



"""simple docstring"""
import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 		__UpperCAmelCase				(	_a  ,       unittest.TestCase					):







 """simple docstring"""




 _snake_case       :							int      	=	LEDTokenizer
 _snake_case       :							Any      	=	LEDTokenizerFast
 _snake_case       :							Any      	=	True





 def  A  (				self					: int	)->	List[Any]:
   super().setUp()
   __UpperCamelCase  						=							[
       "l",
       "o",
       "w",
       "e",
       "r",
       "s",
       "t",
       "i",
       "d",
       "n",
       "\u0120",
       "\u0120l",
       "\u0120n",
       "\u0120lo",
       "\u0120low",
       "er",
       "\u0120lowest",
       "\u0120newer",
       "\u0120wider",
       "<unk>",
   ]
   __UpperCamelCase  						=							dict(zip(lowerCAmelCase_						, range(len(lowerCAmelCase_	)	)	)	)
   __UpperCamelCase  						=							["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
   __UpperCamelCase  						=							{"unk_token": "<unk>"}

   __UpperCamelCase  						=							os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES["vocab_file"]	)
   __UpperCamelCase  						=							os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES["merges_file"]	)
   with open(self.vocab_file						, "w"						, encoding="utf-8"	) as fp:
     fp.write(json.dumps(lowerCAmelCase_	) + "\n"	)
   with open(self.merges_file						, "w"						, encoding="utf-8"	) as fp:
     fp.write("\n".join(lowerCAmelCase_	)	)





 def  A  (				self					: Union[str, Any]						, **A_					: int	)->	Optional[int]:
   kwargs.update(self.special_tokens_map	)
   return self.tokenizer_class.from_pretrained(self.tmpdirname						, **lowerCAmelCase_	)





 def  A  (				self					: Optional[Any]						, **A_					: Any	)->	int:
   kwargs.update(self.special_tokens_map	)
   return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						, **lowerCAmelCase_	)





 def  A  (				self					: Optional[Any]						, A_					: Dict	)->	List[str]:
   return "lower newer", "lower newer"





 @cached_property
 def  A  (				self					: Dict	)->	int:
   return LEDTokenizer.from_pretrained("allenai/led-base-16384"	)





 @cached_property
 def  A  (				self					: List[str]	)->	Union[str, Any]:
   return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"	)





 @require_torch
 def  A  (				self					: int	)->	Tuple:
   __UpperCamelCase  						=							["A long paragraph for summarization.", "Another paragraph for summarization."]
   __UpperCamelCase  						=							[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __UpperCamelCase  						=							tokenizer(lowerCAmelCase_						, max_length=len(lowerCAmelCase_	)						, padding=lowerCAmelCase_						, return_tensors="pt"	)
     self.assertIsInstance(lowerCAmelCase_						, lowerCAmelCase_	)

     self.assertEqual((2, 9)						, batch.input_ids.shape	)
     self.assertEqual((2, 9)						, batch.attention_mask.shape	)
     __UpperCamelCase  						=							batch.input_ids.tolist()[0]
     self.assertListEqual(lowerCAmelCase_						, lowerCAmelCase_	)





 @require_torch
 def  A  (				self					: Tuple	)->	List[Any]:
   __UpperCamelCase  						=							["A long paragraph for summarization.", "Another paragraph for summarization."]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __UpperCamelCase  						=							tokenizer(lowerCAmelCase_						, padding=lowerCAmelCase_						, return_tensors="pt"	)
     self.assertIn("input_ids"						, lowerCAmelCase_	)
     self.assertIn("attention_mask"						, lowerCAmelCase_	)
     self.assertNotIn("labels"						, lowerCAmelCase_	)
     self.assertNotIn("decoder_attention_mask"						, lowerCAmelCase_	)





