






'''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				: int=None    ,		__A				: str=None   ):
				return field(default_factory=lambda: default    ,		metadata=__A   )





@dataclass
class 						SCREAMING_SNAKE_CASE       :
			snake_case__	=					field(
			    metadata={"help": "The csv file to plot."}						, )
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}						, )
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}						, )
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "Disable logarithmic scale when plotting"}						, )
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={
			        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
			    }						, )
			snake_case__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}						, )
			snake_case__	=					list_field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "List of model names that are used instead of the ones in the csv file."}							)





def 		_UpperCAmelCase (				__A				: int   ):
				try:
								int(__A   )
								return True
				except ValueError:
								return False





def 		_UpperCAmelCase (				__A				: List[str]   ):
				try:
								float(__A   )
								return True
				except ValueError:
								return False





class 						SCREAMING_SNAKE_CASE       :






			def __init__(			self   :      Optional[int]	,    __SCREAMING_SNAKE_CASE   :      Tuple       )      ->      List[Any]:
							a_		:	List[Any]									=   args
							a_		:	Tuple									=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )

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







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

											((a_)					,      (a_))		:	Union[str, Any]									=   (
											    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
											)

											a_		:	List[str]									=   (
											    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_		:	Dict									=   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:
																			a_		:	Tuple									=   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_))		:	List[str]									=   (
															    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
															)

															a_		:	str									=   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.'

							a_		:	List[str]									=   title_str[:-4]
							a_		:	List[Any]									=   '''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 		_UpperCAmelCase (				):
				a_		:	Tuple									=   HfArgumentParser(__A   )
				a_		:	Tuple									=   parser.parse_args_into_dataclasses()[0]
				a_		:	int									=   Plot(args=__A   )
				plot.plot()


if __name__ == "__main__":
		main()












'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCAmelCase									=   {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   ['DeiTFeatureExtractor']
		__lowerCAmelCase									=   ['DeiTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DeiTForImageClassification',
		    'DeiTForImageClassificationWithTeacher',
		    'DeiTForMaskedImageModeling',
		    'DeiTModel',
		    'DeiTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFDeiTForImageClassification',
		    'TFDeiTForImageClassificationWithTeacher',
		    'TFDeiTForMaskedImageModeling',
		    'TFDeiTModel',
		    'TFDeiTPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		__lowerCAmelCase									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




