
'''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      UpperCamelCase_				(      A__=None  ,      A__=None						):
	return field(default_factory=lambda: default  ,      metadata=A__						)







@dataclass
class   a_ :
			lowerCamelCase__	:			str       =    field(
			    metadata={'help': 'The csv file to plot.'}		,  )
			lowerCamelCase__	:			bool       =    field(
			    default=UpperCamelCase__		,  metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}		,  )
			lowerCamelCase__	:			bool       =    field(
			    default=UpperCamelCase__		,  metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}		,  )
			lowerCamelCase__	:			bool       =    field(
			    default=UpperCamelCase__		,  metadata={'help': 'Disable logarithmic scale when plotting'}		,  )
			lowerCamelCase__	:			bool       =    field(
			    default=UpperCamelCase__		,  metadata={
			        'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
			    }		,  )
			lowerCamelCase__	:			Optional[str]       =    field(
			    default=UpperCamelCase__		,  metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}		,  )
			lowerCamelCase__	:			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      UpperCamelCase_				(      A__						):
	try:
		int(A__						)
		return True
	except ValueError:
		return False






def      UpperCamelCase_				(      A__						):
	try:
		float(A__						)
		return True
	except ValueError:
		return False







class   a_ :




			def __init__(       self					,   UpperCAmelCase	):
				a_      				=						args
				a_      				=						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

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






			def 					lowerCAmelCase__					(       self	):
				a_				,  a_      				=						plt.subplots()
				a_      				=						"""Time usage""" if self.args.is_time else """Memory usage"""
				a_      				=						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()	):
					a_      				=						sorted(set(self.result_dict[model_name]["""bsz"""]	)	)
					a_      				=						sorted(set(self.result_dict[model_name]["""seq_len"""]	)	)
					a_      				=						self.result_dict[model_name]["""result"""]

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

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

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

						a_      				=						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.'''

				a_      				=						title_str[:-4]
				a_      				=						"""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_				(      ):
	a_      				=						HfArgumentParser(A__						)
	a_      				=						parser.parse_args_into_dataclasses()[0]
	a_      				=						Plot(args=A__						)
	plot.plot()


if __name__ == "__main__":
		main()


'''simple docstring'''
import comet  # From: unbabel-comet
import torch

import datasets


lowercase__											=datasets.logging.get_logger(__name__)

lowercase__											='\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = "{COMET}: A Neural Framework for {MT} Evaluation",\n   author = "Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon",\n   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n   month = nov,\n   year = "2020",\n   address = "Online",\n   publisher = "Association for Computational Linguistics",\n   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n   pages = "2685--2702",\n}\n'

lowercase__											='\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'

lowercase__											='\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric(\'comet\')\n    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use\n    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [0.19, 0.92]\n'







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




			def 					lowerCAmelCase__					(       self	):
				return datasets.MetricInfo(
				    description=_DESCRIPTION					,   citation=_CITATION					,   homepage="""https://unbabel.github.io/COMET/html/index.html"""					,   inputs_description=_KWARGS_DESCRIPTION					,   features=datasets.Features(
				        {
				            """sources""": datasets.Value("""string"""					,   id="""sequence"""	),
				            """predictions""": datasets.Value("""string"""					,   id="""sequence"""	),
				            """references""": datasets.Value("""string"""					,   id="""sequence"""	),
				        }	)					,   codebase_urls=["""https://github.com/Unbabel/COMET"""]					,   reference_urls=[
				        """https://github.com/Unbabel/COMET""",
				        """https://www.aclweb.org/anthology/2020.emnlp-main.213/""",
				        """http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6""",
				    ]					,   )




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				if self.config_name == "default":
					a_      				=						comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da"""	)	)
				else:
					a_      				=						comet.load_from_checkpoint(comet.download_model(self.config_name	)	)






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase=None					,   UpperCAmelCase=False	):
				if gpus is None:
					a_      				=						1 if torch.cuda.is_available() else 0
				a_      				=						{"""src""": sources, """mt""": predictions, """ref""": references}
				a_      				=						[dict(zip(UpperCAmelCase					,   UpperCAmelCase	)	) for t in zip(*data.values()	)]
				a_				,  a_      				=						self.scorer.predict(UpperCAmelCase					,   gpus=UpperCAmelCase					,   progress_bar=UpperCAmelCase	)
				return {"mean_score": mean_score, "scores": scores}
