





import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led 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







@require_tokenizers
class       __A							(   lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   LEDTokenizer
      UpperCAmelCase__							=   LEDTokenizerFast
      UpperCAmelCase__							=   True


      def 					__snake_case      (		self):



             """simple docstring"""





             super().setUp()
             _lowerCamelCase				:  Union[str, Any]						=		[
                 '''l''',
                 '''o''',
                 '''w''',
                 '''e''',
                 '''r''',
                 '''s''',
                 '''t''',
                 '''i''',
                 '''d''',
                 '''n''',
                 '''\u0120''',
                 '''\u0120l''',
                 '''\u0120n''',
                 '''\u0120lo''',
                 '''\u0120low''',
                 '''er''',
                 '''\u0120lowest''',
                 '''\u0120newer''',
                 '''\u0120wider''',
                 '''<unk>''',
             ]
             _lowerCamelCase				:  Union[str, Any]						=		dict(zip(a__   ,				range(len(a__))))
             _lowerCamelCase				:  str						=		['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
             _lowerCamelCase				:  str						=		{'''unk_token''': '''<unk>'''}

             _lowerCamelCase				:  Dict						=		os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file'''])
             _lowerCamelCase				:  Any						=		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(a__) + '''\n''')
             with open(self.merges_file   ,				'''w'''   ,				encoding='''utf-8''') as fp:
                    fp.write('''\n'''.join(a__))


      def 					__snake_case      (		self   ,				**a__):



             """simple docstring"""





             kwargs.update(self.special_tokens_map)
             return self.tokenizer_class.from_pretrained(self.tmpdirname   ,				**a__)


      def 					__snake_case      (		self   ,				**a__):



             """simple docstring"""





             kwargs.update(self.special_tokens_map)
             return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,				**a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return "lower newer", "lower newer"


      @cached_property
      def 					__snake_case      (		self):



             """simple docstring"""





             return LEDTokenizer.from_pretrained('''allenai/led-base-16384''')


      @cached_property
      def 					__snake_case      (		self):



             """simple docstring"""





             return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384''')


      @require_torch
      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
             _lowerCamelCase				:  List[Any]						=		[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    _lowerCamelCase				:  str						=		tokenizer(a__   ,				max_length=len(a__)   ,				padding=a__   ,				return_tensors='''pt''')
                    self.assertIsInstance(a__   ,				a__)

                    self.assertEqual((2, 9)   ,				batch.input_ids.shape)
                    self.assertEqual((2, 9)   ,				batch.attention_mask.shape)
                    _lowerCamelCase				:  List[Any]						=		batch.input_ids.tolist()[0]
                    self.assertListEqual(a__   ,				a__)


      @require_torch
      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  int						=		['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    _lowerCamelCase				:  Any						=		tokenizer(a__   ,				padding=a__   ,				return_tensors='''pt''')
                    self.assertIn('''input_ids'''   ,				a__)
                    self.assertIn('''attention_mask'''   ,				a__)
                    self.assertNotIn('''labels'''   ,				a__)
                    self.assertNotIn('''decoder_attention_mask'''   ,				a__)


      @require_torch
      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		[
                 '''Summary of the text.''',
                 '''Another summary.''',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    _lowerCamelCase				:  Dict						=		tokenizer(text_target=a__   ,				max_length=32   ,				padding='''max_length'''   ,				return_tensors='''pt''')
                    self.assertEqual(32   ,				targets['''input_ids'''].shape[1])


      @require_torch
      def 					__snake_case      (		self):



             """simple docstring"""





             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    _lowerCamelCase				:  List[Any]						=		tokenizer(
                        ['''I am a small frog''' * 1024, '''I am a small frog''']   ,				padding=a__   ,				truncation=a__   ,				return_tensors='''pt''')
                    self.assertIsInstance(a__   ,				a__)
                    self.assertEqual(batch.input_ids.shape   ,				(2, 5122))


      @require_torch
      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		['''A long paragraph for summarization.''']
             _lowerCamelCase				:  int						=		[
                 '''Summary of the text.''',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    _lowerCamelCase				:  Any						=		tokenizer(a__   ,				return_tensors='''pt''')
                    _lowerCamelCase				:  List[Any]						=		tokenizer(text_target=a__   ,				return_tensors='''pt''')
                    _lowerCamelCase				:  Union[str, Any]						=		inputs['''input_ids''']
                    _lowerCamelCase				:  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())


