



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   snake_case_ (__lowerCAmelCase , unittest.TestCase  ):
       _lowerCamelCase			    =				LEDTokenizer
       _lowerCamelCase			    =				LEDTokenizerFast
       _lowerCamelCase			    =				True



       def  A_   (     self):

             """simple docstring"""





             super().setUp()
             UpperCAmelCase_  :	List[Any]				      =		[
                 '''l''',
                 '''o''',
                 '''w''',
                 '''e''',
                 '''r''',
                 '''s''',
                 '''t''',
                 '''i''',
                 '''d''',
                 '''n''',
                 '''\u0120''',
                 '''\u0120l''',
                 '''\u0120n''',
                 '''\u0120lo''',
                 '''\u0120low''',
                 '''er''',
                 '''\u0120lowest''',
                 '''\u0120newer''',
                 '''\u0120wider''',
                 '''<unk>''',
             ]
             UpperCAmelCase_  :	Tuple				      =		dict(zip(lowerCamelCase__     ,range(len(lowerCamelCase__))))
             UpperCAmelCase_  :	Optional[Any]				      =		['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
             UpperCAmelCase_  :	Dict				      =		{'''unk_token''': '''<unk>'''}

             UpperCAmelCase_  :	Any				      =		os.path.join(self.tmpdirname     ,VOCAB_FILES_NAMES["vocab_file"])
             UpperCAmelCase_  :	int				      =		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     ,**lowercase):

             """simple docstring"""





             kwargs.update(self.special_tokens_map)
             return self.tokenizer_class.from_pretrained(self.tmpdirname     ,**lowerCamelCase__)



       def  A_   (     self     ,**lowercase):

             """simple docstring"""





             kwargs.update(self.special_tokens_map)
             return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,**lowerCamelCase__)



       def  A_   (     self     ,lowercase):

             """simple docstring"""





             return "lower newer", "lower newer"



       @cached_property
       def  A_   (     self):

             """simple docstring"""





             return LEDTokenizer.from_pretrained("allenai/led-base-16384")



       @cached_property
       def  A_   (     self):

             """simple docstring"""





             return LEDTokenizerFast.from_pretrained("allenai/led-base-16384")



       @require_torch
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
             UpperCAmelCase_  :	Union[str, Any]				      =		[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   UpperCAmelCase_  :	str				      =		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_  :	Dict				      =		batch.input_ids.tolist()[0]
                   self.assertListEqual(lowerCamelCase__     ,lowerCamelCase__)



       @require_torch
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   UpperCAmelCase_  :	Union[str, Any]				      =		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):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		[
                 '''Summary of the text.''',
                 '''Another summary.''',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   UpperCAmelCase_  :	List[Any]				      =		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):

             """simple docstring"""





             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   UpperCAmelCase_  :	Dict				      =		tokenizer(
                       ["I am a small frog" * 1024, "I am a small frog"]     ,padding=lowerCamelCase__     ,truncation=lowerCamelCase__     ,return_tensors="pt")
                   self.assertIsInstance(lowerCamelCase__     ,lowerCamelCase__)
                   self.assertEqual(batch.input_ids.shape     ,(2, 5122))



       @require_torch
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		['''A long paragraph for summarization.''']
             UpperCAmelCase_  :	str				      =		[
                 '''Summary of the text.''',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   UpperCAmelCase_  :	Dict				      =		tokenizer(lowerCamelCase__     ,return_tensors="pt")
                   UpperCAmelCase_  :	int				      =		tokenizer(text_target=lowerCamelCase__     ,return_tensors="pt")
                   UpperCAmelCase_  :	Union[str, Any]				      =		inputs['''input_ids''']
                   UpperCAmelCase_  :	Dict				      =		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):

             """simple docstring"""





             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                   UpperCAmelCase_  :	Union[str, Any]				      =		['''Summary of the text.''', '''Another summary.''']
                   UpperCAmelCase_  :	Optional[Any]				      =		[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

                   UpperCAmelCase_  :	List[str]				      =		tokenizer(lowerCamelCase__     ,padding=lowerCamelCase__)
                   UpperCAmelCase_  :	Union[str, Any]				      =		[[0] * len(lowerCamelCase__) for x in encoded_output['''input_ids''']]
                   UpperCAmelCase_  :	Optional[Any]				      =		tokenizer.pad(lowerCamelCase__)
                   self.assertSequenceEqual(outputs["global_attention_mask"]     ,lowerCamelCase__)



       def  A_   (     self):

             """simple docstring"""





             pass



       def  A_   (     self):

             """simple docstring"""





             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                         UpperCAmelCase_  :	Union[str, Any]				      =		self.rust_tokenizer_class.from_pretrained(lowerCamelCase__     ,**lowerCamelCase__)
                         UpperCAmelCase_  :	Dict				      =		self.tokenizer_class.from_pretrained(lowerCamelCase__     ,**lowerCamelCase__)
                         UpperCAmelCase_  :	Optional[Any]				      =		'''A, <mask> AllenNLP sentence.'''
                         UpperCAmelCase_  :	Optional[Any]				      =		tokenizer_r.encode_plus(lowerCamelCase__     ,add_special_tokens=lowerCamelCase__     ,return_token_type_ids=lowerCamelCase__)
                         UpperCAmelCase_  :	int				      =		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_  :	int				      =		tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
                         UpperCAmelCase_  :	Optional[int]				      =		tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])
                         self.assertSequenceEqual(tokens_p["input_ids"]     ,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
                         self.assertSequenceEqual(tokens_r["input_ids"]     ,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])

