
'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


snake_case_			:	Optional[Any] 	=       logging.get_logger(__name__)


snake_case_			:	Dict 	=       {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all MVP models at https://huggingface.co/models?filter=mvp
snake_case_			:	Any 	=       {
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json',
    },
}

snake_case_			:	List[str] 	=       {
    'RUCAIBox/mvp': 1_024,
}


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']
   _snake_case		 =				MvpTokenizer







   def __init__(      self			, lowerCamelCase__=None			, lowerCamelCase__=None			, lowerCamelCase__=None			, lowerCamelCase__="replace"			, lowerCamelCase__="<s>"			, lowerCamelCase__="</s>"			, lowerCamelCase__="</s>"			, lowerCamelCase__="<s>"			, lowerCamelCase__="<unk>"			, lowerCamelCase__="<pad>"			, lowerCamelCase__="<mask>"			, lowerCamelCase__=False			, lowerCamelCase__=True			, **lowerCamelCase__			, ):

      '''simple docstring'''







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

      UpperCamelCase	  =	json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
      if pre_tok_state.get('''add_prefix_space'''			, lowerCAmelCase_ ) != add_prefix_space:
         UpperCamelCase	  =	getattr(lowerCAmelCase_			, pre_tok_state.pop('''type''' ) )
         UpperCamelCase	  =	add_prefix_space
         UpperCamelCase	  =	pre_tok_class(**lowerCAmelCase_ )

      UpperCamelCase	  =	add_prefix_space

      # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
      UpperCamelCase	  =	'post_processor'
      UpperCamelCase	  =	getattr(self.backend_tokenizer			, lowerCAmelCase_			, lowerCAmelCase_ )
      if tokenizer_component_instance:
         UpperCamelCase	  =	json.loads(tokenizer_component_instance.__getstate__() )

         # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
         if "sep" in state:
            UpperCamelCase	  =	tuple(state['''sep'''] )
         if "cls" in state:
            UpperCamelCase	  =	tuple(state['''cls'''] )

         UpperCamelCase	  =	False

         if state.get('''add_prefix_space'''			, lowerCAmelCase_ ) != add_prefix_space:
            UpperCamelCase	  =	add_prefix_space
            UpperCamelCase	  =	True

         if state.get('''trim_offsets'''			, lowerCAmelCase_ ) != trim_offsets:
            UpperCamelCase	  =	trim_offsets
            UpperCamelCase	  =	True

         if changes_to_apply:
            UpperCamelCase	  =	getattr(lowerCAmelCase_			, state.pop('''type''' ) )
            UpperCamelCase	  =	component_class(**lowerCAmelCase_ )
            setattr(self.backend_tokenizer			, lowerCAmelCase_			, lowerCAmelCase_ )







   @property
   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      if self._mask_token is None:
         if self.verbose:
            logger.error('''Using mask_token, but it is not set yet.''' )
         return None
      return str(self._mask_token )







   @mask_token.setter
   def    UpperCAmelCase					(      self			, lowerCamelCase__ ):

      '''simple docstring'''







      UpperCamelCase	  =	AddedToken(lowerCAmelCase_			, lstrip=lowerCAmelCase_			, rstrip=lowerCAmelCase_ ) if isinstance(lowerCAmelCase_			, lowerCAmelCase_ ) else value
      UpperCamelCase	  =	value







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

      '''simple docstring'''







      UpperCamelCase	  =	kwargs.get('''is_split_into_words'''			, lowerCAmelCase_ )

      if is_split_into_words and not self.add_prefix_space:
         raise ValueError(
             f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
             '''to use it with pretokenized inputs.''' )

      return super()._batch_encode_plus(*lowerCAmelCase_			, **lowerCAmelCase_ )







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

      '''simple docstring'''







      UpperCamelCase	  =	kwargs.get('''is_split_into_words'''			, lowerCAmelCase_ )

      if is_split_into_words and not self.add_prefix_space:
         raise ValueError(
             f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
             '''to use it with pretokenized inputs.''' )

      return super()._encode_plus(*lowerCAmelCase_			, **lowerCAmelCase_ )







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

      '''simple docstring'''







      UpperCamelCase	  =	self._tokenizer.model.save(lowerCAmelCase_			, name=lowerCAmelCase_ )
      return tuple(lowerCAmelCase_ )







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

      '''simple docstring'''







      UpperCamelCase	  =	[self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
         return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







   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]













"""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()

