


"""simple docstring"""







import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__lowercase          = 16
__lowercase          = 32


def 	lowercase     (							A_			)-> str:
  '''simple docstring'''







  return int(x / 2**20			)






class 		_A    :







    """simple docstring"""




    def __enter__(   self							:       List[str]):
      gc.collect()
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
      a		:						Dict 				=     torch.cuda.memory_allocated()
      a		:						List[Any] 				=     psutil.Process()

      a		:						Union[str, Any] 				=     self.cpu_mem_used()
      a		:						Optional[int] 				=     True
      a		:						List[str] 				=     threading.Thread(target=self.peak_monitor_func)
      a		:						Tuple 				=     True
      peak_monitor_thread.start()
      return self






    def       __snake_case				(   self							:       Tuple):
      return self.process.memory_info().rss






    def       __snake_case				(   self							:       Optional[int]):
      a		:						Optional[int] 				=     -1

      while True:
        a		:						Tuple 				=     max(self.cpu_mem_used()							,					self.cpu_peak)

        # can't sleep or will not catch the peak right (this comment is here on purpose)
        # time.sleep(0.001) # 1msec

        if not self.peak_monitoring:
          break






    def __exit__(   self							:       Optional[Any]							,					*__UpperCAmelCase							:       Optional[int]):
      a		:						Dict 				=     False

      gc.collect()
      torch.cuda.empty_cache()
      a		:						Dict 				=     torch.cuda.memory_allocated()
      a		:						Union[str, Any] 				=     torch.cuda.max_memory_allocated()
      a		:						List[str] 				=     bamb(self.end - self.begin)
      a		:						Optional[Any] 				=     bamb(self.peak - self.begin)

      a		:						Any 				=     self.cpu_mem_used()
      a		:						List[Any] 				=     bamb(self.cpu_end - self.cpu_begin)
      a		:						str 				=     bamb(self.cpu_peak - self.cpu_begin)
      # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
 from accelerate.test_utils.training import mocked_dataloaders

 __lowercase          = mocked_dataloaders  # noqa: F811


def 	lowercase     (							A_    ,  A_			)-> str:
  '''simple docstring'''







  if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,  lowerCAmelCase__			) == "1":
    a		:						Tuple 				=     2

  # New Code #
  # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
  a		:						List[str] 				=     FullyShardedDataParallelPlugin(
      state_dict_config=FullStateDictConfig(offload_to_cpu=lowerCAmelCase__    ,  ranka_only=lowerCAmelCase__			)    ,  optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowerCAmelCase__    ,  ranka_only=lowerCAmelCase__			)    ,  )

  # Initialize accelerator
  if args.with_tracking:
    a		:						List[str] 				=     Accelerator(
        cpu=args.cpu    ,  mixed_precision=args.mixed_precision    ,  log_with="wandb"    ,  project_dir=args.logging_dir    ,  fsdp_plugin=lowerCAmelCase__    ,  )
  else:
    a		:						Any 				=     Accelerator(fsdp_plugin=lowerCAmelCase__			)
  accelerator.print(accelerator.distributed_type			)

  if hasattr(args.checkpointing_steps    ,  "isdigit"			):
    if args.checkpointing_steps == "epoch":
      a		:						Dict 				=     args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      a		:						Any 				=     int(args.checkpointing_steps			)
    else:
      raise ValueError(
          F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''			)
  else:
    a		:						Optional[Any] 				=     None
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  a		:						int 				=     config['lr']
  a		:						List[Any] 				=     int(config["num_epochs"]			)
  a		:						List[str] 				=     int(config["seed"]			)
  a		:						Optional[Any] 				=     int(config["batch_size"]			)

  # We need to initialize the trackers we use, and also store our configuration
  if args.with_tracking:
    a		:						Tuple 				=     vars(lowerCAmelCase__			)
    accelerator.init_trackers("fsdp_glue_no_trainer"    ,  lowerCAmelCase__			)

  a		:						int 				=     AutoTokenizer.from_pretrained(args.model_name_or_path			)
  a		:						Any 				=     load_dataset("glue"    ,  "mrpc"			)
  a		:						List[Any] 				=     evaluate.load("glue"    ,  "mrpc"			)

  def tokenize_function(A_			):
    # max_length=None => use the model max length (it's actually the default)
    a		:						Any 				=     tokenizer(examples["sentence1"]    ,  examples["sentence2"]    ,  truncation=lowerCAmelCase__    ,  max_length=lowerCAmelCase__			)
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  # starting with the main process first:
  with accelerator.main_process_first():
    a		:						Union[str, Any] 				=     datasets.map(
        lowerCAmelCase__    ,  batched=lowerCAmelCase__    ,  remove_columns=["idx", "sentence1", "sentence2"]    ,  )

