






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__						(    lowerCamelCase	:			str=None			,lowerCamelCase	:			Any=None    ):
   return field(default_factory=lambda: default			,metadata=__UpperCamelCase    )


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






def  UpperCAmelCase__						(    lowerCamelCase	:			str    ):


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






def  UpperCAmelCase__						(    lowerCamelCase	:			Tuple    ):


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


class 		lowerCAmelCase_				:





     def __init__(	self							, _UpperCAmelCase  )		->				Optional[int]:
        lowerCamelCase									=args
        lowerCamelCase									=defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}  )

        with open(self.args.csv_file							, newline=""""""  ) as csv_file:
           lowerCamelCase									=csv.DictReader(UpperCamelCase__  )
           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 						lowerCamelCase							(	self  )		->				Optional[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=UpperCamelCase__							, )
              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(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									=title_str[:-4]
        lowerCamelCase									="""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  UpperCAmelCase__						(    ):
   lowerCamelCase									=HfArgumentParser(__UpperCamelCase    )
   lowerCamelCase									=parser.parse_args_into_dataclasses()[0]
   lowerCamelCase									=Plot(args=__UpperCamelCase    )
   plot.plot()


if __name__ == "__main__":
   main()
"""simple docstring"""

import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info


def    lowerCAmelCase			(__UpperCamelCase							:    Optional[Any]			):
							"""simple docstring"""


							class 		_lowercase :



													"""simple docstring"""




													def __init__(				self							:    int     , UpperCamelCase__							:    Union[str, Any]				)				->				None:
																				'''simple docstring'''



																				assert isinstance(UpperCamelCase__     , (bytes, str)				)
																				__UpperCamelCase							=content





													def   UpperCAmelCase_ (				self							:    Dict				)				->				Any:
																				'''simple docstring'''



																				return json.loads(self.content				)

							def mock_response(*__UpperCamelCase							:    Any							,					**__UpperCamelCase							:    Union[str, Any]			):
														assert args[0] == AUTHENTICATED_USER_ENDPOINT
														assert "Authorization" in kwargs["headers"]
														assert kwargs["headers"]["Authorization"].startswith('''token '''			)
														assert "Accept" in kwargs["headers"]
														return FakeResponse(b'''{"login":"test","id":1}'''			)

							monkeypatch.setattr(__UpperCamelCase							,					'''get'''							,					__UpperCamelCase			)
							__UpperCamelCase							=fetch_github_info('''token'''			)
							assert result["login"] == "test"
							assert result["id"] == 1



