
"""simple docstring"""


import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_lowercase							   =				16
_lowercase							   =				32






def       _snake_case				(		snake_case__   :     str	,      snake_case__   :     List[str] = 16	,      snake_case__   :     Optional[Any] = "bert-base-cased"     ):
	A						   =   AutoTokenizer.from_pretrained(_lowerCamelCase     )
	A						   =   load_dataset('glue'	,      'mrpc'     )

	def tokenize_function(snake_case__   :     Dict     ):
		# max_length=None => use the model max length (it's actually the default)
		A						   =   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
	A						   =   datasets.map(
	    _lowerCamelCase	,      batched=_lowerCamelCase	,      remove_columns=['idx', 'sentence1', 'sentence2']	,      load_from_cache_file=_lowerCamelCase     )

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

	def collate_fn(snake_case__   :     Dict     ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(_lowerCamelCase	,      padding='max_length'	,      max_length=128	,      return_tensors='pt'     )
		return tokenizer.pad(_lowerCamelCase	,      padding='longest'	,      return_tensors='pt'     )

	# Instantiate dataloaders.
	A						   =   DataLoader(
	    tokenized_datasets['train']	,      shuffle=_lowerCamelCase	,      collate_fn=_lowerCamelCase	,      batch_size=_lowerCamelCase     )
	A						   =   DataLoader(
	    tokenized_datasets['validation']	,      shuffle=_lowerCamelCase	,      collate_fn=_lowerCamelCase	,      batch_size=_lowerCamelCase     )

	return train_dataloader, eval_dataloader






def       _snake_case				(		snake_case__   :     Dict	,      snake_case__   :     int     ):
	# Initialize accelerator
	A						   =   Accelerator()

	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	A						   =   config["lr"]
	A						   =   int(config['num_epochs']     )
	A						   =   int(config['seed']     )
	A						   =   int(config['batch_size']     )
	A						   =   args.model_name_or_path

	set_seed(_lowerCamelCase     )
	A						   =   get_dataloaders(_lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase     )

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A						   =   AutoModelForSequenceClassification.from_pretrained(_lowerCamelCase	,      return_dict=_lowerCamelCase     )

	# Instantiate optimizer
	A						   =   (
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	A						   =   optimizer_cls(params=model.parameters()	,      lr=_lowerCamelCase     )

	if accelerator.state.deepspeed_plugin is not None:
		A						   =   accelerator.state.deepspeed_plugin.deepspeed_config[
		    "gradient_accumulation_steps"
		]
	else:
		A						   =   1
	A						   =   (len(_lowerCamelCase     ) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		A						   =   get_linear_schedule_with_warmup(
		    optimizer=_lowerCamelCase	,      num_warmup_steps=0	,      num_training_steps=_lowerCamelCase	,      )
	else:
		A						   =   DummyScheduler(_lowerCamelCase	,      total_num_steps=_lowerCamelCase	,      warmup_num_steps=0     )

	# 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						   =   accelerator.prepare(
	    _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase     )

	# We need to keep track of how many total steps we have iterated over
	A						   =   0
	# We also need to keep track of the stating epoch so files are named properly
	A						   =   0

	# Now we train the model
	A						   =   evaluate.load('glue'	,      'mrpc'     )
	A						   =   0
	A						   =   {}
	for epoch in range(_lowerCamelCase	,      _lowerCamelCase     ):
		model.train()
		for step, batch in enumerate(_lowerCamelCase     ):
			A						   =   model(**_lowerCamelCase     )
			A						   =   outputs.loss
			A						   =   loss / gradient_accumulation_steps
			accelerator.backward(_lowerCamelCase     )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

		model.eval()
		A						   =   0
		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						   =   model(**_lowerCamelCase     )
			A						   =   outputs.logits.argmax(dim=-1     )
			# It is slightly faster to call this once, than multiple times
			A						   =   accelerator.gather(
			    (predictions, batch['labels'])     )  # If we are in a multiprocess environment, the last batch has duplicates
			if accelerator.use_distributed:
				if step == len(_lowerCamelCase     ) - 1:
					A						   =   predictions[: len(eval_dataloader.dataset     ) - samples_seen]
					A						   =   references[: len(eval_dataloader.dataset     ) - samples_seen]
				else:
					samples_seen += references.shape[0]
			metric.add_batch(
			    predictions=_lowerCamelCase	,      references=_lowerCamelCase	,      )

