




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       ):






    """simple docstring"""





    lowerCAmelCase        =				GPTaTokenizer
    lowerCAmelCase        =				GPTaTokenizerFast
    lowerCAmelCase        =				True
    lowerCAmelCase        =				{'''add_prefix_space''': True}
    lowerCAmelCase        =				False





    def 							a__				(				self  )			->		Tuple:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         UpperCAmelCase_      :							Dict       =      [
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''\u0120''',
             '''\u0120l''',
             '''\u0120n''',
             '''\u0120lo''',
             '''\u0120low''',
             '''er''',
             '''\u0120lowest''',
             '''\u0120newer''',
             '''\u0120wider''',
             '''<unk>''',
             '''<|endoftext|>''',
         ]
         UpperCAmelCase_      :							Any       =      dict(zip(_SCREAMING_SNAKE_CASE       ,range(len(_SCREAMING_SNAKE_CASE  )  )  )  )
         UpperCAmelCase_      :							str       =      ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
         UpperCAmelCase_      :							int       =      {'''unk_token''': '''<unk>'''}

         UpperCAmelCase_      :							Any       =      os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES['''vocab_file''']  )
         UpperCAmelCase_      :							Tuple       =      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(_SCREAMING_SNAKE_CASE  ) + '''\n'''  )
         with open(self.merges_file       ,'''w'''       ,encoding='''utf-8'''  ) as fp:
              fp.write('''\n'''.join(_SCREAMING_SNAKE_CASE  )  )





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		List[Any]:
         kwargs.update(self.special_tokens_map  )
         return GPTaTokenizer.from_pretrained(self.tmpdirname       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		str:
         kwargs.update(self.special_tokens_map  )
         return GPTaTokenizerFast.from_pretrained(self.tmpdirname       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		int:
         UpperCAmelCase_      :							Dict       =      '''lower newer'''
         UpperCAmelCase_      :							int       =      '''lower newer'''
         return input_text, output_text





    def 							a__				(				self  )			->		str:
         UpperCAmelCase_      :							str       =      GPTaTokenizer(self.vocab_file       ,self.merges_file       ,**self.special_tokens_map  )
         UpperCAmelCase_      :							List[str]       =      '''lower newer'''
         UpperCAmelCase_      :							str       =      ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
         UpperCAmelCase_      :							List[str]       =      tokenizer.tokenize(_SCREAMING_SNAKE_CASE       ,add_prefix_space=_SCREAMING_SNAKE_CASE  )
         self.assertListEqual(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Dict       =      tokens + [tokenizer.unk_token]
         UpperCAmelCase_      :							List[Any]       =      [14, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE  )       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		List[str]:
         if not self.test_rust_tokenizer:
              return

         UpperCAmelCase_      :							int       =      self.get_tokenizer()
         UpperCAmelCase_      :							Dict       =      self.get_rust_tokenizer(add_prefix_space=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Tuple       =      '''lower newer'''

         # Testing tokenization
         UpperCAmelCase_      :							Optional[Any]       =      tokenizer.tokenize(_SCREAMING_SNAKE_CASE       ,add_prefix_space=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE  )
         self.assertListEqual(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

         # Testing conversion to ids without special tokens
         UpperCAmelCase_      :							List[str]       =      tokenizer.encode(_SCREAMING_SNAKE_CASE       ,add_special_tokens=_SCREAMING_SNAKE_CASE       ,add_prefix_space=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							str       =      rust_tokenizer.encode(_SCREAMING_SNAKE_CASE       ,add_special_tokens=_SCREAMING_SNAKE_CASE  )
         self.assertListEqual(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

         # Testing conversion to ids with special tokens
         UpperCAmelCase_      :							List[Any]       =      self.get_rust_tokenizer(add_prefix_space=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Any       =      tokenizer.encode(_SCREAMING_SNAKE_CASE       ,add_prefix_space=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      rust_tokenizer.encode(_SCREAMING_SNAKE_CASE  )
         self.assertListEqual(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

         # Testing the unknown token
         UpperCAmelCase_      :							List[str]       =      tokens + [rust_tokenizer.unk_token]
         UpperCAmelCase_      :							Any       =      [14, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE  )       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		Any:
         # 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 							a__				(				self       ,_SCREAMING_SNAKE_CASE=15  )			->		str:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
                   UpperCAmelCase_      :							int       =      self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )

