'''simple docstring'''


from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
       import timm


if is_torch_available():
       from torch import Tensor





class       _a    (		__a ,				__a      ):


  """simple docstring"""

  A_    							=						'''pixel_values'''
  A_    							=						False
  A_    							=						TimmBackboneConfig




  def __init__(		self      :       List[str]   ,				lowercase_      :       int   ,				**lowercase_      :       Any      ):







    '''simple docstring'''


    requires_backends(self   ,				"""timm"""      )
    super().__init__(lowercase_      )
    lowercase_     =       config

    if config.backbone is None:
      raise ValueError("""backbone is not set in the config. Please set it to a timm model name."""      )

    if config.backbone not in timm.list_models():
      raise ValueError(F"""backbone {config.backbone} is not supported by timm."""      )

    if hasattr(lowercase_   ,				"""out_features"""      ) and config.out_features is not None:
      raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead."""      )

    lowercase_     =       getattr(lowercase_   ,				"""use_pretrained_backbone"""   ,				lowercase_      )
    if pretrained is None:
      raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False."""      )

    # We just take the final layer by default. This matches the default for the transformers models.
    lowercase_     =       config.out_indices if getattr(lowercase_   ,				"""out_indices"""   ,				lowercase_      ) is not None else (-1,)

    lowercase_     =       timm.create_model(
        config.backbone   ,				pretrained=lowercase_   ,				features_only=config.features_only   ,				in_chans=config.num_channels   ,				out_indices=lowercase_   ,				**lowercase_   ,				)
    # These are used to control the output of the model when called. If output_hidden_states is True, then
    # return_layers is modified to include all layers.
    lowercase_     =       self._backbone.return_layers
    lowercase_     =       {layer["""module"""]: str(lowercase_      ) for i, layer in enumerate(self._backbone.feature_info.info      )}
    super()._init_backbone(lowercase_      )




  @classmethod
  def 		lowerCamelCase__  (		cls      :       Tuple   ,				lowercase_      :       List[Any]   ,				*lowercase_      :       List[str]   ,				**lowercase_      :       str      ):







    '''simple docstring'''


    requires_backends(cls   ,				["""vision""", """timm"""]      )
    from ...models.timm_backbone import TimmBackboneConfig

    lowercase_     =       kwargs.pop("""config"""   ,				TimmBackboneConfig()      )

    lowercase_     =       kwargs.pop("""use_timm_backbone"""   ,				lowercase_      )
    if not use_timm:
      raise ValueError("""use_timm_backbone must be True for timm backbones"""      )

    lowercase_     =       kwargs.pop("""num_channels"""   ,				config.num_channels      )
    lowercase_     =       kwargs.pop("""features_only"""   ,				config.features_only      )
    lowercase_     =       kwargs.pop("""use_pretrained_backbone"""   ,				config.use_pretrained_backbone      )
    lowercase_     =       kwargs.pop("""out_indices"""   ,				config.out_indices      )
    lowercase_     =       TimmBackboneConfig(
        backbone=lowercase_   ,				num_channels=lowercase_   ,				features_only=lowercase_   ,				use_pretrained_backbone=lowercase_   ,				out_indices=lowercase_   ,				)
    return super()._from_config(lowercase_   ,				**lowercase_      )




  def 		lowerCamelCase__  (		self      :       Any   ,				lowercase_      :       Tuple      ):







    '''simple docstring'''


    pass





  def 		lowerCamelCase__  (		self      :       str   ,				lowercase_      :       Dict   ,				lowercase_      :       Optional[int]=None   ,				lowercase_      :       Union[str, Any]=None   ,				lowercase_      :       Any=None   ,				**lowercase_      :       List[Any]      ):







    '''simple docstring'''


    lowercase_     =       return_dict if return_dict is not None else self.config.use_return_dict
    lowercase_     =       (
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    lowercase_     =       output_attentions if output_attentions is not None else self.config.output_attentions

    if output_attentions:
      raise ValueError("""Cannot output attentions for timm backbones at the moment"""      )

    if output_hidden_states:
      # We modify the return layers to include all the stages of the backbone
      lowercase_     =       self._all_layers
      lowercase_     =       self._backbone(lowercase_   ,				**lowercase_      )
      lowercase_     =       self._return_layers
      lowercase_     =       tuple(hidden_states[i] for i in self.out_indices      )
    else:
      lowercase_     =       self._backbone(lowercase_   ,				**lowercase_      )
      lowercase_     =       None

    lowercase_     =       tuple(lowercase_      )
    lowercase_     =       tuple(lowercase_      ) if hidden_states is not None else None

    if not return_dict:
      lowercase_     =       (feature_maps,)
      if output_hidden_states:
        lowercase_     =       output + (hidden_states,)
      return output

    return BackboneOutput(feature_maps=lowercase_   ,				hidden_states=lowercase_   ,				attentions=lowercase_      )


'''simple docstring'''


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   A_      (       SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=None						)			->str:
  return field(default_factory=lambda: default ,	metadata=SCREAMING_SNAKE_CASE_						)





@dataclass
class       _a    :


  """simple docstring"""

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






def   A_      (       SCREAMING_SNAKE_CASE_						)			->Dict:
  try:
    int(SCREAMING_SNAKE_CASE_						)
    return True
  except ValueError:
    return False

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:
  try:
    float(SCREAMING_SNAKE_CASE_						)
    return True
  except ValueError:
    return False





class       _a    :


  """simple docstring"""

  def __init__(		self      :       str   ,				lowercase_      :       List[str]      ):







    '''simple docstring'''


    lowercase_     =       args
    lowercase_     =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}      )

    with open(self.args.csv_file   ,				newline=""""""      ) as csv_file:
      lowercase_     =       csv.DictReader(lowercase_      )
      for row in reader:
        lowercase_     =       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
          lowercase_     =       int(row["""result"""]      )
        elif can_convert_to_float(row["""result"""]      ):
          # value is not None
          lowercase_     =       float(row["""result"""]      )





  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    lowercase_     ,	lowercase_     =       plt.subplots()
    lowercase_     =       """Time usage""" if self.args.is_time else """Memory usage"""
    lowercase_     =       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()      ):
      lowercase_     =       sorted(set(self.result_dict[model_name]["""bsz"""]      )      )
      lowercase_     =       sorted(set(self.result_dict[model_name]["""seq_len"""]      )      )
      lowercase_     =       self.result_dict[model_name]["""result"""]

      ((lowercase_)     ,	(lowercase_))     =       (
          (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
      )

      lowercase_     =       (
          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:
          lowercase_     =       np.asarray(
              [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]   ,				dtype=lowercase_   ,				)
        else:
          lowercase_     =       np.asarray(
              [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]   ,				dtype=np.floataa   ,				)

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

        lowercase_     =       np.asarray(lowercase_   ,				lowercase_      )[: len(lowercase_      )]
        plt.scatter(
            lowercase_   ,				lowercase_   ,				label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""      )
        plt.plot(lowercase_   ,				lowercase_   ,				"""--"""      )

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

    lowercase_     =       title_str[:-4]
    lowercase_     =       """Time in s""" if self.args.is_time else """Memory in MB"""

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

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






def   A_      (       )			->Tuple:
  lowercase_     =       HfArgumentParser(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       parser.parse_args_into_dataclasses()[0]
  lowercase_     =       Plot(args=SCREAMING_SNAKE_CASE_						)
  plot.plot()


if __name__ == "__main__":
       main()