		A						   =   metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'epoch {epoch}:'	,      _lowerCamelCase     )
		A						   =   eval_metric["accuracy"]

		if best_performance < eval_metric["accuracy"]:
			A						   =   eval_metric["accuracy"]

	if args.performance_lower_bound is not None:
		assert (
		    args.performance_lower_bound <= best_performance
		), F'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
		with open(os.path.join(args.output_dir	,      'all_results.json'     )	,      'w'     ) as f:
			json.dump(_lowerCamelCase	,      _lowerCamelCase     )






def       _snake_case				(		):
	A						   =   argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'     )
	parser.add_argument(
	    '--model_name_or_path'	,      type=_lowerCamelCase	,      default='bert-base-cased'	,      help='Path to pretrained model or model identifier from huggingface.co/models.'	,      required=_lowerCamelCase	,      )
	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(
	    '--performance_lower_bound'	,      type=_lowerCamelCase	,      default=_lowerCamelCase	,      help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'	,      )
	parser.add_argument(
	    '--num_epochs'	,      type=_lowerCamelCase	,      default=3	,      help='Number of train epochs.'	,      )
	A						   =   parser.parse_args()
	A						   =   {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
	training_function(_lowerCamelCase	,      _lowerCamelCase     )


if __name__ == "__main__":
		main()







"""simple docstring"""


import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
		from cookiecutter.main import cookiecutter

		_lowercase							   =				True
except ImportError:
		_lowercase							   =				False

_lowercase							   =				logging.get_logger(__name__)  # pylint: disable=invalid-name






def       _snake_case				(		snake_case__   :     Namespace     ):
	return AddNewModelCommand(args.testing	,      args.testing_file	,      path=args.path     )







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						@staticmethod
						def 						_SCREAMING_SNAKE_CASE      ( A_     :      ArgumentParser      )     ->   Any:
							A						   =   parser.add_parser('add-new-model'      )
							add_new_model_parser.add_argument('--testing'  ,action='store_true'  ,help='If in testing mode.'      )
							add_new_model_parser.add_argument('--testing_file'  ,type=A_  ,help='Configuration file on which to run.'      )
							add_new_model_parser.add_argument(
							    '--path'  ,type=A_  ,help='Path to cookiecutter. Should only be used for testing purposes.'      )
							add_new_model_parser.set_defaults(func=A_      )


						def __init__( self     :      Tuple  ,A_     :      bool  ,A_     :      str  ,A_     :      Tuple=None  ,*A_     :      List[str]      )     ->   Union[str, Any]:
							A						   =   testing
							A						   =   testing_file
							A						   =   path




						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   int:
							warnings.warn(
							    'The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. '
							    'It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality '
							    'checks, you should use `transformers-cli add-new-model-like` instead.'      )
							if not _has_cookiecutter:
								raise ImportError(
								    'Model creation dependencies are required to use the `add_new_model` command. Install them by running '
								    'the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n'      )
							# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
							A						   =   [directory for directory in os.listdir() if 'cookiecutter-template-' == directory[:22]]
							if len(A_      ) > 0:
								raise ValueError(
								    'Several directories starting with `cookiecutter-template-` in current working directory. '
								    'Please clean your directory by removing all folders starting with `cookiecutter-template-` or '
								    'change your working directory.'      )

							A						   =   (
							    Path(A_      ).parent.parent.parent.parent if self._path is None else Path(self._path      ).parent.parent
							)
							A						   =   path_to_transformer_root / 'templates' / 'adding_a_new_model'

							# Execute cookiecutter
							if not self._testing:
								cookiecutter(str(A_      )      )
							else:
								with open(self._testing_file  ,'r'      ) as configuration_file:
									A						   =   json.load(A_      )

								cookiecutter(
								    str(path_to_cookiecutter if self._path is None else self._path      )  ,no_input=A_  ,extra_context=A_  ,)

							A						   =   [directory for directory in os.listdir() if 'cookiecutter-template-' in directory[:22]][0]

							# Retrieve configuration
							with open(directory + '/configuration.json'  ,'r'      ) as configuration_file:
								A						   =   json.load(A_      )

							A						   =   configuration['lowercase_modelname']
							A						   =   configuration['generate_tensorflow_pytorch_and_flax']
							os.remove(F'{directory}/configuration.json'      )

							A						   =   'PyTorch' in generate_tensorflow_pytorch_and_flax
							A						   =   'TensorFlow' in generate_tensorflow_pytorch_and_flax
							A						   =   'Flax' in generate_tensorflow_pytorch_and_flax

							A						   =   F'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
							os.makedirs(A_  ,exist_ok=A_      )
							os.makedirs(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}'  ,exist_ok=A_      )