                   # Simple input
                   UpperCAmelCase_      :							List[Any]       =      '''This is a simple input'''
                   UpperCAmelCase_      :							Union[str, Any]       =      ['''This is a simple input 1''', '''This is a simple input 2''']
                   UpperCAmelCase_      :							List[str]       =      ('''This is a simple input''', '''This is a pair''')
                   UpperCAmelCase_      :							List[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(_SCREAMING_SNAKE_CASE       ,tokenizer_r.encode       ,_SCREAMING_SNAKE_CASE       ,max_length=_SCREAMING_SNAKE_CASE       ,padding='''max_length'''  )

                   # Simple input
                   self.assertRaises(_SCREAMING_SNAKE_CASE       ,tokenizer_r.encode_plus       ,_SCREAMING_SNAKE_CASE       ,max_length=_SCREAMING_SNAKE_CASE       ,padding='''max_length'''  )

                   # Simple input
                   self.assertRaises(
                       _SCREAMING_SNAKE_CASE       ,tokenizer_r.batch_encode_plus       ,_SCREAMING_SNAKE_CASE       ,max_length=_SCREAMING_SNAKE_CASE       ,padding='''max_length'''       ,)

                   # Pair input
                   self.assertRaises(_SCREAMING_SNAKE_CASE       ,tokenizer_r.encode       ,_SCREAMING_SNAKE_CASE       ,max_length=_SCREAMING_SNAKE_CASE       ,padding='''max_length'''  )

                   # Pair input
                   self.assertRaises(_SCREAMING_SNAKE_CASE       ,tokenizer_r.encode_plus       ,_SCREAMING_SNAKE_CASE       ,max_length=_SCREAMING_SNAKE_CASE       ,padding='''max_length'''  )

                   # Pair input
                   self.assertRaises(
                       _SCREAMING_SNAKE_CASE       ,tokenizer_r.batch_encode_plus       ,_SCREAMING_SNAKE_CASE       ,max_length=_SCREAMING_SNAKE_CASE       ,padding='''max_length'''       ,)





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							str       =      GPTaTokenizer.from_pretrained(self.tmpdirname       ,pad_token='''<pad>'''  )

         # Simple input
         UpperCAmelCase_      :							List[str]       =      '''This is a simple input'''
         UpperCAmelCase_      :							int       =      ['''This is a simple input looooooooong''', '''This is a simple input''']
         UpperCAmelCase_      :							Union[str, Any]       =      ('''This is a simple input''', '''This is a pair''')
         UpperCAmelCase_      :							Optional[int]       =      [
             ('''This is a simple input loooooong''', '''This is a simple input'''),
             ('''This is a simple pair loooooong''', '''This is a simple pair'''),
         ]

         UpperCAmelCase_      :							Optional[Any]       =      tokenizer.pad_token_id

         UpperCAmelCase_      :							Any       =      tokenizer(_SCREAMING_SNAKE_CASE       ,padding='''max_length'''       ,max_length=30       ,return_tensors='''np'''  )
         UpperCAmelCase_      :							Tuple       =      tokenizer(_SCREAMING_SNAKE_CASE       ,padding=_SCREAMING_SNAKE_CASE       ,truncate=_SCREAMING_SNAKE_CASE       ,return_tensors='''np'''  )
         UpperCAmelCase_      :							str       =      tokenizer(*_SCREAMING_SNAKE_CASE       ,padding='''max_length'''       ,max_length=60       ,return_tensors='''np'''  )
         UpperCAmelCase_      :							Any       =      tokenizer(_SCREAMING_SNAKE_CASE       ,padding=_SCREAMING_SNAKE_CASE       ,truncate=_SCREAMING_SNAKE_CASE       ,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 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							List[Any]       =      '''$$$'''
         UpperCAmelCase_      :							List[str]       =      GPTaTokenizer.from_pretrained(self.tmpdirname       ,bos_token=_SCREAMING_SNAKE_CASE       ,add_bos_token=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Union[str, Any]       =      '''This is a simple input'''
         UpperCAmelCase_      :							Optional[int]       =      ['''This is a simple input 1''', '''This is a simple input 2''']

         UpperCAmelCase_      :							Any       =      tokenizer.bos_token_id

         UpperCAmelCase_      :							Optional[Any]       =      tokenizer(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[Any]       =      tokenizer(_SCREAMING_SNAKE_CASE  )