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  a		:						Dict 				=     tokenized_datasets.rename_column("label"    ,  "labels"			)

  # If the batch size is too big we use gradient accumulation
  a		:						Tuple 				=     1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    a		:						Optional[Any] 				=     batch_size // MAX_GPU_BATCH_SIZE
    a		:						Optional[int] 				=     MAX_GPU_BATCH_SIZE

  def collate_fn(A_			):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    a		:						str 				=     128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      a		:						Any 				=     16
    elif accelerator.mixed_precision != "no":
      a		:						Optional[Any] 				=     8
    else:
      a		:						Optional[int] 				=     None

    return tokenizer.pad(
        lowerCAmelCase__    ,  padding="longest"    ,  max_length=lowerCAmelCase__    ,  pad_to_multiple_of=lowerCAmelCase__    ,  return_tensors="pt"    ,  )

  # Instantiate dataloaders.
  a		:						str 				=     DataLoader(
      tokenized_datasets["train"]    ,  shuffle=lowerCAmelCase__    ,  collate_fn=lowerCAmelCase__    ,  batch_size=lowerCAmelCase__			)
  a		:						int 				=     DataLoader(
      tokenized_datasets["validation"]    ,  shuffle=lowerCAmelCase__    ,  collate_fn=lowerCAmelCase__    ,  batch_size=lowerCAmelCase__			)

  set_seed(lowerCAmelCase__			)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  a		:						Optional[int] 				=     AutoModelForSequenceClassification.from_pretrained(
      args.model_name_or_path    ,  return_dict=lowerCAmelCase__    ,  low_cpu_mem_usage=lowerCAmelCase__			)

  # New Code #
  # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
  a		:						Optional[Any] 				=     accelerator.prepare(lowerCAmelCase__			)
  accelerator.print(lowerCAmelCase__			)

  # Instantiate optimizer
  # New Code #
  # For FSDP feature, at present it doesn't support multiple parameter groups,
  # so we need to create a single parameter group for the whole model
  a		:						List[str] 				=     torch.optim.AdamW(params=model.parameters()    ,  lr=lowerCAmelCase__    ,  weight_decay=2e-4			)

  # Instantiate scheduler
  a		:						Tuple 				=     get_linear_schedule_with_warmup(
      optimizer=lowerCAmelCase__    ,  num_warmup_steps=10    ,  num_training_steps=(len(lowerCAmelCase__			) * num_epochs) // gradient_accumulation_steps    ,  )

  # New Code #
  # For FSDP feature, prepare everything except the model as we have already prepared the model
  # before creating the optimizer
  # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  # prepare method.
  a		:						Optional[int] 				=     accelerator.prepare(
      lowerCAmelCase__    ,  lowerCAmelCase__    ,  lowerCAmelCase__    ,  lowerCAmelCase__			)

  a		:						int 				=     0

  # Potentially load in the weights and states from a previous save
  if args.resume_from_checkpoint:
    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''			)
      accelerator.load_state(args.resume_from_checkpoint			)
      a		:						List[str] 				=     os.path.basename(args.resume_from_checkpoint			)
    else:
      # Get the most recent checkpoint
      a		:						int 				=     [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
      dirs.sort(key=os.path.getctime			)
      a		:						Dict 				=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    a		:						int 				=     os.path.splitext(lowerCAmelCase__			)[0]

    if "epoch" in training_difference:
      num_epochs -= int(training_difference.replace("epoch_"    ,  ""			)			)
      a		:						List[str] 				=     None
    else:
      a		:						Dict 				=     int(training_difference.replace("step_"    ,  ""			)			)
      num_epochs -= resume_step // len(lowerCAmelCase__			)
      # If resuming by step, we also need to know exactly how far into the DataLoader we went
      a		:						Dict 				=     (num_epochs * len(lowerCAmelCase__			)) - resume_step

