





"""simple docstring"""
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_  :	Any=None				,     a_  :	Tuple=None							)    ->   Dict:
						return field(default_factory=lambda: default				,     metadata=lowercase__							)






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




def   __lowerCamelCase     (							a_  :	Optional[Any]							)    ->   Optional[Any]:
						try:
												int(lowercase__							)
												return True
						except ValueError:
												return False



def   __lowerCamelCase     (							a_  :	Tuple							)    ->   Union[str, Any]:
						try:
												float(lowercase__							)
												return True
						except ValueError:
												return False






class       _SCREAMING_SNAKE_CASE:




							def __init__(		self	,SCREAMING_SNAKE_CASE__							)    ->   int:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE     :Optional[int]	       =						args
													__SCREAMING_SNAKE_CASE     :Tuple	       =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}							)

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




							def 					_UpperCamelCase  (		self							)    ->   Tuple:





													"""simple docstring"""






													__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Tuple	       =						plt.subplots()
													__SCREAMING_SNAKE_CASE     :Dict	       =						'''Time usage''' if self.args.is_time else '''Memory usage'''
													__SCREAMING_SNAKE_CASE     :List[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()							):
																			__SCREAMING_SNAKE_CASE     :Optional[int]	       =						sorted(set(self.result_dict[model_name]['''bsz''']							)							)
																			__SCREAMING_SNAKE_CASE     :Any	       =						sorted(set(self.result_dict[model_name]['''seq_len''']							)							)
																			__SCREAMING_SNAKE_CASE     :List[Any]	       =						self.result_dict[model_name]['''result''']

																			((__SCREAMING_SNAKE_CASE)       ,(__SCREAMING_SNAKE_CASE))     :int	       =						(
																			    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																			)

																			__SCREAMING_SNAKE_CASE     :str	       =						(
																			    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     :Tuple	       =						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     :str	       =						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))     :List[Any]	       =						(
																									    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
																									)

																									__SCREAMING_SNAKE_CASE     :Optional[int]	       =						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     :Union[str, Any]	       =						title_str[:-4]
													__SCREAMING_SNAKE_CASE     :Optional[int]	       =						'''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   __lowerCamelCase     (							)    ->   str:
						__SCREAMING_SNAKE_CASE     :Tuple	       =						HfArgumentParser(lowercase__							)
						__SCREAMING_SNAKE_CASE     :Tuple	       =						parser.parse_args_into_dataclasses()[0]
						__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						Plot(args=lowercase__							)
						plot.plot()


if __name__ == "__main__":
		main()






"""simple docstring"""
import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowerCamelCase_          =	logging.getLogger(__name__)



def   __lowerCamelCase     (							)    ->   Optional[Any]:
      __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						argparse.ArgumentParser(description='''PyTorch Transformer Language Model'''							)
      parser.add_argument('''--model_name'''				,     type=a_				,     default='''transfo-xl-wt103'''				,     help='''pretrained model name'''							)
      parser.add_argument(
          '''--split'''				,     type=a_				,     default='''test'''				,     choices=['''all''', '''valid''', '''test''']				,     help='''which split to evaluate'''							)
      parser.add_argument('''--batch_size'''				,     type=a_				,     default=10				,     help='''batch size'''							)
      parser.add_argument('''--tgt_len'''				,     type=a_				,     default=1_28				,     help='''number of tokens to predict'''							)
      parser.add_argument('''--ext_len'''				,     type=a_				,     default=0				,     help='''length of the extended context'''							)
      parser.add_argument('''--mem_len'''				,     type=a_				,     default=16_00				,     help='''length of the retained previous heads'''							)
      parser.add_argument('''--clamp_len'''				,     type=a_				,     default=10_00				,     help='''max positional embedding index'''							)
      parser.add_argument('''--no_cuda'''				,     action='''store_true'''				,     help='''Do not use CUDA even though CUA is available'''							)
      parser.add_argument('''--work_dir'''				,     type=a_				,     required=a_				,     help='''path to the work_dir'''							)
      parser.add_argument('''--no_log'''				,     action='''store_true'''				,     help='''do not log the eval result'''							)
      parser.add_argument('''--same_length'''				,     action='''store_true'''				,     help='''set same length attention with masking'''							)
      parser.add_argument('''--server_ip'''				,     type=a_				,     default=''''''				,     help='''Can be used for distant debugging.'''							)
      parser.add_argument('''--server_port'''				,     type=a_				,     default=''''''				,     help='''Can be used for distant debugging.'''							)
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						parser.parse_args()
      assert args.ext_len >= 0, "extended context length must be non-negative"