							# Tests require submodules as they have parent imports
							with open(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py'  ,'w'      ):
								pass

							shutil.move(
							    F'{directory}/__init__.py'  ,F'{model_dir}/__init__.py'  ,)
							shutil.move(
							    F'{directory}/configuration_{lowercase_model_name}.py'  ,F'{model_dir}/configuration_{lowercase_model_name}.py'  ,)

							def remove_copy_lines(A_     :      int      ):
								with open(A_  ,'r'      ) as f:
									A						   =   f.readlines()
								with open(A_  ,'w'      ) as f:
									for line in lines:
										if "# Copied from transformers." not in line:
											f.write(A_      )

							if output_pytorch:
								if not self._testing:
									remove_copy_lines(F'{directory}/modeling_{lowercase_model_name}.py'      )

								shutil.move(
								    F'{directory}/modeling_{lowercase_model_name}.py'  ,F'{model_dir}/modeling_{lowercase_model_name}.py'  ,)

								shutil.move(
								    F'{directory}/test_modeling_{lowercase_model_name}.py'  ,F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py'  ,)
							else:
								os.remove(F'{directory}/modeling_{lowercase_model_name}.py'      )
								os.remove(F'{directory}/test_modeling_{lowercase_model_name}.py'      )

							if output_tensorflow:
								if not self._testing:
									remove_copy_lines(F'{directory}/modeling_tf_{lowercase_model_name}.py'      )

								shutil.move(
								    F'{directory}/modeling_tf_{lowercase_model_name}.py'  ,F'{model_dir}/modeling_tf_{lowercase_model_name}.py'  ,)

								shutil.move(
								    F'{directory}/test_modeling_tf_{lowercase_model_name}.py'  ,F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py'  ,)
							else:
								os.remove(F'{directory}/modeling_tf_{lowercase_model_name}.py'      )
								os.remove(F'{directory}/test_modeling_tf_{lowercase_model_name}.py'      )

							if output_flax:
								if not self._testing:
									remove_copy_lines(F'{directory}/modeling_flax_{lowercase_model_name}.py'      )

								shutil.move(
								    F'{directory}/modeling_flax_{lowercase_model_name}.py'  ,F'{model_dir}/modeling_flax_{lowercase_model_name}.py'  ,)

								shutil.move(
								    F'{directory}/test_modeling_flax_{lowercase_model_name}.py'  ,F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py'  ,)
							else:
								os.remove(F'{directory}/modeling_flax_{lowercase_model_name}.py'      )
								os.remove(F'{directory}/test_modeling_flax_{lowercase_model_name}.py'      )

							shutil.move(
							    F'{directory}/{lowercase_model_name}.md'  ,F'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md'  ,)

							shutil.move(
							    F'{directory}/tokenization_{lowercase_model_name}.py'  ,F'{model_dir}/tokenization_{lowercase_model_name}.py'  ,)

							shutil.move(
							    F'{directory}/tokenization_fast_{lowercase_model_name}.py'  ,F'{model_dir}/tokenization_{lowercase_model_name}_fast.py'  ,)

							from os import fdopen, remove
							from shutil import copymode, move
							from tempfile import mkstemp

							def replace(A_     :      str  ,A_     :      str  ,A_     :      List[str]      ):
								# Create temp file
								A   ,      A						   =   mkstemp()
								A						   =   False
								with fdopen(A_  ,'w'      ) as new_file:
									with open(A_      ) as old_file:
										for line in old_file:
											new_file.write(A_      )
											if line_to_copy_below in line:
												A						   =   True
												for line_to_copy in lines_to_copy:
													new_file.write(A_      )

								if not line_found:
									raise ValueError(F'Line {line_to_copy_below} was not found in file.'      )

								# Copy the file permissions from the old file to the new file
								copymode(A_  ,A_      )
								# Remove original file
								remove(A_      )
								# Move new file
								move(A_  ,A_      )

							def skip_units(A_     :      Dict      ):
								return (
								    ("generating PyTorch" in line and not output_pytorch)
								    or ("generating TensorFlow" in line and not output_tensorflow)
								    or ("generating Flax" in line and not output_flax)
								)

							def replace_in_files(A_     :      Tuple      ):
								with open(A_      ) as datafile:
									A						   =   []
									A						   =   False
									A						   =   False
									for line in datafile:
										if "# To replace in: " in line and "##" not in line:
											A						   =   line.split('"'      )[1]
											A						   =   skip_units(A_      )
										elif "# Below: " in line and "##" not in line:
											A						   =   line.split('"'      )[1]
											A						   =   skip_units(A_      )
										elif "# End." in line and "##" not in line:
											if not skip_file and not skip_snippet:
												replace(A_  ,A_  ,A_      )

											A						   =   []
										elif "# Replace with" in line and "##" not in line:
											A						   =   []
										elif "##" not in line:
											lines_to_copy.append(A_      )

								remove(A_      )

							replace_in_files(F'{directory}/to_replace_{lowercase_model_name}.py'      )
							os.rmdir(A_      )