    # Now we train the model
  for epoch in range(lowerCAmelCase__			):
    # New Code #
    # context manager to track the peak memory usage during the training epoch
    with TorchTracemalloc() as tracemalloc:
      model.train()
      if args.with_tracking:
        a		:						List[Any] 				=     0
      for step, batch in enumerate(lowerCAmelCase__			):
        # We need to skip steps until we reach the resumed step
        if args.resume_from_checkpoint and epoch == 0:
          if resume_step is not None and step < resume_step:
            pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device			)
        a		:						Union[str, Any] 				=     model(**lowerCAmelCase__			)
        a		:						Optional[Any] 				=     outputs.loss
        a		:						str 				=     loss / gradient_accumulation_steps
        # We keep track of the loss at each epoch
        if args.with_tracking:
          total_loss += loss.detach().float()
        accelerator.backward(lowerCAmelCase__			)
        if step % gradient_accumulation_steps == 0:
          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()
          # accelerator.print(lr_scheduler.get_lr())

        overall_step += 1

        if isinstance(lowerCAmelCase__    ,  lowerCAmelCase__			):
          a		:						Optional[int] 				=     F'''step_{overall_step}'''
          if overall_step % checkpointing_steps == 0:
            if args.output_dir is not None:
              a		:						Optional[Any] 				=     os.path.join(args.output_dir    ,  lowerCAmelCase__			)
            accelerator.save_state(lowerCAmelCase__			)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
    accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin			)			)			)
    accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used			)			)
    accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked			)			)
    accelerator.print(
        "Total Peak Memory consumed during the train (max): {}".format(
            tracemalloc.peaked + bamb(tracemalloc.begin			)			)			)
    # Logging the peak memory usage of the GPU to the tracker
    if args.with_tracking:
      accelerator.log(
          {
              "train_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin			),
          }    ,  step=lowerCAmelCase__    ,  )

    # New Code #
    # context manager to track the peak memory usage during the evaluation
    with TorchTracemalloc() as tracemalloc:
      model.eval()
      for step, batch in enumerate(lowerCAmelCase__			):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device			)
        with torch.no_grad():
          a		:						int 				=     model(**lowerCAmelCase__			)
        a		:						Optional[Any] 				=     outputs.logits.argmax(dim=-1			)
        a		:						Dict 				=     accelerator.gather_for_metrics((predictions, batch["labels"])			)
        metric.add_batch(
            predictions=lowerCAmelCase__    ,  references=lowerCAmelCase__    ,  )

      a		:						Optional[Any] 				=     metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''    ,  lowerCAmelCase__			)
      if args.with_tracking:
        accelerator.log(
            {
                "accuracy": eval_metric["accuracy"],
                "f1": eval_metric["f1"],
                "train_loss": total_loss.item() / len(lowerCAmelCase__			),
            }    ,  step=lowerCAmelCase__    ,  )

      if checkpointing_steps == "epoch":
        a		:						List[str] 				=     F'''epoch_{epoch}'''
        if args.output_dir is not None:
          a		:						str 				=     os.path.join(args.output_dir    ,  lowerCAmelCase__			)
        accelerator.save_state(lowerCAmelCase__			)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
    accelerator.print("Memory before entering the eval : {}".format(bamb(tracemalloc.begin			)			)			)
    accelerator.print("Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used			)			)
    accelerator.print("Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked			)			)
    accelerator.print(
        "Total Peak Memory consumed during the eval (max): {}".format(tracemalloc.peaked + bamb(tracemalloc.begin			)			)			)
    # Logging the peak memory usage of the GPU to the tracker
    if args.with_tracking:
      accelerator.log(
          {
              "eval_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin			),
          }    ,  step=lowerCAmelCase__    ,  )

  if args.with_tracking:
    accelerator.end_training()


def 	lowercase     (							)-> Union[str, Any]:
  '''simple docstring'''







  a		:						Union[str, Any] 				=     argparse.ArgumentParser(description="Simple example of training script."			)
  parser.add_argument(
      "--mixed_precision"    ,  type=lowerCAmelCase__    ,  default=lowerCAmelCase__    ,  choices=["no", "fp16", "bf16", "fp8"]    ,  help="Whether to use mixed precision. Choose"
      "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
      "and an Nvidia Ampere GPU."    ,  )
  parser.add_argument("--cpu"    ,  action="store_true"    ,  help="If passed, will train on the CPU."			)
  parser.add_argument(
      "--checkpointing_steps"    ,  type=lowerCAmelCase__    ,  default=lowerCAmelCase__    ,  help="Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch."    ,  )
  parser.add_argument(
      "--resume_from_checkpoint"    ,  type=lowerCAmelCase__    ,  default=lowerCAmelCase__    ,  help="If the training should continue from a checkpoint folder."    ,  )
  parser.add_argument(
      "--with_tracking"    ,  action="store_true"    ,  help="Whether to load in all available experiment trackers from the environment and use them for logging."    ,  )
  parser.add_argument(
      "--output_dir"    ,  type=lowerCAmelCase__    ,  default="."    ,  help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."    ,  )
  parser.add_argument(
      "--logging_dir"    ,  type=lowerCAmelCase__    ,  default="logs"    ,  help="Location on where to store experiment tracking logs`"    ,  )
  parser.add_argument(
      "--model_name_or_path"    ,  type=lowerCAmelCase__    ,  help="Path to pretrained model or model identifier from huggingface.co/models."    ,  required=lowerCAmelCase__    ,  )
  a		:						Optional[Any] 				=     parser.parse_args()
  a		:						int 				=     {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(lowerCAmelCase__    ,  lowerCAmelCase__			)