      if args.server_ip and args.server_port:
            # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
            import ptvsd

            print('''Waiting for debugger attach'''							)
            ptvsd.enable_attach(address=(args.server_ip, args.server_port)				,     redirect_output=a_							)
            ptvsd.wait_for_attach()

      __SCREAMING_SNAKE_CASE     :Tuple	       =						torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''							)
      logger.info('''device: {}'''.format(a_							)							)

      # Load a pre-processed dataset
      # You can also build the corpus yourself using TransfoXLCorpus methods
      # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
      # and tokenizing the dataset
      # The pre-processed corpus is a convertion (using the conversion script )
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						TransfoXLCorpus.from_pretrained(args.model_name							)

      __SCREAMING_SNAKE_CASE     :Tuple	       =						corpus.get_iterator('''valid'''				,     args.batch_size				,     args.tgt_len				,     device=a_				,     ext_len=args.ext_len							)
      __SCREAMING_SNAKE_CASE     :Tuple	       =						corpus.get_iterator('''test'''				,     args.batch_size				,     args.tgt_len				,     device=a_				,     ext_len=args.ext_len							)

      # Load a pre-trained model
      __SCREAMING_SNAKE_CASE     :int	       =						TransfoXLLMHeadModel.from_pretrained(args.model_name							)
      model.to(a_							)

      logger.info(
          '''Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'''.format(
              args.batch_size				,     args.tgt_len				,     args.ext_len				,     args.mem_len				,     args.clamp_len							)							)

      model.reset_memory_length(args.mem_len							)
      if args.clamp_len > 0:
            __SCREAMING_SNAKE_CASE     :Dict	       =						args.clamp_len
      if args.same_length:
            __SCREAMING_SNAKE_CASE     :int	       =						True

      ###############################################################################
      # Evaluation code
      ###############################################################################
      def evaluate(a_  :	Optional[int]							):
            # Turn on evaluation mode which disables dropout.
            model.eval()
            __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						0, 0.0
            __SCREAMING_SNAKE_CASE     :Tuple	       =						time.time()
            with torch.no_grad():
                  __SCREAMING_SNAKE_CASE     :Any	       =						None
                  for idx, (data, target, seq_len) in enumerate(a_							):
                        __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						model(a_				,     lm_labels=a_				,     mems=a_							)
                        __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :List[str]	       =						ret
                        __SCREAMING_SNAKE_CASE     :Tuple	       =						loss.mean()
                        total_loss += seq_len * loss.item()
                        total_len += seq_len
                  __SCREAMING_SNAKE_CASE     :List[str]	       =						time.time() - start_time
            logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(a_				,     10_00 * total_time / (idx + 1)							)							)
            return total_loss / total_len

      # Run on test data.
      if args.split == "all":
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						evaluate(a_							)
            __SCREAMING_SNAKE_CASE     :Any	       =						evaluate(a_							)
      elif args.split == "valid":
            __SCREAMING_SNAKE_CASE     :Tuple	       =						evaluate(a_							)
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						None
      elif args.split == "test":
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						evaluate(a_							)
            __SCREAMING_SNAKE_CASE     :List[Any]	       =						None

      def format_log(a_  :	int				,     a_  :	List[Any]							):
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						'''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(a_				,     a_				,     math.exp(a_							)							)
            return log_str

      __SCREAMING_SNAKE_CASE     :Tuple	       =						''''''
      if valid_loss is not None:
            log_str += format_log(a_				,     '''valid'''							)
      if test_loss is not None:
            log_str += format_log(a_				,     '''test'''							)

      logger.info('''=''' * 1_00							)
      logger.info(a_							)
      logger.info('''=''' * 1_00							)


if __name__ == "__main__":
  main()