"""simple docstring"""



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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase = None			,  ):
							A__       :     Dict								=			{}

							if train_file is not None:
														A__       :     int								=			[train_file]
							if eval_file is not None:
														A__       :     Optional[Any]								=			[eval_file]
							if test_file is not None:
														A__       :     Tuple								=			[test_file]

							A__       :     List[str]								=			datasets.load_dataset("""csv"""			,  data_files=lowerCAmelCase							)
							A__       :     Union[str, Any]								=			list(ds[list(files.keys()							)[0]].features.keys()							)
							A__       :     Optional[Any]								=			features_name.pop(lowerCAmelCase							)
							A__       :     Tuple								=			list(set(ds[list(files.keys()							)[0]][label_name]							)							)
							A__       :     Any								=			{label: i for i, label in enumerate(lowerCAmelCase							)}
							A__       :     Union[str, Any]								=			tokenizer.model_input_names
							A__       :     Any								=			{}

							if len(lowerCAmelCase							) == 1:
														for k in files.keys():
																					A__       :     int								=			ds[k].map(
																					    lambda lowerCAmelCase							: tokenizer.batch_encode_plus(
																					        example[features_name[0]]			,  truncation=lowerCAmelCase			,  max_length=lowerCAmelCase			,  padding="""max_length"""							)			,  batched=lowerCAmelCase			,  )
							elif len(lowerCAmelCase							) == 2:
														for k in files.keys():
																					A__       :     Tuple								=			ds[k].map(
																					    lambda lowerCAmelCase							: tokenizer.batch_encode_plus(
																					        (example[features_name[0]], example[features_name[1]])			,  truncation=lowerCAmelCase			,  max_length=lowerCAmelCase			,  padding="""max_length"""			,  )			,  batched=lowerCAmelCase			,  )

							def gen_train():
														for ex in transformed_ds[datasets.Split.TRAIN]:
																					A__       :     Optional[Any]								=			{k: v for k, v in ex.items() if k in input_names}
																					A__       :     Dict								=			labelaid[ex[label_name]]
																					yield (d, label)

							def gen_val():
														for ex in transformed_ds[datasets.Split.VALIDATION]:
																					A__       :     List[str]								=			{k: v for k, v in ex.items() if k in input_names}
																					A__       :     int								=			labelaid[ex[label_name]]
																					yield (d, label)

							def gen_test():
														for ex in transformed_ds[datasets.Split.TEST]:
																					A__       :     Optional[int]								=			{k: v for k, v in ex.items() if k in input_names}
																					A__       :     str								=			labelaid[ex[label_name]]
																					yield (d, label)

							A__       :     Tuple								=			(
							    tf.data.Dataset.from_generator(
							        lowerCAmelCase			,  ({k: tf.intaa for k in input_names}, tf.intaa)			,  ({k: tf.TensorShape([None]							) for k in input_names}, tf.TensorShape([]							))			,  )
							    if datasets.Split.TRAIN in transformed_ds
							    else None
							)

							if train_ds is not None:
														A__       :     Optional[Any]								=			train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]							)							)							)

							A__       :     Dict								=			(
							    tf.data.Dataset.from_generator(
							        lowerCAmelCase			,  ({k: tf.intaa for k in input_names}, tf.intaa)			,  ({k: tf.TensorShape([None]							) for k in input_names}, tf.TensorShape([]							))			,  )
							    if datasets.Split.VALIDATION in transformed_ds
							    else None
							)

							if val_ds is not None:
														A__       :     Optional[int]								=			val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]							)							)							)

							A__       :     Union[str, Any]								=			(
							    tf.data.Dataset.from_generator(
							        lowerCAmelCase			,  ({k: tf.intaa for k in input_names}, tf.intaa)			,  ({k: tf.TensorShape([None]							) for k in input_names}, tf.TensorShape([]							))			,  )
							    if datasets.Split.TEST in transformed_ds
							    else None
							)

							if test_ds is not None:
														A__       :     List[Any]								=			test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]							)							)							)

							return train_ds, val_ds, test_ds, labelaid


_UpperCamelCase								=		logging.getLogger(__name__)
@dataclass
class 						__UpperCAmelCase    :







