




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_    (    SCREAMING_SNAKE_CASE=None					,	SCREAMING_SNAKE_CASE=None   ):
			'''simple docstring'''
			return field(default_factory=lambda: default					,	metadata=SCREAMING_SNAKE_CASE   )




@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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			try:
						int(SCREAMING_SNAKE_CASE   )
						return True
			except ValueError:
						return False
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			try:
						float(SCREAMING_SNAKE_CASE   )
						return True
			except ValueError:
						return False




class 			UpperCAmelCase_      :




							'''simple docstring'''



							def __init__(					self       :	List[Any]       ,     a       :	Dict			)			->					Any:
										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(a			)
													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    _UpperCAmelCase     (					self       :	Optional[int]			)			->					List[Any]:
										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=a       ,     )
																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(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."""

										SCREAMING_SNAKE_CASE           =					title_str[:-4]
										SCREAMING_SNAKE_CASE           =					"""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 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					HfArgumentParser(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					parser.parse_args_into_dataclasses()[0]
			SCREAMING_SNAKE_CASE           =					Plot(args=SCREAMING_SNAKE_CASE   )
			plot.plot()


if __name__ == "__main__":
		main()












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							@slow
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					Tuple:
										SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""       ,     return_dict=a			).to(a			)
										SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained("""google/mt5-small"""			)

										SCREAMING_SNAKE_CASE           =					tokenizer("""Hello there"""       ,     return_tensors="""pt"""			).input_ids
										SCREAMING_SNAKE_CASE           =					tokenizer("""Hi I am"""       ,     return_tensors="""pt"""			).input_ids

										SCREAMING_SNAKE_CASE           =					model(input_ids.to(a			)       ,     labels=labels.to(a			)			).loss
										SCREAMING_SNAKE_CASE           =					-(labels.shape[-1] * loss.item())

										SCREAMING_SNAKE_CASE           =					-84.9127
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE			) < 1E-4			)






