

"""simple docstring"""


import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class   __a				(UpperCamelCase_ ,						unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE    :Tuple							=			CLIPTokenizer
     _SCREAMING_SNAKE_CASE    :List[str]							=			CLIPTokenizerFast
     _SCREAMING_SNAKE_CASE    :Tuple							=			True
     _SCREAMING_SNAKE_CASE    :str							=			{}
     _SCREAMING_SNAKE_CASE    :Tuple							=			False

     def        _a      (    self	)	->   Union[str, Any]:


           """simple docstring"""


           super().setUp()

           # fmt: off
           SCREAMING_SNAKE_CASE__ :   Any   		=						['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
           # fmt: on
           SCREAMING_SNAKE_CASE__ :   List[str]   		=						dict(zip(UpperCamelCase_						,     range(len(UpperCamelCase_	)	)	)	)
           SCREAMING_SNAKE_CASE__ :   List[Any]   		=						['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						{'unk_token': '<unk>'}

           SCREAMING_SNAKE_CASE__ :   List[Any]   		=						os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["""vocab_file"""]	)
           SCREAMING_SNAKE_CASE__ :   Union[str, 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(UpperCamelCase_	) + """\n"""	)
           with open(self.merges_file						,     """w"""						,     encoding="""utf-8"""	) as fp:
                 fp.write("""\n""".join(UpperCamelCase_	)	)

     def        _a      (    self						,     **_a	)	->   int:


           """simple docstring"""


           kwargs.update(self.special_tokens_map	)
           return CLIPTokenizer.from_pretrained(self.tmpdirname						,     **UpperCamelCase_	)

     def        _a      (    self						,     **_a	)	->   Optional[int]:


           """simple docstring"""


           kwargs.update(self.special_tokens_map	)
           return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,     **UpperCamelCase_	)

     def        _a      (    self						,     _a	)	->   List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Any   		=						'lower newer'
           SCREAMING_SNAKE_CASE__ :   Dict   		=						'lower newer'
           return input_text, output_text

     def        _a      (    self	)	->   List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						CLIPTokenizer(self.vocab_file						,     self.merges_file						,     **self.special_tokens_map	)
           SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						'lower newer'
           SCREAMING_SNAKE_CASE__ :   int   		=						['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
           SCREAMING_SNAKE_CASE__ :   Dict   		=						tokenizer.tokenize(UpperCamelCase_	)
           self.assertListEqual(UpperCamelCase_						,     UpperCamelCase_	)

           SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						tokens + [tokenizer.unk_token]
           SCREAMING_SNAKE_CASE__ :   int   		=						[10, 2, 16, 9, 3, 2, 16, 20]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_	)						,     UpperCamelCase_	)

     @require_ftfy
     def        _a      (    self	)	->   Union[str, Any]:


           """simple docstring"""


           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
                       SCREAMING_SNAKE_CASE__ :   List[str]   		=						self.tokenizer_class.from_pretrained(UpperCamelCase_						,     **UpperCamelCase_	)
                       SCREAMING_SNAKE_CASE__ :   Dict   		=						self.rust_tokenizer_class.from_pretrained(UpperCamelCase_						,     **UpperCamelCase_	)

                       SCREAMING_SNAKE_CASE__ :   int   		=						'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
                       SCREAMING_SNAKE_CASE__ :   List[str]   		=						tokenizer_s.tokenize(UpperCamelCase_	)
                       SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						tokenizer_r.tokenize(UpperCamelCase_	)

                       self.assertListEqual(UpperCamelCase_						,     UpperCamelCase_	)

                       # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
                       # with Tilde) encoded in 2 different ways
                       SCREAMING_SNAKE_CASE__ :   Tuple   		=						'xa\u0303y' + ' ' + 'x\xe3y'
                       SCREAMING_SNAKE_CASE__ :   Dict   		=						tokenizer_s.tokenize(UpperCamelCase_	)
                       SCREAMING_SNAKE_CASE__ :   List[str]   		=						tokenizer_r.tokenize(UpperCamelCase_	)

                       self.assertListEqual(UpperCamelCase_						,     UpperCamelCase_	)

                       # Test that the tokenization is identical on unicode of space type
                       SCREAMING_SNAKE_CASE__ :   Tuple   		=						[
                           '\u0009',  # (horizontal tab, '\t')
                           '\u000B',  # (vertical tab)
                           '\u000C',  # (form feed)
                           '\u0020',  # (space, ' ')
                           '\u200E',  # (left-to-right mark):w
                           '\u200F',  # (right-to-left mark)
                       ]
                       for unicode_seq in spaces_unicodes:
                             SCREAMING_SNAKE_CASE__ :   Dict   		=						tokenizer_s.tokenize(UpperCamelCase_	)
                             SCREAMING_SNAKE_CASE__ :   List[Any]   		=						tokenizer_r.tokenize(UpperCamelCase_	)

