





'''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	:
      _lowercase:     Any			=  MBartConfig
      _lowercase:     Any			=  {}
      _lowercase:     str			=  '''gelu'''




      def __init__(     self     :					str     ,     __snake_case     :					Union[str, Any]     ,     __snake_case     :					List[Any]=13     ,     __snake_case     :					str=7     ,     __snake_case     :					Any=True     ,     __snake_case     :					Optional[int]=False     ,     __snake_case     :					Union[str, Any]=99     ,     __snake_case     :					str=32     ,     __snake_case     :					Dict=2     ,     __snake_case     :					Dict=4     ,     __snake_case     :					List[Any]=37     ,     __snake_case     :					str=0.1     ,     __snake_case     :					int=0.1     ,     __snake_case     :					str=20     ,     __snake_case     :					Optional[int]=2     ,     __snake_case     :					Optional[int]=1     ,     __snake_case     :					Dict=0     ,     )							-> int:
            _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 							lowercase__       (     self     :					Optional[int]    )							-> Any:
            _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(__snake_case     ,     __snake_case     ,     __snake_case    )
            return config, inputs_dict




      def 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					Optional[Any]     ,     __snake_case     :					Tuple    )							-> Any:
            _lowerCAmelCase					=				TFMBartModel(config=__snake_case    ).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(__snake_case     ,     attention_mask=__snake_case     ,     head_mask=__snake_case     ,     use_cache=__snake_case    )

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




def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase=None				,      lowerCAmelCase=None				,      lowerCAmelCase=None				,      lowerCAmelCase=None				,      lowerCAmelCase=None				,      ):





      """simple docstring"""


      if attention_mask is None:
            _lowerCAmelCase					=				tf.cast(tf.math.not_equal(lowerCAmelCase				,      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 					UpperCAmelCase	(     snake_case_				,						snake_case_				,						unittest.TestCase  ):
      _lowercase:     Dict			=  (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
      _lowercase:     Optional[Any]			=  (TFMBartForConditionalGeneration,) if is_tf_available() else ()
      _lowercase:     List[str]			=  (
          {
              '''conversational''': TFMBartForConditionalGeneration,
              '''feature-extraction''': TFMBartModel,
              '''summarization''': TFMBartForConditionalGeneration,
              '''text2text-generation''': TFMBartForConditionalGeneration,
              '''translation''': TFMBartForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      _lowercase:     Optional[int]			=  True
      _lowercase:     List[Any]			=  False
      _lowercase:     str			=  False




      def 							lowercase__       (     self     :					Optional[int]     ,     __snake_case     :					Dict     ,     __snake_case     :					Optional[int]     ,     __snake_case     :					int     ,     __snake_case     :					Optional[Any]     ,     __snake_case     :					Dict    )							-> Optional[Any]:
            if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
                  # Exception encountered when calling layer '...'
                  return True

            return False




      def 							lowercase__       (     self     :					Optional[int]    )							-> str:
            _lowerCAmelCase					=				TFMBartModelTester(self    )
            _lowerCAmelCase					=				ConfigTester(self     ,     config_class=__snake_case    )




      def 							lowercase__       (     self     :					str    )							-> Optional[int]:
            self.config_tester.run_common_tests()




      def 							lowercase__       (     self     :					Optional[int]    )							-> Optional[int]:
            _lowerCAmelCase					=				self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_decoder_model_past_large_inputs(*__snake_case    )




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




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




      @cached_property
      def 							lowercase__       (     self     :					Optional[Any]    )							-> List[Any]:
            _lowerCAmelCase					=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
            return model




      def 							lowercase__       (     self     :					List[str]     ,     **__snake_case     :					Optional[Any]    )							-> Union[str, Any]:
            _lowerCAmelCase					=				self.translate_src_text(**__snake_case    )
            self.assertListEqual(self.expected_text     ,     __snake_case    )




      def 							lowercase__       (     self     :					int     ,     **__snake_case     :					Any    )							-> List[Any]:
            _lowerCAmelCase					=				self.tokenizer(self.src_text     ,     **__snake_case     ,     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(__snake_case     ,     skip_special_tokens=__snake_case    )
            return generated_words




