



"""simple docstring"""
import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


A:     int										=	logging.get_logger(__name__)



class    SCREAMING_SNAKE_CASE__						:

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			question_encoder
    UpperCAmelCase			:      Tuple			=			generator
    UpperCAmelCase			:      Dict			=			self.question_encoder

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   str:





    '''simple docstring'''




    if os.path.isfile(_SCREAMING_SNAKE_CASE		):
     raise ValueError(F"Provided path ({save_directory}) should be a directory, not a file"		)
    os.makedirs(_SCREAMING_SNAKE_CASE   ,  exist_ok=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[int]			=			os.path.join(_SCREAMING_SNAKE_CASE   ,  """question_encoder_tokenizer"""		)
    UpperCAmelCase			:      Optional[Any]			=			os.path.join(_SCREAMING_SNAKE_CASE   ,  """generator_tokenizer"""		)
    self.question_encoder.save_pretrained(_SCREAMING_SNAKE_CASE		)
    self.generator.save_pretrained(_SCREAMING_SNAKE_CASE		)

   @classmethod
   def 			SCREAMING_SNAKE_CASE  (     cls   ,  _SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




    from ..auto.tokenization_auto import AutoTokenizer

    UpperCAmelCase			:      Optional[Any]			=			kwargs.pop("""config"""   ,  _SCREAMING_SNAKE_CASE		)

    if config is None:
     UpperCAmelCase			:      Tuple			=			RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Any			=			AutoTokenizer.from_pretrained(
        _SCREAMING_SNAKE_CASE   ,  config=config.question_encoder   ,  subfolder="""question_encoder_tokenizer"""		)
    UpperCAmelCase			:      List[Any]			=			AutoTokenizer.from_pretrained(
        _SCREAMING_SNAKE_CASE   ,  config=config.generator   ,  subfolder="""generator_tokenizer"""		)
    return cls(question_encoder=_SCREAMING_SNAKE_CASE   ,  generator=_SCREAMING_SNAKE_CASE		)

   def __call__(     self   ,  *_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




    return self.current_tokenizer(*_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  *_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   Optional[Any]:





    '''simple docstring'''




    return self.generator.batch_decode(*_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  *_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)						->   Union[str, Any]:





    '''simple docstring'''




    return self.generator.decode(*_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE		)

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





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			self.question_encoder

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			self.generator




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = "longest"   ,  _SCREAMING_SNAKE_CASE = None   ,  _SCREAMING_SNAKE_CASE = True   ,  **_SCREAMING_SNAKE_CASE   ,  )						->   BatchEncoding:





    '''simple docstring'''




    warnings.warn(
        """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
        """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
        """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
        """details"""   ,  _SCREAMING_SNAKE_CASE   ,  )
    if max_length is None:
     UpperCAmelCase			:      Tuple			=			self.current_tokenizer.model_max_length
    UpperCAmelCase			:      Any			=			self(
        _SCREAMING_SNAKE_CASE   ,  add_special_tokens=_SCREAMING_SNAKE_CASE   ,  return_tensors=_SCREAMING_SNAKE_CASE   ,  max_length=_SCREAMING_SNAKE_CASE   ,  padding=_SCREAMING_SNAKE_CASE   ,  truncation=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )
    if tgt_texts is None:
     return model_inputs
    # Process tgt_texts
    if max_target_length is None:
     UpperCAmelCase			:      Optional[int]			=			self.current_tokenizer.model_max_length
    UpperCAmelCase			:      str			=			self(
        text_target=_SCREAMING_SNAKE_CASE   ,  add_special_tokens=_SCREAMING_SNAKE_CASE   ,  return_tensors=_SCREAMING_SNAKE_CASE   ,  padding=_SCREAMING_SNAKE_CASE   ,  max_length=_SCREAMING_SNAKE_CASE   ,  truncation=_SCREAMING_SNAKE_CASE   ,  **_SCREAMING_SNAKE_CASE   ,  )
    UpperCAmelCase			:      Any			=			labels["""input_ids"""]
    return model_inputs





"""simple docstring"""
import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    unittest.TestCase				):
   __lowerCAmelCase						:     Any		=		XLMTokenizer
   __lowerCAmelCase						:     List[Any]		=		False

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





    '''simple docstring'''




    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    UpperCAmelCase			:      Optional[int]			=			[
        """l""",
        """o""",
        """w""",
        """e""",
        """r""",
        """s""",
        """t""",
        """i""",
        """d""",
        """n""",
        """w</w>""",
        """r</w>""",
        """t</w>""",
        """lo""",
        """low""",
        """er</w>""",
        """low</w>""",
        """lowest</w>""",
        """newer</w>""",
        """wider</w>""",
        """<unk>""",
    ]
    UpperCAmelCase			:      str			=			dict(zip(_SCREAMING_SNAKE_CASE   ,  range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
    UpperCAmelCase			:      List[Any]			=			["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

    UpperCAmelCase			:      Optional[Any]			=			os.path.join(self.tmpdirname   ,  VOCAB_FILES_NAMES["""vocab_file"""]		)
    UpperCAmelCase			:      Optional[Any]			=			os.path.join(self.tmpdirname   ,  VOCAB_FILES_NAMES["""merges_file"""]		)
    with open(self.vocab_file   ,  """w"""		) as fp:
     fp.write(json.dumps(_SCREAMING_SNAKE_CASE		)		)
    with open(self.merges_file   ,  """w"""		) as fp:
     fp.write("""\n""".join(_SCREAMING_SNAKE_CASE		)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			"""lower newer"""
    UpperCAmelCase			:      Dict			=			"""lower newer"""
    return input_text, output_text

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





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			XLMTokenizer(self.vocab_file   ,  self.merges_file		)

    UpperCAmelCase			:      Optional[int]			=			"""lower"""
    UpperCAmelCase			:      Optional[int]			=			["""low""", """er</w>"""]
    UpperCAmelCase			:      List[Any]			=			tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)
    self.assertListEqual(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			tokens + ["""<unk>"""]
    UpperCAmelCase			:      List[Any]			=			[14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)   ,  _SCREAMING_SNAKE_CASE		)




   @slow
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			XLMTokenizer.from_pretrained("""xlm-mlm-en-2048"""		)

    UpperCAmelCase			:      int			=			tokenizer.encode("""sequence builders"""   ,  add_special_tokens=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			tokenizer.encode("""multi-sequence build"""   ,  add_special_tokens=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Tuple			=			tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      str			=			tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    assert encoded_sentence == [0] + text + [1]
    assert encoded_pair == [0] + text + [1] + text_a + [1]
