							Optional[Any]									=				layer_outputs[0]

          return hidden_states


@add_start_docstrings(
    """The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.""",     a_,     )
class 						a__		( a_		):
   def __init__(     self      ,       _a							):
          super().__init__(_a							)

          lowercase       :							Union[str, Any]									=				BertEncoderWithPabee(_a							)

          self.init_weights()
          lowercase       :							int									=				0
          lowercase       :							Optional[Any]									=				0
          lowercase       :							Tuple									=				0

          lowercase       :							Dict									=				0
   def      __magic_name__     (     self      ,       _a							):
          lowercase       :							List[str]									=				threshold
   def      __magic_name__     (     self      ,       _a							):
          lowercase       :							Dict									=				patience
   def      __magic_name__     (     self							):
          lowercase       :							int									=				0
          lowercase       :							Tuple									=				0
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				self.inference_layers_num / self.inference_instances_num
          lowercase       :							List[str]									=				(
              f"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
              f""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
          )
          print(_a							)

   @add_start_docstrings_to_model_forward(_a							)
   def      __magic_name__     (     self      ,       _a=None      ,       _a=None      ,       _a=None      ,       _a=None      ,       _a=None      ,       _a=None      ,       _a=None      ,       _a=None      ,       _a=None      ,       _a=None      ,       _a=False      ,       ):

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

          lowercase       :							Tuple									=				input_ids.device if input_ids is not None else inputs_embeds.device

          if attention_mask is None:
                 lowercase       :							Tuple									=				torch.ones(_a      ,       device=_a							)
          if token_type_ids is None:
                 lowercase       :							Tuple									=				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.
          lowercase       :							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 self.config.is_decoder and encoder_hidden_states is not None:
                 lowercase						,       lowercase						,       lowercase       :							List[str]									=				encoder_hidden_states.size()
                 lowercase       :							List[str]									=				(encoder_batch_size, encoder_sequence_length)
                 if encoder_attention_mask is None:
                        lowercase       :							List[str]									=				torch.ones(_a      ,       device=_a							)
                 lowercase       :							Optional[Any]									=				self.invert_attention_mask(_a							)
          else:
                 lowercase       :							Union[str, Any]									=				None

          # 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       :							List[Any]									=				self.get_head_mask(_a      ,       self.config.num_hidden_layers							)

          lowercase       :							int									=				self.embeddings(
              input_ids=_a      ,       position_ids=_a      ,       token_type_ids=_a      ,       inputs_embeds=_a							)
          lowercase       :							Tuple									=				embedding_output

          if self.training:
                 lowercase       :							str									=				[]
                 for i in range(self.config.num_hidden_layers							):
                        lowercase       :							Union[str, Any]									=				self.encoder.adaptive_forward(
                            _a      ,       current_layer=_a      ,       attention_mask=_a      ,       head_mask=_a							)

                        lowercase       :							Optional[int]									=				self.pooler(_a							)
                        lowercase       :							Optional[int]									=				output_layers[i](output_dropout(_a							)							)
                        res.append(_a							)
          elif self.patience == 0:  # Use all layers for inference
                 lowercase       :							Optional[Any]									=				self.encoder(
                     _a      ,       attention_mask=_a      ,       head_mask=_a      ,       encoder_hidden_states=_a      ,       encoder_attention_mask=_a      ,       )
                 lowercase       :							Tuple									=				self.pooler(encoder_outputs[0]							)
                 lowercase       :							int									=				[output_layers[self.config.num_hidden_layers - 1](_a							)]
          else:
                 lowercase       :							List[Any]									=				0
                 lowercase       :							Union[str, Any]									=				None
                 lowercase       :							Union[str, Any]									=				0
                 for i in range(self.config.num_hidden_layers							):
                        calculated_layer_num += 1
                        lowercase       :							str									=				self.encoder.adaptive_forward(
                            _a      ,       current_layer=_a      ,       attention_mask=_a      ,       head_mask=_a							)

                        lowercase       :							int									=				self.pooler(_a							)
                        lowercase       :							List[Any]									=				output_layers[i](_a							)
                        if regression:
                               lowercase       :							List[Any]									=				logits.detach()
                               if patient_result is not None:
                                      lowercase       :							Union[str, Any]									=				patient_result.detach()
                               if (patient_result is not None) and torch.abs(patient_result - labels							) < self.regression_threshold:
                                      patient_counter += 1
                               else:
                                      lowercase       :							List[Any]									=				0
                        else:
                               lowercase       :							List[Any]									=				logits.detach().argmax(dim=1							)
                               if patient_result is not None:
                                      lowercase       :							Optional[Any]									=				patient_result.detach().argmax(dim=1							)
                               if (patient_result is not None) and torch.all(labels.eq(_a							)							):
                                      patient_counter += 1
                               else:
                                      lowercase       :							Dict									=				0

                        lowercase       :							int									=				logits
                        if patient_counter == self.patience:
                               break
                 lowercase       :							List[Any]									=				[patient_result]
                 self.inference_layers_num += calculated_layer_num
                 self.inference_instances_num += 1

          return res


@add_start_docstrings(
    """Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. """,     a_,     )
class 						a__		( a_		):
   def __init__(     self      ,       _a							):
          super().__init__(_a							)
          lowercase       :							Tuple									=				config.num_labels

          lowercase       :							List[Any]									=				BertModelWithPabee(_a							)
          lowercase       :							Any									=				nn.Dropout(config.hidden_dropout_prob							)
          lowercase       :							List[str]									=				nn.ModuleList(
              [nn.Linear(config.hidden_size      ,       self.config.num_labels							) for _ in range(config.num_hidden_layers							)]							)

          self.init_weights()

