


import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa







class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					Union[str, Any]   =							["""loss""", """ce_loss""", """mlm_loss""", """hid_loss_enc""", """hid_loss_dec"""]




						def __init__(			self				,  __lowercase							)   ->      Dict:
										assert Path(hparams.data_dir							).exists()
										lowerCAmelCase_	: List[str]      			=			Path(hparams.output_dir							)
										self.output_dir.mkdir(exist_ok=__lowercase							)

										lowerCAmelCase_	: List[Any]      			=			self.output_dir.joinpath('''student'''							)

										lowerCAmelCase_	: str      			=			str(__lowercase							)  # Tell lightning we are training the student
										lowerCAmelCase_	: Tuple      			=			AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher							).eval()
										use_task_specific_params(__lowercase				,  hparams.task							)  # We copy good generation parameters to student by default
										if hparams.student is not None:
														lowerCAmelCase_	: Dict      			=			AutoModelForSeqaSeqLM.from_pretrained(hparams.student							)
														use_task_specific_params(__lowercase				,  hparams.task							)
														lowerCAmelCase_			,					lowerCAmelCase_	: Any      			=			None, None
										else:
														lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_	: Dict      			=			create_student_by_copying_alternating_layers(
														    __lowercase				,  e=hparams.student_encoder_layers				,  d=hparams.student_decoder_layers				,  save_path=__lowercase							)

										if hparams.length_penalty != -1:
														lowerCAmelCase_	: List[str]      			=			hparams.length_penalty
										lowerCAmelCase_	: List[Any]      			=			hparams.teacher  # Use teacher's tokenizer
										super().__init__(__lowercase				,  model=__lowercase				,  config=student.config							)
										assert student.config.model_type == teacher.config.model_type, (
										    f"""teacher, student model types should be the same, got {student.config.model_type} !="""
										    f""" {teacher.config.model_type}"""
										)

										if student.config.model_type == "t5":
														lowerCAmelCase_	: Optional[Any]      			=			len(student.get_encoder().block							)
														lowerCAmelCase_	: Any      			=			len(student.get_decoder().block							)
														lowerCAmelCase_	: int      			=			len(teacher.get_encoder().block							)
														lowerCAmelCase_	: Dict      			=			len(teacher.get_decoder().block							)
										else:
														lowerCAmelCase_	: str      			=			student.config.encoder_layers
														lowerCAmelCase_	: Optional[Any]      			=			student.config.decoder_layers
														lowerCAmelCase_	: str      			=			teacher.config.encoder_layers
														lowerCAmelCase_	: List[Any]      			=			teacher.config.decoder_layers

										lowerCAmelCase_	: List[Any]      			=			not (hparams.student is None or hparams.teacher == hparams.student)
										lowerCAmelCase_	: Optional[int]      			=			(not self.different_base_models) and hparams.alpha_hid > 0
										lowerCAmelCase_	: List[Any]      			=			self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
										# self.different_encoder determines whether we need to run the teacher encoder
										lowerCAmelCase_	: str      			=			teacher
										freeze_params(self.teacher							)

										if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
														try:
																		del self.teacher.model.encoder
														except AttributeError:  # T5
																		del self.teacher.encoder

										if e_layer_ids is None:
														lowerCAmelCase_	: Tuple      			=			list(range(__lowercase							)							)
										if d_layer_ids is None:
														lowerCAmelCase_	: Optional[Any]      			=			list(range(__lowercase							)							)

										lowerCAmelCase_			,					lowerCAmelCase_	: List[Any]      			=			e_layer_ids, d_layer_ids  # type: List[int], List[int]

										if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
														if hparams.supervise_forward:
																		lowerCAmelCase_	: Any      			=			get_layers_to_supervise(
																		    n_student=len(self.e_layer_ids							)				,  n_teacher=__lowercase							)
																		lowerCAmelCase_	: int      			=			get_layers_to_supervise(
																		    n_student=len(self.d_layer_ids							)				,  n_teacher=__lowercase							)
														else:  # student layer should emulate hidden states of the teacher layer it was copied from
																		lowerCAmelCase_	: Optional[Any]      			=			self.e_layer_ids
																		lowerCAmelCase_	: Union[str, Any]      			=			self.d_layer_ids
										else:
														lowerCAmelCase_	: int      			=			None
														lowerCAmelCase_	: List[Any]      			=			None

