






import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors

@require_tokenizers
class        _SCREAMING_SNAKE_CASE			(     _a						,  unittest.TestCase ):
	snake_case__				:	Optional[int]	      =    BartTokenizer
	snake_case__				:	Optional[int]	      =    BartTokenizerFast
	snake_case__				:	Optional[Any]	      =    True
	snake_case__				:	int	      =    filter_roberta_detectors





	def 		_A					(						self							:   str      ):
		super().setUp()
		UpperCamelCase	:Tuple		=      [
		    """l""",
		    """o""",
		    """w""",
		    """e""",
		    """r""",
		    """s""",
		    """t""",
		    """i""",
		    """d""",
		    """n""",
		    """\u0120""",
		    """\u0120l""",
		    """\u0120n""",
		    """\u0120lo""",
		    """\u0120low""",
		    """er""",
		    """\u0120lowest""",
		    """\u0120newer""",
		    """\u0120wider""",
		    """<unk>""",
		]
		UpperCamelCase	:List[Any]		=      dict(zip(__lowerCamelCase					,    range(len(__lowerCamelCase      )      )      )      )
		UpperCamelCase	:Union[str, Any]		=      ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
		UpperCamelCase	:List[str]		=      {"""unk_token""": """<unk>"""}

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





	def 		_A					(						self							:   List[Any]					,    **__lowerCamelCase							:   Optional[int]      ):
		kwargs.update(self.special_tokens_map      )
		return self.tokenizer_class.from_pretrained(self.tmpdirname					,    **__lowerCamelCase      )





	def 		_A					(						self							:   int					,    **__lowerCamelCase							:   List[Any]      ):
		kwargs.update(self.special_tokens_map      )
		return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					,    **__lowerCamelCase      )





	def 		_A					(						self							:   List[Any]					,    __lowerCamelCase							:   Optional[Any]      ):
		return "lower newer", "lower newer"





	@cached_property
	def 		_A					(						self							:   Optional[Any]      ):
		return BartTokenizer.from_pretrained("""facebook/bart-large"""      )





	@cached_property
	def 		_A					(						self							:   Dict      ):
		return BartTokenizerFast.from_pretrained("""facebook/bart-large"""      )





	@require_torch
	def 		_A					(						self							:   List[Any]      ):
		UpperCamelCase	:Union[str, Any]		=      ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
		UpperCamelCase	:Tuple		=      [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			UpperCamelCase	:Optional[Any]		=      tokenizer(__lowerCamelCase					,    max_length=len(__lowerCamelCase      )					,    padding=__lowerCamelCase					,    return_tensors="""pt"""      )
			self.assertIsInstance(__lowerCamelCase					,    __lowerCamelCase      )

			self.assertEqual((2, 9)					,    batch.input_ids.shape      )
			self.assertEqual((2, 9)					,    batch.attention_mask.shape      )
			UpperCamelCase	:Optional[Any]		=      batch.input_ids.tolist()[0]
			self.assertListEqual(__lowerCamelCase					,    __lowerCamelCase      )
			# Test that special tokens are reset





	@require_torch
	def 		_A					(						self							:   Optional[int]      ):
		UpperCamelCase	:Optional[Any]		=      ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			UpperCamelCase	:Tuple		=      tokenizer(__lowerCamelCase					,    padding=__lowerCamelCase					,    return_tensors="""pt"""      )
			# check if input_ids are returned and no labels
			self.assertIn("""input_ids"""					,    __lowerCamelCase      )
			self.assertIn("""attention_mask"""					,    __lowerCamelCase      )
			self.assertNotIn("""labels"""					,    __lowerCamelCase      )
			self.assertNotIn("""decoder_attention_mask"""					,    __lowerCamelCase      )





	@require_torch
	def 		_A					(						self							:   int      ):
		UpperCamelCase	:Tuple		=      [
		    """Summary of the text.""",
		    """Another summary.""",
		]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			UpperCamelCase	:str		=      tokenizer(text_target=__lowerCamelCase					,    max_length=32					,    padding="""max_length"""					,    return_tensors="""pt"""      )
			self.assertEqual(32					,    targets["""input_ids"""].shape[1]      )





	@require_torch
	def 		_A					(						self							:   Tuple      ):
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			UpperCamelCase	:Optional[int]		=      tokenizer(
			    ["""I am a small frog""" * 1_024, """I am a small frog"""]					,    padding=__lowerCamelCase					,    truncation=__lowerCamelCase					,    return_tensors="""pt"""      )
			self.assertIsInstance(__lowerCamelCase					,    __lowerCamelCase      )
			self.assertEqual(batch.input_ids.shape					,    (2, 1_024)      )





