import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


UpperCamelCase__											=					logging.get_logger(__name__)


UpperCamelCase__											=					'▁'

UpperCamelCase__											=					{'vocab_file': 'spiece.model'}

UpperCamelCase__											=					{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

UpperCamelCase__											=					{
    'google/reformer-crime-and-punishment': 5_2_4_2_8_8,
}





class      A							(				_UpperCAmelCase     ):
   __UpperCAmelCase : List[Any]      =			VOCAB_FILES_NAMES
   __UpperCAmelCase : int      =			PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase : str      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase : Any      =			["input_ids", "attention_mask"]


   def __init__(self     :						List[Any]       ,  __UpperCAmelCase     :						List[Any]       ,  __UpperCAmelCase     :						Any="</s>"       ,  __UpperCAmelCase     :						Optional[int]="<unk>"       ,  __UpperCAmelCase     :						List[Any]=[]       ,  __UpperCAmelCase     :						List[str] = None       ,  **__UpperCAmelCase     :						Any       ,  )						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             eos_token=_UpperCAmelCase       ,  unk_token=_UpperCAmelCase       ,  additional_special_tokens=_UpperCAmelCase       ,  sp_model_kwargs=self.sp_model_kwargs       ,  **_UpperCAmelCase       ,  )

         UpperCAmelCase__								=  vocab_file
         UpperCAmelCase__								=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(_UpperCAmelCase		)


   @property
   def 			lowercase_      (self     :						Optional[Any]		)						->				Union[str, Any]:
         """simple docstring"""



         return self.sp_model.get_piece_size()


   def 			lowercase_      (self     :						Optional[int]		)						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  {self.convert_ids_to_tokens(_UpperCAmelCase		): i for i in range(self.vocab_size		)}
         vocab.update(self.added_tokens_encoder		)
         return vocab


   def __getstate__(self     :						Any		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  self.__dict__.copy()
         UpperCAmelCase__								=  None
         return state


   def __setstate__(self     :						str       ,  __UpperCAmelCase     :						int		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  d

         # for backward compatibility
         if not hasattr(self       ,  "sp_model_kwargs"		):
               UpperCAmelCase__								=  {}

         UpperCAmelCase__								=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(self.vocab_file		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						List[Any]		)						->				Tuple:
         """simple docstring"""



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


   def 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						Any		)						->				Any:
         """simple docstring"""



         return self.sp_model.piece_to_id(_UpperCAmelCase		)


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						Any		)						->				Optional[int]:
         """simple docstring"""



         if index < self.sp_model.get_piece_size():
               UpperCAmelCase__								=  self.sp_model.IdToPiece(_UpperCAmelCase		)
         return token


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  []
         UpperCAmelCase__								=  ''''''
         for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                     out_string += self.sp_model.decode(_UpperCAmelCase		) + token
                     UpperCAmelCase__								=  []
               else:
                     current_sub_tokens.append(_UpperCAmelCase		)
         out_string += self.sp_model.decode(_UpperCAmelCase		)
         return out_string.strip()





   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						Any = None		)						->				Optional[Any]:
         """simple docstring"""



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

         if os.path.abspath(self.vocab_file		) != os.path.abspath(_UpperCAmelCase		) and os.path.isfile(self.vocab_file		):
               copyfile(self.vocab_file       ,  _UpperCAmelCase		)
         elif not os.path.isfile(self.vocab_file		):
               with open(_UpperCAmelCase       ,  "wb"		) as fi:
                     UpperCAmelCase__								=  self.sp_model.serialized_model_proto()
                     fi.write(_UpperCAmelCase		)

         return (out_vocab_file,)




"""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     UpperCAmelCase						:



    """simple docstring"""

    _UpperCAmelCase	:Union[str, Any]						    = MBartConfig
    _UpperCAmelCase	:str						    = {}
    _UpperCAmelCase	:Union[str, Any]						    = "gelu"






    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=13    ,			_UpperCAmelCase=7    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=99    ,			_UpperCAmelCase=32    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=37    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=20    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=0    ,			):
       lowercase__:   List[str]					=      parent
       lowercase__:   List[Any]					=      batch_size
       lowercase__:   List[Any]					=      seq_length
       lowercase__:   str					=      is_training
       lowercase__:   List[str]					=      use_labels
       lowercase__:   Optional[int]					=      vocab_size
       lowercase__:   int					=      hidden_size
       lowercase__:   Optional[int]					=      num_hidden_layers
       lowercase__:   int					=      num_attention_heads
       lowercase__:   Tuple					=      intermediate_size
       lowercase__:   Optional[Any]					=      hidden_dropout_prob
       lowercase__:   Optional[int]					=      attention_probs_dropout_prob
       lowercase__:   str					=      max_position_embeddings
       lowercase__:   Union[str, Any]					=      eos_token_id
       lowercase__:   int					=      pad_token_id
       lowercase__:   List[str]					=      bos_token_id






    def _snake_case			(   self							):
       lowercase__:   int					=      ids_tensor([self.batch_size, self.seq_length - 1]    ,			self.vocab_size							)
       lowercase__:   str					=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)    ,			1							)
       lowercase__:   List[Any]					=      tf.concat([input_ids, eos_tensor]    ,			axis=1							)

