





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_			(	__a=None     ,   __a=None		)					->		Union[str, Any]:







  """simple docstring"""





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



@dataclass
class        _SCREAMING_SNAKE_CASE						:







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





def 					lowerCAmelCase_			(	__a		)					->		int:







  """simple docstring"""





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





def 					lowerCAmelCase_			(	__a		)					->		Any:







  """simple docstring"""





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



class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self    :       Any	,			UpperCAmelCase_    :       str)	->Optional[int]:



   '''simple docstring'''



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

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







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->Any:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Optional[int]        =plt.subplots()
   lowerCamelCase__:				int        ="Time usage" if self.args.is_time else "Memory usage"
   lowerCamelCase__:				Optional[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()):
     lowerCamelCase__:				Tuple        =sorted(set(self.result_dict[model_name]["bsz"]))
     lowerCamelCase__:				str        =sorted(set(self.result_dict[model_name]["seq_len"]))
     lowerCamelCase__:				Union[str, Any]        =self.result_dict[model_name]["result"]

     ((lowerCamelCase__)  , (lowerCamelCase__)):				Union[str, Any]        =(
         (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
     )

     lowerCamelCase__:				Optional[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:
         lowerCamelCase__:				str        =np.asarray(
             [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]	,			dtype=UpperCAmelCase_	,			)
       else:
         lowerCamelCase__:				Union[str, Any]        =np.asarray(
             [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]	,			dtype=np.floataa	,			)

       ((lowerCamelCase__)  , (lowerCamelCase__)):				List[Any]        =(
           ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
       )

       lowerCamelCase__:				List[Any]        =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."""

   lowerCamelCase__:				Union[str, Any]        =title_str[:-4]
   lowerCamelCase__:				List[Any]        ="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 					lowerCAmelCase_			(	)					->		List[str]:







  """simple docstring"""





  lowerCamelCase__:				List[str]        =HfArgumentParser(__a		)
  lowerCamelCase__:				Any        =parser.parse_args_into_dataclasses()[0]
  lowerCamelCase__:				Tuple        =Plot(args=__a		)
  plot.plot()


if __name__ == "__main__":
     main()








import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__A     =    "src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
__A     =    direct_transformers_import(PATH_TO_TRANSFORMERS)

__A     =    transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
__A     =    re.compile(R"\[(.+?)\]\((https://huggingface\.co/.+?)\)")


__A     =    {
    "DecisionTransformerConfig",
    "EncoderDecoderConfig",
    "MusicgenConfig",
    "RagConfig",
    "SpeechEncoderDecoderConfig",
    "TimmBackboneConfig",
    "VisionEncoderDecoderConfig",
    "VisionTextDualEncoderConfig",
    "LlamaConfig",
}





def 					lowerCAmelCase_			(	__a		)					->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__:				List[Any]        =None

  # source code of `config_class`
  lowerCamelCase__:				List[Any]        =inspect.getsource(__a		)
  lowerCamelCase__:				str        =_re_checkpoint.findall(__a		)

  # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
  # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
  for ckpt_name, ckpt_link in checkpoints:
    # allow the link to end with `/`
    if ckpt_link.endswith("/"		):
      lowerCamelCase__:				str        =ckpt_link[:-1]

    # verify the checkpoint name corresponds to the checkpoint link
    lowerCamelCase__:				Optional[int]        =F"""https://huggingface.co/{ckpt_name}"""
    if ckpt_link == ckpt_link_from_name:
      lowerCamelCase__:				List[Any]        =ckpt_name
      break

  return checkpoint





def 					lowerCAmelCase_			(	)					->		Tuple:







  """simple docstring"""





  lowerCamelCase__:				Union[str, Any]        =[]

  for config_class in list(CONFIG_MAPPING.values()		):
    # Skip deprecated models
    if "models.deprecated" in config_class.__module__:
      continue
    lowerCamelCase__:				List[Any]        =get_checkpoint_from_config_class(__a		)

    lowerCamelCase__:				Tuple        =config_class.__name__
    if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
      configs_without_checkpoint.append(__a		)

  if len(__a		) > 0:
    lowerCamelCase__:				List[str]        ="\n".join(sorted(__a		)		)
    raise ValueError(F"""The following configurations don't contain any valid checkpoint:\n{message}"""		)


if __name__ == "__main__":
     check_config_docstrings_have_checkpoints()