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





import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
   raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
__lowerCamelCase         =			False
__lowerCamelCase         =			False







def    _snake_case (       __snake_case    )     ->     Any:






      '''simple docstring'''







      return TrainCommand(__snake_case    )





class   snake_case_ (lowercase__  ):




       """simple docstring"""






       @staticmethod
       def  A_   (     lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		parser.add_parser("train"     ,help="CLI tool to train a model on a task.")

             train_parser.add_argument(
                 "--train_data"     ,type=lowercase     ,required=lowercase     ,help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."     ,)
             train_parser.add_argument(
                 "--column_label"     ,type=lowercase     ,default=0     ,help="Column of the dataset csv file with example labels.")
             train_parser.add_argument(
                 "--column_text"     ,type=lowercase     ,default=1     ,help="Column of the dataset csv file with example texts.")
             train_parser.add_argument(
                 "--column_id"     ,type=lowercase     ,default=2     ,help="Column of the dataset csv file with example ids.")
             train_parser.add_argument(
                 "--skip_first_row"     ,action="store_true"     ,help="Skip the first row of the csv file (headers).")

             train_parser.add_argument("--validation_data"     ,type=lowercase     ,default=""     ,help="path to validation dataset.")
             train_parser.add_argument(
                 "--validation_split"     ,type=lowercase     ,default=0.1     ,help="if validation dataset is not provided, fraction of train dataset to use as validation dataset."     ,)

             train_parser.add_argument("--output"     ,type=lowercase     ,default="./"     ,help="path to saved the trained model.")

             train_parser.add_argument(
                 "--task"     ,type=lowercase     ,default="text_classification"     ,help="Task to train the model on.")
             train_parser.add_argument(
                 "--model"     ,type=lowercase     ,default="bert-base-uncased"     ,help="Model's name or path to stored model.")
             train_parser.add_argument("--train_batch_size"     ,type=lowercase     ,default=32     ,help="Batch size for training.")
             train_parser.add_argument("--valid_batch_size"     ,type=lowercase     ,default=64     ,help="Batch size for validation.")
             train_parser.add_argument("--learning_rate"     ,type=lowercase     ,default=3E-5     ,help="Learning rate.")
             train_parser.add_argument("--adam_epsilon"     ,type=lowercase     ,default=1E-08     ,help="Epsilon for Adam optimizer.")
             train_parser.set_defaults(func=lowercase)



       def __init__(     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		logging.get_logger("transformers-cli/training")

             UpperCAmelCase_  :	List[Any]				      =		"tf" if is_tf_available() else "torch"

             os.makedirs(args.output     ,exist_ok=lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		args.output

             UpperCAmelCase_  :	Optional[int]				      =		args.column_label
             UpperCAmelCase_  :	Dict				      =		args.column_text
             UpperCAmelCase_  :	Optional[Any]				      =		args.column_id

             self.logger.info(F"""Loading {args.task} pipeline for {args.model}""")
             if args.task == "text_classification":
                   UpperCAmelCase_  :	List[Any]				      =		TextClassificationPipeline.from_pretrained(args.model)
             elif args.task == "token_classification":
                   raise NotImplementedError
             elif args.task == "question_answering":
                   raise NotImplementedError

             self.logger.info(F"""Loading dataset from {args.train_data}""")
             UpperCAmelCase_  :	str				      =		Processor.create_from_csv(
                 args.train_data     ,column_label=args.column_label     ,column_text=args.column_text     ,column_id=args.column_id     ,skip_first_row=args.skip_first_row     ,)
             UpperCAmelCase_  :	str				      =		None
             if args.validation_data:
                   self.logger.info(F"""Loading validation dataset from {args.validation_data}""")
                   UpperCAmelCase_  :	Union[str, Any]				      =		Processor.create_from_csv(
                       args.validation_data     ,column_label=args.column_label     ,column_text=args.column_text     ,column_id=args.column_id     ,skip_first_row=args.skip_first_row     ,)

             UpperCAmelCase_  :	List[str]				      =		args.validation_split
             UpperCAmelCase_  :	Tuple				      =		args.train_batch_size
             UpperCAmelCase_  :	Optional[int]				      =		args.valid_batch_size
             UpperCAmelCase_  :	Dict				      =		args.learning_rate
             UpperCAmelCase_  :	List[str]				      =		args.adam_epsilon



       def  A_   (     self):

             """simple docstring"""





             if self.framework == "tf":
                   return self.run_tf()
             return self.run_torch()



       def  A_   (     self):

             """simple docstring"""





             raise NotImplementedError



       def  A_   (     self):

             """simple docstring"""





             self.pipeline.fit(
                 self.train_dataset     ,validation_data=self.valid_dataset     ,validation_split=self.validation_split     ,learning_rate=self.learning_rate     ,adam_epsilon=self.adam_epsilon     ,train_batch_size=self.train_batch_size     ,valid_batch_size=self.valid_batch_size     ,)

             # Save trained pipeline
             self.pipeline.save_pretrained(self.output)
