






'''simple docstring'''







import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
_A     :      Any   					="""src/transformers"""
_A     :      Dict   					="""docs/source/en"""
_A     :      List[str]   					="""."""





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	Optional[Any]:





							with open(_snake_case    ,      """r"""    ,      encoding="""utf-8"""    ,      newline="""\n"""				) as f:
														lowerCamelCase__ :     List[Any]	  =	f.readlines()
							# Find the start prompt.
							lowerCamelCase__ :     Dict	  =	0
							while not lines[start_index].startswith(_snake_case				):
														start_index += 1
							start_index += 1

							lowerCamelCase__ :     Optional[int]	  =	start_index
							while not lines[end_index].startswith(_snake_case				):
														end_index += 1
							end_index -= 1

							while len(lines[start_index]				) <= 1:
														start_index += 1
							while len(lines[end_index]				) <= 1:
														end_index -= 1
							end_index += 1
							return "".join(lines[start_index:end_index]				), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
_A     :      Optional[int]   					="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_A     :      List[Any]   					=re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
_A     :      Union[str, Any]   					=re.compile(r'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_A     :      Optional[int]   					=re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# This is to make sure the transformers module imported is the one in the repo.
_A     :      Optional[Any]   					=direct_transformers_import(TRANSFORMERS_PATH)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Optional[int]:
							lowerCamelCase__ :     str	  =	re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""    ,      _snake_case				)
							return [m.group(0				) for m in matches]





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	List[Any]:
							lowerCamelCase__ :     Optional[int]	  =	2 if text == '''✅''' or text == '''❌''' else len(_snake_case				)
							lowerCamelCase__ :     str	  =	(width - text_length) // 2
							lowerCamelCase__ :     List[str]	  =	width - text_length - left_indent
							return " " * left_indent + text + " " * right_indent





def 			SCREAMING_SNAKE_CASE_	()				->	List[str]:
							lowerCamelCase__ :     Optional[int]	  =	transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
							lowerCamelCase__ :     Optional[int]	  =	{
							    name: config_maping_names[code]
							    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
							    if code in config_maping_names
							}
							lowerCamelCase__ :     Union[str, Any]	  =	{name: config.replace("""Config"""    ,      """"""				) for name, config in model_name_to_config.items()}

							# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
							lowerCamelCase__ :     List[Any]	  =	collections.defaultdict(_snake_case				)
							lowerCamelCase__ :     List[Any]	  =	collections.defaultdict(_snake_case				)
							lowerCamelCase__ :     Optional[Any]	  =	collections.defaultdict(_snake_case				)
							lowerCamelCase__ :     Optional[int]	  =	collections.defaultdict(_snake_case				)
							lowerCamelCase__ :     Any	  =	collections.defaultdict(_snake_case				)

							# Let's lookup through all transformers object (once).
							for attr_name in dir(_snake_case				):
														lowerCamelCase__ :     List[str]	  =	None
														if attr_name.endswith("""Tokenizer"""				):
																					lowerCamelCase__ :     Tuple	  =	slow_tokenizers
																					lowerCamelCase__ :     Union[str, Any]	  =	attr_name[:-9]
														elif attr_name.endswith("""TokenizerFast"""				):
																					lowerCamelCase__ :     List[Any]	  =	fast_tokenizers
																					lowerCamelCase__ :     Optional[int]	  =	attr_name[:-13]
														elif _re_tf_models.match(_snake_case				) is not None:
																					lowerCamelCase__ :     List[Any]	  =	tf_models
																					lowerCamelCase__ :     int	  =	_re_tf_models.match(_snake_case				).groups()[0]
														elif _re_flax_models.match(_snake_case				) is not None:
																					lowerCamelCase__ :     Tuple	  =	flax_models
																					lowerCamelCase__ :     Optional[int]	  =	_re_flax_models.match(_snake_case				).groups()[0]
														elif _re_pt_models.match(_snake_case				) is not None:
																					lowerCamelCase__ :     Optional[Any]	  =	pt_models
																					lowerCamelCase__ :     List[Any]	  =	_re_pt_models.match(_snake_case				).groups()[0]

														if lookup_dict is not None:
																					while len(_snake_case				) > 0:
																												if attr_name in model_name_to_prefix.values():
																																			lowerCamelCase__ :     Optional[int]	  =	True
																																			break
																												# Try again after removing the last word in the name
																												lowerCamelCase__ :     Dict	  =	''''''.join(camel_case_split(_snake_case				)[:-1]				)