 @require_torch
 def  A  (				self					: List[str]	)->	str:
   __UpperCamelCase  						=							[
       "Summary of the text.",
       "Another summary.",
   ]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __UpperCamelCase  						=							tokenizer(text_target=lowerCAmelCase_						, max_length=32						, padding="max_length"						, return_tensors="pt"	)
     self.assertEqual(32						, targets["input_ids"].shape[1]	)





 @require_torch
 def  A  (				self					: Any	)->	Union[str, Any]:


   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __UpperCamelCase  						=							tokenizer(
         ["I am a small frog" * 10_24, "I am a small frog"]						, padding=lowerCAmelCase_						, truncation=lowerCAmelCase_						, return_tensors="pt"	)
     self.assertIsInstance(lowerCAmelCase_						, lowerCAmelCase_	)
     self.assertEqual(batch.input_ids.shape						, (2, 51_22)	)





 @require_torch
 def  A  (				self					: Optional[Any]	)->	Union[str, Any]:
   __UpperCamelCase  						=							["A long paragraph for summarization."]
   __UpperCamelCase  						=							[
       "Summary of the text.",
   ]
   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __UpperCamelCase  						=							tokenizer(lowerCAmelCase_						, return_tensors="pt"	)
     __UpperCamelCase  						=							tokenizer(text_target=lowerCAmelCase_						, return_tensors="pt"	)
     __UpperCamelCase  						=							inputs["input_ids"]
     __UpperCamelCase  						=							targets["input_ids"]
     self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()	)
     self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()	)
     self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()	)
     self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()	)





 @require_torch
 def  A  (				self					: Any	)->	Union[str, Any]:


   for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
     __UpperCamelCase  						=							["Summary of the text.", "Another summary."]
     __UpperCamelCase  						=							[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

     __UpperCamelCase  						=							tokenizer(lowerCAmelCase_						, padding=lowerCAmelCase_	)
     __UpperCamelCase  						=							[[0] * len(lowerCAmelCase_	) for x in encoded_output["input_ids"]]
     __UpperCamelCase  						=							tokenizer.pad(lowerCAmelCase_	)
     self.assertSequenceEqual(outputs["global_attention_mask"]						, lowerCAmelCase_	)





 def  A  (				self					: Any	)->	Dict:
   pass





 def  A  (				self					: Any	)->	Optional[Any]:


   for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
       __UpperCamelCase  						=							self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_						, **lowerCAmelCase_	)
       __UpperCamelCase  						=							self.tokenizer_class.from_pretrained(lowerCAmelCase_						, **lowerCAmelCase_	)
       __UpperCamelCase  						=							"A, <mask> AllenNLP sentence."
       __UpperCamelCase  						=							tokenizer_r.encode_plus(lowerCAmelCase_						, add_special_tokens=lowerCAmelCase_						, return_token_type_ids=lowerCAmelCase_	)
       __UpperCamelCase  						=							tokenizer_p.encode_plus(lowerCAmelCase_						, add_special_tokens=lowerCAmelCase_						, return_token_type_ids=lowerCAmelCase_	)
       self.assertEqual(sum(tokens_r["token_type_ids"]	)						, sum(tokens_p["token_type_ids"]	)	)
       self.assertEqual(
           sum(tokens_r["attention_mask"]	) / len(tokens_r["attention_mask"]	)						, sum(tokens_p["attention_mask"]	) / len(tokens_p["attention_mask"]	)						, )

       __UpperCamelCase  						=							tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]	)
       __UpperCamelCase  						=							tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]	)
       self.assertSequenceEqual(tokens_p["input_ids"]						, [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]	)
       self.assertSequenceEqual(tokens_r["input_ids"]						, [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]	)

       self.assertSequenceEqual(
           lowerCAmelCase_						, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)
       self.assertSequenceEqual(
           lowerCAmelCase_						, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]	)





'''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 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    str					,		UpperCamelCase			:    str					,		UpperCamelCase			:    PreTrainedTokenizer					,		UpperCamelCase			:    int					,		UpperCamelCase			:    Optional[int] = None					,		):





							'''simple docstring'''


