





"""simple docstring"""




import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# 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)
#
# 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
#
########################################################################


UpperCAmelCase						:       Optional[Any]        =					16
UpperCAmelCase						:       Dict        =					32







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				Any:
					# Initialize accelerator
					if args.with_tracking:
										__A     :						Dict          =    Accelerator(
										    cpu=args.cpu ,						mixed_precision=args.mixed_precision ,						log_with='all' ,						project_dir=args.project_dir  )
					else:
										__A     :						Optional[int]          =    Accelerator(cpu=args.cpu ,						mixed_precision=args.mixed_precision  )

					if hasattr(args.checkpointing_steps ,						'isdigit'  ):
										if args.checkpointing_steps == "epoch":
															__A     :						List[Any]          =    args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															__A     :						str          =    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[int]          =    None
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__A     :						str          =    config['lr']
					__A     :						List[Any]          =    int(config['num_epochs']  )
					__A     :						Tuple          =    int(config['seed']  )
					__A     :						str          =    int(config['batch_size']  )

					# We need to initialize the trackers we use, and also store our configuration
					if args.with_tracking:
										__A     :						Dict          =    os.path.split(a  )[-1].split('.'  )[0]
										accelerator.init_trackers(a ,						a  )

					__A     :						str          =    AutoTokenizer.from_pretrained('bert-base-cased'  )
					__A     :						Dict          =    load_dataset('glue' ,						'mrpc'  )
					__A     :						Tuple          =    evaluate.load('glue' ,						'mrpc'  )

					def tokenize_function(a  ):
										# max_length=None => use the model max length (it's actually the default)
										__A     :						Optional[Any]          =    tokenizer(examples['sentence1'] ,						examples['sentence2'] ,						truncation=a ,						max_length=a  )
										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(
										    a ,						batched=a ,						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     :						List[Any]          =    tokenized_datasets.rename_column('label' ,						'labels'  )

					# If the batch size is too big we use gradient accumulation
					__A     :						int          =    1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										__A     :						Union[str, Any]          =    batch_size // MAX_GPU_BATCH_SIZE
										__A     :						Any          =    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     :						Optional[Any]          =    1_28 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     :						int          =    16
										elif accelerator.mixed_precision != "no":
															__A     :						int          =    8
										else:
															__A     :						Union[str, Any]          =    None

										return tokenizer.pad(
										    a ,						padding='longest' ,						max_length=a ,						pad_to_multiple_of=a ,						return_tensors='pt' ,						)

					# Instantiate dataloaders.
					__A     :						Optional[int]          =    DataLoader(
					    tokenized_datasets['train'] ,						shuffle=a ,						collate_fn=a ,						batch_size=a  )
					__A     :						Dict          =    DataLoader(
					    tokenized_datasets['validation'] ,						shuffle=a ,						collate_fn=a ,						batch_size=a  )

					set_seed(a  )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					__A     :						Optional[int]          =    AutoModelForSequenceClassification.from_pretrained('bert-base-cased' ,						return_dict=a  )

					# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
					# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
					# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
					__A     :						Tuple          =    model.to(accelerator.device  )

					# Instantiate optimizer
					__A     :						Optional[int]          =    AdamW(params=model.parameters() ,						lr=a  )

					# Instantiate scheduler
					__A     :						Optional[int]          =    get_linear_schedule_with_warmup(
					    optimizer=a ,						num_warmup_steps=1_00 ,						num_training_steps=(len(a  ) * num_epochs) // gradient_accumulation_steps ,						)

					# Prepare everything
					# 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     :						Dict          =    accelerator.prepare(
					    a ,						a ,						a ,						a ,						a  )

					# We need to keep track of how many total steps we have iterated over
					__A     :						Union[str, Any]          =    0
					# We also need to keep track of the stating epoch so files are named properly
					__A     :						List[Any]          =    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     :						Union[str, Any]          =    os.path.basename(args.resume_from_checkpoint  )
										else:
															# Get the most recent checkpoint
															__A     :						Any          =    [f.name for f in os.scandir(os.getcwd()  ) if f.is_dir()]
															dirs.sort(key=os.path.getctime  )
															__A     :						Optional[int]          =    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										__A     :						int          =    os.path.splitext(a  )[0]

										if "epoch" in training_difference:
															__A     :						Dict          =    int(training_difference.replace('epoch_' ,						''  )  ) + 1
															__A     :						str          =    None
										else:
															__A     :						Union[str, Any]          =    int(training_difference.replace('step_' ,						''  )  )
															__A     :						Any          =    resume_step // len(a  )
															resume_step -= starting_epoch * len(a  )