			'''simple docstring'''
			_UpperCamelCase  :						int					     =				field(metadata={'help': 'Which column contains the label'}     )
			_UpperCamelCase  :						str					     =				field(default=__A							,   metadata={'help': 'The path of the training file'}     )
			_UpperCamelCase  :						Optional[str]					     =				field(default=__A							,   metadata={'help': 'The path of the development file'}     )
			_UpperCamelCase  :						Optional[str]					     =				field(default=__A							,   metadata={'help': 'The path of the test file'}     )
			_UpperCamelCase  :						int					     =				field(
			    default=128							,   metadata={
			        'help': (
			            'The maximum total input sequence length after tokenization. Sequences longer '
			            'than this will be truncated, sequences shorter will be padded.'
			        )
			    }							,   )
			_UpperCamelCase  :						bool					     =				field(
			    default=__A							,   metadata={'help': 'Overwrite the cached training and evaluation sets'}     )





@dataclass
class 						__UpperCAmelCase    :







			'''simple docstring'''
			_UpperCamelCase  :						str					     =				field(
			    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}     )
			_UpperCamelCase  :						Optional[str]					     =				field(
			    default=__A							,   metadata={'help': 'Pretrained config name or path if not the same as model_name'}     )
			_UpperCamelCase  :						Optional[str]					     =				field(
			    default=__A							,   metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}     )
			_UpperCamelCase  :						bool					     =				field(default=__A							,   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  :						Optional[str]					     =				field(
			    default=__A							,   metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}							,   )





def  _A(		):
							# 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.
							A__       :     Optional[int]								=			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)							)
							A__				,							A__				,							A__       :     int								=			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			,  )
							logger.info(
							    F'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1							)}, '''
							    F'''16-bits training: {training_args.fpaa}'''							)
							logger.info(F'''Training/evaluation parameters {training_args}'''							)

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

							A__       :     str								=			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			,  )

							A__				,							A__				,							A__				,							A__       :     Optional[Any]								=			get_tfds(
							    train_file=data_args.train_file			,  eval_file=data_args.dev_file			,  test_file=data_args.test_file			,  tokenizer=lowerCAmelCase			,  label_column_id=data_args.label_column_id			,  max_seq_length=data_args.max_seq_length			,  )

							A__       :     Tuple								=			AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,  num_labels=len(lowerCAmelCase							)			,  labelaid=lowerCAmelCase			,  idalabel={id: label for label, id in labelaid.items()}			,  finetuning_task="""text-classification"""			,  cache_dir=model_args.cache_dir			,  )

							with training_args.strategy.scope():
														A__       :     List[str]								=			TFAutoModelForSequenceClassification.from_pretrained(
														    model_args.model_name_or_path			,  from_pt=bool(""".bin""" in model_args.model_name_or_path							)			,  config=lowerCAmelCase			,  cache_dir=model_args.cache_dir			,  )

							def compute_metrics(lowerCAmelCase							) -> Dict:
														A__       :     Tuple								=			np.argmax(p.predictions			,  axis=1							)

														return {"acc": (preds == p.label_ids).mean()}

							# Initialize our Trainer
							A__       :     Optional[Any]								=			TFTrainer(
							    model=lowerCAmelCase			,  args=lowerCAmelCase			,  train_dataset=lowerCAmelCase			,  eval_dataset=lowerCAmelCase			,  compute_metrics=lowerCAmelCase			,  )

							# Training
							if training_args.do_train:
														trainer.train()
														trainer.save_model()
														tokenizer.save_pretrained(training_args.output_dir							)

