import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_			    =							get_tests_dir('''fixtures/test_sentencepiece.model''')




@require_sentencepiece
@require_tokenizers
class  __magic_name__  (		__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    Dict  			=   XLMRobertaTokenizer
   lowerCAmelCase      :    Optional[int]  			=   XLMRobertaTokenizerFast
   lowerCAmelCase      :    int  			=   True
   lowerCAmelCase      :    Any  			=   True



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    super().setUp()

    # We have a SentencePiece fixture for testing
    _UpperCamelCase:     Dict							= XLMRobertaTokenizer(_lowercase		,  keep_accents=_lowercase							)
    tokenizer.save_pretrained(self.tmpdirname							)



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= '''<pad>'''
    _UpperCamelCase:     Tuple							= 1

    self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase							)		,  _lowercase							)
    self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase							)		,  _lowercase							)



   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= list(self.get_tokenizer().get_vocab().keys()							)

    self.assertEqual(vocab_keys[0]		,  '''<s>'''							)
    self.assertEqual(vocab_keys[1]		,  '''<pad>'''							)
    self.assertEqual(vocab_keys[-1]		,  '''<mask>'''							)
    self.assertEqual(len(_lowercase							)		,  1_002							)



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    self.assertEqual(self.get_tokenizer().vocab_size		,  1_002							)



   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= XLMRobertaTokenizer(_lowercase		,  keep_accents=_lowercase							)

    _UpperCamelCase:     Any							= tokenizer.tokenize('''This is a test'''							)
    self.assertListEqual(_lowercase		,  ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']							)

    self.assertListEqual(
        tokenizer.convert_tokens_to_ids(_lowercase							)		,  [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]		,  )

    _UpperCamelCase:     List[Any]							= tokenizer.tokenize('''I was born in 92000, and this is falsé.'''							)
    self.assertListEqual(
        _lowercase		,  [
            SPIECE_UNDERLINE + '''I''',
            SPIECE_UNDERLINE + '''was''',
            SPIECE_UNDERLINE + '''b''',
            '''or''',
            '''n''',
            SPIECE_UNDERLINE + '''in''',
            SPIECE_UNDERLINE + '''''',
            '''9''',
            '''2''',
            '''0''',
            '''0''',
            '''0''',
            ''',''',
            SPIECE_UNDERLINE + '''and''',
            SPIECE_UNDERLINE + '''this''',
            SPIECE_UNDERLINE + '''is''',
            SPIECE_UNDERLINE + '''f''',
            '''al''',
            '''s''',
            '''é''',
            '''.''',
        ]		,  )
    _UpperCamelCase:     List[Any]							= tokenizer.convert_tokens_to_ids(_lowercase							)
    self.assertListEqual(
        _lowercase		,  [
            value + tokenizer.fairseq_offset
            for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
            #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
        ]		,  )

    _UpperCamelCase:     List[Any]							= tokenizer.convert_ids_to_tokens(_lowercase							)
    self.assertListEqual(
        _lowercase		,  [
            SPIECE_UNDERLINE + '''I''',
            SPIECE_UNDERLINE + '''was''',
            SPIECE_UNDERLINE + '''b''',
            '''or''',
            '''n''',
            SPIECE_UNDERLINE + '''in''',
            SPIECE_UNDERLINE + '''''',
            '''<unk>''',
            '''2''',
            '''0''',
            '''0''',
            '''0''',
            ''',''',
            SPIECE_UNDERLINE + '''and''',
            SPIECE_UNDERLINE + '''this''',
            SPIECE_UNDERLINE + '''is''',
            SPIECE_UNDERLINE + '''f''',
            '''al''',
            '''s''',
            '''<unk>''',
            '''.''',
        ]		,  )



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    if not self.test_slow_tokenizer:
     # as we don't have a slow version, we can't compare the outputs between slow and fast versions
     return

    _UpperCamelCase:     Optional[Any]							= (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
      _UpperCamelCase:     Tuple							= self.rust_tokenizer_class.from_pretrained(_lowercase		,  **_lowercase							)
      _UpperCamelCase:     str							= self.tokenizer_class.from_pretrained(_lowercase		,  **_lowercase							)