										lowerCAmelCase_	: Dict      			=			nn.KLDivLoss(reduction='''batchmean'''							)
										lowerCAmelCase_	: Tuple      			=			2.0
										lowerCAmelCase_	: Tuple      			=			hparams.alpha_mlm
										lowerCAmelCase_	: Any      			=			hparams.alpha_ce
										lowerCAmelCase_	: Any      			=			hparams.alpha_hid
										gc.collect()
										torch.cuda.empty_cache()




						def   lowercase_	(			self				,  __lowercase				,  __lowercase				,  __lowercase							)   ->      List[Any]:
										lowerCAmelCase_	: int      			=			mask[:, :, None].expand_as(__lowercase							)
										lowerCAmelCase_	: List[Any]      			=			s_logits.size(-1							)
										lowerCAmelCase_	: int      			=			torch.masked_select(__lowercase				,  __lowercase							)  # (bs * seq_length * voc_size) modulo the 1s in mask
										lowerCAmelCase_	: Dict      			=			torch.masked_select(__lowercase				,  __lowercase							)  # (bs * seq_length * voc_size) modulo the 1s in mask
										lowerCAmelCase_	: Optional[Any]      			=			s_logits_slct.view(-1				,  __lowercase							)  # (bs * seq_length, voc_size) modulo the 1s in mask
										lowerCAmelCase_	: Dict      			=			t_logits_slct.view(-1				,  __lowercase							)  # (bs * seq_length, voc_size) modulo the 1s in mask
										assert t_logits_slct.size() == s_logits_slct.size()
										lowerCAmelCase_	: Any      			=			(
										    self.ce_loss_fct(
										        nn.functional.log_softmax(s_logits_slct / self.temperature				,  dim=-1							)				,  nn.functional.softmax(t_logits_slct / self.temperature				,  dim=-1							)				,  )
										    * (self.temperature) ** 2
										)
										return loss_ce




						@staticmethod
						def   lowercase_	(			__lowercase				,  __lowercase							)   ->      Any:
										SummarizationModule.add_model_specific_args(__lowercase				,  __lowercase							)
										add_distill_args(__lowercase							)
										return parser




						def   lowercase_	(			self				,  __lowercase							)   ->      tuple:
										lowerCAmelCase_	: List[Any]      			=			self.tokenizer.pad_token_id
										lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_	: int      			=			batch['''input_ids'''], batch['''attention_mask'''], batch['''labels''']
										if isinstance(self.model				,  __lowercase							):
														lowerCAmelCase_	: Optional[int]      			=			self.model._shift_right(__lowercase							)
										else:
														lowerCAmelCase_	: Optional[int]      			=			shift_tokens_right(__lowercase				,  __lowercase							)

										# noinspection PyCallingNonCallable
										lowerCAmelCase_	: Any      			=			self(
										    __lowercase				,  attention_mask=__lowercase				,  decoder_input_ids=__lowercase				,  output_hidden_states=self.do_calc_hidden_loss				,  output_attentions=__lowercase				,  use_cache=__lowercase				,  )
										lowerCAmelCase_	: Any      			=			student_outputs['''logits''']

										# Same cross entropy vs. label smoothing logic as finetune.py
										assert lm_logits.shape[-1] == self.model.config.vocab_size
										if self.hparams.label_smoothing == 0:
														# Same behavior as modeling_bart.py, besides ignoring pad_token_id
														lowerCAmelCase_	: str      			=			nn.CrossEntropyLoss(ignore_index=__lowercase							)
														lowerCAmelCase_	: Dict      			=			loss_fct(lm_logits.view(-1				,  lm_logits.shape[-1]							)				,  labels.view(-1							)							)
										else:
														lowerCAmelCase_	: List[str]      			=			nn.functional.log_softmax(__lowercase				,  dim=-1							)
														lowerCAmelCase_			,					lowerCAmelCase_	: List[str]      			=			label_smoothed_nll_loss(
														    __lowercase				,  __lowercase				,  self.hparams.label_smoothing				,  ignore_index=__lowercase							)