    # Now we train the model
					for epoch in range(a ,						a  ):
										model.train()
										if args.with_tracking:
															__A     :						Optional[int]          =    0
										if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
															# We need to skip steps until we reach the resumed step
															__A     :						str          =    accelerator.skip_first_batches(a ,						a  )
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															__A     :						Optional[Any]          =    train_dataloader
										for step, batch in enumerate(a  ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device  )
															__A     :						Optional[int]          =    model(**a  )
															__A     :						int          =    outputs.loss
															__A     :						Tuple          =    loss / gradient_accumulation_steps
															# We keep track of the loss at each epoch
															if args.with_tracking:
																				total_loss += loss.detach().float()
															accelerator.backward(a  )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

															overall_step += 1

															if isinstance(a ,						a  ):
																				__A     :						List[Any]          =    F"""step_{overall_step}"""
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														__A     :						int          =    os.path.join(args.output_dir ,						a  )
																									accelerator.save_state(a  )

										model.eval()
										for step, batch in enumerate(a  ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device  )
															with torch.no_grad():
																				__A     :						Optional[Any]          =    model(**a  )
															__A     :						Optional[Any]          =    outputs.logits.argmax(dim=-1  )
															__A     :						Any          =    accelerator.gather_for_metrics((predictions, batch['labels'])  )
															metric.add_batch(
															    predictions=a ,						references=a ,						)

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

										if checkpointing_steps == "epoch":
															__A     :						int          =    F"""epoch_{epoch}"""
															if args.output_dir is not None:
																				__A     :						Tuple          =    os.path.join(args.output_dir ,						a  )
															accelerator.save_state(a  )

					if args.with_tracking:
										accelerator.end_training()







def 			_SCREAMING_SNAKE_CASE    (       )				->				str:
					__A     :						str          =    argparse.ArgumentParser(description='Simple example of training script.'  )
					parser.add_argument(
					    '--mixed_precision' ,						type=a ,						default=a ,						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=a ,						default=a ,						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=a ,						default=a ,						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=a ,						default='.' ,						help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.' ,						)
					parser.add_argument(
					    '--project_dir' ,						type=a ,						default='logs' ,						help='Location on where to store experiment tracking logs` and relevent project information' ,						)
					__A     :						Union[str, Any]          =    parser.parse_args()
					__A     :						Tuple          =    {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
					training_function(a ,						a  )


if __name__ == "__main__":
				main()











import logging
from pathlib import Path
from typing import Any, List, Tuple

import yaml


# loading package files: https://stackoverflow.com/a/20885799
try:
				import importlib.resources as pkg_resources
except ImportError:
				# Try backported to PY<37 `importlib_resources`.
				import importlib_resources as pkg_resources

from . import resources


UpperCAmelCase						:       Optional[Any]        =					'''https://github.com/huggingface/datasets/tree/main/src/datasets/utils'''
UpperCAmelCase						:       Any        =					F"""{BASE_REF_URL}/{__file__}"""
UpperCAmelCase						:       int        =					logging.getLogger(__name__)







def 			_SCREAMING_SNAKE_CASE    (       a  )				->				Tuple[Any, str]:
					__A     :						List[Any]          =    pkg_resources.read_text(a ,						a  )
					return yaml.safe_load(a  ), F"""{BASE_REF_URL}/resources/{resource}"""


UpperCAmelCase  ,			UpperCAmelCase						:       Tuple        =					load_yaml_resource('''readme_structure.yaml''')

UpperCAmelCase						:       List[Any]        =					[
    '''[Needs More Information]''',
    '''[More Information Needed]''',
    '''(https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)''',
]

# Dictionary representation of section/readme, error_list, warning_list
UpperCAmelCase						:       Union[str, Any]        =					Tuple[dict, List[str], List[str]]





class 						_A:


				"""simple docstring"""


				def __init__(      self					, _A					, _A					, _A = None					, _A = False					):
									__A     :						Dict          =    name
									__A     :						str          =    level
									__A     :						List[str]          =    lines
									__A     :						List[Any]          =    ''
									__A     :						Optional[Any]          =    True
									__A     :						List[Any]          =    {}
									__A     :						Optional[Any]          =    []
									__A     :						Optional[int]          =    []
									if self.lines is not None:
														self.parse(suppress_parsing_errors=_A					)



				def  UpperCAmelCase_		(      self					, _A = False					):
									__A     :						Any          =    ''
									__A     :						Union[str, Any]          =    []
									__A     :						Tuple          =    False
									for line in self.lines:
														if line.strip(' \n'					) == "":
																			continue
														elif line.strip(' \n'					)[:3] == "```":
																			__A     :						int          =    not code_start
														elif line.split()[0] == self.level + "#" and not code_start:
																			if current_sub_level != "":
																								__A     :						Dict          =    Section(_A					, self.level + '#'					, _A					)
																								__A     :						Optional[int]          =    []
																			else:
																								if current_lines != []:
																													self.text += "".join(_A					).strip()
																													if self.text != "" and self.text not in FILLER_TEXT:
																																		__A     :						Union[str, Any]          =    False
																													__A     :						Dict          =    []

																			__A     :						Optional[Any]          =    ' '.join(line.split()[1:]					).strip(' \n'					)
														else:
																			current_lines.append(_A					)
									else:
														if current_sub_level != "":
																			if current_sub_level in self.content:
																								self.parsing_error_list.append(
																								    F"""Multiple sections with the same heading `{current_sub_level}` have been found. Please keep only one of these sections."""					)
																			__A     :						Any          =    Section(_A					, self.level + '#'					, _A					)
														else:
																			if current_lines != []:
																								self.text += "".join(_A					).strip()
																								if self.text != "" and self.text not in FILLER_TEXT:
																													__A     :						str          =    False

									if self.level == "" and not suppress_parsing_errors:
														if self.parsing_error_list != [] or self.parsing_warning_list != []:
																			__A     :						Optional[int]          =    '\n'.join('-\t' + x for x in self.parsing_error_list + self.parsing_warning_list					)
																			__A     :						List[Any]          =    F"""The following issues were found while parsing the README at `{self.name}`:\n""" + errors
																			raise ValueError(_A					)



				def  UpperCAmelCase_		(      self					, _A					):
									__A     :						Any          =    []
									__A     :						Tuple          =    []
									if structure["allow_empty"] is False:
														# If content is expected
														if self.is_empty_text and self.content == {}:
																			# If no content is found, mention it in the error_list
																			error_list.append(F"""Expected some content in section `{self.name}` but it is empty."""					)

									if structure["allow_empty_text"] is False:
														# If some text is expected
														if self.is_empty_text:
																			# If no text is found, mention it in the error_list
																			error_list.append(
																			    F"""Expected some text in section `{self.name}` but it is empty (text in subsections are ignored)."""					)
        # Subsections Validation
									if structure["subsections"] is not None:
														# If subsections are expected
														if self.content == {}:
																			# If no subsections are present
																			__A     :						int          =    [subsection['name'] for subsection in structure['subsections']]
																			# Mention the expected values in the error_list
																			error_list.append(
																			    F"""Section `{self.name}` expected the following subsections: {", ".join(["`"+x+"`" for x in values]					)}. Found 'None'."""					)
														else:
																			# If some subsections are present
																			__A     :						Optional[Any]          =    [subsection['name'] for subsection in structure['subsections']]
																			__A     :						str          =    False
																			for idx, name in enumerate(_A					):
																								if name not in self.content:
																													# If the expected subsection is not present
																													error_list.append(F"""Section `{self.name}` is missing subsection: `{name}`."""					)
																													__A     :						Optional[Any]          =    True
																								else:
																													# If the subsection is present, validate subsection, return the result
																													# and concat the errors from subsection to section error_list

																													# Skip sublevel validation if current level is `###`
																													if self.level == "###":
																																		continue
																													else:
																																		__A   ,    __A   ,    __A     :						Optional[int]          =    self.content[name].validate(
																																		    structure['subsections'][idx]					)
																													error_list += subsec_error_list
																													warning_list += subsec_warning_list

																			if has_missing_subsections:  # we only allow to have extra subsections if all the other ones are here
																								for name in self.content:
																													if name not in structure_names:
																																		# If an extra subsection is present
																																		warning_list.append(
																																		    F"""`{self.name}` has an extra subsection: `{name}`. Skipping further validation checks for this subsection as expected structure is unknown."""					)
									if error_list:
														# If there are errors, do not return the dictionary as it is invalid
														return {}, error_list, warning_list
									else:
														return self.to_dict(), error_list, warning_list





				def  UpperCAmelCase_		(      self					):
									return {
									    "name": self.name,
									    "text": self.text,
									    "is_empty_text": self.is_empty_text,
									    "subsections": [value.to_dict() for value in self.content.values()],
									}