      _UpperCamelCase:     Dict							= tempfile.mkdtemp()

      _UpperCamelCase:     Any							= tokenizer_r.save_pretrained(_lowercase							)
      _UpperCamelCase:     str							= tokenizer_p.save_pretrained(_lowercase							)

      # Checks it save with the same files + the tokenizer.json file for the fast one
      self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files							)							)
      _UpperCamelCase:     Tuple							= tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f							)
      self.assertSequenceEqual(_lowercase		,  _lowercase							)

      # Checks everything loads correctly in the same way
      _UpperCamelCase:     Dict							= tokenizer_r.from_pretrained(_lowercase							)
      _UpperCamelCase:     int							= tokenizer_p.from_pretrained(_lowercase							)

      # Check special tokens are set accordingly on Rust and Python
      for key in tokenizer_pp.special_tokens_map:
       self.assertTrue(hasattr(_lowercase		,  _lowercase							)							)
       # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
       # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

      shutil.rmtree(_lowercase							)

      # Save tokenizer rust, legacy_format=True
      _UpperCamelCase:     Union[str, Any]							= tempfile.mkdtemp()

      _UpperCamelCase:     Union[str, Any]							= tokenizer_r.save_pretrained(_lowercase		,  legacy_format=_lowercase							)
      _UpperCamelCase:     Optional[int]							= tokenizer_p.save_pretrained(_lowercase							)

      # Checks it save with the same files
      self.assertSequenceEqual(_lowercase		,  _lowercase							)

      # Checks everything loads correctly in the same way
      _UpperCamelCase:     Dict							= tokenizer_r.from_pretrained(_lowercase							)
      _UpperCamelCase:     Optional[Any]							= tokenizer_p.from_pretrained(_lowercase							)

      # Check special tokens are set accordingly on Rust and Python
      for key in tokenizer_pp.special_tokens_map:
       self.assertTrue(hasattr(_lowercase		,  _lowercase							)							)

      shutil.rmtree(_lowercase							)

      # Save tokenizer rust, legacy_format=False
      _UpperCamelCase:     Dict							= tempfile.mkdtemp()

      _UpperCamelCase:     Optional[Any]							= tokenizer_r.save_pretrained(_lowercase		,  legacy_format=_lowercase							)
      _UpperCamelCase:     Union[str, Any]							= tokenizer_p.save_pretrained(_lowercase							)

      # Checks it saved the tokenizer.json file
      self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files							)							)

      # Checks everything loads correctly in the same way
      _UpperCamelCase:     List[str]							= tokenizer_r.from_pretrained(_lowercase							)
      _UpperCamelCase:     Tuple							= tokenizer_p.from_pretrained(_lowercase							)

      # Check special tokens are set accordingly on Rust and Python
      for key in tokenizer_pp.special_tokens_map:
       self.assertTrue(hasattr(_lowercase		,  _lowercase							)							)

      shutil.rmtree(_lowercase							)



   @cached_property
   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''							)



   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    with tempfile.NamedTemporaryFile() as f:
     shutil.copyfile(_lowercase		,  f.name							)
     _UpperCamelCase:     Any							= XLMRobertaTokenizer(f.name		,  keep_accents=_lowercase							)
     _UpperCamelCase:     Union[str, Any]							= pickle.dumps(_lowercase							)
    pickle.loads(_lowercase							)



   def   lowerCAmelCase						(	self					:  int							):



    """simple docstring"""



    if not self.test_rust_tokenizer:
     return

    _UpperCamelCase:     int							= self.get_tokenizer()
    _UpperCamelCase:     str							= self.get_rust_tokenizer()

    _UpperCamelCase:     Optional[Any]							= '''I was born in 92000, and this is falsé.'''