							_a      	=							{}

							if train_file is not None:
														_a      	=							[train_file]
							if eval_file is not None:
														_a      	=							[eval_file]
							if test_file is not None:
														_a      	=							[test_file]

							_a      	=							datasets.load_dataset('''csv'''					,		data_files=UpperCamelCase )
							_a      	=							list(ds[list(files.keys() )[0]].features.keys() )
							_a      	=							features_name.pop(UpperCamelCase )
							_a      	=							list(set(ds[list(files.keys() )[0]][label_name] ) )
							_a      	=							{label: i for i, label in enumerate(UpperCamelCase )}
							_a      	=							tokenizer.model_input_names
							_a      	=							{}

							if len(UpperCamelCase ) == 1:
														for k in files.keys():
																					_a      	=							ds[k].map(
																					    lambda UpperCamelCase : tokenizer.batch_encode_plus(
																					        example[features_name[0]]					,		truncation=UpperCamelCase					,		max_length=UpperCamelCase					,		padding='''max_length''' )					,		batched=UpperCamelCase					,		)
							elif len(UpperCamelCase ) == 2:
														for k in files.keys():
																					_a      	=							ds[k].map(
																					    lambda UpperCamelCase : tokenizer.batch_encode_plus(
																					        (example[features_name[0]], example[features_name[1]])					,		truncation=UpperCamelCase					,		max_length=UpperCamelCase					,		padding='''max_length'''					,		)					,		batched=UpperCamelCase					,		)

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

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

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

							_a      	=							(
							    tf.data.Dataset.from_generator(
							        UpperCamelCase					,		({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      	=							train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )

							_a      	=							(
							    tf.data.Dataset.from_generator(
							        UpperCamelCase					,		({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      	=							val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )

							_a      	=							(
							    tf.data.Dataset.from_generator(
							        UpperCamelCase					,		({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      	=							test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )

							return train_ds, val_ds, test_ds, labelaid


_snake_case : str  				=						logging.getLogger(__name__)

@dataclass
class      A       :
							lowercase_							     =							field(metadata={'help': 'Which column contains the label'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the training file'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the development file'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the test file'}       )
							lowercase_							     =							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.'
							        )
							    }	,)
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Overwrite the cached training and evaluation sets'}       )



@dataclass
class      A       :
							lowercase_							     =							field(
							    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}       )
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}       )
							lowercase_							     =							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.
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,)



def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
							_a	,				_a	,				_a      	=							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      	=							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      	=							get_tfds(
							    train_file=data_args.train_file					,		eval_file=data_args.dev_file					,		test_file=data_args.test_file					,		tokenizer=UpperCamelCase					,		label_column_id=data_args.label_column_id					,		max_seq_length=data_args.max_seq_length					,		)

							_a      	=							AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,		num_labels=len(UpperCamelCase )					,		labelaid=UpperCamelCase					,		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      	=							TFAutoModelForSequenceClassification.from_pretrained(
														    model_args.model_name_or_path					,		from_pt=bool('''.bin''' in model_args.model_name_or_path )					,		config=UpperCamelCase					,		cache_dir=model_args.cache_dir					,		)

							def compute_metrics(UpperCamelCase			:    EvalPrediction ) -> Dict:
														_a      	=							np.argmax(p.predictions					,		axis=1 )

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

							# Initialize our Trainer
							_a      	=							TFTrainer(
							    model=UpperCamelCase					,		args=UpperCamelCase					,		train_dataset=UpperCamelCase					,		eval_dataset=UpperCamelCase					,		compute_metrics=UpperCamelCase					,		)

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

							# Evaluation
							_a      	=							{}
							if training_args.do_eval:
														logger.info('''*** Evaluate ***''' )
														_a      	=							trainer.evaluate()
														_a      	=							os.path.join(training_args.output_dir					,		'''eval_results.txt''' )

														with open(UpperCamelCase					,		'''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(UpperCamelCase )

							return results


if __name__ == "__main__":
				main()