      @require_torch
      def 					__snake_case      (		self):



             """simple docstring"""





             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    _lowerCamelCase				:  Tuple						=		['''Summary of the text.''', '''Another summary.''']
                    _lowerCamelCase				:  Optional[int]						=		[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

                    _lowerCamelCase				:  Optional[Any]						=		tokenizer(a__   ,				padding=a__)
                    _lowerCamelCase				:  Dict						=		[[0] * len(a__) for x in encoded_output['''input_ids''']]
                    _lowerCamelCase				:  str						=		tokenizer.pad(a__)
                    self.assertSequenceEqual(outputs['''global_attention_mask''']   ,				a__)


      def 					__snake_case      (		self):



             """simple docstring"""





             pass



      def 					__snake_case      (		self):



             """simple docstring"""





             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
                           _lowerCamelCase				:  List[Any]						=		self.rust_tokenizer_class.from_pretrained(a__   ,				**a__)
                           _lowerCamelCase				:  Dict						=		self.tokenizer_class.from_pretrained(a__   ,				**a__)
                           _lowerCamelCase				:  str						=		'''A, <mask> AllenNLP sentence.'''
                           _lowerCamelCase				:  Optional[Any]						=		tokenizer_r.encode_plus(a__   ,				add_special_tokens=a__   ,				return_token_type_ids=a__)
                           _lowerCamelCase				:  Optional[int]						=		tokenizer_p.encode_plus(a__   ,				add_special_tokens=a__   ,				return_token_type_ids=a__)
                           self.assertEqual(sum(tokens_r['''token_type_ids'''])   ,				sum(tokens_p['''token_type_ids''']))
                           self.assertEqual(
                               sum(tokens_r['''attention_mask''']) / len(tokens_r['''attention_mask'''])   ,				sum(tokens_p['''attention_mask''']) / len(tokens_p['''attention_mask'''])   ,				)

                           _lowerCamelCase				:  str						=		tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''])
                           _lowerCamelCase				:  Dict						=		tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids'''])
                           self.assertSequenceEqual(tokens_p['''input_ids''']   ,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])
                           self.assertSequenceEqual(tokens_r['''input_ids''']   ,				[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])

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








import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase      							=   logging.get_logger(__name__)

_lowerCamelCase      							=   '▁'

_lowerCamelCase      							=   {
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
}

_lowerCamelCase      							=   {
    'vocab_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
        ),
    },
    'spm_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
        )
    },
}

_lowerCamelCase      							=   {
    'facebook/s2t-small-librispeech-asr': 1024,
}

_lowerCamelCase      							=   ['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']

_lowerCamelCase      							=   {'mustc': MUSTC_LANGS}







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   VOCAB_FILES_NAMES
      UpperCAmelCase__							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase__							=   MAX_MODEL_INPUT_SIZES
      UpperCAmelCase__							=   ["""input_ids""", """attention_mask"""]

      UpperCAmelCase__							=   []


      def __init__(		self   ,				a__   ,				a__   ,				a__="<s>"   ,				a__="</s>"   ,				a__="<pad>"   ,				a__="<unk>"   ,				a__=False   ,				a__=False   ,				a__=None   ,				a__=None   ,				a__ = None   ,				**a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  str						=		{} if sp_model_kwargs is None else sp_model_kwargs

             super().__init__(
                 bos_token=a__   ,				eos_token=a__   ,				unk_token=a__   ,				pad_token=a__   ,				do_upper_case=a__   ,				do_lower_case=a__   ,				tgt_lang=a__   ,				lang_codes=a__   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**a__   ,				)
             _lowerCamelCase				:  Optional[int]						=		do_upper_case
             _lowerCamelCase				:  Optional[Any]						=		do_lower_case

             _lowerCamelCase				:  Tuple						=		load_json(a__)
             _lowerCamelCase				:  Union[str, Any]						=		{v: k for k, v in self.encoder.items()}
             _lowerCamelCase				:  Tuple						=		spm_file
             _lowerCamelCase				:  Any						=		load_spm(a__   ,				self.sp_model_kwargs)

             if lang_codes is not None:
                    _lowerCamelCase				:  List[Any]						=		lang_codes
                    _lowerCamelCase				:  List[str]						=		LANGUAGES[lang_codes]
                    _lowerCamelCase				:  Any						=		[F"""<lang:{lang}>""" for lang in self.langs]
                    _lowerCamelCase				:  Optional[Any]						=		{lang: self.sp_model.PieceToId(F"""<lang:{lang}>""") for lang in self.langs}