							# Evaluation
							A__       :     Optional[Any]								=			{}
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""							)
														A__       :     str								=			trainer.evaluate()
														A__       :     List[str]								=			os.path.join(training_args.output_dir			,  """eval_results.txt"""							)

														with open(lowerCAmelCase			,  """w"""							) as writer:
																					logger.info("""***** Eval results *****"""							)

																					for key, value in result.items():
																												logger.info(F'''  {key} = {value}'''							)
																												writer.write(F'''{key} = {value}\n'''							)

																					results.update(lowerCAmelCase							)

							return results


if __name__ == "__main__":
				main()






"""simple docstring"""



import os
from collections import deque

import torch
from torch.utils.data import Dataset
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_=""   ,      snake_case_="train"							):







										'''simple docstring'''


										assert os.path.isdir(snake_case_							)

										A__       :     Dict								=			[]
										A__       :     Optional[Any]								=			os.listdir(snake_case_							)
										for story_filename in story_filenames_list:
																	if "summary" in story_filename:
																								continue
																	A__       :     Any								=			os.path.join(snake_case_   ,      snake_case_							)
																	if not os.path.isfile(snake_case_							):
																								continue
																	self.documents.append(snake_case_							)





			def __len__(			self							):







										'''simple docstring'''


										return len(self.documents							)





			def __getitem__(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     List[str]								=			self.documents[idx]
										A__       :     List[Any]								=			document_path.split("""/"""							)[-1]
										with open(snake_case_   ,      encoding="""utf-8"""							) as source:
																	A__       :     Dict								=			source.read()
																	A__				,							A__       :     Dict								=			process_story(snake_case_							)
										return document_name, story_lines, summary_lines





def  _A(		lowerCAmelCase							):
							A__       :     Optional[int]								=			list(filter(lambda lowerCAmelCase							: len(lowerCAmelCase							) != 0			,  [line.strip() for line in raw_story.split("""\n"""							)]							)							)

							# for some unknown reason some lines miss a period, add it
							A__       :     List[str]								=			[_add_missing_period(lowerCAmelCase							) for line in nonempty_lines]

							# gather article lines
							A__       :     Union[str, Any]								=			[]
							A__       :     Optional[Any]								=			deque(lowerCAmelCase							)
							while True:
														try:
																					A__       :     List[str]								=			lines.popleft()
																					if element.startswith("""@highlight"""							):
																												break
																					story_lines.append(lowerCAmelCase							)
														except IndexError:
																					# if "@highlight" is absent from the file we pop
																					# all elements until there is None, raising an exception.
																					return story_lines, []

    # gather summary lines
							A__       :     List[str]								=			list(filter(lambda lowerCAmelCase							: not t.startswith("""@highlight"""							)			,  lowerCAmelCase							)							)

							return story_lines, summary_lines

def  _A(		lowerCAmelCase							):
							A__       :     Tuple								=			[""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
							if line.startswith("""@highlight"""							):
														return line
							if line[-1] in END_TOKENS:
														return line
							return line + "."

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							if len(lowerCAmelCase							) > block_size:
														return sequence[:block_size]
							else:
														sequence.extend([pad_token_id] * (block_size - len(lowerCAmelCase							))							)
														return sequence

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Any								=			torch.ones_like(lowerCAmelCase							)
							A__       :     Any								=			sequence == pad_token_id
							A__       :     Any								=			0
							return mask

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Dict								=			[tokenizer.encode(lowerCAmelCase							) for line in story_lines]
							A__       :     Any								=			[token for sentence in story_lines_token_ids for token in sentence]
							A__       :     List[str]								=			[tokenizer.encode(lowerCAmelCase							) for line in summary_lines]
							A__       :     int								=			[token for sentence in summary_lines_token_ids for token in sentence]

							return story_token_ids, summary_token_ids



def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     str								=			[]
							for sequence in batch:
														A__       :     List[Any]								=			-1
														A__       :     Dict								=			[]
														for s in sequence:
																					if s == separator_token_id:
																												sentence_num += 1
																					embeddings.append(sentence_num % 2							)
														batch_embeddings.append(lowerCAmelCase							)
							return torch.tensor(lowerCAmelCase							)