      @slow
      def 							lowercase__       (     self     :					Tuple    )							-> List[str]:
            self._assert_generated_batch_equal_expected()













'''simple docstring'''






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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_xlnet import XLNetTokenizer
else:
      A__							:  Any    =None


A__							:  Optional[int]    =logging.get_logger(__name__)

A__							:  Union[str, Any]    ={'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

A__							:  List[str]    ={
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json''',
    },
}

A__							:  List[str]    ={
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

A__							:  List[str]    ='''▁'''

# Segments (not really needed)
A__							:  str    =0
A__							:  str    =1
A__							:  List[Any]    =2
A__							:  str    =3
A__							:  Optional[Any]    =4




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Optional[int]			=  VOCAB_FILES_NAMES
      _lowercase:     Optional[int]			=  PRETRAINED_VOCAB_FILES_MAP
      _lowercase:     Dict			=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase:     Optional[Any]			=  '''left'''
      _lowercase:     Dict			=  XLNetTokenizer




      def __init__(     self     :					List[str]     ,     __snake_case     :					Optional[Any]=None     ,     __snake_case     :					str=None     ,     __snake_case     :					Union[str, Any]=False     ,     __snake_case     :					str=True     ,     __snake_case     :					Union[str, Any]=False     ,     __snake_case     :					List[Any]="<s>"     ,     __snake_case     :					List[Any]="</s>"     ,     __snake_case     :					str="<unk>"     ,     __snake_case     :					int="<sep>"     ,     __snake_case     :					int="<pad>"     ,     __snake_case     :					Dict="<cls>"     ,     __snake_case     :					int="<mask>"     ,     __snake_case     :					Optional[int]=["<eop>", "<eod>"]     ,     **__snake_case     :					List[str]     ,     )							-> List[str]:
            # Mask token behave like a normal word, i.e. include the space before it
            _lowerCAmelCase					=				AddedToken(__snake_case     ,     lstrip=__snake_case     ,     rstrip=__snake_case    ) if isinstance(__snake_case     ,     __snake_case    ) else mask_token

            super().__init__(
                vocab_file=__snake_case     ,     tokenizer_file=__snake_case     ,     do_lower_case=__snake_case     ,     remove_space=__snake_case     ,     keep_accents=__snake_case     ,     bos_token=__snake_case     ,     eos_token=__snake_case     ,     unk_token=__snake_case     ,     sep_token=__snake_case     ,     pad_token=__snake_case     ,     cls_token=__snake_case     ,     mask_token=__snake_case     ,     additional_special_tokens=__snake_case     ,     **__snake_case     ,     )

            _lowerCAmelCase					=				3
            _lowerCAmelCase					=				do_lower_case
            _lowerCAmelCase					=				remove_space
            _lowerCAmelCase					=				keep_accents
            _lowerCAmelCase					=				vocab_file
            _lowerCAmelCase					=				False if not self.vocab_file else True




      def 							lowercase__       (     self     :					Optional[int]     ,     __snake_case     :					List[int]     ,     __snake_case     :					Optional[List[int]] = None    )							-> List[int]:
            _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 							lowercase__       (     self     :					str     ,     __snake_case     :					List[int]     ,     __snake_case     :					Optional[List[int]] = None    )							-> List[int]:
            _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 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					str     ,     __snake_case     :					Optional[str] = None    )							-> Tuple[str]:
            if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                      """tokenizer."""    )

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

            if os.path.abspath(self.vocab_file    ) != os.path.abspath(__snake_case    ):
                  copyfile(self.vocab_file     ,     __snake_case    )

            return (out_vocab_file,)






