
'''simple docstring'''


import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case_			:	str 	=       logging.get_logger(__name__)


snake_case_			:	Tuple 	=       {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

snake_case_			:	Optional[int] 	=       {
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
}

snake_case_			:	str 	=       {
    'allenai/longformer-base-4096': 4_096,
    'allenai/longformer-large-4096': 4_096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4_096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4_096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4_096,
}



@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 			__snake_case  (     ):
   UpperCamelCase	  =	(
       list(range(ord('''!'''),   ord('''~''') + 1)) + list(range(ord('''¡'''),   ord('''¬''') + 1)) + list(range(ord('''®'''),   ord('''ÿ''') + 1))
   )
   UpperCamelCase	  =	bs[:]
   UpperCamelCase	  =	0
   for b in range(2**8):
      if b not in bs:
         bs.append(_UpperCAmelCase)
         cs.append(2**8 + n)
         n += 1
   UpperCamelCase	  =	[chr(_UpperCAmelCase) for n in cs]
   return dict(zip(_UpperCAmelCase,   _UpperCAmelCase))



def 			__snake_case  (     _UpperCAmelCase       :							List[Any]):
   UpperCamelCase	  =	set()
   UpperCamelCase	  =	word[0]
   for char in word[1:]:
      pairs.add((prev_char, char))
      UpperCamelCase	  =	char
   return pairs


class     lowercase__						(	__UpperCAmelCase ):






   '''simple docstring'''







   _snake_case		 =				VOCAB_FILES_NAMES
   _snake_case		 =				PRETRAINED_VOCAB_FILES_MAP
   _snake_case		 =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _snake_case		 =				['input_ids', 'attention_mask']







   def __init__(      self			, lowerCamelCase__			, lowerCamelCase__			, lowerCamelCase__="replace"			, lowerCamelCase__="<s>"			, lowerCamelCase__="</s>"			, lowerCamelCase__="</s>"			, lowerCamelCase__="<s>"			, lowerCamelCase__="<unk>"			, lowerCamelCase__="<pad>"			, lowerCamelCase__="<mask>"			, lowerCamelCase__=False			, **lowerCamelCase__			, ):

      '''simple docstring'''







      UpperCamelCase	  =	AddedToken(lowerCAmelCase_			, lstrip=lowerCAmelCase_			, rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_			, lowerCAmelCase_ ) else bos_token
      UpperCamelCase	  =	AddedToken(lowerCAmelCase_			, lstrip=lowerCAmelCase_			, rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_			, lowerCAmelCase_ ) else eos_token
      UpperCamelCase	  =	AddedToken(lowerCAmelCase_			, lstrip=lowerCAmelCase_			, rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_			, lowerCAmelCase_ ) else sep_token
      UpperCamelCase	  =	AddedToken(lowerCAmelCase_			, lstrip=lowerCAmelCase_			, rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_			, lowerCAmelCase_ ) else cls_token
      UpperCamelCase	  =	AddedToken(lowerCAmelCase_			, lstrip=lowerCAmelCase_			, rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_			, lowerCAmelCase_ ) else unk_token
      UpperCamelCase	  =	AddedToken(lowerCAmelCase_			, lstrip=lowerCAmelCase_			, rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_			, lowerCAmelCase_ ) else pad_token

      # Mask token behave like a normal word, i.e. include the space before it
      UpperCamelCase	  =	AddedToken(lowerCAmelCase_			, lstrip=lowerCAmelCase_			, rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_			, lowerCAmelCase_ ) else mask_token

      super().__init__(
          errors=lowerCAmelCase_			, bos_token=lowerCAmelCase_			, eos_token=lowerCAmelCase_			, unk_token=lowerCAmelCase_			, sep_token=lowerCAmelCase_			, cls_token=lowerCAmelCase_			, pad_token=lowerCAmelCase_			, mask_token=lowerCAmelCase_			, add_prefix_space=lowerCAmelCase_			, **lowerCAmelCase_			, )

      with open(lowerCAmelCase_			, encoding='''utf-8''' ) as vocab_handle:
         UpperCamelCase	  =	json.load(lowerCAmelCase_ )
      UpperCamelCase	  =	{v: k for k, v in self.encoder.items()}
      UpperCamelCase	  =	errors  # how to handle errors in decoding
      UpperCamelCase	  =	bytes_to_unicode()
      UpperCamelCase	  =	{v: k for k, v in self.byte_encoder.items()}
      with open(lowerCAmelCase_			, encoding='''utf-8''' ) as merges_handle:
         UpperCamelCase	  =	merges_handle.read().split('''\n''' )[1:-1]
      UpperCamelCase	  =	[tuple(merge.split() ) for merge in bpe_merges]
      UpperCamelCase	  =	dict(zip(lowerCAmelCase_			, range(len(lowerCAmelCase_ ) ) ) )
      UpperCamelCase	  =	{}
      UpperCamelCase	  =	add_prefix_space

