




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			__a  :			Union[str, Any]      			=						['''image_processor''', '''tokenizer''']
			__a  :			int      			=						'''VideoMAEImageProcessor'''
			__a  :			int      			=						('''CLIPTokenizer''', '''CLIPTokenizerFast''')





			def __init__(       self				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       **lowerCAmelCase__						)					-> int:

							'''simple docstring'''


							__lowercase			      =							None
							if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''				,       lowerCAmelCase__				,       )
											__lowercase			      =							kwargs.pop('''feature_extractor'''						)

							__lowercase			      =							image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
											raise ValueError('''You need to specify an `image_processor`.'''						)
							if tokenizer is None:
											raise ValueError('''You need to specify a `tokenizer`.'''						)

							super().__init__(lowerCAmelCase__				,       lowerCAmelCase__						)
							__lowercase			      =							self.image_processor





			def __call__(       self				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       lowerCAmelCase__=None				,       **lowerCAmelCase__						)					-> List[Any]:

							'''simple docstring'''


							if text is None and videos is None:
											raise ValueError('''You have to specify either text or videos. Both cannot be none.'''						)

							if text is not None:
											__lowercase			      =							self.tokenizer(lowerCAmelCase__				,       return_tensors=lowerCAmelCase__				,       **lowerCAmelCase__						)

							if videos is not None:
											__lowercase			      =							self.image_processor(lowerCAmelCase__				,       return_tensors=lowerCAmelCase__				,       **lowerCAmelCase__						)

							if text is not None and videos is not None:
											__lowercase			      =							image_features.pixel_values
											return encoding
							elif text is not None:
											return encoding
							else:
											return BatchEncoding(data=dict(**lowerCAmelCase__						)				,       tensor_type=lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       *lowerCAmelCase__				,       **lowerCAmelCase__						)					-> List[Any]:

							'''simple docstring'''


							return self.tokenizer.batch_decode(*lowerCAmelCase__				,       **lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       *lowerCAmelCase__				,       **lowerCAmelCase__						)					-> List[Any]:

							'''simple docstring'''


							return self.tokenizer.decode(*lowerCAmelCase__				,       **lowerCAmelCase__						)





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''simple docstring'''


							return ["input_ids", "attention_mask", "position_ids", "pixel_values"]





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,       lowerCAmelCase__				,       )
							return self.image_processor_class





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[str]:

							'''simple docstring'''


							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,       lowerCAmelCase__				,       )
							return self.image_processor
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						(   lowercase=None							,      lowercase=None							):







				"""simple docstring"""



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




@dataclass
class        _UpperCamelCase				:


			"""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     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



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





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



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




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__						)					-> str:

							'''simple docstring'''


							__lowercase			      =							args
							__lowercase			      =							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

							with open(self.args.csv_file				,       newline=''''''						) as csv_file:
											__lowercase			      =							csv.DictReader(lowerCAmelCase__						)
											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 	_SCREAMING_SNAKE_CASE      (       self						)					-> Union[str, Any]:

							'''simple docstring'''


							__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=lowerCAmelCase__				,       )
															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(lowerCAmelCase__				,       lowerCAmelCase__						)[: len(lowerCAmelCase__						)]
															plt.scatter(
															    lowerCAmelCase__				,       lowerCAmelCase__				,       label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"						)
															plt.plot(lowerCAmelCase__				,       lowerCAmelCase__				,       '''--'''						)

											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(lowerCAmelCase__						)
							plt.xlabel(lowerCAmelCase__						)
							plt.ylabel(lowerCAmelCase__						)
							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"""



				__lowercase			      =							HfArgumentParser(lowercase							)
				__lowercase			      =							parser.parse_args_into_dataclasses()[0]
				__lowercase			      =							Plot(args=lowercase							)
				plot.plot()


if __name__ == "__main__":
		main()