
"""simple docstring"""




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
_A     :			Union[str, Any]  						=			False
_A     :			List[str]  						=			False


def     __magic_name__      ( __snake_case							:			Namespace    )					->							Tuple:
       return TrainCommand(__snake_case    )

class 						a__		( a_		):
   @staticmethod
   def      __magic_name__     (     _a							):
          lowercase       :							Optional[Any]									=				parser.add_parser("train"      ,       help="CLI tool to train a model on a task."							)

          train_parser.add_argument(
              "--train_data"      ,       type=_a      ,       required=_a      ,       help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."      ,       )
          train_parser.add_argument(
              "--column_label"      ,       type=_a      ,       default=0      ,       help="Column of the dataset csv file with example labels."							)
          train_parser.add_argument(
              "--column_text"      ,       type=_a      ,       default=1      ,       help="Column of the dataset csv file with example texts."							)
          train_parser.add_argument(
              "--column_id"      ,       type=_a      ,       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=_a      ,       default=""      ,       help="path to validation dataset."							)
          train_parser.add_argument(
              "--validation_split"      ,       type=_a      ,       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=_a      ,       default="./"      ,       help="path to saved the trained model."							)

          train_parser.add_argument(
              "--task"      ,       type=_a      ,       default="text_classification"      ,       help="Task to train the model on."							)
          train_parser.add_argument(
              "--model"      ,       type=_a      ,       default="bert-base-uncased"      ,       help="Model's name or path to stored model."							)
          train_parser.add_argument("--train_batch_size"      ,       type=_a      ,       default=32      ,       help="Batch size for training."							)
          train_parser.add_argument("--valid_batch_size"      ,       type=_a      ,       default=64      ,       help="Batch size for validation."							)
          train_parser.add_argument("--learning_rate"      ,       type=_a      ,       default=3E-5      ,       help="Learning rate."							)
          train_parser.add_argument("--adam_epsilon"      ,       type=_a      ,       default=1E-08      ,       help="Epsilon for Adam optimizer."							)
          train_parser.set_defaults(func=_a							)
   def __init__(     self      ,       _a							):
          lowercase       :							Optional[Any]									=				logging.get_logger("transformers-cli/training"							)

          lowercase       :							List[str]									=				"tf" if is_tf_available() else "torch"

          os.makedirs(args.output      ,       exist_ok=_a							)
          lowercase       :							Optional[int]									=				args.output

          lowercase       :							Optional[Any]									=				args.column_label
          lowercase       :							List[Any]									=				args.column_text
          lowercase       :							Optional[Any]									=				args.column_id

          self.logger.info(f"""Loading {args.task} pipeline for {args.model}"""							)
          if args.task == "text_classification":
                 lowercase       :							str									=				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}"""							)
          lowercase       :							Dict									=				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      ,       )
          lowercase       :							Union[str, Any]									=				None
          if args.validation_data:
                 self.logger.info(f"""Loading validation dataset from {args.validation_data}"""							)
                 lowercase       :							Tuple									=				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      ,       )

          lowercase       :							Tuple									=				args.validation_split
          lowercase       :							Tuple									=				args.train_batch_size
          lowercase       :							Optional[int]									=				args.valid_batch_size
          lowercase       :							Any									=				args.learning_rate
          lowercase       :							Optional[int]									=				args.adam_epsilon
   def      __magic_name__     (     self							):
          if self.framework == "tf":
                 return self.run_tf()
          return self.run_torch()
   def      __magic_name__     (     self							):
          raise NotImplementedError

   def      __magic_name__     (     self							):
          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							)




