

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_       (						_A=None				,		_A=None    ):





     '''simple docstring'''




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






@dataclass
class 	UpperCAmelCase__		:






  """simple docstring"""


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



def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     try:
          int(_A    )
          return True
     except ValueError:
          return False
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




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






class 	UpperCAmelCase__		:






  """simple docstring"""


  def __init__(							self						:		Tuple     ,       __lowerCamelCase						:		str )		->							str:
       SCREAMING_SNAKE_CASE__   =   args
       SCREAMING_SNAKE_CASE__   =   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )

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







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

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

            SCREAMING_SNAKE_CASE__   =   (
                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:
                      SCREAMING_SNAKE_CASE__   =   np.asarray(
                          [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]     ,       dtype=__lowerCamelCase     ,       )
                 else:
                      SCREAMING_SNAKE_CASE__   =   np.asarray(
                          [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]     ,       dtype=np.floataa     ,       )

                 ((SCREAMING_SNAKE_CASE__),(SCREAMING_SNAKE_CASE__))   =   (
                     ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
                 )

                 SCREAMING_SNAKE_CASE__   =   np.asarray(__lowerCamelCase     ,       __lowerCamelCase )[: len(__lowerCamelCase )]
                 plt.scatter(
                     __lowerCamelCase     ,       __lowerCamelCase     ,       label=f'''{label_model_name} - {inner_loop_label}: {inner_loop_value}''' )
                 plt.plot(__lowerCamelCase     ,       __lowerCamelCase     ,       '''--''' )

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

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

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

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



def     UpperCAmelCase_       (						):





     '''simple docstring'''




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


if __name__ == "__main__":
       main()





import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_SCREAMING_SNAKE_CASE					:  Tuple             =      data_utils.TransfoXLTokenizer
_SCREAMING_SNAKE_CASE					:  Dict             =      data_utils.TransfoXLCorpus
_SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      data_utils
_SCREAMING_SNAKE_CASE					:  Any             =      data_utils
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A    ):





     '''simple docstring'''




     if transfo_xl_dataset_file:
          # Convert a pre-processed corpus (see original TensorFlow repo)
          with open(_A				,		'''rb'''    ) as fp:
               SCREAMING_SNAKE_CASE__   =   pickle.load(_A				,		encoding='''latin1'''    )
          # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
          SCREAMING_SNAKE_CASE__   =   pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
          print(F'''Save vocabulary to {pytorch_vocab_dump_path}'''    )
          SCREAMING_SNAKE_CASE__   =   corpus.vocab.__dict__
          torch.save(_A				,		_A    )

          SCREAMING_SNAKE_CASE__   =   corpus.__dict__
          corpus_dict_no_vocab.pop('''vocab'''				,		_A    )
          SCREAMING_SNAKE_CASE__   =   pytorch_dump_folder_path + '''/''' + CORPUS_NAME
          print(F'''Save dataset to {pytorch_dataset_dump_path}'''    )
          torch.save(_A				,		_A    )

     if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          SCREAMING_SNAKE_CASE__   =   os.path.abspath(_A    )
          SCREAMING_SNAKE_CASE__   =   os.path.abspath(_A    )

          print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''    )
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
               SCREAMING_SNAKE_CASE__   =   TransfoXLConfig()
          else:
               SCREAMING_SNAKE_CASE__   =   TransfoXLConfig.from_json_file(_A    )
          print(F'''Building PyTorch model from configuration: {config}'''    )
          SCREAMING_SNAKE_CASE__   =   TransfoXLLMHeadModel(_A    )

          SCREAMING_SNAKE_CASE__   =   load_tf_weights_in_transfo_xl(_A				,		_A				,		_A    )
          # Save pytorch-model
          SCREAMING_SNAKE_CASE__   =   os.path.join(_A				,		_A    )
          SCREAMING_SNAKE_CASE__   =   os.path.join(_A				,		_A    )
          print(F'''Save PyTorch model to {os.path.abspath(_A    )}'''    )
          torch.save(model.state_dict()				,		_A    )
          print(F'''Save configuration file to {os.path.abspath(_A    )}'''    )
          with open(_A				,		'''w'''				,		encoding='''utf-8'''    ) as f:
               f.write(config.to_json_string()    )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  Any             =      argparse.ArgumentParser()
       parser.add_argument(
           '''--pytorch_dump_folder_path''',
           default=None,
           type=str,
           required=True,
           help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
       )
       parser.add_argument(
           '''--tf_checkpoint_path''',
           default='''''',
           type=str,
           help='''An optional path to a TensorFlow checkpoint path to be converted.''',
       )
       parser.add_argument(
           '''--transfo_xl_config_file''',
           default='''''',
           type=str,
           help=(
               '''An optional config json file corresponding to the pre-trained BERT model. \n'''
               '''This specifies the model architecture.'''
           ),
       )
       parser.add_argument(
           '''--transfo_xl_dataset_file''',
           default='''''',
           type=str,
           help='''An optional dataset file to be converted in a vocabulary.''',
       )
       _SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      parser.parse_args()
       convert_transfo_xl_checkpoint_to_pytorch(
           args.tf_checkpoint_path,
           args.transfo_xl_config_file,
           args.pytorch_dump_folder_path,
           args.transfo_xl_dataset_file,
       )