										def zero_tensor():
														return torch.tensor(0.0							).type_as(__lowercase							)

										lowerCAmelCase_	: Union[str, Any]      			=			student_outputs[
										    '''encoder_last_hidden_state'''
										]  # use this unless self.different_base_models
										lowerCAmelCase_			,					lowerCAmelCase_	: List[str]      			=			zero_tensor(), zero_tensor()
										if self.different_encoder:  # compute encoder hidden state loss
														lowerCAmelCase_	: Union[str, Any]      			=			self.teacher.get_encoder()(
														    __lowercase				,  attention_mask=__lowercase				,  output_hidden_states=self.do_calc_hidden_loss				,  )
														if self.different_base_models:
																		lowerCAmelCase_	: int      			=			all_teacher_encoder_outputs['''last_hidden_state''']
														elif self.do_calc_hidden_loss:
																		lowerCAmelCase_	: List[str]      			=			self.calc_hidden_loss(
																		    __lowercase				,  student_outputs['''encoder_hidden_states''']				,  all_teacher_encoder_outputs['''hidden_states''']				,  self.e_matches				,  normalize_hidden=self.hparams.normalize_hidden				,  )

										lowerCAmelCase_	: List[str]      			=			self.teacher(
										    __lowercase				,  attention_mask=__lowercase				,  encoder_outputs=(teacher_enc_outputs,)				,  decoder_input_ids=__lowercase				,  output_hidden_states=self.do_calc_hidden_loss				,  use_cache=__lowercase				,  )
										lowerCAmelCase_	: str      			=			decoder_input_ids.ne(__lowercase							)
										lowerCAmelCase_	: Dict      			=			self.calc_ce_loss(__lowercase				,  __lowercase				,  teacher_outputs['''logits''']							)
										if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
														lowerCAmelCase_	: Dict      			=			self.calc_hidden_loss(
														    __lowercase				,  student_outputs['''decoder_hidden_states''']				,  teacher_outputs['''decoder_hidden_states''']				,  self.d_matches				,  normalize_hidden=self.hparams.normalize_hidden				,  )

										lowerCAmelCase_	: str      			=			(
										    self.alpha_ce * loss_ce
										    + self.alpha_mlm * student_lm_loss
										    + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
										)
										return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec




						@staticmethod
						def   lowercase_	(			__lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)   ->      Dict:
										lowerCAmelCase_	: str      			=			'''expected list or tuple for hidden_states, got tensor of shape: '''
										assert not isinstance(__lowercase				,  torch.Tensor							), f"""{msg}{hidden_states.shape}"""
										assert not isinstance(__lowercase				,  torch.Tensor							), f"""{msg}{hidden_states_T.shape}"""
										lowerCAmelCase_	: Tuple      			=			attention_mask.to(hidden_states[0]							)
										lowerCAmelCase_	: str      			=			mask.sum() * hidden_states[0].size(-1							)
										lowerCAmelCase_	: int      			=			torch.stack([hidden_states[i] for i in range(len(__lowercase							)							)]							)
										lowerCAmelCase_	: List[Any]      			=			torch.stack([hidden_states_T[j] for j in matches]							)
										assert student_states.shape == teacher_states.shape, f"""{student_states.shape} != {teacher_states.shape}"""
										if normalize_hidden:
														lowerCAmelCase_	: List[Any]      			=			nn.functional.layer_norm(__lowercase				,  student_states.shape[1:]							)
														lowerCAmelCase_	: int      			=			nn.functional.layer_norm(__lowercase				,  teacher_states.shape[1:]							)
										lowerCAmelCase_	: Dict      			=			nn.functional.mse_loss(__lowercase				,  __lowercase				,  reduction='''none'''							)
										lowerCAmelCase_	: Union[str, Any]      			=			(mse * mask.unsqueeze(0							).unsqueeze(-1							)).sum() / valid_count
										return masked_mse






