




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      UpperCAmelCase  (						UpperCamelCase__=None						,			UpperCamelCase__=None   )     ->     Union[str, Any]:







				'''simple docstring'''


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

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





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Any:







				'''simple docstring'''


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





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Union[str, Any]:







				'''simple docstring'''


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

class 		lowercase_       :




					def __init__(   self:		str,     _lowercase:		Union[str, Any]):


									'''simple docstring'''


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

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







					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


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

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

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

																	__lowerCAmelCase					=  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.'''

									__lowerCAmelCase					=  title_str[:-4]
									__lowerCAmelCase					=  """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      UpperCAmelCase  (						)     ->     Optional[int]:







				'''simple docstring'''


				__lowerCAmelCase					=  HfArgumentParser(UpperCamelCase__   )
				__lowerCAmelCase					=  parser.parse_args_into_dataclasses()[0]
				__lowerCAmelCase					=  Plot(args=UpperCamelCase__   )
				plot.plot()


if __name__ == "__main__":
	main()












# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


__lowercase				    =      """Update an existing config file with the latest defaults while maintaining the old configuration."""


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Optional[Any]):



  '''simple docstring'''






  __lowerCAmelCase								=					args.config_file
  if config_file is None and Path(__lowercase).exists():
    __lowerCAmelCase								=					default_config_file
  elif not Path(__lowercase).exists():
    raise ValueError(F'''The passed config file located at {config_file} doesn\'t exist.''')
  __lowerCAmelCase								=					load_config_from_file(__lowercase)

  if config_file.endswith('''.json'''):
    config.to_json_file(__lowercase)
  else:
    config.to_yaml_file(__lowercase)
  return config_file


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple  ,				__lowercase:		int):



  '''simple docstring'''






  __lowerCAmelCase								=					parser.add_parser('''update'''  ,				parents=__lowercase  ,				help=__lowercase  ,				formatter_class=__lowercase)
  parser.add_argument(
      '''--config_file'''  ,				default=__lowercase  ,				help=(
          '''The path to the config file to update. Will default to a file named default_config.yaml in the cache '''
          '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
          '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
          '''with \'huggingface\'.'''
      )  ,				)

  parser.set_defaults(func=__lowercase)
  return parser




def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any):



  '''simple docstring'''






  __lowerCAmelCase								=					update_config(__lowercase)
  print(F'''Sucessfully updated the configuration file at {config_file}.''')
