





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   __snake_case				(			_a       ,				unittest.TestCase   ):






    '''simple docstring'''
    _lowerCAmelCase     =	BartTokenizer
    _lowerCAmelCase     =	BartTokenizerFast
    _lowerCAmelCase     =	True
    _lowerCAmelCase     =	filter_roberta_detectors





    def 	A		(   self						:		Optional[Any]							):

      """simple docstring"""





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

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





    def 	A		(   self						:		Dict			,						**snake_case						:		Tuple							):

      """simple docstring"""





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





    def 	A		(   self						:		List[Any]			,						**snake_case						:		List[str]							):

      """simple docstring"""





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





    def 	A		(   self						:		Optional[int]			,						snake_case						:		Optional[Any]							):

      """simple docstring"""





      return "lower newer", "lower newer"





    @cached_property
    def 	A		(   self						:		Any							):

      """simple docstring"""





      return BartTokenizer.from_pretrained('''facebook/bart-large'''							)





    @cached_property
    def 	A		(   self						:		Optional[Any]							):

      """simple docstring"""





      return BartTokenizerFast.from_pretrained('''facebook/bart-large'''							)





    @require_torch
    def 	A		(   self						:		Tuple							):

      """simple docstring"""





      __lowerCAmelCase			=				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      __lowerCAmelCase			=				[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

        self.assertEqual((2, 9)			,						batch.input_ids.shape							)
        self.assertEqual((2, 9)			,						batch.attention_mask.shape							)
        __lowerCAmelCase			=				batch.input_ids.tolist()[0]
        self.assertListEqual(snake_case			,						snake_case							)
        # Test that special tokens are reset





    @require_torch
    def 	A		(   self						:		int							):

      """simple docstring"""





      __lowerCAmelCase			=				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        __lowerCAmelCase			=				tokenizer(snake_case			,						padding=snake_case			,						return_tensors='''pt'''							)
        # check if input_ids are returned and no labels
        self.assertIn('''input_ids'''			,						snake_case							)
        self.assertIn('''attention_mask'''			,						snake_case							)
        self.assertNotIn('''labels'''			,						snake_case							)
        self.assertNotIn('''decoder_attention_mask'''			,						snake_case							)





    @require_torch
    def 	A		(   self						:		List[str]							):

      """simple docstring"""





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





    @require_torch
    def 	A		(   self						:		List[str]							):

      """simple docstring"""





      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
        __lowerCAmelCase			=				tokenizer(
            ['''I am a small frog''' * 1_024, '''I am a small frog''']			,						padding=snake_case			,						truncation=snake_case			,						return_tensors='''pt'''							)
        self.assertIsInstance(snake_case			,						snake_case							)
        self.assertEqual(batch.input_ids.shape			,						(2, 1_024)							)





    @require_torch
    def 	A		(   self						:		Tuple							):

      """simple docstring"""





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

      """simple docstring"""





      pass





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

      """simple docstring"""





      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
          __lowerCAmelCase			=				self.rust_tokenizer_class.from_pretrained(snake_case			,						**snake_case							)
          __lowerCAmelCase			=				self.tokenizer_class.from_pretrained(snake_case			,						**snake_case							)
          __lowerCAmelCase			=				'''A, <mask> AllenNLP sentence.'''
          __lowerCAmelCase			=				tokenizer_r.encode_plus(snake_case			,						add_special_tokens=snake_case			,						return_token_type_ids=snake_case							)
          __lowerCAmelCase			=				tokenizer_p.encode_plus(snake_case			,						add_special_tokens=snake_case			,						return_token_type_ids=snake_case							)

          # 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''']							)			,						)

          __lowerCAmelCase			=				tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']							)
          __lowerCAmelCase			=				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(
              snake_case			,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']							)
          self.assertSequenceEqual(
              snake_case			,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']							)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__lowercase													=			{
    """configuration_speech_to_text_2""": ["""SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2Text2Config"""],
    """processing_speech_to_text_2""": ["""Speech2Text2Processor"""],
    """tokenization_speech_to_text_2""": ["""Speech2Text2Tokenizer"""],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowercase													=			[
          """SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """Speech2Text2ForCausalLM""",
          """Speech2Text2PreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
      from .processing_speech_to_text_a import SpeechaTextaProcessor
      from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speech_to_text_a import (
                SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechaTextaForCausalLM,
                SpeechaTextaPreTrainedModel,
            )

else:
      import sys

      __lowercase													=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
