




"""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 						_snake_case					( _snake_case							: List[Any]=None    ,  _snake_case							: Optional[int]=None							)   -> Dict:






      '''simple docstring'''


      return field(default_factory=lambda: default    ,  metadata=_snake_case							)





@dataclass
class     lowercase_	:



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






def 						_snake_case					( _snake_case							: Optional[Any]							)   -> List[str]:






      '''simple docstring'''


      try:
            int(_snake_case							)
            return True
      except ValueError:
            return False






def 						_snake_case					( _snake_case							: Dict							)   -> str:






      '''simple docstring'''


      try:
            float(_snake_case							)
            return True
      except ValueError:
            return False





class     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	Optional[int]		):
       _A        =       args
       _A        =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)

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



 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       _A       ,   _A        =       plt.subplots()
       _A        =       'Time usage' if self.args.is_time else 'Memory usage'
       _A        =       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        =       sorted(set(self.result_dict[model_name]['bsz']		)		)
             _A        =       sorted(set(self.result_dict[model_name]['seq_len']		)		)
             _A        =       self.result_dict[model_name]['result']

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

             _A        =       (
                 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        =       np.asarray(
                             [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]   ,							dtype=_UpperCAmelCase   ,							)
                   else:
                         _A        =       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))        =       (
                       ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
                   )

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

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

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

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

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






def 						_snake_case					( )   -> Any:






      '''simple docstring'''


      _A        =       HfArgumentParser(_snake_case							)
      _A        =       parser.parse_args_into_dataclasses()[0]
      _A        =       Plot(args=_snake_case							)
      plot.plot()


if __name__ == "__main__":
       main()







"""simple docstring"""

import argparse
from collections import defaultdict

import yaml


a         =      '''docs/source/en/_toctree.yml'''






def 						_snake_case					( _snake_case							: List[Any]							)   -> Optional[Any]:






      '''simple docstring'''


      _A        =       defaultdict(_snake_case							)
      _A        =       []
      _A        =       []
      for doc in doc_list:
            if "local" in doc:
                  counts[doc["local"]] += 1

            if doc["title"].lower() == "overview":
                  overview_doc.append({'local': doc['local'], 'title': doc['title']}							)
            else:
                  new_doc_list.append(_snake_case							)

      _A        =       new_doc_list
      _A        =       [key for key, value in counts.items() if value > 1]

      _A        =       []
      for duplicate_key in duplicates:
            _A        =       list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}							)
            if len(_snake_case							) > 1:
                  raise ValueError(
                      F'''{duplicate_key} is present several times in the documentation table of content at '''
                      '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                      'others.'							)
            # Only add this once
            new_doc.append({'local': duplicate_key, 'title': titles[0]}							)

      # Add none duplicate-keys
      new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]							)
      _A        =       sorted(_snake_case    ,  key=lambda _snake_case							: s["title"].lower()							)

      # "overview" gets special treatment and is always first
      if len(_snake_case							) > 1:
            raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'							)

      overview_doc.extend(_snake_case							)

      # Sort
      return overview_doc






def 						_snake_case					( _snake_case							: Tuple=False							)   -> List[Any]:






      '''simple docstring'''


      with open(_snake_case    ,  encoding='utf-8'							) as f:
            _A        =       yaml.safe_load(f.read()							)

      # Get to the API doc
      _A        =       0
      while content[api_idx]["title"] != "API":
            api_idx += 1
      _A        =       content[api_idx]['sections']

      # Then to the model doc
      _A        =       0
      while api_doc[scheduler_idx]["title"] != "Schedulers":
            scheduler_idx += 1

      _A        =       api_doc[scheduler_idx]['sections']
      _A        =       clean_doc_toc(_snake_case							)

      _A        =       False
      if new_scheduler_doc != scheduler_doc:
            _A        =       True
            if overwrite:
                  _A        =       new_scheduler_doc

      if diff:
            if overwrite:
                  _A        =       api_doc
                  with open(_snake_case    ,  'w'    ,  encoding='utf-8'							) as f:
                        f.write(yaml.dump(_snake_case    ,  allow_unicode=_snake_case							)							)
            else:
                  raise ValueError(
                      'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'							)






def 						_snake_case					( _snake_case							: str=False							)   -> Union[str, Any]:






      '''simple docstring'''


      with open(_snake_case    ,  encoding='utf-8'							) as f:
            _A        =       yaml.safe_load(f.read()							)

      # Get to the API doc
      _A        =       0
      while content[api_idx]["title"] != "API":
            api_idx += 1
      _A        =       content[api_idx]['sections']

      # Then to the model doc
      _A        =       0
      while api_doc[pipeline_idx]["title"] != "Pipelines":
            pipeline_idx += 1

      _A        =       False
      _A        =       api_doc[pipeline_idx]['sections']
      _A        =       []

      # sort sub pipeline docs
      for pipeline_doc in pipeline_docs:
            if "section" in pipeline_doc:
                  _A        =       pipeline_doc['section']
                  _A        =       clean_doc_toc(_snake_case							)
                  if overwrite:
                        _A        =       new_sub_pipeline_doc
            new_pipeline_docs.append(_snake_case							)

      # sort overall pipeline doc
      _A        =       clean_doc_toc(_snake_case							)

      if new_pipeline_docs != pipeline_docs:
            _A        =       True
            if overwrite:
                  _A        =       new_pipeline_docs

      if diff:
            if overwrite:
                  _A        =       api_doc
                  with open(_snake_case    ,  'w'    ,  encoding='utf-8'							) as f:
                        f.write(yaml.dump(_snake_case    ,  allow_unicode=_snake_case							)							)
            else:
                  raise ValueError(
                      'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'							)


if __name__ == "__main__":
       a         =      argparse.ArgumentParser()
       parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
       a         =      parser.parse_args()

       check_scheduler_doc(args.fix_and_overwrite)
       check_pipeline_doc(args.fix_and_overwrite)