    _UpperCamelCase:     int							= tokenizer.tokenize(_lowercase							)
    _UpperCamelCase:     Union[str, Any]							= rust_tokenizer.tokenize(_lowercase							)
    self.assertListEqual(_lowercase		,  _lowercase							)

    _UpperCamelCase:     int							= tokenizer.encode(_lowercase		,  add_special_tokens=_lowercase							)
    _UpperCamelCase:     Union[str, Any]							= rust_tokenizer.encode(_lowercase		,  add_special_tokens=_lowercase							)
    self.assertListEqual(_lowercase		,  _lowercase							)

    _UpperCamelCase:     List[str]							= self.get_rust_tokenizer()
    _UpperCamelCase:     Optional[int]							= tokenizer.encode(_lowercase							)
    _UpperCamelCase:     Optional[int]							= rust_tokenizer.encode(_lowercase							)
    self.assertListEqual(_lowercase		,  _lowercase							)



   @slow
   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= '''Hello World!'''
    _UpperCamelCase:     int							= [0, 35_378, 6_661, 38, 2]
    # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
    # xlmr.eval()
    # xlmr.encode(symbols)

    self.assertListEqual(_lowercase		,  self.big_tokenizer.encode(_lowercase							)							)



   @slow
   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     int							= (
        '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
        ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
    )
    _UpperCamelCase:     Dict							= [
        0,
        3_293,
        83,
        10,
        4_552,
        4_989,
        7_986,
        678,
        10,
        5_915,
        111,
        179_459,
        124_850,
        4,
        6_044,
        237,
        12,
        6,
        5,
        6,
        4,
        6_780,
        705,
        15,
        1_388,
        44,
        378,
        10_114,
        711,
        152,
        20,
        6,
        5,
        22_376,
        642,
        1_221,
        15_190,
        34_153,
        450,
        5_608,
        959,
        1_119,
        57_702,
        136,
        186,
        47,
        1_098,
        29_367,
        47,
        # 4426, # What fairseq tokenizes from "<unk>": "_<"
        # 3678, # What fairseq tokenizes from "<unk>": "unk"
        # 2740, # What fairseq tokenizes from "<unk>": ">"
        3,  # What we tokenize from "<unk>": "<unk>"
        6,  # Residue from the tokenization: an extra sentencepiece underline
        4,
        6_044,
        237,
        6_284,
        50_901,
        528,
        31,
        90,
        34,
        927,
        2,
    ]
    # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
    # xlmr.eval()
    # xlmr.encode(symbols)

    self.assertListEqual(_lowercase		,  self.big_tokenizer.encode(_lowercase							)							)



   @slow
   def   lowerCAmelCase						(	self					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     List[str]							= {'''input_ids''': [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
    # fmt: on

    self.tokenizer_integration_test_util(
        expected_encoding=_lowercase		,  model_name='''xlm-roberta-base'''		,  revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''		,  )
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool




class  __magic_name__  (		__a    ):





   """simple docstring"""




   lowerCAmelCase      :    Optional[Any]  			=   '''philschmid/bart-large-cnn-samsum'''
   lowerCAmelCase      :    Any  			=   (
       '''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
       '''and returns a summary of the text.'''
   )
   lowerCAmelCase      :    Any  			=   '''summarizer'''
   lowerCAmelCase      :    Tuple  			=   AutoTokenizer
   lowerCAmelCase      :    Optional[Any]  			=   AutoModelForSeqaSeqLM

   lowerCAmelCase      :    Union[str, Any]  			=   ['''text''']
   lowerCAmelCase      :    Dict  			=   ['''text''']



   def   lowerCAmelCase						(	self					:  str		,  _lowercase					:  Union[str, Any]							):



    """simple docstring"""



    return self.pre_processor(_lowercase		,  return_tensors='''pt'''		,  truncation=_lowercase							)



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  Optional[Any]							):



    """simple docstring"""



    return self.model.generate(**_lowercase							)[0]



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  List[str]							):



    """simple docstring"""



    return self.pre_processor.decode(_lowercase		,  skip_special_tokens=_lowercase		,  clean_up_tokenization_spaces=_lowercase							)