


'''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      snake_case    :





  """simple docstring"""




  _lowerCamelCase		    =  MBartConfig
  _lowerCamelCase		    =  {}
  _lowerCamelCase		    =  "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			,  ):







   """simple docstring"""
   lowerCamelCase_      =  parent
   lowerCamelCase_      =  batch_size
   lowerCamelCase_      =  seq_length
   lowerCamelCase_      =  is_training
   lowerCamelCase_      =  use_labels
   lowerCamelCase_      =  vocab_size
   lowerCamelCase_      =  hidden_size
   lowerCamelCase_      =  num_hidden_layers
   lowerCamelCase_      =  num_attention_heads
   lowerCamelCase_      =  intermediate_size
   lowerCamelCase_      =  hidden_dropout_prob
   lowerCamelCase_      =  attention_probs_dropout_prob
   lowerCamelCase_      =  max_position_embeddings
   lowerCamelCase_      =  eos_token_id
   lowerCamelCase_      =  pad_token_id
   lowerCamelCase_      =  bos_token_id






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  ids_tensor([self.batch_size, self.seq_length - 1]			,  self.vocab_size     )
   lowerCamelCase_      =  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )			,  1     )
   lowerCamelCase_      =  tf.concat([input_ids, eos_tensor]			,  axis=1     )

   lowerCamelCase_      =  ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size     )

   lowerCamelCase_      =  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			,  )
   lowerCamelCase_      =  prepare_mbart_inputs_dict(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )
   return config, inputs_dict







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  TFMBartModel(config=UpperCamelCase     ).get_decoder()
   lowerCamelCase_      =  inputs_dict["input_ids"]

   lowerCamelCase_      =  input_ids[:1, :]
   lowerCamelCase_      =  inputs_dict["attention_mask"][:1, :]
   lowerCamelCase_      =  inputs_dict["head_mask"]
   lowerCamelCase_      =  1

   # first forward pass
   lowerCamelCase_      =  model(UpperCamelCase			,  attention_mask=UpperCamelCase			,  head_mask=UpperCamelCase			,  use_cache=UpperCamelCase     )

   lowerCamelCase_ ,lowerCamelCase_      =  outputs.to_tuple()
   lowerCamelCase_      =  past_key_values[1]





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						List[str]=None					,				UpperCAmelCase_						:						Optional[int]=None					,				UpperCAmelCase_						:						List[Any]=None					,				UpperCAmelCase_						:						str=None					,				UpperCAmelCase_						:						Any=None					,				):
 if attention_mask is None:
  lowerCamelCase_      =  tf.cast(tf.math.not_equal(UpperCAmelCase_					,				config.pad_token_id		)					,				tf.inta		)
 if decoder_attention_mask is None:
  lowerCamelCase_      =  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:
  lowerCamelCase_      =  tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
 if decoder_head_mask is None:
  lowerCamelCase_      =  tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
 if cross_attn_head_mask is None:
  lowerCamelCase_      =  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      snake_case    (    lowercase			,						lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
  _lowerCamelCase		    =  (TFMBartForConditionalGeneration,) if is_tf_available() else ()
  _lowerCamelCase		    =  (
      {
          "conversational": TFMBartForConditionalGeneration,
          "feature-extraction": TFMBartModel,
          "summarization": TFMBartForConditionalGeneration,
          "text2text-generation": TFMBartForConditionalGeneration,
          "translation": TFMBartForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  _lowerCamelCase		    =  True
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
    # Exception encountered when calling layer '...'
    return True

   return False






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFMBartModelTester(self     )
   lowerCamelCase_      =  ConfigTester(self			,  config_class=UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   self.config_tester.run_common_tests()







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  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      snake_case    (    unittest.TestCase			):





  """simple docstring"""




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






  @cached_property
  def   snake_case		(    self     ):







   """simple docstring"""
   return AutoTokenizer.from_pretrained(self.model_name     )






  @cached_property
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
   return model






  def   snake_case		(    self			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.translate_src_text(**UpperCamelCase     )
   self.assertListEqual(self.expected_text			,  UpperCamelCase     )






  def   snake_case		(    self			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.tokenizer(self.src_text			,  **UpperCamelCase			,  return_tensors="tf"     )
   lowerCamelCase_      =  self.model.generate(
       model_inputs.input_ids			,  attention_mask=model_inputs.attention_mask			,  num_beams=2     )
   lowerCamelCase_      =  self.tokenizer.batch_decode(UpperCamelCase			,  skip_special_tokens=UpperCamelCase     )
   return generated_words







  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   self._assert_generated_batch_equal_expected()







'''simple docstring'''