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      UpperCamelCase	  =	re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+''' )







   @property
   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      return len(self.encoder )







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      return dict(self.encoder			, **self.added_tokens_encoder )







   def    UpperCAmelCase					(      self			, lowerCamelCase__ ):

      '''simple docstring'''







      if token in self.cache:
         return self.cache[token]
      UpperCamelCase	  =	tuple(lowerCAmelCase_ )
      UpperCamelCase	  =	get_pairs(lowerCAmelCase_ )

      if not pairs:
         return token

      while True:
         UpperCamelCase	  =	min(lowerCAmelCase_			, key=lambda lowerCamelCase__ : self.bpe_ranks.get(lowerCAmelCase_			, float('''inf''' ) ) )
         if bigram not in self.bpe_ranks:
            break
         UpperCamelCase	  =	bigram
         UpperCamelCase	  =	[]
         UpperCamelCase	  =	0
         while i < len(lowerCAmelCase_ ):
            try:
               UpperCamelCase	  =	word.index(lowerCAmelCase_			, lowerCAmelCase_ )
            except ValueError:
               new_word.extend(word[i:] )
               break
            else:
               new_word.extend(word[i:j] )
               UpperCamelCase	  =	j

            if word[i] == first and i < len(lowerCAmelCase_ ) - 1 and word[i + 1] == second:
               new_word.append(first + second )
               i += 2
            else:
               new_word.append(word[i] )
               i += 1
         UpperCamelCase	  =	tuple(lowerCAmelCase_ )
         UpperCamelCase	  =	new_word
         if len(lowerCAmelCase_ ) == 1:
            break
         else:
            UpperCamelCase	  =	get_pairs(lowerCAmelCase_ )
      UpperCamelCase	  =	' '.join(lowerCAmelCase_ )
      UpperCamelCase	  =	word
      return word







   def    UpperCAmelCase					(      self			, lowerCamelCase__ ):

      '''simple docstring'''







      UpperCamelCase	  =	[]
      for token in re.findall(self.pat			, lowerCAmelCase_ ):
         UpperCamelCase	  =	''.join(
             self.byte_encoder[b] for b in token.encode('''utf-8''' ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
         bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase_ ).split(''' ''' ) )
      return bpe_tokens







   def    UpperCAmelCase					(      self			, lowerCamelCase__ ):

      '''simple docstring'''







      return self.encoder.get(lowerCAmelCase_			, self.encoder.get(self.unk_token ) )







   def    UpperCAmelCase					(      self			, lowerCamelCase__ ):

      '''simple docstring'''







      return self.decoder.get(lowerCAmelCase_ )







   def    UpperCAmelCase					(      self			, lowerCamelCase__ ):

      '''simple docstring'''







      UpperCamelCase	  =	''.join(lowerCAmelCase_ )
      UpperCamelCase	  =	bytearray([self.byte_decoder[c] for c in text] ).decode('''utf-8'''			, errors=self.errors )
      return text







   def    UpperCAmelCase					(      self			, lowerCamelCase__			, lowerCamelCase__ = None ):

      '''simple docstring'''







      if not os.path.isdir(lowerCAmelCase_ ):
         logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
         return
      UpperCamelCase	  =	os.path.join(
          lowerCAmelCase_			, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
      UpperCamelCase	  =	os.path.join(
          lowerCAmelCase_			, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )

      with open(lowerCAmelCase_			, '''w'''			, encoding='''utf-8''' ) as f:
         f.write(json.dumps(self.encoder			, indent=2			, sort_keys=lowerCAmelCase_			, ensure_ascii=lowerCAmelCase_ ) + '''\n''' )