	@require_torch
	def 		_A					(						self							:   Optional[Any]      ):
		UpperCamelCase	:List[Any]		=      ["""A long paragraph for summarization."""]
		UpperCamelCase	:int		=      [
		    """Summary of the text.""",
		]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			UpperCamelCase	:Union[str, Any]		=      tokenizer(__lowerCamelCase					,    return_tensors="""pt"""      )
			UpperCamelCase	:Union[str, Any]		=      tokenizer(text_target=__lowerCamelCase					,    return_tensors="""pt"""      )
			UpperCamelCase	:Any		=      inputs["""input_ids"""]
			UpperCamelCase	:Optional[Any]		=      targets["""input_ids"""]
			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()      )
			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()      )
			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()      )
			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()      )





	def 		_A					(						self							:   Any      ):
		pass





	def 		_A					(						self							:   List[str]      ):
		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
				UpperCamelCase	:Dict		=      self.rust_tokenizer_class.from_pretrained(__lowerCamelCase					,    **__lowerCamelCase      )
				UpperCamelCase	:Optional[Any]		=      self.tokenizer_class.from_pretrained(__lowerCamelCase					,    **__lowerCamelCase      )
				UpperCamelCase	:Optional[Any]		=      """A, <mask> AllenNLP sentence."""
				UpperCamelCase	:int		=      tokenizer_r.encode_plus(__lowerCamelCase					,    add_special_tokens=__lowerCamelCase					,    return_token_type_ids=__lowerCamelCase      )
				UpperCamelCase	:Optional[Any]		=      tokenizer_p.encode_plus(__lowerCamelCase					,    add_special_tokens=__lowerCamelCase					,    return_token_type_ids=__lowerCamelCase      )

				# token_type_ids should put 0 everywhere
				self.assertEqual(sum(tokens_r["""token_type_ids"""]      )					,    sum(tokens_p["""token_type_ids"""]      )      )

				# attention_mask should put 1 everywhere, so sum over length should be 1
				self.assertEqual(
				    sum(tokens_r["""attention_mask"""]      ) / len(tokens_r["""attention_mask"""]      )					,    sum(tokens_p["""attention_mask"""]      ) / len(tokens_p["""attention_mask"""]      )					,    )

				UpperCamelCase	:List[Any]		=      tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]      )
				UpperCamelCase	:List[Any]		=      tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]      )

				# Rust correctly handles the space before the mask while python doesnt
				self.assertSequenceEqual(tokens_p["""input_ids"""]					,    [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]      )
				self.assertSequenceEqual(tokens_r["""input_ids"""]					,    [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]      )

				self.assertSequenceEqual(
				    __lowerCamelCase					,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]      )
				self.assertSequenceEqual(
				    __lowerCamelCase					,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]      )








import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
	UpperCAmelCase_					:		str           = None


UpperCAmelCase_					:		int           = logging.get_logger(__name__)

UpperCAmelCase_					:		List[str]           = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase_					:		Any           = {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase_					:		Any           = {
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}

class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	Dict	      =    VOCAB_FILES_NAMES
	snake_case__				:	Optional[int]	      =    PRETRAINED_VOCAB_FILES_MAP
	snake_case__				:	str	      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__				:	int	      =    ["""input_ids""", """attention_mask"""]
	snake_case__				:	Dict	      =    XLMRobertaTokenizer





	def __init__(						self							:   List[str]					,    __lowerCamelCase							:   List[Any]=None					,    __lowerCamelCase							:   int=None					,    __lowerCamelCase							:   Dict="<s>"					,    __lowerCamelCase							:   Optional[int]="</s>"					,    __lowerCamelCase							:   int="</s>"					,    __lowerCamelCase							:   Dict="<s>"					,    __lowerCamelCase							:   Optional[Any]="<unk>"					,    __lowerCamelCase							:   Union[str, Any]="<pad>"					,    __lowerCamelCase							:   Any="<mask>"					,    **__lowerCamelCase							:   List[str]					,    ):
		# Mask token behave like a normal word, i.e. include the space before it
		UpperCamelCase	:int		=      AddedToken(__lowerCamelCase					,    lstrip=__lowerCamelCase					,    rstrip=__lowerCamelCase      ) if isinstance(__lowerCamelCase					,    __lowerCamelCase      ) else mask_token

		super().__init__(
		    __lowerCamelCase					,    tokenizer_file=__lowerCamelCase					,    bos_token=__lowerCamelCase					,    eos_token=__lowerCamelCase					,    sep_token=__lowerCamelCase					,    cls_token=__lowerCamelCase					,    unk_token=__lowerCamelCase					,    pad_token=__lowerCamelCase					,    mask_token=__lowerCamelCase					,    **__lowerCamelCase					,    )

		UpperCamelCase	:Optional[int]		=      vocab_file
		UpperCamelCase	:List[Any]		=      False if not self.vocab_file else True





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   List[int]					,    __lowerCamelCase							:   Optional[List[int]] = None      ):

		if token_ids_a is None:
			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
		UpperCamelCase	:int		=      [self.cls_token_id]
		UpperCamelCase	:Any		=      [self.sep_token_id]
		return cls + token_ids_a + sep + sep + token_ids_a + sep





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   List[int]					,    __lowerCamelCase							:   Optional[List[int]] = None      ):
		UpperCamelCase	:str		=      [self.sep_token_id]
		UpperCamelCase	: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 + sep + token_ids_a + sep      ) * [0]





	def 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   str					,    __lowerCamelCase							:   Optional[str] = None      ):
		if not self.can_save_slow_tokenizer:
			raise ValueError(
			    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
			    """tokenizer."""      )

		if not os.path.isdir(__lowerCamelCase      ):
			logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""      )
			return
		UpperCamelCase	:Union[str, Any]		=      os.path.join(
		    __lowerCamelCase					,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

		if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowerCamelCase      ):
			copyfile(self.vocab_file					,    __lowerCamelCase      )

		return (out_vocab_file,)
