






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


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



@require_sentencepiece
@require_tokenizers
class       a					(       lowercase__     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[int]       		=  XLMRobertaTokenizer
     lowerCAmelCase    :	Union[str, Any]       		=  XLMRobertaTokenizerFast
     lowerCAmelCase    :	List[Any]       		=  True
     lowerCAmelCase    :	Optional[int]       		=  True
     def   lowerCamelCase_       (     self       :      Tuple    ):
         super().setUp()

         # We have a SentencePiece fixture for testing
         UpperCAmelCase_										=					XLMRobertaTokenizer(_a ,   keep_accents=_a    )
         tokenizer.save_pretrained(self.tmpdirname    )
     def   lowerCamelCase_       (     self       :      str    ):
         UpperCAmelCase_										=					'<pad>'
         UpperCAmelCase_										=					1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a    ) ,   _a    )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a    ) ,   _a    )
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					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(_a    ) ,   10_02    )
     def   lowerCamelCase_       (     self       :      int    ):
         self.assertEqual(self.get_tokenizer().vocab_size ,   10_02    )
     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					XLMRobertaTokenizer(_a ,   keep_accents=_a    )

         UpperCAmelCase_										=					tokenizer.tokenize('''This is a test'''    )
         self.assertListEqual(_a ,   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']    )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(_a    ) ,   [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] ,   )

         UpperCAmelCase_										=					tokenizer.tokenize('''I was born in 92000, and this is falsé.'''    )
         self.assertListEqual(
             _a ,   [
                 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_										=					tokenizer.convert_tokens_to_ids(_a    )
         self.assertListEqual(
             _a ,   [
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                 #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
             ] ,   )

         UpperCAmelCase_										=					tokenizer.convert_ids_to_tokens(_a    )
         self.assertListEqual(
             _a ,   [
                 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       :      List[str]    ):






         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_										=					(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_										=					self.rust_tokenizer_class.from_pretrained(_a ,   **_a    )
                 UpperCAmelCase_										=					self.tokenizer_class.from_pretrained(_a ,   **_a    )

                 UpperCAmelCase_										=					tempfile.mkdtemp()

                 UpperCAmelCase_										=					tokenizer_r.save_pretrained(_a    )
                 UpperCAmelCase_										=					tokenizer_p.save_pretrained(_a    )

                 # 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(f for f in tokenizer_r_files if '''tokenizer.json''' not in f    )
                 self.assertSequenceEqual(_a ,   _a    )

                 # Checks everything loads correctly in the same way
                 UpperCAmelCase_										=					tokenizer_r.from_pretrained(_a    )
                 UpperCAmelCase_										=					tokenizer_p.from_pretrained(_a    )

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

                 shutil.rmtree(_a    )

                 # Save tokenizer rust, legacy_format=True
                 UpperCAmelCase_										=					tempfile.mkdtemp()

                 UpperCAmelCase_										=					tokenizer_r.save_pretrained(_a ,   legacy_format=_a    )
                 UpperCAmelCase_										=					tokenizer_p.save_pretrained(_a    )

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

                 # Checks everything loads correctly in the same way
                 UpperCAmelCase_										=					tokenizer_r.from_pretrained(_a    )
                 UpperCAmelCase_										=					tokenizer_p.from_pretrained(_a    )

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

                 shutil.rmtree(_a    )

                 # Save tokenizer rust, legacy_format=False
                 UpperCAmelCase_										=					tempfile.mkdtemp()

                 UpperCAmelCase_										=					tokenizer_r.save_pretrained(_a ,   legacy_format=_a    )
                 UpperCAmelCase_										=					tokenizer_p.save_pretrained(_a    )

                 # 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_										=					tokenizer_r.from_pretrained(_a    )
                 UpperCAmelCase_										=					tokenizer_p.from_pretrained(_a    )

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

                 shutil.rmtree(_a    )
     @cached_property
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''    )
     def   lowerCamelCase_       (     self       :      List[str]    ):






         with tempfile.NamedTemporaryFile() as f:
             shutil.copyfile(_a ,   f.name    )
             UpperCAmelCase_										=					XLMRobertaTokenizer(f.name ,   keep_accents=_a    )
             UpperCAmelCase_										=					pickle.dumps(_a    )
         pickle.loads(_a    )
     def   lowerCamelCase_       (     self       :      int    ):






         if not self.test_rust_tokenizer:
             return

         UpperCAmelCase_										=					self.get_tokenizer()
         UpperCAmelCase_										=					self.get_rust_tokenizer()

         UpperCAmelCase_										=					'I was born in 92000, and this is falsé.'

         UpperCAmelCase_										=					tokenizer.tokenize(_a    )
         UpperCAmelCase_										=					rust_tokenizer.tokenize(_a    )
         self.assertListEqual(_a ,   _a    )

         UpperCAmelCase_										=					tokenizer.encode(_a ,   add_special_tokens=_a    )
         UpperCAmelCase_										=					rust_tokenizer.encode(_a ,   add_special_tokens=_a    )
         self.assertListEqual(_a ,   _a    )