class 						_A( snake_case__       ):  # Level 0


				"""simple docstring"""


				def __init__(      self					, _A					, _A					, _A = None					, _A = False					):
									super().__init__(name=_A					, level=''					)  # Not using lines here as we need to use a child class parse
									__A     :						List[str]          =    structure
									__A     :						str          =    -2
									__A     :						Tuple          =    0
									__A     :						Dict          =    lines
									if self.lines is not None:
														self.parse(suppress_parsing_errors=_A					)



				def  UpperCAmelCase_		(      self					):
									if self.structure is None:
														__A   ,    __A   ,    __A     :						List[Any]          =    self._validate(_A					)
									else:
														__A   ,    __A   ,    __A     :						Any          =    self._validate(self.structure					)
									if error_list != [] or warning_list != []:
														__A     :						Optional[Any]          =    '\n'.join(['-\t' + x for x in error_list + warning_list]					)
														__A     :						Union[str, Any]          =    F"""The following issues were found for the README at `{self.name}`:\n""" + errors
														raise ValueError(_A					)



				@classmethod
				def  UpperCAmelCase_		(      cls					, _A					, _A = None					, _A = False					):
									with open(_A					, encoding='utf-8'					) as f:
														__A     :						str          =    f.readlines()
									return cls(_A					, _A					, _A					, suppress_parsing_errors=_A					)



				@classmethod
				def  UpperCAmelCase_		(      cls					, _A					, _A = None					, _A = "root"					, _A = False					):
									__A     :						Any          =    string.split('\n'					)
									return cls(_A					, _A					, _A					, suppress_parsing_errors=_A					)



				def  UpperCAmelCase_		(      self					, _A = False					):
									# Skip Tags
									__A     :						Any          =    0

									for line in self.lines:
														self.yaml_tags_line_count += 1
														if line.strip(' \n'					) == "---":
																			self.tag_count += 1
																			if self.tag_count == 2:
																								break
														line_count += 1
									if self.tag_count == 2:
														__A     :						List[str]          =    self.lines[line_count + 1 :]  # Get the last + 1 th item.
									else:
														__A     :						List[Any]          =    self.lines[self.tag_count :]
									super().parse(suppress_parsing_errors=_A					)



				def __str__(      self					):
									return str(self.to_dict()					)





				def  UpperCAmelCase_		(      self					, _A					):
									__A     :						List[str]          =    []
									__A     :						List[Any]          =    []
									if self.yaml_tags_line_count == 0:
														warning_list.append('Empty YAML markers are present in the README.'					)
									elif self.tag_count == 0:
														warning_list.append('No YAML markers are present in the README.'					)
									elif self.tag_count == 1:
														warning_list.append('Only the start of YAML tags present in the README.'					)
									# Check how many first level sections are present.
									__A     :						Optional[int]          =    len(self.content.keys()					)
									if num_first_level_keys > 1:
														# If more than one, add to the error list, continue
														error_list.append(
														    F"""The README has several first-level headings: {", ".join(["`"+x+"`" for x in list(self.content.keys()					)]					)}. Only one heading is expected. Skipping further validation for this README."""					)
									elif num_first_level_keys < 1:
														# If less than one, append error.
														error_list.append(
														    'The README has no first-level headings. One heading is expected. Skipping further validation for this README.'					)

									else:
														# If one exactly
														__A     :						List[Any]          =    list(self.content.keys()					)[0]  # Get the key
														if start_key.startswith('Dataset Card for'					):  # Check correct start
																			# If the starting is correct, validate all the sections
																			__A   ,    __A   ,    __A     :						List[str]          =    self.content[start_key].validate(
																			    readme_structure['subsections'][0]					)
																			error_list += sec_error_list
																			warning_list += sec_warning_list
														else:
																			# If not found, append error
																			error_list.append(
																			    'No first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README.'					)
									if error_list:
														# If there are errors, do not return the dictionary as it is invalid
														return {}, error_list, warning_list
									else:
														return self.to_dict(), error_list, warning_list


if __name__ == "__main__":
				from argparse import ArgumentParser

				UpperCAmelCase						:       Dict        =					ArgumentParser(usage='''Validate the content (excluding YAML tags) of a README.md file.''')
				ap.add_argument('''readme_filepath''')
				UpperCAmelCase						:       Tuple        =					ap.parse_args()
				UpperCAmelCase						:       List[str]        =					Path(args.readme_filepath)
				UpperCAmelCase						:       int        =					ReadMe.from_readme(readme_filepath)




