


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


snake_case_  :	Optional[Any]							  =							logging.getLogger(__name__)

@dataclass
class   A__							:
						UpperCAmelCase									=  field(
						    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}     )
						UpperCAmelCase									=  field(
						    default=UpperCamelCase__      ,   metadata={"help": "Pretrained config name or path if not the same as model_name"}     )
						UpperCAmelCase									=  field(
						    default=UpperCamelCase__      ,   metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}     )
						UpperCAmelCase									=  field(default=UpperCamelCase__      ,   metadata={"help": "Set this flag to use fast tokenization."}     )
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						UpperCAmelCase									=  field(
						    default=UpperCamelCase__      ,   metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}      ,   )







def   lowerCamelCase(      ):
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				_SCREAMING_SNAKE_CASE	     =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))

				if len(sys.argv) == 2 and sys.argv[1].endswith('''.json'''):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
				else:
								_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE	     =parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir)
				    and os.listdir(training_args.output_dir)
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
								    ''' --overwrite_output_dir to overcome.''')

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''		,datefmt='''%m/%d/%Y %H:%M:%S'''		,level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN		,)
				logger.warning(
				    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''		,training_args.local_rank		,training_args.device		,training_args.n_gpu		,bool(training_args.local_rank != -1)		,training_args.fpaa		,)
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info('''Training/evaluation parameters %s'''		,a__)

				# Prepare Question-Answering task
				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				_SCREAMING_SNAKE_CASE	     =AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path		,cache_dir=model_args.cache_dir		,)
				_SCREAMING_SNAKE_CASE	     =AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,cache_dir=model_args.cache_dir		,use_fast=a__		,)
				_SCREAMING_SNAKE_CASE	     =AutoModelForQuestionAnswering.from_pretrained(
				    model_args.model_name_or_path		,from_tf=bool('''.ckpt''' in model_args.model_name_or_path)		,config=a__		,cache_dir=model_args.cache_dir		,)

				# Get datasets
				_SCREAMING_SNAKE_CASE	     =hasattr(model.config		,'''lang2id''')
				_SCREAMING_SNAKE_CASE	     =(
				    SquadDataset(
				        a__		,tokenizer=a__		,is_language_sensitive=a__		,cache_dir=model_args.cache_dir)
				    if training_args.do_train
				    else None
				)
				_SCREAMING_SNAKE_CASE	     =(
				    SquadDataset(
				        a__		,tokenizer=a__		,mode='''dev'''		,is_language_sensitive=a__		,cache_dir=model_args.cache_dir		,)
				    if training_args.do_eval
				    else None
				)

				# Data collator
				_SCREAMING_SNAKE_CASE	     =DataCollatorWithPadding(a__		,pad_to_multiple_of=8) if training_args.fpaa else None

				# Initialize our Trainer
				_SCREAMING_SNAKE_CASE	     =Trainer(
				    model=a__		,args=a__		,train_dataset=a__		,eval_dataset=a__		,data_collator=a__		,)

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None)
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_master():
												tokenizer.save_pretrained(training_args.output_dir)







def   lowerCamelCase(      a__):
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()


'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List


def       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]     ,							_SCREAMING_SNAKE_CASE					:    str							)			-> Optional[Any]:


	"""simple docstring"""





	if name != "training_function" and name != "main":
		raise ValueError(F'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''							)
	UpperCAmelCase_ , UpperCAmelCase_			:	int      =   [], False
	for line in lines:
		if not found_start and F'''def {name}''' in line:
			UpperCAmelCase_			:	List[str]      =   True
			good_lines.append(_SCREAMING_SNAKE_CASE							)
			continue
		if found_start:
			if name == "training_function" and "def main" in line:
				return good_lines
			if name == "main" and "if __name__" in line:
				return good_lines
			good_lines.append(_SCREAMING_SNAKE_CASE							)


def       a__    (  _SCREAMING_SNAKE_CASE					:    List[str]							)			-> Optional[Any]:


	"""simple docstring"""





	return [line for line in lines if not line.lstrip().startswith("#"							) and line != "\n"]





def       a__    (  _SCREAMING_SNAKE_CASE					:    str     ,							_SCREAMING_SNAKE_CASE					:    str     ,							_SCREAMING_SNAKE_CASE					:    bool     ,							_SCREAMING_SNAKE_CASE					:    str = None							)			-> int:


	"""simple docstring"""





	with open(_SCREAMING_SNAKE_CASE     ,							"r"							) as f:
		UpperCAmelCase_			:	Union[str, Any]      =   f.readlines()
	with open(os.path.abspath(os.path.join("examples"     ,							"nlp_example.py"							)							)     ,							"r"							) as f:
		UpperCAmelCase_			:	Dict      =   f.readlines()
	with open(_SCREAMING_SNAKE_CASE     ,							"r"							) as f:
		UpperCAmelCase_			:	Any      =   f.readlines()
	if secondary_filename is not None:
		with open(_SCREAMING_SNAKE_CASE     ,							"r"							) as f:
			UpperCAmelCase_			:	Tuple      =   f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
	if parser_only:
		UpperCAmelCase_			:	List[str]      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"main"							)							)
		UpperCAmelCase_			:	Tuple      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"main"							)							)
		UpperCAmelCase_			:	Tuple      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"main"							)							)
		if secondary_filename is not None:
			UpperCAmelCase_			:	List[Any]      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"main"							)							)
	else:
		UpperCAmelCase_			:	Union[str, Any]      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"training_function"							)							)
		UpperCAmelCase_			:	str      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"training_function"							)							)
		UpperCAmelCase_			:	List[Any]      =   clean_lines(get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"training_function"							)							)
		if secondary_filename is not None:
			UpperCAmelCase_			:	Tuple      =   clean_lines(
			    get_function_contents_by_name(_SCREAMING_SNAKE_CASE     ,							"training_function"							)							)

	UpperCAmelCase_			:	List[Any]      =   "train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"

	# Specific code in our script that differs from the full version, aka what is new
	UpperCAmelCase_			:	Dict      =   []
	UpperCAmelCase_			:	Any      =   []  # We keep track of the idxs just in case it's a repeated statement
	UpperCAmelCase_			:	Optional[int]      =   iter(_SCREAMING_SNAKE_CASE							)
	for i in range(len(_SCREAMING_SNAKE_CASE							) - 1							):
		if i not in passed_idxs:
			UpperCAmelCase_			:	str      =   next(_SCREAMING_SNAKE_CASE							)
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_feature_code.append(_SCREAMING_SNAKE_CASE							)
					passed_idxs.append(_SCREAMING_SNAKE_CASE							)
				else:
					# Skip over the `config['num_epochs'] = 2` statement
					UpperCAmelCase_			:	List[Any]      =   next(_SCREAMING_SNAKE_CASE							)

    # Extract out just the new parts from the full_file_training_func
	UpperCAmelCase_			:	Tuple      =   []
	UpperCAmelCase_			:	Optional[Any]      =   []  # We keep track of the idxs just in case it's a repeated statement
	for i, line in enumerate(_SCREAMING_SNAKE_CASE							):
		if i not in passed_idxs:
			if (line not in full_file_func) and (line.lstrip() != _dl_line):
				if "TESTING_MOCKED_DATALOADERS" not in line:
					new_full_example_parts.append(_SCREAMING_SNAKE_CASE							)
					passed_idxs.append(_SCREAMING_SNAKE_CASE							)

    # Finally, get the overall diff
	UpperCAmelCase_			:	Union[str, Any]      =   [line for line in new_feature_code if line not in new_full_example_parts]
	if secondary_filename is not None:
		UpperCAmelCase_			:	int      =   [line for line in full_file_contents if line not in secondary_file_func]
		UpperCAmelCase_			:	str      =   [line for line in diff_from_example if line not in diff_from_two]

	return diff_from_example