"""simple docstring"""




import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						a__		( a_,     unittest.TestCase		):
   __lowerCAmelCase				=							GPTaTokenizer
   __lowerCAmelCase				=							GPTaTokenizerFast
   __lowerCAmelCase				=							True
   __lowerCAmelCase				=							{"""add_prefix_space""": True}
   __lowerCAmelCase				=							False
   def      __magic_name__     (     self							):
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          lowercase       :							Any									=				[
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "\u0120",
              "\u0120l",
              "\u0120n",
              "\u0120lo",
              "\u0120low",
              "er",
              "\u0120lowest",
              "\u0120newer",
              "\u0120wider",
              "<unk>",
              "<|endoftext|>",
          ]
          lowercase       :							Any									=				dict(zip(_a      ,       range(len(_a							)							)							)							)
          lowercase       :							Dict									=				["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
          lowercase       :							int									=				{"unk_token": "<unk>"}

          lowercase       :							Tuple									=				os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES["vocab_file"]							)
          lowercase       :							Optional[Any]									=				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(_a							) + "\n"							)
          with open(self.merges_file      ,       "w"      ,       encoding="utf-8"							) as fp:
                 fp.write("\n".join(_a							)							)
   def      __magic_name__     (     self      ,       **_a							):
          kwargs.update(self.special_tokens_map							)
          return GPTaTokenizer.from_pretrained(self.tmpdirname      ,       **_a							)
   def      __magic_name__     (     self      ,       **_a							):
          kwargs.update(self.special_tokens_map							)
          return GPTaTokenizerFast.from_pretrained(self.tmpdirname      ,       **_a							)
   def      __magic_name__     (     self      ,       _a							):
          lowercase       :							Tuple									=				"lower newer"
          lowercase       :							Dict									=				"lower newer"
          return input_text, output_text
   def      __magic_name__     (     self							):
          lowercase       :							str									=				GPTaTokenizer(self.vocab_file      ,       self.merges_file      ,       **self.special_tokens_map							)
          lowercase       :							Union[str, Any]									=				"lower newer"
          lowercase       :							Any									=				["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
          lowercase       :							int									=				tokenizer.tokenize(_a      ,       add_prefix_space=_a							)
          self.assertListEqual(_a      ,       _a							)

          lowercase       :							int									=				tokens + [tokenizer.unk_token]
          lowercase       :							List[Any]									=				[14, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_a							)      ,       _a							)
   def      __magic_name__     (     self							):
          if not self.test_rust_tokenizer:
                 return

          lowercase       :							Union[str, Any]									=				self.get_tokenizer()
          lowercase       :							int									=				self.get_rust_tokenizer(add_prefix_space=_a							)

          lowercase       :							Union[str, Any]									=				"lower newer"

          # Testing tokenization
          lowercase       :							Union[str, Any]									=				tokenizer.tokenize(_a      ,       add_prefix_space=_a							)
          lowercase       :							List[Any]									=				rust_tokenizer.tokenize(_a							)
          self.assertListEqual(_a      ,       _a							)

          # Testing conversion to ids without special tokens
          lowercase       :							Dict									=				tokenizer.encode(_a      ,       add_special_tokens=_a      ,       add_prefix_space=_a							)
          lowercase       :							Dict									=				rust_tokenizer.encode(_a      ,       add_special_tokens=_a							)
          self.assertListEqual(_a      ,       _a							)

          # Testing conversion to ids with special tokens
          lowercase       :							Any									=				self.get_rust_tokenizer(add_prefix_space=_a							)
          lowercase       :							Any									=				tokenizer.encode(_a      ,       add_prefix_space=_a							)
          lowercase       :							Any									=				rust_tokenizer.encode(_a							)
          self.assertListEqual(_a      ,       _a							)

          # Testing the unknown token
          lowercase       :							Union[str, Any]									=				tokens + [rust_tokenizer.unk_token]
          lowercase       :							str									=				[14, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_a							)      ,       _a							)
   def      __magic_name__     (     self      ,       *_a      ,       **_a							):
          # It's very difficult to mix/test pretokenization with byte-level
          # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
          pass
   def      __magic_name__     (     self      ,       _a=15							):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
                        lowercase       :							int									=				self.rust_tokenizer_class.from_pretrained(_a      ,       **_a							)

                        # Simple input
                        lowercase       :							int									=				"This is a simple input"
                        lowercase       :							Optional[int]									=				["This is a simple input 1", "This is a simple input 2"]
                        lowercase       :							Union[str, Any]									=				("This is a simple input", "This is a pair")
                        lowercase       :							Any									=				[
                            ("This is a simple input 1", "This is a simple input 2"),
                            ("This is a simple pair 1", "This is a simple pair 2"),
                        ]