                             self.assertListEqual(UpperCamelCase_						,     UpperCamelCase_	)

                       # Test that the tokenization is identical on unicode of line break type
                       SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						[
                           '\u000A',  # (line feed, '\n')
                           '\r\n',  # (carriage return and line feed, '\r\n')
                           '\u000D',  # (carriage return, '\r')
                           '\r',  # (carriage return, '\r')
                           '\u000D',  # (carriage return, '\r')
                           '\u2028',  # (line separator)
                           '\u2029',  # (paragraph separator)
                           # "\u0085", # (next line)
                       ]

                       # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
                       # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
                       # space (and thus into an empty list).

                       for unicode_seq in line_break_unicodes:
                             SCREAMING_SNAKE_CASE__ :   List[Any]   		=						tokenizer_s.tokenize(UpperCamelCase_	)
                             SCREAMING_SNAKE_CASE__ :   str   		=						tokenizer_r.tokenize(UpperCamelCase_	)

                             self.assertListEqual(UpperCamelCase_						,     UpperCamelCase_	)

     def        _a      (    self	)	->   Union[str, Any]:


           """simple docstring"""


           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
                       SCREAMING_SNAKE_CASE__ :   str   		=						'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                       SCREAMING_SNAKE_CASE__ :   int   		=						f'''{text_of_1_token} {text_of_1_token}'''

                       SCREAMING_SNAKE_CASE__ :   Dict   		=						self.rust_tokenizer_class.from_pretrained(
                           UpperCamelCase_						,     use_fast=UpperCamelCase_						,     )
                       SCREAMING_SNAKE_CASE__ :   List[Any]   		=						tokenizer_r(UpperCamelCase_						,     return_offsets_mapping=UpperCamelCase_						,     add_special_tokens=UpperCamelCase_	)
                       self.assertEqual(encoding.offset_mapping[0]						,     (0, len(UpperCamelCase_	))	)
                       self.assertEqual(
                           encoding.offset_mapping[1]						,     (len(UpperCamelCase_	) + 1, len(UpperCamelCase_	) + 1 + len(UpperCamelCase_	))						,     )

                       SCREAMING_SNAKE_CASE__ :   Dict   		=						f''' {text}'''

                       SCREAMING_SNAKE_CASE__ :   Dict   		=						self.rust_tokenizer_class.from_pretrained(
                           UpperCamelCase_						,     use_fast=UpperCamelCase_						,     )
                       SCREAMING_SNAKE_CASE__ :   Tuple   		=						tokenizer_r(UpperCamelCase_						,     return_offsets_mapping=UpperCamelCase_						,     add_special_tokens=UpperCamelCase_	)
                       self.assertEqual(encoding.offset_mapping[0]						,     (1, 1 + len(UpperCamelCase_	))	)
                       self.assertEqual(
                           encoding.offset_mapping[1]						,     (1 + len(UpperCamelCase_	) + 1, 1 + len(UpperCamelCase_	) + 1 + len(UpperCamelCase_	))						,     )

     def        _a      (    self	)	->   Optional[int]:


           """simple docstring"""


           with self.assertRaises(UpperCamelCase_	) as context:
                 self.rust_tokenizer_class.from_pretrained("""robot-test/old-clip-tokenizer"""	)

           self.assertTrue(
               context.exception.args[0].startswith(
                   """The `backend_tokenizer` provided does not match the expected format."""	)	)

     @require_ftfy
     def        _a      (    self	)	->   Any:


           """simple docstring"""


           super().test_tokenization_python_rust_equals()





     def        _a      (    self	)	->   int:


           """simple docstring"""


           pass






"""simple docstring"""



import argparse
from copy import deepcopy

import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    set_seed,
)
def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      List[Any]     							=					argparse.ArgumentParser()
    parser.add_argument('--model_ckpt'    ,type=_snake_case    ,default='microsoft/unixcoder-base-nine' )
    parser.add_argument('--num_epochs'    ,type=_snake_case    ,default=5 )
    parser.add_argument('--batch_size'    ,type=_snake_case    ,default=6 )
    parser.add_argument('--gradient_accumulation_steps'    ,type=_snake_case    ,default=1 )
    parser.add_argument('--freeze'    ,type=_snake_case    ,default=_snake_case )
    parser.add_argument('--learning_rate'    ,type=_snake_case    ,default=5e-4 )
    parser.add_argument('--seed'    ,type=_snake_case    ,default=0 )
    parser.add_argument('--lr_scheduler_type'    ,type=_snake_case    ,default='cosine' )
    parser.add_argument('--num_warmup_steps'    ,type=_snake_case    ,default=10 )
    parser.add_argument('--weight_decay'    ,type=_snake_case    ,default=0.01 )
    parser.add_argument('--output_dir'    ,type=_snake_case    ,default='./results' )
    return parser.parse_args()