                    _lowerCamelCase				:  List[str]						=		self.lang_tokens
                    _lowerCamelCase				:  str						=		tgt_lang if tgt_lang is not None else self.langs[0]

                    self.set_tgt_lang_special_tokens(self._tgt_lang)
             else:
                    _lowerCamelCase				:  Any						=		{}


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return len(self.encoder)


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self._tgt_lang


      @tgt_lang.setter
      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		new_tgt_lang
             self.set_tgt_lang_special_tokens(a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		self.lang_code_to_id[tgt_lang]
             _lowerCamelCase				:  Any						=		[lang_code_id]


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return self.sp_model.encode(a__   ,				out_type=a__)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return self.encoder.get(a__   ,				self.encoder[self.unk_token])


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             return self.decoder.get(a__   ,				self.unk_token)


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		[]
             _lowerCamelCase				:  List[str]						=		''''''
             for token in tokens:
                    # make sure that special tokens are not decoded using sentencepiece model
                    if token in self.all_special_tokens:
                           _lowerCamelCase				:  List[Any]						=		self.sp_model.decode(a__)
                           out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                           _lowerCamelCase				:  Optional[int]						=		[]
                    else:
                           current_sub_tokens.append(a__)
             _lowerCamelCase				:  Tuple						=		self.sp_model.decode(a__)
             out_string += decoded.upper() if self.do_upper_case else decoded
             return out_string.strip()


      def 					__snake_case      (		self   ,				a__   ,				a__=None):



             """simple docstring"""





             if token_ids_a is None:
                    return self.prefix_tokens + token_ids_a + [self.eos_token_id]
             # We don't expect to process pairs, but leave the pair logic for API consistency
             return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]


      def 					__snake_case      (		self   ,				a__   ,				a__ = None   ,				a__ = False):



             """simple docstring"""





             if already_has_special_tokens:
                    return super().get_special_tokens_mask(
                        token_ids_a=a__   ,				token_ids_a=a__   ,				already_has_special_tokens=a__)

             _lowerCamelCase				:  Tuple						=		[1] * len(self.prefix_tokens)
             _lowerCamelCase				:  Tuple						=		[1]
             if token_ids_a is None:
                    return prefix_ones + ([0] * len(a__)) + suffix_ones
             return prefix_ones + ([0] * len(a__)) + ([0] * len(a__)) + suffix_ones


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		self.encoder.copy()
             vocab.update(self.added_tokens_encoder)
             return vocab


      def __getstate__(		self):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		self.__dict__.copy()
             _lowerCamelCase				:  str						=		None
             return state


      def __setstate__(		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		d

             # for backward compatibility
             if not hasattr(self   ,				'''sp_model_kwargs'''):
                    _lowerCamelCase				:  List[Any]						=		{}

             _lowerCamelCase				:  Dict						=		load_spm(self.spm_file   ,				self.sp_model_kwargs)



      def 					__snake_case      (		self   ,				a__   ,				a__ = None):



             """simple docstring"""





             _lowerCamelCase				:  str						=		Path(a__)
             assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
             _lowerCamelCase				:  Any						=		save_dir / (
                 (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
             )
             _lowerCamelCase				:  Optional[Any]						=		save_dir / (
                 (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
             )

             save_json(self.encoder   ,				a__)

             if os.path.abspath(self.spm_file) != os.path.abspath(a__) and os.path.isfile(self.spm_file):
                    copyfile(self.spm_file   ,				a__)
             elif not os.path.isfile(self.spm_file):
                    with open(a__   ,				'''wb''') as fi:
                           _lowerCamelCase				:  List[str]						=		self.sp_model.serialized_model_proto()
                           fi.write(a__)

             return (str(a__), str(a__))




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       _lowerCamelCase				:  Optional[Any]						=		sentencepiece.SentencePieceProcessor(**lowercase_   )
       spm.Load(str(lowercase_   )   )
       return spm




def  __UpperCAmelCase(   lowercase_   ):
       with open(lowercase_			,     '''r'''   ) as f:
              return json.load(lowercase_   )




def  __UpperCAmelCase(   lowercase_			,     lowercase_   ):
       with open(lowercase_			,     '''w'''   ) as f:
              json.dump(lowercase_			,     lowercase_			,     indent=2   )