   @add_start_docstrings_to_model_forward(_a							)
   def      __magic_name__     (     self      ,       _a=None      ,       _a=None      ,       _a=None      ,       _a=None      ,       _a=None      ,       _a=None      ,       _a=None      ,       ):
          lowercase       :							List[str]									=				self.bert(
              input_ids=_a      ,       attention_mask=_a      ,       token_type_ids=_a      ,       position_ids=_a      ,       head_mask=_a      ,       inputs_embeds=_a      ,       output_dropout=self.dropout      ,       output_layers=self.classifiers      ,       regression=self.num_labels == 1      ,       )

          lowercase       :							Any									=				(logits[-1],)

          if labels is not None:
                 lowercase       :							List[str]									=				None
                 lowercase       :							str									=				0
                 for ix, logits_item in enumerate(_a							):
                        if self.num_labels == 1:
                               #  We are doing regression
                               lowercase       :							Dict									=				MSELoss()
                               lowercase       :							List[Any]									=				loss_fct(logits_item.view(-1							)      ,       labels.view(-1							)							)
                        else:
                               lowercase       :							Optional[Any]									=				CrossEntropyLoss()
                               lowercase       :							List[str]									=				loss_fct(logits_item.view(-1      ,       self.num_labels							)      ,       labels.view(-1							)							)
                        if total_loss is None:
                               lowercase       :							Dict									=				loss
                        else:
                               total_loss += loss * (ix + 1)
                        total_weights += ix + 1
                 lowercase       :							List[str]									=				(total_loss / total_weights,) + outputs

          return outputs




"""simple docstring"""




from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


_A     :			Union[str, Any]  						=			logging.get_logger(__name__)  # pylint: disable=invalid-name


def     __magic_name__      ( __snake_case							:			str    )					->							List[str]:
       if not path:
              return "pipe"

       for ext in PipelineDataFormat.SUPPORTED_FORMATS:
              if path.endswith(__snake_case    ):
                     return ext

       raise Exception(
           f"""Unable to determine file format from file extension {path}. """
           f"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""    )


def     __magic_name__      ( __snake_case							:			str    )					->							Optional[int]:
       lowercase       :							int									=				pipeline(
           task=args.task	,					model=args.model if args.model else None	,					config=args.config	,					tokenizer=args.tokenizer	,					device=args.device	,					)
       lowercase       :							Optional[Any]									=				try_infer_format_from_ext(args.input    ) if args.format == "infer" else args.format
       lowercase       :							Optional[int]									=				PipelineDataFormat.from_str(
           format=__snake_case	,					output_path=args.output	,					input_path=args.input	,					column=args.column if args.column else nlp.default_input_names	,					overwrite=args.overwrite	,					)
       return RunCommand(__snake_case	,					__snake_case    )

class 						a__		( a_		):
   def __init__(     self      ,       _a      ,       _a							):
          lowercase       :							List[str]									=				nlp
          lowercase       :							Optional[Any]									=				reader
   @staticmethod
   def      __magic_name__     (     _a							):
          lowercase       :							Any									=				parser.add_parser("run"      ,       help="Run a pipeline through the CLI"							)
          run_parser.add_argument("--task"      ,       choices=get_supported_tasks()      ,       help="Task to run"							)
          run_parser.add_argument("--input"      ,       type=_a      ,       help="Path to the file to use for inference"							)
          run_parser.add_argument("--output"      ,       type=_a      ,       help="Path to the file that will be used post to write results."							)
          run_parser.add_argument("--model"      ,       type=_a      ,       help="Name or path to the model to instantiate."							)
          run_parser.add_argument("--config"      ,       type=_a      ,       help="Name or path to the model's config to instantiate."							)
          run_parser.add_argument(
              "--tokenizer"      ,       type=_a      ,       help="Name of the tokenizer to use. (default: same as the model name)"							)
          run_parser.add_argument(
              "--column"      ,       type=_a      ,       help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"      ,       )
          run_parser.add_argument(
              "--format"      ,       type=_a      ,       default="infer"      ,       choices=PipelineDataFormat.SUPPORTED_FORMATS      ,       help="Input format to read from"      ,       )
          run_parser.add_argument(
              "--device"      ,       type=_a      ,       default=-1      ,       help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"      ,       )
          run_parser.add_argument("--overwrite"      ,       action="store_true"      ,       help="Allow overwriting the output file."							)
          run_parser.set_defaults(func=_a							)

   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							int									=				self._nlp, []

          for entry in self._reader:
                 lowercase       :							str									=				nlp(**_a							) if self._reader.is_multi_columns else nlp(_a							)
                 if isinstance(_a      ,       _a							):
                        outputs.append(_a							)
                 else:
                        outputs += output

        # Saving data
          if self._nlp.binary_output:
                 lowercase       :							Dict									=				self._reader.save_binary(_a							)
                 logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""							)
          else:
                 self._reader.save(_a							)


