
"""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	(						__lowerCamelCase=None,						__lowerCamelCase=None    ):
   return field(default_factory=lambda: default,						metadata=__lowerCamelCase    )


@dataclass
class A_      :




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		str    	=						field(
      metadata={"""help""": """The csv file to plot."""}							,)
  SCREAMING_SNAKE_CASE__     :		bool    	=						field(
      default=lowercase__							,metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}							,)
  SCREAMING_SNAKE_CASE__     :		bool    	=						field(
      default=lowercase__							,metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}							,)
  SCREAMING_SNAKE_CASE__     :		bool    	=						field(
      default=lowercase__							,metadata={"""help""": """Disable logarithmic scale when plotting"""}							,)
  SCREAMING_SNAKE_CASE__     :		bool    	=						field(
      default=lowercase__							,metadata={
          """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
      }							,)
  SCREAMING_SNAKE_CASE__     :		Optional[str]    	=						field(
      default=lowercase__							,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=lowercase__							,metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""} )







def       __a	(						__lowerCamelCase    ):
   try:
      int(__lowerCamelCase    )
      return True
   except ValueError:
      return False







def       __a	(						__lowerCamelCase    ):
   try:
      float(__lowerCamelCase    )
      return True
   except ValueError:
      return False


class A_      :




  '''simple docstring'''







  def __init__(						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       args
     UpperCAmelCase_   :   Optional[Any]     	=       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}    )

     with open(self.args.csv_file							,  newline=""    ) as csv_file:
        UpperCAmelCase_   :   Dict     	=       csv.DictReader(lowercase_    )
        for row in reader:
           UpperCAmelCase_   :   Dict     	=       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_   :   Union[str, Any]     	=       int(row["result"]    )
           elif can_convert_to_float(row["result"]    ):
              # value is not None
              UpperCAmelCase_   :   int     	=       float(row["result"]    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   List[str]     	=       plt.subplots()
     UpperCAmelCase_   :   List[Any]     	=       "Time usage" if self.args.is_time else "Memory usage"
     UpperCAmelCase_   :   int     	=       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_   :   Union[str, Any]     	=       sorted(set(self.result_dict[model_name]["bsz"]    )    )
        UpperCAmelCase_   :   Any     	=       sorted(set(self.result_dict[model_name]["seq_len"]    )    )
        UpperCAmelCase_   :   Optional[int]     	=       self.result_dict[model_name]["result"]

        ((UpperCAmelCase_)	,  (UpperCAmelCase_))   :   Dict     	=       (
            (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
        )

        UpperCAmelCase_   :   Optional[int]     	=       (
            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_   :   Union[str, Any]     	=       np.asarray(
                  [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							,  dtype=lowercase_							,  )
           else:
              UpperCAmelCase_   :   List[Any]     	=       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_))   :   int     	=       (
               ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
           )

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

        title_str += F""" {label_model_name} vs."""

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

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

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







def       __a	(						):
   UpperCAmelCase_   :   List[str]     	=       HfArgumentParser(__lowerCamelCase    )
   UpperCAmelCase_   :   Any     	=       parser.parse_args_into_dataclasses()[0]
   UpperCAmelCase_   :   Any     	=       Plot(args=__lowerCamelCase    )
   plot.plot()


if __name__ == "__main__":
    main()








import sacrebleu as scb
from packaging import version

import datasets


UpperCAmelCase_    ="""\
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

UpperCAmelCase_    ="""\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
"""

UpperCAmelCase_    ="""
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).
    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
        - `'none'`: no smoothing
        - `'floor'`: increment zero counts
        - `'add-k'`: increment num/denom by k for n>1
        - `'exp'`: exponential decay
    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).
    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:
        - `'none'`: No tokenization.
        - `'zh'`: Chinese tokenization.
        - `'13a'`: mimics the `mteval-v13a` script from Moses.
        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `'char'`: Language-agnostic character-level tokenization.
        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.

Returns:
    'score': BLEU score,
    'counts': Counts,
    'totals': Totals,
    'precisions': Precisions,
    'bp': Brevity penalty,
    'sys_len': predictions length,
    'ref_len': reference length,

Examples:

    Example 1:
        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        100.0

    Example 2:
        >>> predictions = [\"hello there general kenobi\",
        ...                 \"on our way to ankh morpork\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"],
        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        39.8
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,       _KWARGS_DESCRIPTION	)
class 				__UpperCamelCase      (			datasets.Metric	):





				'''simple docstring'''



				def    __snake_case		(			self    ):
											if version.parse(scb.__version__    ) < version.parse('''1.4.12'''    ):
																		raise ImportWarning(
																		    '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
																		    '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''    )
											return datasets.MetricInfo(
											    description=_DESCRIPTION							,			citation=_CITATION							,			homepage='''https://github.com/mjpost/sacreBLEU'''							,			inputs_description=_KWARGS_DESCRIPTION							,			features=datasets.Features(
											        {
											            '''predictions''': datasets.Value('''string'''							,			id='''sequence'''    ),
											            '''references''': datasets.Sequence(datasets.Value('''string'''							,			id='''sequence'''    )							,			id='''references'''    ),
											        }    )							,			codebase_urls=['''https://github.com/mjpost/sacreBLEU''']							,			reference_urls=[
											        '''https://github.com/mjpost/sacreBLEU''',
											        '''https://en.wikipedia.org/wiki/BLEU''',
											        '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
											    ]							,			)






				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_="exp"							,			UpperCAmelCase_=None							,			UpperCAmelCase_=False							,			UpperCAmelCase_=False							,			UpperCAmelCase_=None							,			UpperCAmelCase_=False							,			):
											lowerCAmelCase =		len(references[0]    )
											if any(len(UpperCAmelCase_    ) != references_per_prediction for refs in references    ):
																		raise ValueError('''Sacrebleu requires the same number of references for each prediction'''    )
											lowerCAmelCase =		[[refs[i] for refs in references] for i in range(UpperCAmelCase_    )]
											lowerCAmelCase =		scb.corpus_bleu(
											    UpperCAmelCase_							,			UpperCAmelCase_							,			smooth_method=UpperCAmelCase_							,			smooth_value=UpperCAmelCase_							,			force=UpperCAmelCase_							,			lowercase=UpperCAmelCase_							,			use_effective_order=UpperCAmelCase_							,			**({'''tokenize''': tokenize} if tokenize else {})							,			)
											lowerCAmelCase =		{
											    '''score''': output.score,
											    '''counts''': output.counts,
											    '''totals''': output.totals,
											    '''precisions''': output.precisions,
											    '''bp''': output.bp,
											    '''sys_len''': output.sys_len,
											    '''ref_len''': output.ref_len,
											}
											return output_dict