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

         UpperCAmelCase_      :							Optional[Any]       =      tokenizer.decode(out_s.input_ids  )
         UpperCAmelCase_      :							int       =      tokenizer.batch_decode(out_sa.input_ids  )

         self.assertEqual(decode_s.split()[0]       ,_SCREAMING_SNAKE_CASE  )
         self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa  )  )





    def 							a__				(				self  )			->		Dict:
         pass





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

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


@require_tokenizers
class   __a(					unittest.TestCase       ):






    """simple docstring"""





    def 							a__				(				self  )			->		str:
         # 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

         UpperCAmelCase_      :							Any       =      AutoTokenizer.from_pretrained('''facebook/opt-350m'''       ,from_slow=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[int]       =      '''A photo of a cat'''

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

         UpperCAmelCase_      :							List[Any]       =      AutoTokenizer.from_pretrained('''./test_opt'''  )
         UpperCAmelCase_      :							List[str]       =      tokenizer.encode(
             _SCREAMING_SNAKE_CASE       ,)
         self.assertEqual(_SCREAMING_SNAKE_CASE       ,[2, 250, 1_345, 9, 10, 4_758]  )





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							Optional[Any]       =      AutoTokenizer.from_pretrained('''facebook/opt-350m'''       ,use_slow=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Dict       =      '''A photo of a cat'''

         UpperCAmelCase_      :							Tuple       =      tokenizer.encode(
             _SCREAMING_SNAKE_CASE       ,)
         # Same as above
         self.assertEqual(_SCREAMING_SNAKE_CASE       ,[2, 250, 1_345, 9, 10, 4_758]  )





    @unittest.skip('''This test is failing because of a bug in the fast tokenizer'''  )
    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							int       =      AutoTokenizer.from_pretrained('''facebook/opt-350m'''       ,from_slow=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							str       =      '''bos'''
         UpperCAmelCase_      :							str       =      tokenizer.get_vocab()['''bos''']

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





from dataclasses import dataclass, field
from typing import Optional

@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Model name or path of model to be trained.'''}       )
    lowerCAmelCase        =				field(
        default='''./'''    ,						metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot-clean-train'''    ,						metadata={'''help''': '''Name or path of training dataset.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot-clean-valid'''    ,						metadata={'''help''': '''Name or path of validation dataset.'''}       )
    lowerCAmelCase        =				field(default=2    ,						metadata={'''help''': '''Batch size for training.'''}       )
    lowerCAmelCase        =				field(default=2    ,						metadata={'''help''': '''Batch size for evaluation.'''}       )
    lowerCAmelCase        =				field(default=0.1    ,						metadata={'''help''': '''Value of weight decay.'''}       )
    lowerCAmelCase        =				field(
        default=1_0000    ,						metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''}       )
    lowerCAmelCase        =				field(default=2E-4    ,						metadata={'''help''': '''Learning rate fo training.'''}       )
    lowerCAmelCase        =				field(default='''cosine'''    ,						metadata={'''help''': '''Learning rate.'''}       )
    lowerCAmelCase        =				field(
        default=750    ,						metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}       )
    lowerCAmelCase        =				field(
        default=16    ,						metadata={'''help''': '''Number of gradient accumulation steps.'''}       )
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}       )
    lowerCAmelCase        =				field(default=5_0000    ,						metadata={'''help''': '''Maximum number of training steps.'''}       )
    lowerCAmelCase        =				field(
        default=-1    ,						metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}       )
    lowerCAmelCase        =				field(default=1024    ,						metadata={'''help''': '''Sequence lengths used for training.'''}       )
    lowerCAmelCase        =				field(default=1    ,						metadata={'''help''': '''Training seed.'''}       )
    lowerCAmelCase        =				field(
        default=1024    ,						metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''If True the data is pretokenized.'''}       )


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Model name or path of model to be evaluated.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot-clean-valid'''    ,						metadata={'''help''': '''Name or path of validation dataset.'''}       )
    lowerCAmelCase        =				field(default=2    ,						metadata={'''help''': '''Batch size used for evaluation.'''}       )
    lowerCAmelCase        =				field(
        default=-1    ,						metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}       )
    lowerCAmelCase        =				field(default=1024    ,						metadata={'''help''': '''Length of sequences to be evaluated.'''}       )
    lowerCAmelCase        =				field(default=1    ,						metadata={'''help''': '''Random seed used for evaluation.'''}       )