      UpperCamelCase	  =	0
      with open(lowerCAmelCase_			, '''w'''			, encoding='''utf-8''' ) as writer:
         writer.write('''#version: 0.2\n''' )
         for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			, key=lambda lowerCamelCase__ : kv[1] ):
            if index != token_index:
               logger.warning(
                   f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                   ''' Please check that the tokenizer is not corrupted!''' )
               UpperCamelCase	  =	token_index
            writer.write(''' '''.join(lowerCAmelCase_ ) + '''\n''' )
            index += 1

      return vocab_file, merge_file







   def    UpperCAmelCase					(      self			, lowerCamelCase__			, lowerCamelCase__ = None ):

      '''simple docstring'''







      if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase	  =	[self.cls_token_id]
      UpperCamelCase	  =	[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep







   def    UpperCAmelCase					(      self			, lowerCamelCase__			, lowerCamelCase__ = None			, lowerCamelCase__ = False ):

      '''simple docstring'''







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

      if token_ids_a is None:
         return [1] + ([0] * len(lowerCAmelCase_ )) + [1]
      return [1] + ([0] * len(lowerCAmelCase_ )) + [1, 1] + ([0] * len(lowerCAmelCase_ )) + [1]







   def    UpperCAmelCase					(      self			, lowerCamelCase__			, lowerCamelCase__ = None ):

      '''simple docstring'''







      UpperCamelCase	  =	[self.sep_token_id]
      UpperCamelCase	  =	[self.cls_token_id]

      if token_ids_a is None:
         return len(cls + token_ids_a + sep ) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]







   def    UpperCAmelCase					(      self			, lowerCamelCase__			, lowerCamelCase__=False			, **lowerCamelCase__ ):

      '''simple docstring'''







      UpperCamelCase	  =	kwargs.pop('''add_prefix_space'''			, self.add_prefix_space )
      if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase_ ) > 0 and not text[0].isspace()):
         UpperCamelCase	  =	' ' + text
      return (text, kwargs)













"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel


@require_tf
class _lowerCAmelCase						:
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				MBartConfig
						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				{}
						SCREAMING_SNAKE_CASE_:					Tuple 					=				'gelu'







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_=1_3   ,				lowerCAmelCase_=7   ,				lowerCAmelCase_=True   ,				lowerCAmelCase_=False   ,				lowerCAmelCase_=9_9   ,				lowerCAmelCase_=3_2   ,				lowerCAmelCase_=2   ,				lowerCAmelCase_=4   ,				lowerCAmelCase_=3_7   ,				lowerCAmelCase_=0.1   ,				lowerCAmelCase_=0.1   ,				lowerCAmelCase_=2_0   ,				lowerCAmelCase_=2   ,				lowerCAmelCase_=1   ,				lowerCAmelCase_=0   ,				)		->							int:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    parent
										_SCREAMING_SNAKE_CASE   :		str       		=    batch_size
										_SCREAMING_SNAKE_CASE   :		str       		=    seq_length
										_SCREAMING_SNAKE_CASE   :		Dict       		=    is_training
										_SCREAMING_SNAKE_CASE   :		int       		=    use_labels
										_SCREAMING_SNAKE_CASE   :		str       		=    vocab_size
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    hidden_size
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    num_hidden_layers
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    num_attention_heads
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    intermediate_size
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    hidden_dropout_prob
										_SCREAMING_SNAKE_CASE   :		int       		=    attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    max_position_embeddings
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    eos_token_id
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    pad_token_id
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    bos_token_id







						def 			A      (    self							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    ids_tensor([self.batch_size, self.seq_length - 1]   ,				self.vocab_size							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)   ,				1							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    tf.concat([input_ids, eos_tensor]   ,				axis=1							)

										_SCREAMING_SNAKE_CASE   :		Any       		=    ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.config_cls(
										    vocab_size=self.vocab_size   ,				d_model=self.hidden_size   ,				encoder_layers=self.num_hidden_layers   ,				decoder_layers=self.num_hidden_layers   ,				encoder_attention_heads=self.num_attention_heads   ,				decoder_attention_heads=self.num_attention_heads   ,				encoder_ffn_dim=self.intermediate_size   ,				decoder_ffn_dim=self.intermediate_size   ,				dropout=self.hidden_dropout_prob   ,				attention_dropout=self.attention_probs_dropout_prob   ,				max_position_embeddings=self.max_position_embeddings   ,				eos_token_ids=[2]   ,				bos_token_id=self.bos_token_id   ,				pad_token_id=self.pad_token_id   ,				decoder_start_token_id=self.pad_token_id   ,				**self.config_updates   ,				)
										_SCREAMING_SNAKE_CASE   :		Any       		=    prepare_mbart_inputs_dict(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
										return config, inputs_dict