UpperCamelCase__          =	load('accuracy')
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					eval_pred
    UpperCAmelCase__	:      Optional[int]     							=					np.argmax(_snake_case    ,axis=1 )
    return metric.compute(predictions=_snake_case    ,references=_snake_case )





class 				a			(					lowercase      ):



      def __init__(   self     ,	UpperCamelCase_      ):
          super().__init__()
          UpperCAmelCase__	:      List[str]     							=					trainer



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ):
          if control.should_evaluate:
              UpperCAmelCase__	:      int     							=					deepcopy(UpperCamelCase_      )
              self._trainer.evaluate(eval_dataset=self._trainer.train_dataset     ,	metric_key_prefix='train'      )
              return control_copy




def 					lowerCamelCase   (     ):
    UpperCAmelCase__	:      int     							=					get_args()
    set_seed(args.seed )

    UpperCAmelCase__	:      Optional[int]     							=					load_dataset('codeparrot/codecomplex'    ,split='train' )
    UpperCAmelCase__	:      Tuple     							=					dataset.train_test_split(test_size=0.2 )
    UpperCAmelCase__	:      List[Any]     							=					train_test['test'].train_test_split(test_size=0.5 )
    UpperCAmelCase__	:      Tuple     							=					DatasetDict(
        {
            'train': train_test['train'],
            'test': test_validation['train'],
            'valid': test_validation['test'],
        } )

    print('Loading tokenizer and model' )
    UpperCAmelCase__	:      str     							=					AutoTokenizer.from_pretrained(args.model_ckpt )
    UpperCAmelCase__	:      Optional[Any]     							=					tokenizer.eos_token
    UpperCAmelCase__	:      Optional[Any]     							=					AutoModelForSequenceClassification.from_pretrained(args.model_ckpt    ,num_labels=7 )
    UpperCAmelCase__	:      Dict     							=					model.config.eos_token_id

    if args.freeze:
        for param in model.roberta.parameters():
            UpperCAmelCase__	:      Optional[int]     							=					False

    UpperCAmelCase__	:      Optional[Any]     							=					ClassLabel(num_classes=7    ,names=list(set(train_test_validation['train']['complexity'] ) ) )

    def tokenize(_snake_case ):
        UpperCAmelCase__	:      Dict     							=					tokenizer(example['src']    ,truncation=_snake_case    ,max_length=1024 )
        UpperCAmelCase__	:      Any     							=					labels.straint(example['complexity'] )
        return {
            "input_ids": inputs["input_ids"],
            "attention_mask": inputs["attention_mask"],
            "label": label,
        }

    UpperCAmelCase__	:      List[Any]     							=					train_test_validation.map(
        _snake_case    ,batched=_snake_case    ,remove_columns=train_test_validation['train'].column_names    ,)
    UpperCAmelCase__	:      Tuple     							=					DataCollatorWithPadding(tokenizer=_snake_case )

    UpperCAmelCase__	:      Union[str, Any]     							=					TrainingArguments(
        output_dir=args.output_dir    ,learning_rate=args.learning_rate    ,lr_scheduler_type=args.lr_scheduler_type    ,evaluation_strategy='epoch'    ,save_strategy='epoch'    ,logging_strategy='epoch'    ,per_device_train_batch_size=args.batch_size    ,per_device_eval_batch_size=args.batch_size    ,num_train_epochs=args.num_epochs    ,gradient_accumulation_steps=args.gradient_accumulation_steps    ,weight_decay=0.01    ,metric_for_best_model='accuracy'    ,run_name='complexity-java'    ,report_to='wandb'    ,)

    UpperCAmelCase__	:      Dict     							=					Trainer(
        model=_snake_case    ,args=_snake_case    ,train_dataset=tokenized_datasets['train']    ,eval_dataset=tokenized_datasets['valid']    ,tokenizer=_snake_case    ,data_collator=_snake_case    ,compute_metrics=_snake_case    ,)

    print('Training...' )
    trainer.add_callback(CustomCallback(_snake_case ) )
    trainer.train()


if __name__ == "__main__":
    main()