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Model name or path of model to be evaluated.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''Number of workers used for code evaluation.'''}       )
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''Sample from the language model\'s output distribution.'''}       )
    lowerCAmelCase        =				field(default=0.2    ,						metadata={'''help''': '''Sampling temperature used for generation.'''}       )
    lowerCAmelCase        =				field(default=256    ,						metadata={'''help''': '''Maximum number of newly generated tokens.'''}       )
    lowerCAmelCase        =				field(default=0    ,						metadata={'''help''': '''Top-k parameter used for generation.'''}       )
    lowerCAmelCase        =				field(default=0.95    ,						metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}       )
    lowerCAmelCase        =				field(default=10    ,						metadata={'''help''': '''Number of generations to run in parallel.'''}       )
    lowerCAmelCase        =				field(
        default=200    ,						metadata={'''help''': '''Number of completions to generate for each sample.'''}       )
    lowerCAmelCase        =				field(default=1    ,						metadata={'''help''': '''Random seed used for evaluation.'''}       )
    lowerCAmelCase        =				field(
        default='''eval_results.json'''    ,						metadata={'''help''': '''Random seed used for evaluation.'''}       )
    lowerCAmelCase        =				field(
        default='''0'''    ,						metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}       )
    lowerCAmelCase        =				field(
        default=-1    ,						metadata={
            '''help''': (
                '''Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive'''
                ''' number corresponds to which GPU device id to run on.'''
            )
        }    ,						)


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default=_a    ,						metadata={
            '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
        }    ,						)
    lowerCAmelCase        =				field(
        default='''transformersbook/codeparrot'''    ,						metadata={'''help''': '''Folder or name of dataset to process.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot-clean'''    ,						metadata={'''help''': '''Folder to save processed processed dataset.'''}       )
    lowerCAmelCase        =				field(
        default=10_0000    ,						metadata={'''help''': '''Number of files to save per JSON output file.'''}       )
    lowerCAmelCase        =				field(default='''content'''    ,						metadata={'''help''': '''Column containing text data to process.'''}       )
    lowerCAmelCase        =				field(
        default=1000    ,						metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}       )
    lowerCAmelCase        =				field(
        default=100    ,						metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}       )
    lowerCAmelCase        =				field(
        default=0.25    ,						metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}       )
    lowerCAmelCase        =				field(
        default=1.5    ,						metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}       )
    lowerCAmelCase        =				field(
        default=0.7    ,						metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Name or path to the tokenizer.'''}    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''If True, near-duplicate samples are removed.'''}       )
    lowerCAmelCase        =				field(
        default=0.85    ,						metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}       )


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''gpt2'''    ,						metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}       )
    lowerCAmelCase        =				field(
        default='''transformersbook/codeparrot-train'''    ,						metadata={'''help''': '''Dataset to train tokenizer on.'''}       )
    lowerCAmelCase        =				field(default='''content'''    ,						metadata={'''help''': '''Column containing text data to process.'''}       )
    lowerCAmelCase        =				field(default=20_0000    ,						metadata={'''help''': '''Number of examples to train tokenizer on.'''}       )
    lowerCAmelCase        =				field(
        default=3_2768    ,						metadata={'''help''': '''Number of examples to train the tokenizer on.'''}       )
    lowerCAmelCase        =				field(default='''codeparrot'''    ,						metadata={'''help''': '''Name of new tokenizer.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''Push saved tokenizer to the hub.'''}       )


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Name or path to the tokenizer.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot-clean-train'''    ,						metadata={'''help''': '''Name or path to the dataset to pretokenize.'''}       )
    lowerCAmelCase        =				field(
        default='''tokenized-codeparrot-train'''    ,						metadata={'''help''': '''Repo name of the pretokenized data.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''Number of workers used for code evaluation.'''}       )


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''gpt2-large'''    ,						metadata={'''help''': '''Configuration to use for model initialization.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Tokenizer attached to model.'''}       )
    lowerCAmelCase        =				field(default='''codeparrot'''    ,						metadata={'''help''': '''Name of the created model.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''Push saved tokenizer to the hub.'''}       )