def      lowerCAmelCase	(						lowerCAmelCase_	)->	Dict:
				# NOTE: if --student argument was specified and the teacher and student base models
				# are different, the models still have to have the same tokenizer, specified by
				# --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not
				# from bart to t5. This s because if the tokenizers are different, the output space
				# for the two models is also different and their logits are not comparable.
				parser.add_argument('''--teacher'''	,				type=lowerCAmelCase_	)
				parser.add_argument('''--alpha_ce'''	,				default=0.8	,				type=lowerCAmelCase_	)
				parser.add_argument('''--alpha_mlm'''	,				default=0.2	,				type=lowerCAmelCase_	)
				parser.add_argument('''--alpha_hid'''	,				default=0.0	,				type=lowerCAmelCase_	,				required=lowerCAmelCase_	)
				parser.add_argument('''--student'''	,				type=lowerCAmelCase_	,				required=lowerCAmelCase_	)
				parser.add_argument('''--student_decoder_layers'''	,				default=12	,				type=lowerCAmelCase_	,				required=lowerCAmelCase_	)
				parser.add_argument('''--student_encoder_layers'''	,				default=12	,				type=lowerCAmelCase_	,				required=lowerCAmelCase_	)
				parser.add_argument('''--no_teacher'''	,				action='''store_true'''	,				default=lowerCAmelCase_	)
				parser.add_argument('''--length_penalty'''	,				type=lowerCAmelCase_	,				default=-1	)
				parser.add_argument('''--supervise_forward'''	,				action='''store_true'''	,				default=lowerCAmelCase_	)
				parser.add_argument('''--normalize_hidden'''	,				action='''store_true'''	,				default=lowerCAmelCase_	)







class 			snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__ :					Any   =							"""translation"""
						SCREAMING_SNAKE_CASE__ :					Union[str, Any]   =							["""bleu"""]
						SCREAMING_SNAKE_CASE__ :					str   =							"""bleu"""




						def __init__(			self				,  __lowercase				,  **__lowercase							)   ->      Dict:
										super().__init__(__lowercase				,  **__lowercase							)
										assert hparams.src_lang is not None
										assert hparams.tgt_lang is not None
										lowerCAmelCase_	: Union[str, Any]      			=			hparams.src_lang
										lowerCAmelCase_	: List[Any]      			=			hparams.tgt_lang
										if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer				,  __lowercase							):
														lowerCAmelCase_	: Any      			=			self.tokenizer.lang_code_to_id[hparams.tgt_lang]




						def   lowercase_	(			self				,  __lowercase				,  __lowercase							)   ->      dict:
										return calculate_bleu(__lowercase				,  __lowercase							)




						@staticmethod
						def   lowercase_	(			__lowercase				,  __lowercase							)   ->      List[Any]:
										TranslationModule.add_model_specific_args(__lowercase				,  __lowercase							)
										add_distill_args(__lowercase							)
										return parser






def      lowerCAmelCase	(						lowerCAmelCase_	)->	Any:
				if args.no_teacher:
								lowerCAmelCase_	: Optional[int]      			=			TranslationModule if '''translation''' in args.task else SummarizationModule
				else:  # DISTILL WITH TEACHER
								lowerCAmelCase_	: List[str]      			=			TranslationDistiller if '''translation''' in args.task else SummarizationDistiller
				lowerCAmelCase_	: str      			=			module_cls.__name__
				print(f"""using module {args.setup_cls}"""	)
				lowerCAmelCase_	: Dict      			=			module_cls(lowerCAmelCase_	)
				return model



def      lowerCAmelCase	(						lowerCAmelCase_	)->	List[str]:
				Path(args.output_dir	).mkdir(exist_ok=lowerCAmelCase_	)
				check_output_dir(lowerCAmelCase_	,				expected_items=3	)

				lowerCAmelCase_	: Tuple      			=			create_module(lowerCAmelCase_	)
				return ft_main(lowerCAmelCase_	,				model=lowerCAmelCase_	)


if __name__ == "__main__":
						_UpperCAmelCase     :					Optional[int]       =argparse.ArgumentParser()
						_UpperCAmelCase     :					Tuple       =pl.Trainer.add_argparse_args(parser)
						_UpperCAmelCase     :					List[Any]       =SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
						_UpperCAmelCase     :					List[Any]       =parser.parse_args()

						distill_main(args)



import argparse
import collections
import importlib.util
import os
import re


# 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
_UpperCAmelCase     :					List[Any]       ="""src/diffusers"""
_UpperCAmelCase     :					Dict       ="""docs/source/en"""
_UpperCAmelCase     :					List[str]       ="""."""