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							int:
										_SCREAMING_SNAKE_CASE   :		int       		=    TFMBartModel(config=lowerCAmelCase_							).get_decoder()
										_SCREAMING_SNAKE_CASE   :		int       		=    inputs_dict['input_ids']

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    input_ids[:1, :]
										_SCREAMING_SNAKE_CASE   :		Any       		=    inputs_dict['attention_mask'][:1, :]
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    inputs_dict['head_mask']
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    1

										# first forward pass
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    model(lowerCAmelCase_   ,				attention_mask=lowerCAmelCase_   ,				head_mask=lowerCAmelCase_   ,				use_cache=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		str       		=    outputs.to_tuple()
										_SCREAMING_SNAKE_CASE   :		str       		=    past_key_values[1]







def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase=None,						lowerCamelCase=None,						lowerCamelCase=None,						lowerCamelCase=None,						lowerCamelCase=None,						):
				if attention_mask is None:
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    tf.cast(tf.math.not_equal(lowerCamelCase,						config.pad_token_id       ),						tf.inta       )
				if decoder_attention_mask is None:
								_SCREAMING_SNAKE_CASE   :		Dict       		=    tf.concat(
								    [
								        tf.ones(decoder_input_ids[:, :1].shape,						dtype=tf.inta       ),
								        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:],						config.pad_token_id       ),						tf.inta       ),
								    ],						axis=-1,						)
				if head_mask is None:
								_SCREAMING_SNAKE_CASE   :		Dict       		=    tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
				if decoder_head_mask is None:
								_SCREAMING_SNAKE_CASE   :		int       		=    tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
				if cross_attn_head_mask is None:
								_SCREAMING_SNAKE_CASE   :		int       		=    tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}


@require_tf
class _lowerCAmelCase						(    __UpperCAmelCase   ,  __UpperCAmelCase   ,  unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					List[str] 					=				(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
						SCREAMING_SNAKE_CASE_:					Dict 					=				(TFMBartForConditionalGeneration,) if is_tf_available() else ()
						SCREAMING_SNAKE_CASE_:					Tuple 					=				(
						    {
						        'conversational': TFMBartForConditionalGeneration,
						        'feature-extraction': TFMBartModel,
						        'summarization': TFMBartForConditionalGeneration,
						        'text2text-generation': TFMBartForConditionalGeneration,
						        'translation': TFMBartForConditionalGeneration,
						    }
						    if is_tf_available()
						    else {}
						)
						SCREAMING_SNAKE_CASE_:					Any 					=				True
						SCREAMING_SNAKE_CASE_:					int 					=				False
						SCREAMING_SNAKE_CASE_:					Optional[int] 					=				False







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							List[Any]:
										if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
														# Exception encountered when calling layer '...'
														return True

										return False







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    TFMBartModelTester(self							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    ConfigTester(self   ,				config_class=lowerCAmelCase_							)







						def 			A      (    self							)		->							List[Any]:
										self.config_tester.run_common_tests()







						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_							)







@require_sentencepiece
@require_tokenizers
@require_tf
class _lowerCAmelCase						(    unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					str 					=				[
						    ' UN Chief Says There Is No Military Solution in Syria',
						]
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				[
						    'Şeful ONU declară că nu există o soluţie militară în Siria',
						]
						SCREAMING_SNAKE_CASE_:					Dict 					=				'facebook/mbart-large-en-ro'







						@cached_property
						def 			A      (    self							)		->							Union[str, Any]:
										return AutoTokenizer.from_pretrained(self.model_name							)







						@cached_property
						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
										return model







						def 			A      (    self   ,				**lowerCAmelCase_							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.translate_src_text(**lowerCAmelCase_							)
										self.assertListEqual(self.expected_text   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				**lowerCAmelCase_							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.tokenizer(self.src_text   ,				**lowerCAmelCase_   ,				return_tensors='tf'							)
										_SCREAMING_SNAKE_CASE   :		int       		=    self.model.generate(
										    model_inputs.input_ids   ,				attention_mask=model_inputs.attention_mask   ,				num_beams=2							)
										_SCREAMING_SNAKE_CASE   :		int       		=    self.tokenizer.batch_decode(lowerCAmelCase_   ,				skip_special_tokens=lowerCAmelCase_							)
										return generated_words







						@slow
						def 			A      (    self							)		->							Any:
										self._assert_generated_batch_equal_expected()