                        # Simple input tests
                        self.assertRaises(_a      ,       tokenizer_r.encode      ,       _a      ,       max_length=_a      ,       padding="max_length"							)

                        # Simple input
                        self.assertRaises(_a      ,       tokenizer_r.encode_plus      ,       _a      ,       max_length=_a      ,       padding="max_length"							)

                        # Simple input
                        self.assertRaises(
                            _a      ,       tokenizer_r.batch_encode_plus      ,       _a      ,       max_length=_a      ,       padding="max_length"      ,       )

                        # Pair input
                        self.assertRaises(_a      ,       tokenizer_r.encode      ,       _a      ,       max_length=_a      ,       padding="max_length"							)

                        # Pair input
                        self.assertRaises(_a      ,       tokenizer_r.encode_plus      ,       _a      ,       max_length=_a      ,       padding="max_length"							)

                        # Pair input
                        self.assertRaises(
                            _a      ,       tokenizer_r.batch_encode_plus      ,       _a      ,       max_length=_a      ,       padding="max_length"      ,       )
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				GPTaTokenizer.from_pretrained(self.tmpdirname      ,       pad_token="<pad>"							)

          # Simple input
          lowercase       :							int									=				"This is a simple input"
          lowercase       :							Tuple									=				["This is a simple input looooooooong", "This is a simple input"]
          lowercase       :							Any									=				("This is a simple input", "This is a pair")
          lowercase       :							str									=				[
              ("This is a simple input loooooong", "This is a simple input"),
              ("This is a simple pair loooooong", "This is a simple pair"),
          ]

          lowercase       :							List[Any]									=				tokenizer.pad_token_id

          lowercase       :							List[Any]									=				tokenizer(_a      ,       padding="max_length"      ,       max_length=30      ,       return_tensors="np"							)
          lowercase       :							List[str]									=				tokenizer(_a      ,       padding=_a      ,       truncate=_a      ,       return_tensors="np"							)
          lowercase       :							List[Any]									=				tokenizer(*_a      ,       padding="max_length"      ,       max_length=60      ,       return_tensors="np"							)
          lowercase       :							Any									=				tokenizer(_a      ,       padding=_a      ,       truncate=_a      ,       return_tensors="np"							)

          # s
          # test single string max_length padding
          self.assertEqual(out_s["input_ids"].shape[-1]      ,       30							)
          self.assertTrue(pad_token_id in out_s["input_ids"]							)
          self.assertTrue(0 in out_s["attention_mask"]							)

          # s2
          # test automatic padding
          self.assertEqual(out_sa["input_ids"].shape[-1]      ,       33							)
          # long slice doesn't have padding
          self.assertFalse(pad_token_id in out_sa["input_ids"][0]							)
          self.assertFalse(0 in out_sa["attention_mask"][0]							)
          # short slice does have padding
          self.assertTrue(pad_token_id in out_sa["input_ids"][1]							)
          self.assertTrue(0 in out_sa["attention_mask"][1]							)

          # p
          # test single pair max_length padding
          self.assertEqual(out_p["input_ids"].shape[-1]      ,       60							)
          self.assertTrue(pad_token_id in out_p["input_ids"]							)
          self.assertTrue(0 in out_p["attention_mask"]							)

          # p2
          # test automatic padding pair
          self.assertEqual(out_pa["input_ids"].shape[-1]      ,       52							)
          # long slice pair doesn't have padding
          self.assertFalse(pad_token_id in out_pa["input_ids"][0]							)
          self.assertFalse(0 in out_pa["attention_mask"][0]							)
          # short slice pair does have padding
          self.assertTrue(pad_token_id in out_pa["input_ids"][1]							)
          self.assertTrue(0 in out_pa["attention_mask"][1]							)
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				"$$$"
          lowercase       :							List[str]									=				GPTaTokenizer.from_pretrained(self.tmpdirname      ,       bos_token=_a      ,       add_bos_token=_a							)

          lowercase       :							List[Any]									=				"This is a simple input"
          lowercase       :							str									=				["This is a simple input 1", "This is a simple input 2"]

