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






 '''simple docstring'''

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







@dataclass
class     _lowercase					:


 '''simple docstring'''






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







def 					UpperCamelCase_						( snake_case_	:	Tuple				) ->					str:






 '''simple docstring'''

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







def 					UpperCamelCase_						( snake_case_	:	int				) ->					Optional[Any]:






 '''simple docstring'''

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







class     _lowercase					:


 '''simple docstring'''






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

  with open(self.args.csv_file				,	newline=""""""							) as csv_file:
   __lowerCAmelCase    							=						csv.DictReader(snake_case_							)
   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 a (				self       :     Optional[Any]							)  ->   List[Any]:
  __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=snake_case_				,	)
    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(snake_case_				,	snake_case_							)[: len(snake_case_							)]
    plt.scatter(
        snake_case_				,	snake_case_				,	label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""							)
    plt.plot(snake_case_				,	snake_case_				,	"""--"""							)

   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(snake_case_							)
  plt.xlabel(snake_case_							)
  plt.ylabel(snake_case_							)
  plt.legend()

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







def 					UpperCamelCase_						( ) ->					Tuple:






 '''simple docstring'''

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


if __name__ == "__main__":
 main()







'''simple docstring'''

import functools
from math import sqrt
from time import time







def 					UpperCamelCase_						( snake_case_	:	Dict						,		*snake_case_	:	Union[str, Any]						,		**snake_case_	:	List[str]				) ->					List[str]:






 '''simple docstring'''

 __lowerCAmelCase    							=						time()
 __lowerCAmelCase    							=						func(*snake_case_						,		**snake_case_				)
 __lowerCAmelCase    							=						time()
 if int(end - start				) > 0:
  print(f"""{func.__name__} runtime: {(end - start):0.4f} s"""				)
 else:
  print(f"""{func.__name__} runtime: {(end - start) * 10_00:0.4f} ms"""				)
 return output







def 					UpperCamelCase_						( snake_case_	:	int				) ->					list[int]:






 '''simple docstring'''

 if n < 0:
  raise Exception("""n is negative"""				)
 if n == 0:
  return [0]
 __lowerCAmelCase    							=						[0, 1]
 for _ in range(n - 1				):
  fib.append(fib[-1] + fib[-2]				)
 return fib







def 					UpperCamelCase_						( snake_case_	:	int				) ->					list[int]:






 '''simple docstring'''

 def fib_recursive_term(snake_case_	:	int				) -> int:
  if i < 0:
   raise Exception("""n is negative"""				)
  if i < 2:
   return i
  return fib_recursive_term(i - 1				) + fib_recursive_term(i - 2				)

 if n < 0:
  raise Exception("""n is negative"""				)
 return [fib_recursive_term(snake_case_				) for i in range(n + 1				)]







def 					UpperCamelCase_						( snake_case_	:	int				) ->					list[int]:






 '''simple docstring'''

 @functools.cache
 def fib_recursive_term(snake_case_	:	int				) -> int:
  if i < 0:
   raise Exception("""n is negative"""				)
  if i < 2:
   return i
  return fib_recursive_term(i - 1				) + fib_recursive_term(i - 2				)

 if n < 0:
  raise Exception("""n is negative"""				)
 return [fib_recursive_term(snake_case_				) for i in range(n + 1				)]







def 					UpperCamelCase_						( snake_case_	:	int				) ->					list[int]:






 '''simple docstring'''

 if n < 0:
  raise Exception("""n is negative"""				)
 # Cache must be outside recursuive function
 # other it will reset every time it calls itself.
 __lowerCAmelCase    							=						{0: 0, 1: 1, 2: 1}  # Prefilled cache

 def rec_fn_memoized(snake_case_	:	int				) -> int:
  if num in cache:
   return cache[num]

  __lowerCAmelCase    							=						rec_fn_memoized(num - 1				) + rec_fn_memoized(num - 2				)
  __lowerCAmelCase    							=						value
  return value

 return [rec_fn_memoized(snake_case_				) for i in range(n + 1				)]







def 					UpperCamelCase_						( snake_case_	:	int				) ->					list[int]:






 '''simple docstring'''

 if n < 0:
  raise Exception("""n is negative"""				)
 if n >= 14_75:
  raise Exception("""n is too large"""				)
 __lowerCAmelCase    							=						sqrt(5				)
 __lowerCAmelCase    							=						(1 + sqrt_a) / 2
 return [round(phi**i / sqrt_a				) for i in range(n + 1				)]


if __name__ == "__main__":
 _A      :				Optional[Any] 				=    30
 time_func(fib_iterative, num)
 time_func(fib_recursive, num)  # Around 3s runtime
 time_func(fib_recursive_cached, num)  # Around 0ms runtime
 time_func(fib_memoization, num)
 time_func(fib_binet, num)