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

import sentencepiece as spm

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


a_  :				Any						= logging.get_logger(__name__)

a_  :				Optional[Any]						= {"""vocab_file""": """spiece.model"""}

a_  :				Tuple						= {
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    }
}





class      snake_case    (    lowercase			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase=False			,  UpperCamelCase=True			,  UpperCamelCase=False			,  UpperCamelCase="<s>"			,  UpperCamelCase="</s>"			,  UpperCamelCase="<unk>"			,  UpperCamelCase="<sep>"			,  UpperCamelCase="<pad>"			,  UpperCamelCase="<cls>"			,  UpperCamelCase="<mask>"			,  UpperCamelCase=["<eop>", "<eod>"]			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  AddedToken(UpperCamelCase			,  lstrip=UpperCamelCase			,  rstrip=UpperCamelCase     ) if isinstance(UpperCamelCase			,  UpperCamelCase     ) else mask_token

   lowerCamelCase_      =  {} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       do_lower_case=UpperCamelCase			,  remove_space=UpperCamelCase			,  keep_accents=UpperCamelCase			,  bos_token=UpperCamelCase			,  eos_token=UpperCamelCase			,  unk_token=UpperCamelCase			,  sep_token=UpperCamelCase			,  pad_token=UpperCamelCase			,  cls_token=UpperCamelCase			,  mask_token=UpperCamelCase			,  additional_special_tokens=UpperCamelCase			,  sp_model_kwargs=self.sp_model_kwargs			,  **UpperCamelCase			,  )

   lowerCamelCase_      =  3

   lowerCamelCase_      =  do_lower_case
   lowerCamelCase_      =  remove_space
   lowerCamelCase_      =  keep_accents
   lowerCamelCase_      =  vocab_file

   lowerCamelCase_      =  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
   self.sp_model.Load(UpperCamelCase     )

   try:
    import jieba
   except ModuleNotFoundError as error:
    raise error.__class__(
        "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
        "See https://pypi.org/project/jieba/ for installation."     )
   lowerCamelCase_      =  jieba
   lowerCamelCase_      =  str.maketrans(" \n"			,  "\u2582\u2583"     )






  @property
  # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
  def   snake_case		(    self     ):







   """simple docstring"""
   return len(self.sp_model     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  {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     ):







   """simple docstring"""
   lowerCamelCase_      =  self.__dict__.copy()
   lowerCamelCase_      =  None
   return state






  def __setstate__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  d

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

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






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   if self.remove_space:
    lowerCamelCase_      =  " ".join(inputs.strip().split()     )
   else:
    lowerCamelCase_      =  inputs
   lowerCamelCase_      =  outputs.replace("``"			,  "\""     ).replace("''"			,  "\""     )

   if not self.keep_accents:
    lowerCamelCase_      =  unicodedata.normalize("NFKD"			,  UpperCamelCase     )
    lowerCamelCase_      =  "".join([c for c in outputs if not unicodedata.combining(UpperCamelCase     )]     )
   if self.do_lower_case:
    lowerCamelCase_      =  outputs.lower()

   return outputs






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.preprocess_text(UpperCamelCase     )
   lowerCamelCase_      =  self.sp_model.encode(UpperCamelCase			,  out_type=UpperCamelCase     )
   lowerCamelCase_      =  []
   for piece in pieces:
    if len(UpperCamelCase     ) > 1 and piece[-1] == str(","     ) and piece[-2].isdigit():
     lowerCamelCase_      =  self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase			,  ""     )     )
     if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
      if len(cur_pieces[0]     ) == 1:
       lowerCamelCase_      =  cur_pieces[1:]
      else:
       lowerCamelCase_      =  cur_pieces[0][1:]
     cur_pieces.append(piece[-1]     )
     new_pieces.extend(UpperCamelCase     )
    else:
     new_pieces.append(UpperCamelCase     )

   return new_pieces






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.sp_model.PieceToId(UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.sp_model.IdToPiece(UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  "".join(UpperCamelCase     ).replace(UpperCamelCase			,  " "     ).strip()
   return out_string






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   lowerCamelCase_      =  [self.sep_token_id]
   lowerCamelCase_      =  [self.cls_token_id]
   if token_ids_a is None:
    return token_ids_a + sep + cls
   return token_ids_a + sep + token_ids_a + sep + cls






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None			,  UpperCamelCase = False     ):







   """simple docstring"""

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

   if token_ids_a is not None:
    return ([0] * len(UpperCamelCase     )) + [1] + ([0] * len(UpperCamelCase     )) + [1, 1]
   return ([0] * len(UpperCamelCase     )) + [1, 1]






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   lowerCamelCase_      =  [self.sep_token_id]
   lowerCamelCase_      =  [2]

   if token_ids_a is None:
    return len(token_ids_a + sep     ) * [0] + cls_segment_id
   return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1] + cls_segment_id






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   if not os.path.isdir(UpperCamelCase     ):
    logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
    return
   lowerCamelCase_      =  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:
     lowerCamelCase_      =  self.sp_model.serialized_model_proto()
     fi.write(UpperCamelCase     )

   return (out_vocab_file,)







  def   snake_case		(    self			,  *UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  super()._decode(*UpperCamelCase			,  **UpperCamelCase     )
   lowerCamelCase_      =  text.replace(" "			,  ""     ).replace("\u2582"			,  " "     ).replace("\u2583"			,  "\n"     )
   return text