          lowercase       :							Union[str, Any]									=				tokenizer.bos_token_id

          lowercase       :							List[str]									=				tokenizer(_a							)
          lowercase       :							Any									=				tokenizer(_a							)

          self.assertEqual(out_s.input_ids[0]      ,       _a							)
          self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids							)							)

          lowercase       :							List[Any]									=				tokenizer.decode(out_s.input_ids							)
          lowercase       :							Tuple									=				tokenizer.batch_decode(out_sa.input_ids							)

          self.assertEqual(decode_s.split()[0]      ,       _a							)
          self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa							)							)
   def      __magic_name__     (     self							):
          pass

   def      __magic_name__     (     self							):
          # TODO: change to self.get_tokenizers() when the fast version is implemented
          lowercase       :							Dict									=				[self.get_tokenizer(do_lower_case=_a      ,       add_bos_token=_a							)]
          for tokenizer in tokenizers:
                 with self.subTest(f"""{tokenizer.__class__.__name__}"""							):
                        lowercase       :							Optional[Any]									=				"Encode this."
                        lowercase       :							Union[str, Any]									=				"This one too please."
                        lowercase       :							Dict									=				tokenizer.encode(_a      ,       add_special_tokens=_a							)
                        encoded_sequence += tokenizer.encode(_a      ,       add_special_tokens=_a							)
                        lowercase       :							Optional[Any]									=				tokenizer.encode_plus(
                            _a      ,       _a      ,       add_special_tokens=_a      ,       return_special_tokens_mask=_a      ,       )
                        lowercase       :							Tuple									=				encoded_sequence_dict["input_ids"]
                        lowercase       :							int									=				encoded_sequence_dict["special_tokens_mask"]
                        self.assertEqual(len(_a							)      ,       len(_a							)							)

                        lowercase       :							List[str]									=				[
                            (x if not special_tokens_mask[i] else None) for i, x in enumerate(_a							)
                        ]
                        lowercase       :							List[str]									=				[x for x in filtered_sequence if x is not None]
                        self.assertEqual(_a      ,       _a							)


@require_tokenizers
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          # More context:
          # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
          # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
          # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

          lowercase       :							Tuple									=				AutoTokenizer.from_pretrained("facebook/opt-350m"      ,       from_slow=_a							)
          lowercase       :							Any									=				"A photo of a cat"

          lowercase       :							List[str]									=				tokenizer.encode(
              _a      ,       )
          self.assertEqual(_a      ,       [2, 250, 1_345, 9, 10, 4_758]							)
          tokenizer.save_pretrained("test_opt"							)

          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("./test_opt"							)
          lowercase       :							Tuple									=				tokenizer.encode(
              _a      ,       )
          self.assertEqual(_a      ,       [2, 250, 1_345, 9, 10, 4_758]							)
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("facebook/opt-350m"      ,       use_slow=_a							)
          lowercase       :							Union[str, Any]									=				"A photo of a cat"

          lowercase       :							Union[str, Any]									=				tokenizer.encode(
              _a      ,       )
          # Same as above
          self.assertEqual(_a      ,       [2, 250, 1_345, 9, 10, 4_758]							)

   @unittest.skip("This test is failing because of a bug in the fast tokenizer"							)
   def      __magic_name__     (     self							):
          lowercase       :							int									=				AutoTokenizer.from_pretrained("facebook/opt-350m"      ,       from_slow=_a							)

          lowercase       :							List[Any]									=				"bos"
          lowercase       :							Optional[int]									=				tokenizer.get_vocab()["bos"]

          lowercase       :							List[str]									=				"A photo of a cat"
          lowercase       :							Dict									=				tokenizer.encode(
              _a      ,       )
          # We changed the bos token
          self.assertEqual(_a      ,       [31_957, 250, 1_345, 9, 10, 4_758]							)
          tokenizer.save_pretrained("./tok"							)
          lowercase       :							Tuple									=				AutoTokenizer.from_pretrained("./tok"							)
          self.assertTrue(tokenizer.is_fast							)
          lowercase       :							str									=				tokenizer.encode(
              _a      ,       )
          self.assertEqual(_a      ,       [31_957, 250, 1_345, 9, 10, 4_758]							)


