
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 :
 __lowerCAmelCase	:  Optional[Any]	   =      MBartConfig
 __lowerCAmelCase	:  Tuple	   =      {}
 __lowerCAmelCase	:  Any	   =      'gelu'


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=13       ,	SCREAMING_SNAKE_CASE_=7       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=99       ,	SCREAMING_SNAKE_CASE_=32       ,	SCREAMING_SNAKE_CASE_=2       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=37       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=20       ,	SCREAMING_SNAKE_CASE_=2       ,	SCREAMING_SNAKE_CASE_=1       ,	SCREAMING_SNAKE_CASE_=0       ,	):


   '''simple docstring'''




   lowercase__	: str     		=   parent
   lowercase__	: Tuple     		=   batch_size
   lowercase__	: List[str]     		=   seq_length
   lowercase__	: List[Any]     		=   is_training
   lowercase__	: str     		=   use_labels
   lowercase__	: Any     		=   vocab_size
   lowercase__	: str     		=   hidden_size
   lowercase__	: str     		=   num_hidden_layers
   lowercase__	: Optional[int]     		=   num_attention_heads
   lowercase__	: List[str]     		=   intermediate_size
   lowercase__	: int     		=   hidden_dropout_prob
   lowercase__	: Dict     		=   attention_probs_dropout_prob
   lowercase__	: List[str]     		=   max_position_embeddings
   lowercase__	: Any     		=   eos_token_id
   lowercase__	: Dict     		=   pad_token_id
   lowercase__	: Dict     		=   bos_token_id


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[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__	: Dict     		=   tf.concat([input_ids, eos_tensor]       ,	axis=1)

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

   lowercase__	: List[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__	: Tuple     		=   prepare_mbart_inputs_dict(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   return config, inputs_dict



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   TFMBartModel(config=SCREAMING_SNAKE_CASE_).get_decoder()
   lowercase__	: Optional[Any]     		=   inputs_dict["""input_ids"""]

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

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

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

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			)		->						str:


  '''simple docstring'''






  if attention_mask is None:
    lowercase__	: Optional[int]     		=   tf.cast(tf.math.not_equal(lowercase_			,			config.pad_token_id )			,			tf.inta )
  if decoder_attention_mask is None:
    lowercase__	: Optional[Any]     		=   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__	: Optional[Any]     		=   tf.ones((config.encoder_layers, config.encoder_attention_heads) )
  if decoder_head_mask is None:
    lowercase__	: Tuple     		=   tf.ones((config.decoder_layers, config.decoder_attention_heads) )
  if cross_attn_head_mask is None:
    lowercase__	: Dict     		=   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 (						UpperCAmelCase_	,    UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  Any	   =      (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
 __lowerCAmelCase	:  List[Any]	   =      (TFMBartForConditionalGeneration,) if is_tf_available() else ()
 __lowerCAmelCase	:  Optional[Any]	   =      (
     {
         'conversational': TFMBartForConditionalGeneration,
         'feature-extraction': TFMBartModel,
         'summarization': TFMBartForConditionalGeneration,
         'text2text-generation': TFMBartForConditionalGeneration,
         'translation': TFMBartForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 __lowerCAmelCase	:  Optional[int]	   =      True
 __lowerCAmelCase	:  str	   =      False
 __lowerCAmelCase	:  Union[str, Any]	   =      False


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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

   return False


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   TFMBartModelTester(self)
   lowercase__	: Optional[int]     		=   ConfigTester(self       ,	config_class=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   self.config_tester.run_common_tests()



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.model_tester.prepare_config_and_inputs_for_common()
   self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_)



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


 @cached_property
 def   lowercase__  (       self):


   '''simple docstring'''




   return AutoTokenizer.from_pretrained(self.model_name)


 @cached_property
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
   return model


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.translate_src_text(**SCREAMING_SNAKE_CASE_)
   self.assertListEqual(self.expected_text       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   self.tokenizer(self.src_text       ,	**SCREAMING_SNAKE_CASE_       ,	return_tensors="""tf""")
   lowercase__	: Any     		=   self.model.generate(
       model_inputs.input_ids       ,	attention_mask=model_inputs.attention_mask       ,	num_beams=2)
   lowercase__	: Union[str, Any]     		=   self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE_       ,	skip_special_tokens=SCREAMING_SNAKE_CASE_)
   return generated_words



 @slow
 def   lowercase__  (       self):


   '''simple docstring'''




   self._assert_generated_batch_equal_expected()














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


SCREAMING_SNAKE_CASE__   :      str				=		logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__   :      Tuple				=		"""▁"""

SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		{"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE__   :      List[Any]				=		{
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

SCREAMING_SNAKE_CASE__   :      Optional[int]				=		{
    """google/reformer-crime-and-punishment""": 52_42_88,
}
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	VOCAB_FILES_NAMES
				a__       							=	PRETRAINED_VOCAB_FILES_MAP
				a__       							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				a__       							=	['''input_ids''', '''attention_mask''']







				def __init__(    self							,       __lowerCAmelCase							,       __lowerCAmelCase="</s>"							,       __lowerCAmelCase="<unk>"							,       __lowerCAmelCase=[]							,       __lowerCAmelCase = None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								__magic_name__		:int   =   {} if sp_model_kwargs is None else sp_model_kwargs

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

								__magic_name__		:Optional[Any]   =   vocab_file
								__magic_name__		:int   =   spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(__lowerCAmelCase  )







				@property
				def       A						(    self  ):
								"""simple docstring"""
								return self.sp_model.get_piece_size()







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:str   =   {self.convert_ids_to_tokens(__lowerCAmelCase  ): i for i in range(self.vocab_size  )}
								vocab.update(self.added_tokens_encoder  )
								return vocab







				def __getstate__(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   self.__dict__.copy()
								__magic_name__		:Optional[Any]   =   None
								return state







				def __setstate__(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Any   =   d

								# for backward compatibility
								if not hasattr(self							,       '''sp_model_kwargs'''  ):
												__magic_name__		:Optional[int]   =   {}

								__magic_name__		:Union[str, Any]   =   spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(self.vocab_file  )







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								return self.sp_model.encode(__lowerCAmelCase							,       out_type=__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								return self.sp_model.piece_to_id(__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								if index < self.sp_model.get_piece_size():
												__magic_name__		:int   =   self.sp_model.IdToPiece(__lowerCAmelCase  )
								return token







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   []
								__magic_name__		:Tuple   =   ''''''
								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(__lowerCAmelCase  ) + token
																__magic_name__		:Optional[Any]   =   []
												else:
																current_sub_tokens.append(__lowerCAmelCase  )
								out_string += self.sp_model.decode(__lowerCAmelCase  )
								return out_string.strip()







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase = None  ):
								"""simple docstring"""
								if not os.path.isdir(__lowerCAmelCase  ):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
												return
								__magic_name__		:Optional[int]   =   os.path.join(
								    __lowerCAmelCase							,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

								if os.path.abspath(self.vocab_file  ) != os.path.abspath(__lowerCAmelCase  ) and os.path.isfile(self.vocab_file  ):
												copyfile(self.vocab_file							,       __lowerCAmelCase  )
								elif not os.path.isfile(self.vocab_file  ):
												with open(__lowerCAmelCase							,       '''wb'''  ) as fi:
																__magic_name__		:Dict   =   self.sp_model.serialized_model_proto()
																fi.write(__lowerCAmelCase  )

								return (out_vocab_file,)