    # Let's build that table!
							lowerCamelCase__ :     int	  =	list(model_name_to_config.keys()				)
							model_names.sort(key=str.lower				)
							lowerCamelCase__ :     List[Any]	  =	['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
							# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
							lowerCamelCase__ :     int	  =	[len(_snake_case				) + 2 for c in columns]
							lowerCamelCase__ :     List[Any]	  =	max([len(_snake_case				) for name in model_names]				) + 2

							# Build the table per se
							lowerCamelCase__ :     Optional[int]	  =	'''|''' + '''|'''.join([_center_text(_snake_case    ,      _snake_case				) for c, w in zip(_snake_case    ,      _snake_case				)]				) + '''|\n'''
							# Use ":-----:" format to center-aligned table cell texts
							table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths]				) + "|\n"

							lowerCamelCase__ :     Optional[int]	  =	{True: '''✅''', False: '''❌'''}
							for name in model_names:
														lowerCamelCase__ :     Optional[Any]	  =	model_name_to_prefix[name]
														lowerCamelCase__ :     Optional[Any]	  =	[
														    name,
														    check[slow_tokenizers[prefix]],
														    check[fast_tokenizers[prefix]],
														    check[pt_models[prefix]],
														    check[tf_models[prefix]],
														    check[flax_models[prefix]],
														]
														table += "|" + "|".join([_center_text(_snake_case    ,      _snake_case				) for l, w in zip(_snake_case    ,      _snake_case				)]				) + "|\n"
							return table





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase=False				)				->	Dict:
							lowerCamelCase__ :     int	  =	_find_text_in_file(
							    filename=os.path.join(_snake_case    ,      """index.md"""				)    ,      start_prompt="""<!--This table is updated automatically from the auto modules"""    ,      end_prompt="""<!-- End table-->"""    ,      )
							lowerCamelCase__ :     Optional[Any]	  =	get_model_table_from_auto_modules()

							if current_table != new_table:
														if overwrite:
																					with open(os.path.join(_snake_case    ,      """index.md"""				)    ,      """w"""    ,      encoding="""utf-8"""    ,      newline="""\n"""				) as f:
																												f.writelines(lines[:start_index] + [new_table] + lines[end_index:]				)
														else:
																					raise ValueError(
																					    """The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."""				)


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

			check_model_table(args.fix_and_overwrite)









'''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_	(UpperCamelCase=None    ,      UpperCamelCase=None				)				->	Any:
							return field(default_factory=lambda: default    ,      metadata=UpperCamelCase				)

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





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							try:
														int(UpperCamelCase				)
														return True
							except ValueError:
														return False





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	int:
							try:
														float(UpperCamelCase				)
														return True
							except ValueError:
														return False

class _lowercase     :




							def __init__(							self:    Tuple      ,							UpperCamelCase__:    str					):
														lowerCamelCase__ :     int	  =	args
														lowerCamelCase__ :     Optional[int]	  =	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)

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




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

																					((lowerCamelCase__)  ,		(lowerCamelCase__)) :     Dict	  =	(
																					    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																					)

																					lowerCamelCase__ :     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:
																																			lowerCamelCase__ :     int	  =	np.asarray(
																																			    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,							dtype=UpperCamelCase__      ,							)
																												else:
																																			lowerCamelCase__ :     List[Any]	  =	np.asarray(
																																			    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]      ,							dtype=np.floataa      ,							)

																												((lowerCamelCase__)  ,		(lowerCamelCase__)) :     List[str]	  =	(
																												    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																												)

																												lowerCamelCase__ :     int	  =	np.asarray(UpperCamelCase__      ,							UpperCamelCase__					)[: len(UpperCamelCase__					)]
																												plt.scatter(
																												    UpperCamelCase__      ,							UpperCamelCase__      ,							label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''					)
																												plt.plot(UpperCamelCase__      ,							UpperCamelCase__      ,							"""--"""					)

																					title_str += F''' {label_model_name} vs.'''

														lowerCamelCase__ :     Any	  =	title_str[:-4]
														lowerCamelCase__ :     Optional[int]	  =	"""Time in s""" if self.args.is_time else """Memory in MB"""

														# plot
														plt.title(UpperCamelCase__					)
														plt.xlabel(UpperCamelCase__					)
														plt.ylabel(UpperCamelCase__					)
														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_	()				->	str:
							lowerCamelCase__ :     str	  =	HfArgumentParser(UpperCamelCase				)
							lowerCamelCase__ :     str	  =	parser.parse_args_into_dataclasses()[0]
							lowerCamelCase__ :     Any	  =	Plot(args=UpperCamelCase				)
							plot.plot()


if __name__ == "__main__":
			main()

