


"""simple docstring"""






import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
SCREAMING_SNAKE_CASE__				:  Tuple						= "\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n"

# TODO: Add description of the dataset here
# You can copy an official description
SCREAMING_SNAKE_CASE__				:  Any						= "\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n"

# TODO: Add a link to an official homepage for the dataset here
SCREAMING_SNAKE_CASE__				:  int						= ""

# TODO: Add the licence for the dataset here if you can find it
SCREAMING_SNAKE_CASE__				:  str						= ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
SCREAMING_SNAKE_CASE__				:  str						= {
    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
}


class 	A_       (     datasets.GeneratorBasedBuilder   ):





  """simple docstring"""

  lowercase		:	List[Any]   =						datasets.Version("1.1.0"   )

  # This is an example of a dataset with multiple configurations.
  # If you don't want/need to define several sub-sets in your dataset,
  # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

  # If you need to make complex sub-parts in the datasets with configurable options
  # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
  # BUILDER_CONFIG_CLASS = MyBuilderConfig

  # You will be able to load one or the other configurations in the following list with
  # data = datasets.load_dataset('my_dataset', 'first_domain')
  # data = datasets.load_dataset('my_dataset', 'second_domain')
  lowercase		:	str   =						[
      datasets.BuilderConfig(name="first_domain"      ,  version=_lowercase      ,  description="This part of my dataset covers a first domain"   ),
      datasets.BuilderConfig(name="second_domain"      ,  version=_lowercase      ,  description="This part of my dataset covers a second domain"   ),
  ]

  lowercase		:	Tuple   =						"first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
  def 							lowercase_     (						self						)				->					str:
     # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
     if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
        a   :	Tuple						=    datasets.Features(
            {
                'sentence': datasets.Value('string'						),
                'option1': datasets.Value('string'						),
                'answer': datasets.Value('string'						)
                # These are the features of your dataset like images, labels ...
            }						)
     else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
        a   :	Union[str, Any]						=    datasets.Features(
            {
                'sentence': datasets.Value('string'						),
                'option2': datasets.Value('string'						),
                'second_domain_answer': datasets.Value('string'						)
                # These are the features of your dataset like images, labels ...
            }						)
     return datasets.DatasetInfo(
         # This is the description that will appear on the datasets page.
         description=_DESCRIPTION  ,					features=__a  ,					homepage=_HOMEPAGE  ,					license=_LICENSE  ,					citation=_CITATION  ,					)
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					Dict:
     # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
     # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

     # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
     # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
     # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
     a   :	Any						=    _URLS[self.config.name]
     a   :	List[str]						=    dl_manager.download_and_extract(__a						)
     return [
         datasets.SplitGenerator(
             name=datasets.Split.TRAIN  ,					gen_kwargs={
                 'filepath': os.path.join(__a  ,					'train.jsonl'						),
                 'split': 'train',
             }  ,					),
         datasets.SplitGenerator(
             name=datasets.Split.VALIDATION  ,					gen_kwargs={
                 'filepath': os.path.join(__a  ,					'dev.jsonl'						),
                 'split': 'dev',
             }  ,					),
         datasets.SplitGenerator(
             name=datasets.Split.TEST  ,					gen_kwargs={
                 'filepath': os.path.join(__a  ,					'test.jsonl'						),
                 'split': 'test'
             }  ,					),
     ]






  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					List[Any]:
     # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
     # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
     with open(__a  ,					encoding='utf-8'						) as f:
        for key, row in enumerate(__a						):
           a   :	Tuple						=    json.loads(__a						)
           if self.config.name == "first_domain":
              # Yields examples as (key, example) tuples
              yield key, {
                  "sentence": data["sentence"],
                  "option1": data["option1"],
                  "answer": "" if split == "test" else data["answer"],
              }
           else:
              yield key, {
                  "sentence": data["sentence"],
                  "option2": data["option2"],
                  "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
              }












"""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    a__       (       _SCREAMING_SNAKE_CASE=None	,						_SCREAMING_SNAKE_CASE=None     ):




		"""simple docstring"""


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





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



def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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





class 							_lowerCamelCase   :


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

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




						def      snake_case_ (self		)			-> Optional[int]:
								UpperCamelCase ,					UpperCamelCase =						plt.subplots()
								UpperCamelCase =						"Time usage" if self.args.is_time else "Memory usage"
								UpperCamelCase =						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()		):
										UpperCamelCase =						sorted(set(self.result_dict[model_name]["bsz"]		)		)
										UpperCamelCase =						sorted(set(self.result_dict[model_name]["seq_len"]		)		)
										UpperCamelCase =						self.result_dict[model_name]["result"]

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

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

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

												UpperCamelCase =						np.asarray(__a ,				__a		)[: len(__a		)]
												plt.scatter(
												    __a ,				__a ,				label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"		)
												plt.plot(__a ,				__a ,				"--"		)

										title_str += F" {label_model_name} vs."

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

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

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



def    a__       (       ):




		"""simple docstring"""


		UpperCamelCase =						HfArgumentParser(_SCREAMING_SNAKE_CASE     )
		UpperCamelCase =						parser.parse_args_into_dataclasses()[0]
		UpperCamelCase =						Plot(args=_SCREAMING_SNAKE_CASE     )
		plot.plot()


if __name__ == "__main__":
						main()