if __name__ == "__main__":
 main()














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
A										=      'src/diffusers'
A										=      'docs/source/en'
A										=      '.'



def        _lowerCamelCase(      lowerCAmelCase__					:		List[Any]     ,     lowerCAmelCase__					:		int     ,     lowerCAmelCase__					:		Tuple   ):





				'''simple docstring'''







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

				SCREAMING_SNAKE_CASE_ :						List[str]    				= 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 |
A										=      'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
A										=      re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
A										=      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										=      re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


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



def        _lowerCamelCase(      lowerCAmelCase__					:		Any   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Optional[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__					:		Optional[Any]     ,     lowerCAmelCase__					:		Tuple   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Any    				= 2 if text == '✅' or text == '❌' else len(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						int    				= (width - text_length) // 2
				SCREAMING_SNAKE_CASE_ :						str    				= width - text_length - left_indent
				return " " * left_indent + text + " " * right_indent



def        _lowerCamelCase(      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= {
				    name: config_mapping_names[code]
				    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
				    if code in config_mapping_names
				}
				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= {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.
				SCREAMING_SNAKE_CASE_ :						int    				= collections.defaultdict(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						List[Any]    				= collections.defaultdict(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= collections.defaultdict(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						str    				= collections.defaultdict(lowerCAmelCase__   )
				SCREAMING_SNAKE_CASE_ :						Dict    				= collections.defaultdict(lowerCAmelCase__   )

				# Let's lookup through all diffusers object (once).
				for attr_name in dir(lowerCAmelCase__   ):
								SCREAMING_SNAKE_CASE_ :						List[str]    				= None
								if attr_name.endswith('Tokenizer'   ):
												SCREAMING_SNAKE_CASE_ :						List[Any]    				= slow_tokenizers
												SCREAMING_SNAKE_CASE_ :						Dict    				= attr_name[:-9]
								elif attr_name.endswith('TokenizerFast'   ):
												SCREAMING_SNAKE_CASE_ :						Any    				= fast_tokenizers
												SCREAMING_SNAKE_CASE_ :						int    				= attr_name[:-13]
								elif _re_tf_models.match(lowerCAmelCase__   ) is not None:
												SCREAMING_SNAKE_CASE_ :						List[Any]    				= tf_models
												SCREAMING_SNAKE_CASE_ :						Optional[int]    				= _re_tf_models.match(lowerCAmelCase__   ).groups()[0]
								elif _re_flax_models.match(lowerCAmelCase__   ) is not None:
												SCREAMING_SNAKE_CASE_ :						List[str]    				= flax_models
												SCREAMING_SNAKE_CASE_ :						int    				= _re_flax_models.match(lowerCAmelCase__   ).groups()[0]
								elif _re_pt_models.match(lowerCAmelCase__   ) is not None:
												SCREAMING_SNAKE_CASE_ :						Optional[int]    				= pt_models
												SCREAMING_SNAKE_CASE_ :						Any    				= _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():
																				SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= True
																				break
																# Try again after removing the last word in the name
																SCREAMING_SNAKE_CASE_ :						str    				= ''.join(camel_case_split(lowerCAmelCase__   )[:-1]   )

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

				# Build the table per se
				SCREAMING_SNAKE_CASE_ :						Dict    				= '|' + '|'.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"

				SCREAMING_SNAKE_CASE_ :						int    				= {True: '✅', False: '❌'}
				for name in model_names:
								SCREAMING_SNAKE_CASE_ :						int    				= model_name_to_prefix[name]
								SCREAMING_SNAKE_CASE_ :						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__					:		Optional[int]=False   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						str    				= _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-->'     ,     )
				SCREAMING_SNAKE_CASE_ :						Tuple    				= 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__":
				A										=      argparse.ArgumentParser()
				parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
				A										=      parser.parse_args()

				check_model_table(args.fix_and_overwrite)