




"""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 						_SCREAMING_SNAKE_CASE     (							lowercase_=None    ,				lowercase_=None		)		->	Optional[int]:
	return field(default_factory=lambda: default    ,				metadata=lowercase__		)




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







def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Any:
	try:
		int(lowercase__		)
		return True
	except ValueError:
		return False







def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Dict:
	try:
		float(lowercase__		)
		return True
	except ValueError:
		return False




class 			UpperCAmelCase_						:




				def __init__(					self		: List[Any]			,				snake_case_		: Dict   )   ->							int:




					'''simple docstring'''

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

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




				def 							__magic_name__							(					self		: int   )   ->							Tuple:




					'''simple docstring'''

					A__,   A__ =       plt.subplots()
					A__ =       "Time usage" if self.args.is_time else "Memory usage"
					A__ =       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__ =       sorted(set(self.result_dict[model_name]["bsz"]   )   )
						A__ =       sorted(set(self.result_dict[model_name]["seq_len"]   )   )
						A__ =       self.result_dict[model_name]["result"]

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

						A__ =       (
						    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__ =       np.asarray(
								    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]			,				dtype=lowerCamelCase__			,				)
							else:
								A__ =       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__)) =       (
							    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
							)

							A__ =       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."""

					A__ =       title_str[:-4]
					A__ =       "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 						_SCREAMING_SNAKE_CASE     (							)		->	Dict:
	A__ =       HfArgumentParser(lowercase__		)
	A__ =       parser.parse_args_into_dataclasses()[0]
	A__ =       Plot(args=lowercase__		)
	plot.plot()


if __name__ == "__main__":
						main()











"""simple docstring"""



from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy




class 			UpperCAmelCase_						(		A_							):
    lowercase__		      =   '''MarkupLMFeatureExtractor'''
    lowercase__		      =   ('''MarkupLMTokenizer''', '''MarkupLMTokenizerFast''')
    lowercase__		      =   True




    def __call__(					self		: Dict			,				snake_case_		: Optional[int]=None			,				snake_case_		: Union[str, Any]=None			,				snake_case_		: Tuple=None			,				snake_case_		: str=None			,				snake_case_		: Optional[Any]=None			,				snake_case_		: bool = True			,				snake_case_		: Union[bool, str, PaddingStrategy] = False			,				snake_case_		: Union[bool, str, TruncationStrategy] = None			,				snake_case_		: Optional[int] = None			,				snake_case_		: int = 0			,				snake_case_		: Optional[int] = None			,				snake_case_		: Optional[bool] = None			,				snake_case_		: Optional[bool] = None			,				snake_case_		: bool = False			,				snake_case_		: bool = False			,				snake_case_		: bool = False			,				snake_case_		: bool = False			,				snake_case_		: bool = True			,				snake_case_		: Optional[Union[str, TensorType]] = None			,				**snake_case_		: Dict			,				)   ->							BatchEncoding:




     '''simple docstring'''

     if self.parse_html:
      if html_strings is None:
       raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`"   )

      if nodes is not None or xpaths is not None or node_labels is not None:
       raise ValueError(
           "Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"   )

      A__ =       self.feature_extractor(snake_case_   )
      A__ =       features["nodes"]
      A__ =       features["xpaths"]
     else:
      if html_strings is not None:
       raise ValueError("You have passed HTML strings but `parse_html` is set to `False`."   )
      if nodes is None or xpaths is None:
       raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"   )

        # # second, apply the tokenizer
     if questions is not None and self.parse_html:
      if isinstance(snake_case_			,				snake_case_   ):
       A__ =       [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

     A__ =       self.tokenizer(
         text=questions if questions is not None else nodes			,				text_pair=nodes if questions is not None else None			,				xpaths=snake_case_			,				node_labels=snake_case_			,				add_special_tokens=snake_case_			,				padding=snake_case_			,				truncation=snake_case_			,				max_length=snake_case_			,				stride=snake_case_			,				pad_to_multiple_of=snake_case_			,				return_token_type_ids=snake_case_			,				return_attention_mask=snake_case_			,				return_overflowing_tokens=snake_case_			,				return_special_tokens_mask=snake_case_			,				return_offsets_mapping=snake_case_			,				return_length=snake_case_			,				verbose=snake_case_			,				return_tensors=snake_case_			,				**snake_case_			,				)

     return encoded_inputs




    def 							__magic_name__							(					self		: Optional[Any]			,				*snake_case_		: Any			,				**snake_case_		: Any   )   ->							int:




     '''simple docstring'''

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




    def 							__magic_name__							(					self		: Tuple			,				*snake_case_		: Optional[Any]			,				**snake_case_		: List[Any]   )   ->							Any:




     '''simple docstring'''

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




    @property
    def 							__magic_name__							(					self		: List[Any]   )   ->							Optional[int]:




     '''simple docstring'''

     A__ =       self.tokenizer.model_input_names
     return tokenizer_input_names





