






'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


snake_case_			:     Any  	=  {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

snake_case_			:     int  	=  {
    "vocab_file": {
        "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "unc-nlp/lxmert-base-uncased": (
            "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"
        ),
    },
}

snake_case_			:     List[Any]  	=  {
    "unc-nlp/lxmert-base-uncased": 512,
}

snake_case_			:     Optional[Any]  	=  {
    "unc-nlp/lxmert-base-uncased": {"do_lower_case": True},
}






class 							lowercase__     (							_SCREAMING_SNAKE_CASE       ):
	lowercase__      =      VOCAB_FILES_NAMES
	lowercase__      =      PRETRAINED_VOCAB_FILES_MAP
	lowercase__      =      PRETRAINED_INIT_CONFIGURATION
	lowercase__      =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__      =      LxmertTokenizer





	def __init__(			self :    Dict			,lowerCamelCase__ :    Tuple=None			,lowerCamelCase__ :    List[str]=None			,lowerCamelCase__ :    Dict=True			,lowerCamelCase__ :    List[Any]="[UNK]"			,lowerCamelCase__ :    Tuple="[SEP]"			,lowerCamelCase__ :    str="[PAD]"			,lowerCamelCase__ :    Any="[CLS]"			,lowerCamelCase__ :    str="[MASK]"			,lowerCamelCase__ :    Dict=True			,lowerCamelCase__ :    Union[str, Any]=None			,**lowerCamelCase__ :    Optional[int]			,):







							'''simple docstring'''
							super().__init__(
							    lowerCamelCase__			,tokenizer_file=lowerCamelCase__			,do_lower_case=lowerCamelCase__			,unk_token=lowerCamelCase__			,sep_token=lowerCamelCase__			,pad_token=lowerCamelCase__			,cls_token=lowerCamelCase__			,mask_token=lowerCamelCase__			,tokenize_chinese_chars=lowerCamelCase__			,strip_accents=lowerCamelCase__			,**lowerCamelCase__			,)

							_UpperCamelCase :		Optional[Any]			    =       json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
							if (
							    normalizer_state.get('lowercase'			,lowerCamelCase__			) != do_lower_case
							    or normalizer_state.get('strip_accents'			,lowerCamelCase__			) != strip_accents
							    or normalizer_state.get('handle_chinese_chars'			,lowerCamelCase__			) != tokenize_chinese_chars
							):
													_UpperCamelCase :		Tuple			    =       getattr(lowerCamelCase__			,normalizer_state.pop('type'			)			)
													_UpperCamelCase :		Optional[Any]			    =       do_lower_case
													_UpperCamelCase :		Dict			    =       strip_accents
													_UpperCamelCase :		List[Any]			    =       tokenize_chinese_chars
													_UpperCamelCase :		List[Any]			    =       normalizer_class(**lowerCamelCase__			)

							_UpperCamelCase :		Tuple			    =       do_lower_case





	def      UpperCamelCase_  (			self :    Tuple			,lowerCamelCase__ :    str			,lowerCamelCase__ :    Optional[Any]=None			):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

							if token_ids_a:
													output += token_ids_a + [self.sep_token_id]

							return output





	def      UpperCamelCase_  (			self :    str			,lowerCamelCase__ :    List[int]			,lowerCamelCase__ :    Optional[List[int]] = None			):







							'''simple docstring'''
							_UpperCamelCase :		str			    =       [self.sep_token_id]
							_UpperCamelCase :		Optional[int]			    =       [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      UpperCamelCase_  (			self :    Tuple			,lowerCamelCase__ :    str			,lowerCamelCase__ :    Optional[str] = None			):







							'''simple docstring'''
							_UpperCamelCase :		int			    =       self._tokenizer.model.save(lowerCamelCase__			,name=lowerCamelCase__			)
							return tuple(lowerCamelCase__			)







'''simple docstring'''
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 							lowercase__     (							unittest.TestCase       ):





	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       'hf-internal-testing/tiny-random-t5'
							_UpperCamelCase :		str			    =       AutoTokenizer.from_pretrained(lowerCamelCase__			)
							_UpperCamelCase :		int			    =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__			)

							_UpperCamelCase :		List[str]			    =       tokenizer('This is me'			,return_tensors='pt'			)

							_UpperCamelCase :		str			    =       model.to_bettertransformer()

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

							_UpperCamelCase :		Optional[Any]			    =       model.generate(**lowerCamelCase__			)

							_UpperCamelCase :		Union[str, Any]			    =       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__			)

													_UpperCamelCase :		List[Any]			    =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__			)

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

													_UpperCamelCase :		Optional[Any]			    =       model_reloaded.generate(**lowerCamelCase__			)
													self.assertTrue(torch.allclose(lowerCamelCase__			,lowerCamelCase__			)			)





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		List[Any]			    =       'hf-internal-testing/tiny-random-t5'
							_UpperCamelCase :		List[Any]			    =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__			)

							_UpperCamelCase :		List[str]			    =       model.to_bettertransformer()

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

													_UpperCamelCase :		str			    =       model.reverse_bettertransformer()
													model.save_pretrained(lowerCamelCase__			)


