
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						(							snake_case__:					Optional[Any]=None						,   snake_case__:					Optional[Any]=None					):


				'''simple docstring'''
				return field(default_factory=lambda: default						,   metadata=snake_case__					)
@dataclass
class  lowercase__:



	"""simple docstring"""




	a			:str         =	field(
	    metadata={'help': 'The csv file to plot.'} ,						)
	a			:bool         =	field(
	    default=UpperCAmelCase ,						metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'} ,						)
	a			:bool         =	field(
	    default=UpperCAmelCase ,						metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'} ,						)
	a			:bool         =	field(
	    default=UpperCAmelCase ,						metadata={'help': 'Disable logarithmic scale when plotting'} ,						)
	a			:bool         =	field(
	    default=UpperCAmelCase ,						metadata={
	        'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
	    } ,						)
	a			:Optional[str]         =	field(
	    default=UpperCAmelCase ,						metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'} ,						)
	a			: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 				a						(							snake_case__:					Dict					):


				'''simple docstring'''
				try:
								int(snake_case__					)
								return True
				except ValueError:
								return False




def 				a						(							snake_case__:					Optional[int]					):


				'''simple docstring'''
				try:
								float(snake_case__					)
								return True
				except ValueError:
								return False
class  lowercase__:



	"""simple docstring"""




	def __init__(       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			List[str]       )       ->     Any:
					lowercase_       =					args
					lowercase_       =					defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )

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




	def 						_lowercase  (       self  :			Dict       )       ->     Optional[Any]:
					lowercase_  ,	lowercase_       =					plt.subplots()
					lowercase_       =					'''Time usage''' if self.args.is_time else '''Memory usage'''
					lowercase_       =					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()       ):
									lowercase_       =					sorted(set(self.result_dict[model_name]['''bsz''']       )       )
									lowercase_       =					sorted(set(self.result_dict[model_name]['''seq_len''']       )       )
									lowercase_       =					self.result_dict[model_name]['''result''']

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

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

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

													lowercase_       =					np.asarray(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )[: len(SCREAMING_SNAKE_CASE_       )]
													plt.scatter(
													    SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							label=f'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''       )
													plt.plot(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							'''--'''       )

									title_str += f''' {label_model_name} vs.'''

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

					# plot
					plt.title(SCREAMING_SNAKE_CASE_       )
					plt.xlabel(SCREAMING_SNAKE_CASE_       )
					plt.ylabel(SCREAMING_SNAKE_CASE_       )
					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'''
				lowercase_       =					HfArgumentParser(snake_case__					)
				lowercase_       =					parser.parse_args_into_dataclasses()[0]
				lowercase_       =					Plot(args=snake_case__					)
				plot.plot()


if __name__ == "__main__":
						main()





'''simple docstring'''




import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
						import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin





@require_torch
class     __magic_name__						(    unittest.TestCase,     _UpperCAmelCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			List[str]										=       load_tool("""text-to-speech"""    )
										self.tool.setup()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										# SpeechT5 isn't deterministic
										torch.manual_seed(0    )
										lowercase_					:			List[str]										=       self.tool("""hey"""    )
										lowercase_					:			Optional[int]										=       result.to_raw()
										self.assertTrue(
										    torch.allclose(
										        resulting_tensor[:3]				,			torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85]    )				,			)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										# SpeechT5 isn't deterministic
										torch.manual_seed(0    )
										lowercase_					:			Union[str, Any]										=       self.tool("""hey"""    )
										lowercase_					:			int										=       result.to_raw()
										self.assertTrue(
										    torch.allclose(
										        resulting_tensor[:3]				,			torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85]    )				,			)    )





