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



	'''simple docstring'''



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





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



	'''simple docstring'''



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



def      __UpperCAmelCase    (  snake_case_							:		List[Any]				):



	'''simple docstring'''



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





class 		__lowerCamelCase     :



							def __init__(     self	,				__snake_case							)						->     List[Any]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     args
								UpperCAmelCase:					Union[str, Any] 						=     defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}							)

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




							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase:					Tuple 						=     plt.subplots()
								UpperCAmelCase:					List[Any] 						=     "Time usage" if self.args.is_time else "Memory usage"
								UpperCAmelCase:					List[str] 						=     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:					List[Any] 						=     sorted(set(self.result_dict[model_name]["bsz"]							)							)
									UpperCAmelCase:					Union[str, Any] 						=     sorted(set(self.result_dict[model_name]["seq_len"]							)							)
									UpperCAmelCase:					List[str] 						=     self.result_dict[model_name]["result"]

									((UpperCAmelCase)						,   (UpperCAmelCase)):					Optional[Any] 						=     (
									    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
									)

									UpperCAmelCase:					Any 						=     (
									    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:					Optional[int] 						=     np.asarray(
											    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]	,				dtype=__snake_case	,				)
										else:
											UpperCAmelCase:					Dict 						=     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:					Optional[int] 						=     np.asarray(__snake_case	,				__snake_case							)[: len(__snake_case							)]
										plt.scatter(
										    __snake_case	,				__snake_case	,				label=F'{label_model_name} - {inner_loop_label}: {inner_loop_value}'							)
										plt.plot(__snake_case	,				__snake_case	,				"--"							)

									title_str += F' {label_model_name} vs.'

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

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

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



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	UpperCAmelCase:					Any 						=     HfArgumentParser(snake_case_				)
	UpperCAmelCase:					Union[str, Any] 						=     parser.parse_args_into_dataclasses()[0]
	UpperCAmelCase:					Optional[Any] 						=     Plot(args=snake_case_				)
	plot.plot()


if __name__ == "__main__":
	main()


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_      :					Optional[int]				      =     {
    'configuration_splinter': ['SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SplinterConfig'],
    'tokenization_splinter': ['SplinterTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Dict				      =     ['SplinterTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_      :					Optional[Any]				      =     [
	    'SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SplinterForQuestionAnswering',
	    'SplinterForPreTraining',
	    'SplinterLayer',
	    'SplinterModel',
	    'SplinterPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
	from .tokenization_splinter import SplinterTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_splinter_fast import SplinterTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_splinter import (
		    SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SplinterForPreTraining,
		    SplinterForQuestionAnswering,
		    SplinterLayer,
		    SplinterModel,
		    SplinterPreTrainedModel,
		)


else:
	import sys

	snake_case_      :					Dict				      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