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

       lowercase__:   Optional[Any]					=      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    ,			)
       lowercase__:   Optional[int]					=      prepare_mbart_inputs_dict(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)
       return config, inputs_dict






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   List[str]					=      TFMBartModel(config=_UpperCAmelCase							).get_decoder()
       lowercase__:   Tuple					=      inputs_dict['''input_ids''']

       lowercase__:   Optional[Any]					=      input_ids[:1, :]
       lowercase__:   Optional[int]					=      inputs_dict['''attention_mask'''][:1, :]
       lowercase__:   List[str]					=      inputs_dict['''head_mask''']
       lowercase__:   Optional[int]					=      1

       # first forward pass
       lowercase__:   List[Any]					=      model(_UpperCAmelCase    ,			attention_mask=_UpperCAmelCase    ,			head_mask=_UpperCAmelCase    ,			use_cache=_UpperCAmelCase							)

       lowercase__, lowercase__:   Any					=      outputs.to_tuple()
       lowercase__:   List[str]					=      past_key_values[1]



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    )  ->  int:
   if attention_mask is None:
      lowercase__:   Union[str, Any]					=      tf.cast(tf.math.not_equal(__UpperCAmelCase       ,    config.pad_token_id )       ,    tf.inta )
   if decoder_attention_mask is None:
      lowercase__:   List[str]					=      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:
      lowercase__:   str					=      tf.ones((config.encoder_layers, config.encoder_attention_heads) )
   if decoder_head_mask is None:
      lowercase__:   List[str]					=      tf.ones((config.decoder_layers, config.decoder_attention_heads) )
   if cross_attn_head_mask is None:
      lowercase__:   Union[str, Any]					=      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     UpperCAmelCase						(_UpperCAmelCase    ,_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
    _UpperCAmelCase	:List[Any]						    = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
    _UpperCAmelCase	:List[str]						    = (
        {
            "conversational": TFMBartForConditionalGeneration,
            "feature-extraction": TFMBartModel,
            "summarization": TFMBartForConditionalGeneration,
            "text2text-generation": TFMBartForConditionalGeneration,
            "translation": TFMBartForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    _UpperCAmelCase	:str						    = True
    _UpperCAmelCase	:List[Any]						    = False
    _UpperCAmelCase	:str						    = False






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
          # Exception encountered when calling layer '...'
          return True

       return False






    def _snake_case			(   self							):
       lowercase__:   Tuple					=      TFMBartModelTester(self							)
       lowercase__:   Optional[Any]					=      ConfigTester(self    ,			config_class=_UpperCAmelCase							)






    def _snake_case			(   self							):
       self.config_tester.run_common_tests()






    def _snake_case			(   self							):
       lowercase__:   List[str]					=      self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase							)



@require_sentencepiece
@require_tokenizers
@require_tf
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Optional[int]						    = [
        " UN Chief Says There Is No Military Solution in Syria",
    ]
    _UpperCAmelCase	:Any						    = [
        "Şeful ONU declară că nu există o soluţie militară în Siria",
    ]
    _UpperCAmelCase	:Tuple						    = "facebook/mbart-large-en-ro"






    @cached_property
    def _snake_case			(   self							):
       return AutoTokenizer.from_pretrained(self.model_name							)






    @cached_property
    def _snake_case			(   self							):
       lowercase__:   Optional[int]					=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
       return model






    def _snake_case			(   self    ,			**_UpperCAmelCase							):
       lowercase__:   List[Any]					=      self.translate_src_text(**_UpperCAmelCase							)
       self.assertListEqual(self.expected_text    ,			_UpperCAmelCase							)






    def _snake_case			(   self    ,			**_UpperCAmelCase							):
       lowercase__:   str					=      self.tokenizer(self.src_text    ,			**_UpperCAmelCase    ,			return_tensors='''tf'''							)
       lowercase__:   Optional[int]					=      self.model.generate(
           model_inputs.input_ids    ,			attention_mask=model_inputs.attention_mask    ,			num_beams=2							)
       lowercase__:   Tuple					=      self.tokenizer.batch_decode(_UpperCAmelCase    ,			skip_special_tokens=_UpperCAmelCase							)
       return generated_words






    @slow
    def _snake_case			(   self							):
       self._assert_generated_batch_equal_expected()
