





import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
       import torch




@require_torch
@require_optimum
@slow
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 def 				UpperCamelCase			(							self ):
        A__											=						"hf-internal-testing/tiny-random-t5"
        A__											=						AutoTokenizer.from_pretrained(lowerCAmelCase__ )
        A__											=						AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__ )

        A__											=						tokenizer('''This is me''',return_tensors='''pt''' )

        A__											=						model.to_bettertransformer()

        self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules() ) )

        A__											=						model.generate(**lowerCAmelCase__ )

        A__											=						model.reverse_bettertransformer()

        self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules() ) )

        with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(lowerCAmelCase__ )

               A__											=						AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__ )

               self.assertFalse(
                   any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )

               A__											=						model_reloaded.generate(**lowerCAmelCase__ )
               self.assertTrue(torch.allclose(lowerCAmelCase__,lowerCAmelCase__ ) )




 def 				UpperCamelCase			(							self ):
        A__											=						"hf-internal-testing/tiny-random-t5"
        A__											=						AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__ )

        A__											=						model.to_bettertransformer()

        with tempfile.TemporaryDirectory() as tmpdirname:
               with self.assertRaises(lowerCAmelCase__ ):
                      model.save_pretrained(lowerCAmelCase__ )

               A__											=						model.reverse_bettertransformer()
               model.save_pretrained(lowerCAmelCase__ )












"""simple docstring"""




from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


a						: List[str]     	=			logging.get_logger(__name__)

a						: List[Any]     	=			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a						: str     	=			{
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

a						: Tuple     	=			{'''allegro/herbert-base-cased''': 514}
a						: Optional[int]     	=			{}


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					str      					=VOCAB_FILES_NAMES
							lowerCamelCase   :					Dict      					=PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase   :					Dict      					=PRETRAINED_INIT_CONFIGURATION
							lowerCamelCase   :					int      					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase   :					Optional[Any]      					=HerbertTokenizer






							def __init__(   self      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__="<s>"      ,					lowerCAmelCase__="<unk>"      ,					lowerCAmelCase__="<pad>"      ,					lowerCAmelCase__="<mask>"      ,					lowerCAmelCase__="</s>"      ,					**lowerCAmelCase__      ,					)	-> Optional[int]:
								super().__init__(
								    lowerCAmelCase__      ,					lowerCAmelCase__      ,					tokenizer_file=lowerCAmelCase__      ,					cls_token=lowerCAmelCase__      ,					unk_token=lowerCAmelCase__      ,					pad_token=lowerCAmelCase__      ,					mask_token=lowerCAmelCase__      ,					sep_token=lowerCAmelCase__      ,					**lowerCAmelCase__      ,					)






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> List[int]:
								a      :		Optional[Any]	=				[self.cls_token_id]
								a      :		Any	=				[self.sep_token_id]
								if token_ids_a is None:
									return cls + token_ids_a + sep

								return cls + token_ids_a + sep + token_ids_a + sep






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = False      )	-> List[int]:
								if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=lowerCAmelCase__      ,					token_ids_a=lowerCAmelCase__      ,					already_has_special_tokens=lowerCAmelCase__      )

								if token_ids_a is None:
									return [1] + ([0] * len(lowerCAmelCase__      )) + [1]
								return [1] + ([0] * len(lowerCAmelCase__      )) + [1] + ([0] * len(lowerCAmelCase__      )) + [1]






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> List[int]:
								a      :		Dict	=				[self.sep_token_id]
								a      :		Tuple	=				[self.cls_token_id]

								if token_ids_a is None:
									return len(cls + token_ids_a + sep      ) * [0]
								return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]






							def     __a   (   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      )	-> Tuple[str]:
								a      :		List[str]	=				self._tokenizer.model.save(lowerCAmelCase__      ,					name=lowerCAmelCase__      )
								return tuple(lowerCAmelCase__      )


