




import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser







def 				lowerCAmelCase_	(     snake_case_=None,snake_case_=None     ):
      return field(default_factory=lambda: default,metadata=snake_case_     )







@dataclass
class 						lowercase							:
     _a							 =						field(
         metadata={"help": "The csv file to plot."},)
     _a							 =						field(
         default=UpperCamelCase__,metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."},)
     _a							 =						field(
         default=UpperCamelCase__,metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."},)
     _a							 =						field(
         default=UpperCamelCase__,metadata={"help": "Disable logarithmic scale when plotting"},)
     _a							 =						field(
         default=UpperCamelCase__,metadata={
             "help": "Whether the csv file has training results or inference results. Defaults to inference results."
         },)
     _a							 =						field(
         default=UpperCamelCase__,metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."},)
     _a							 =						list_field(
         default=UpperCamelCase__,metadata={"help": "List of model names that are used instead of the ones in the csv file."}		)







def 				lowerCAmelCase_	(     snake_case_     ):
      try:
            int(snake_case_     )
            return True
      except ValueError:
            return False







def 				lowerCAmelCase_	(     snake_case_     ):
      try:
            float(snake_case_     )
            return True
      except ValueError:
            return False







class 						lowercase							:

     def __init__(  self		,     _a				)						->							Optional[Any]:
           _A							:     int			=						args
           _A							:     Optional[Any]			=						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}				)

           with open(self.args.csv_file		,     newline=""""""				) as csv_file:
                 _A							:     List[str]			=						csv.DictReader(_a				)
                 for row in reader:
                       _A							:     Optional[Any]			=						row["""model"""]
                       self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]				)				)
                       self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]				)				)
                       if can_convert_to_int(row["""result"""]				):
                             # value is not None
                             _A							:     Union[str, Any]			=						int(row["""result"""]				)
                       elif can_convert_to_float(row["""result"""]				):
                             # value is not None
                             _A							:     int			=						float(row["""result"""]				)



     def 					a__	(  self				)						->							Any:
           _A			,   _A							:     Tuple			=						plt.subplots()
           _A							:     Union[str, Any]			=						"""Time usage""" if self.args.is_time else """Memory usage"""
           _A							:     List[Any]			=						title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""

           if not self.args.no_log_scale:
                 # set logarithm scales
                 ax.set_xscale("""log"""				)
                 ax.set_yscale("""log"""				)

           for axis in [ax.xaxis, ax.yaxis]:
                 axis.set_major_formatter(ScalarFormatter()				)

           for model_name_idx, model_name in enumerate(self.result_dict.keys()				):
                 _A							:     Optional[Any]			=						sorted(set(self.result_dict[model_name]["""bsz"""]				)				)
                 _A							:     List[Any]			=						sorted(set(self.result_dict[model_name]["""seq_len"""]				)				)
                 _A							:     List[str]			=						self.result_dict[model_name]["""result"""]

                 ((_A)			,   (_A))							:     Dict			=						(
                     (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
                 )

                 _A							:     Union[str, Any]			=						(
                     model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
                 )

                 for inner_loop_value in inner_loop_array:
                       if self.args.plot_along_batch:
                             _A							:     int			=						np.asarray(
                                 [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		,     dtype=_a		,     )
                       else:
                             _A							:     int			=						np.asarray(
                                 [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]		,     dtype=np.floataa		,     )

                       ((_A)			,   (_A))							:     Any			=						(
                           ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
                       )

                       _A							:     List[Any]			=						np.asarray(_a		,     _a				)[: len(_a				)]
                       plt.scatter(
                           _a		,     _a		,     label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''				)
                       plt.plot(_a		,     _a		,     """--"""				)

                 title_str += F''' {label_model_name} vs.'''

           _A							:     Optional[Any]			=						title_str[:-4]
           _A							:     Optional[int]			=						"""Time in s""" if self.args.is_time else """Memory in MB"""

           # plot
           plt.title(_a				)
           plt.xlabel(_a				)
           plt.ylabel(_a				)
           plt.legend()

           if self.args.figure_png_file is not None:
                 plt.savefig(self.args.figure_png_file				)
           else:
                 plt.show()







def 				lowerCAmelCase_	(     ):
      _A							:     List[Any]			=						HfArgumentParser(snake_case_     )
      _A							:     Tuple			=						parser.parse_args_into_dataclasses()[0]
      _A							:     Dict			=						Plot(args=snake_case_     )
      plot.plot()


if __name__ == "__main__":
  main()







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). ",UpperCamelCase__,)
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						RobertaConfig
     _a							 =						"roberta"

     def __init__(  self		,     _a				)						->							Optional[int]:
           super().__init__(_a				)

           _A							:     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. ",UpperCamelCase__,)
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						RobertaConfig
     _a							 =						"roberta"

     def __init__(  self		,     _a				)						->							str:
           super().__init__(_a				)
           _A							:     Any			=						config.num_labels
           _A							:     Dict			=						config.num_hidden_layers

           _A							:     List[str]			=						DeeRobertaModel(_a				)
           _A							:     int			=						nn.Dropout(config.hidden_dropout_prob				)
           _A							:     int			=						nn.Linear(config.hidden_size		,     self.config.num_labels				)



     @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		,     )						->							Any:
           _A							:     Optional[int]			=						self.num_layers
           try:
                 _A							:     List[str]			=						self.roberta(
                     _a		,     attention_mask=_a		,     token_type_ids=_a		,     position_ids=_a		,     head_mask=_a		,     inputs_embeds=_a		,     )

                 _A							:     List[str]			=						outputs[1]

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

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

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

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