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	List[str]:
				with open(lowerCAmelCase_	,				'''r'''	,				encoding='''utf-8'''	,				newline='''\n'''	) as f:
								lowerCAmelCase_	: Any      			=			f.readlines()
				# Find the start prompt.
				lowerCAmelCase_	: Optional[Any]      			=			0
				while not lines[start_index].startswith(lowerCAmelCase_	):
								start_index += 1
				start_index += 1

				lowerCAmelCase_	: List[Any]      			=			start_index
				while not lines[end_index].startswith(lowerCAmelCase_	):
								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 |
_UpperCAmelCase     :					int       ="""Model|Encoder|Decoder|ForConditionalGeneration"""
# Regexes that match TF/Flax/PT model names.
_UpperCAmelCase     :					Any       =re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_UpperCAmelCase     :					Optional[int]       =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.
_UpperCAmelCase     :					Union[str, Any]       =re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# This is to make sure the diffusers module imported is the one in the repo.
_UpperCAmelCase     :					Dict       =importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(TRANSFORMERS_PATH, """__init__.py"""),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
_UpperCAmelCase     :					Tuple       =spec.loader.load_module()



def      lowerCAmelCase	(						lowerCAmelCase_	)->	Dict:
				lowerCAmelCase_	: Any      			=			re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''	,				lowerCAmelCase_	)
				return [m.group(0	) for m in matches]



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	)->	Any:
				lowerCAmelCase_	: Any      			=			2 if text == '''✅''' or text == '''❌''' else len(lowerCAmelCase_	)
				lowerCAmelCase_	: List[str]      			=			(width - text_length) // 2
				lowerCAmelCase_	: str      			=			width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent



def      lowerCAmelCase	(						)->	Dict:
				lowerCAmelCase_	: Optional[int]      			=			diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				lowerCAmelCase_	: Tuple      			=			{
				    name: config_mapping_names[code]
				    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_mapping_names
				}
				lowerCAmelCase_	: str      			=			{name: config.replace('''ConfigMixin'''	,				''''''	) 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_	: Union[str, Any]      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Dict      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Tuple      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Optional[int]      			=			collections.defaultdict(lowerCAmelCase_	)
				lowerCAmelCase_	: Any      			=			collections.defaultdict(lowerCAmelCase_	)

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

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

    # Let's build that table!
				lowerCAmelCase_	: Union[str, Any]      			=			list(model_name_to_config.keys()	)
				model_names.sort(key=str.lower	)
				lowerCAmelCase_	: Tuple      			=			['''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_	: Optional[Any]      			=			[len(lowerCAmelCase_	) + 2 for c in columns]
				lowerCAmelCase_	: List[Any]      			=			max([len(lowerCAmelCase_	) for name in model_names]	) + 2

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

				lowerCAmelCase_	: Tuple      			=			{True: '''✅''', False: '''❌'''}
				for name in model_names:
								lowerCAmelCase_	: Optional[int]      			=			model_name_to_prefix[name]
								lowerCAmelCase_	: int      			=			[
								    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(lowerCAmelCase_	,				lowerCAmelCase_	) for l, w in zip(lowerCAmelCase_	,				lowerCAmelCase_	)]	) + "|\n"
				return table



def      lowerCAmelCase	(						lowerCAmelCase_=False	)->	str:
				lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_			,					lowerCAmelCase_	: Optional[int]      			=			_find_text_in_file(
				    filename=os.path.join(lowerCAmelCase_	,				'''index.mdx'''	)	,				start_prompt='''<!--This table is updated automatically from the auto modules'''	,				end_prompt='''<!-- End table-->'''	,				)
				lowerCAmelCase_	: int      			=			get_model_table_from_auto_modules()

				if current_table != new_table:
								if overwrite:
												with open(os.path.join(lowerCAmelCase_	,				'''index.mdx'''	)	,				'''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.mdx` has not been updated. Run `make fix-copies` to fix this.'''	)


if __name__ == "__main__":
						_UpperCAmelCase     :					Optional[Any]       =argparse.ArgumentParser()
						parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
						_UpperCAmelCase     :					int       =parser.parse_args()

						check_model_table(args.fix_and_overwrite)