         UpperCAmelCase_										=					self.get_rust_tokenizer()
         UpperCAmelCase_										=					tokenizer.encode(_a    )
         UpperCAmelCase_										=					rust_tokenizer.encode(_a    )
         self.assertListEqual(_a ,   _a    )
     @slow
     def   lowerCamelCase_       (     self       :      str    ):
         UpperCAmelCase_										=					'Hello World!'
         UpperCAmelCase_										=					[0, 3_53_78, 66_61, 38, 2]
         # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
         # xlmr.eval()
         # xlmr.encode(symbols)

         self.assertListEqual(_a ,   self.big_tokenizer.encode(_a    )    )
     @slow
     def   lowerCamelCase_       (     self       :      Tuple    ):
         UpperCAmelCase_										=					(
             '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_										=					[
             0,
             32_93,
             83,
             10,
             45_52,
             49_89,
             79_86,
             6_78,
             10,
             59_15,
             1_11,
             17_94_59,
             12_48_50,
             4,
             60_44,
             2_37,
             12,
             6,
             5,
             6,
             4,
             67_80,
             7_05,
             15,
             13_88,
             44,
             3_78,
             1_01_14,
             7_11,
             1_52,
             20,
             6,
             5,
             2_23_76,
             6_42,
             12_21,
             1_51_90,
             3_41_53,
             4_50,
             56_08,
             9_59,
             11_19,
             5_77_02,
             1_36,
             1_86,
             47,
             10_98,
             2_93_67,
             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,
             60_44,
             2_37,
             62_84,
             5_09_01,
             5_28,
             31,
             90,
             34,
             9_27,
             2,
         ]
         # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
         # xlmr.eval()
         # xlmr.encode(symbols)

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




     @slow
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         UpperCAmelCase_										=					{'input_ids': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 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, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 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=_a ,   model_name='''xlm-roberta-base''' ,   revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' ,   )











from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowerCamelCase      			=						logging.get_logger(__name__)



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	str       		=  ['input_values', 'padding_mask']
     def __init__(     self       :      Optional[Any] ,   __snake_case       :      int = 1 ,   __snake_case       :      int = 2_40_00 ,   __snake_case       :      float = 0.0 ,   __snake_case       :      float = None ,   __snake_case       :      float = None ,   **__snake_case       :      Dict ,   ):
         super().__init__(feature_size=__snake_case ,   sampling_rate=__snake_case ,   padding_value=__snake_case ,   **__snake_case    )
         UpperCAmelCase_										=					chunk_length_s
         UpperCAmelCase_										=					overlap
     @property
     def   lowerCamelCase_       (     self       :      List[str]    ):
         if self.chunk_length_s is None:
             return None
         else:
             return int(self.chunk_length_s * self.sampling_rate    )
     @property
     def   lowerCamelCase_       (     self       :      List[str]    ):
         if self.chunk_length_s is None or self.overlap is None:
             return None
         else:
             return max(1 ,   int((1.0 - self.overlap) * self.chunk_length    )    )




     def __call__(     self       :      List[str] ,   __snake_case       :      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,   __snake_case       :      Optional[Union[bool, str, PaddingStrategy]] = None ,   __snake_case       :      Optional[bool] = False ,   __snake_case       :      Optional[int] = None ,   __snake_case       :      Optional[Union[str, TensorType]] = None ,   __snake_case       :      Optional[int] = None ,   ):
         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                     F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
                     F' {self.sampling_rate} and not {sampling_rate}.'    )
         else:
             logger.warning(
                 '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                 '''Failing to do so can result in silent errors that might be hard to debug.'''    )

         if padding and truncation:
             raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''    )
         elif padding is None:
             # by default let's pad the inputs
             UpperCAmelCase_										=					True

         UpperCAmelCase_										=					bool(
             isinstance(__snake_case ,   (list, tuple)    ) and (isinstance(raw_audio[0] ,   (np.ndarray, tuple, list)    ))    )

         if is_batched:
             UpperCAmelCase_										=					[np.asarray(__snake_case ,   dtype=np.floataa    ).T for audio in raw_audio]
         elif not is_batched and not isinstance(__snake_case ,   np.ndarray    ):
             UpperCAmelCase_										=					np.asarray(__snake_case ,   dtype=np.floataa    )
         elif isinstance(__snake_case ,   np.ndarray    ) and raw_audio.dtype is np.dtype(np.floataa    ):
             UpperCAmelCase_										=					raw_audio.astype(np.floataa    )

         # always return batch
         if not is_batched:
             UpperCAmelCase_										=					[np.asarray(__snake_case    ).T]

         # verify inputs are valid
         for idx, example in enumerate(__snake_case    ):
             if example.ndim > 2:
                 raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'    )
             if self.feature_size == 1 and example.ndim != 1:
                 raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'    )
             if self.feature_size == 2 and example.shape[-1] != 2:
                 raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'    )

         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					BatchFeature({'''input_values''': raw_audio}    )
         if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
             if truncation:
                 UpperCAmelCase_										=					min(array.shape[0] for array in raw_audio    )
                 UpperCAmelCase_										=					int(np.floor(max_length / self.chunk_stride    )    )
                 UpperCAmelCase_										=					(nb_step - 1) * self.chunk_stride + self.chunk_length
             elif padding:
                 UpperCAmelCase_										=					max(array.shape[0] for array in raw_audio    )
                 UpperCAmelCase_										=					int(np.ceil(max_length / self.chunk_stride    )    )
                 UpperCAmelCase_										=					(nb_step - 1) * self.chunk_stride + self.chunk_length
                 UpperCAmelCase_										=					'''max_length'''
             else:
                 UpperCAmelCase_										=					input_values

        # normal padding on batch
         if padded_inputs is None:
             UpperCAmelCase_										=					self.pad(
                 __snake_case ,   max_length=__snake_case ,   truncation=__snake_case ,   padding=__snake_case ,   return_attention_mask=__snake_case ,   )
             if padding:
                 UpperCAmelCase_										=					padded_inputs.pop('''attention_mask'''    )

         UpperCAmelCase_										=					[]
         for example in padded_inputs.pop('''input_values'''    ):
             if self.feature_size == 1:
                 UpperCAmelCase_										=					example[..., None]
             input_values.append(example.T    )

         UpperCAmelCase_										=					input_values
         if return_tensors is not None:
             UpperCAmelCase_										=					padded_inputs.convert_to_tensors(__snake_case    )

         return padded_inputs





