




'''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 		snake_case_   (UpperCamelCase	:  List[Any]=None			,						UpperCamelCase	:  List[str]=None				):





							'''simple docstring'''


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

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



def 		snake_case_   (UpperCamelCase	:  Dict				):





							'''simple docstring'''


							try:
														int(UpperCamelCase				)
														return True
							except ValueError:
														return False
def 		snake_case_   (UpperCamelCase	:  Any				):





							'''simple docstring'''


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

class       A   :







							def __init__(  self	:  List[str]							,      lowerCAmelCase_	:  Union[str, Any] )      ->							List[Any]:


														"""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        __lowerCAmelCase      (  self	:  int )      ->							Dict:


														"""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 		snake_case_   ():





							'''simple docstring'''


							_a     					=    HfArgumentParser(UpperCamelCase				)
							_a     					=    parser.parse_args_into_dataclasses()[0]
							_a     					=    Plot(args=UpperCamelCase				)
							plot.plot()


if __name__ == "__main__":
				main()







'''simple docstring'''


import argparse
from collections import defaultdict

import yaml


_snake_case				:				int       							=		'docs/source/en/_toctree.yml'
def 		snake_case_   (UpperCamelCase	:  Optional[int]				):





							'''simple docstring'''


							_a     					=    defaultdict(UpperCamelCase				)
							for doc in model_doc:
														counts[doc["local"]] += 1
							_a     					=    [key for key, value in counts.items() if value > 1]

							_a     					=    []
							for duplicate_key in duplicates:
														_a     					=    list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}				)
														if len(UpperCamelCase				) > 1:
																					raise ValueError(
																					    f'{duplicate_key} is present several times in the documentation table of content at '
																					    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
																					    '''others.'''				)
														# Only add this once
														new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}				)

							# Add none duplicate-keys
							new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]				)

							# Sort
							return sorted(UpperCamelCase			,						key=lambda UpperCamelCase				: s["title"].lower()				)
def 		snake_case_   (UpperCamelCase	:  str=False				):





							'''simple docstring'''


							with open(UpperCamelCase			,						encoding='''utf-8'''				) as f:
														_a     					=    yaml.safe_load(f.read()				)

							# Get to the API doc
							_a     					=    0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							_a     					=    content[api_idx]['''sections''']

							# Then to the model doc
							_a     					=    0
							while api_doc[model_idx]["title"] != "Models":
														model_idx += 1

							_a     					=    api_doc[model_idx]['''sections''']

							_a     					=    [(idx, section) for idx, section in enumerate(UpperCamelCase				) if '''sections''' in section]
							_a     					=    False
							for idx, modality_doc in modalities_docs:
														_a     					=    modality_doc['''sections''']
														_a     					=    clean_model_doc_toc(UpperCamelCase				)

														if old_modality_doc != new_modality_doc:
																					_a     					=    True
																					if overwrite:
																												_a     					=    new_modality_doc

							if diff:
														if overwrite:
																					_a     					=    model_doc
																					_a     					=    api_doc
																					with open(UpperCamelCase			,						'''w'''			,						encoding='''utf-8'''				) as f:
																												f.write(yaml.dump(UpperCamelCase			,						allow_unicode=UpperCamelCase				)				)
														else:
																					raise ValueError(
																					    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''				)


if __name__ == "__main__":
				_snake_case				:				List[str]       							=		argparse.ArgumentParser()
				parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
				_snake_case				:				List[Any]       							=		parser.parse_args()

				check_model_doc(args.fix_and_overwrite)

