





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	:






      """simple docstring"""




      _lowercase    : Optional[Any]            =      MBartConfig
      _lowercase    : Union[str, Any]            =      {}
      _lowercase    : List[Any]            =      '''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		,				)       ->						List[Any]:



        '''simple docstring'''




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







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




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

        a__     : Optional[Any]  =ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size							)

        a__     : 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		,				)
        a__     : int  =prepare_mbart_inputs_dict(_a		,				_a		,				_a							)
        return config, inputs_dict







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Tuple:



        '''simple docstring'''




        a__     : Any  =TFMBartModel(config=_a							).get_decoder()
        a__     : Tuple  =inputs_dict['input_ids']

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

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

        a__     : List[str]  =outputs.to_tuple()
        a__     : Union[str, Any]  =past_key_values[1]
def 					_A	(		SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       List[str]	,	SCREAMING_SNAKE_CASE					:       int=None	,	SCREAMING_SNAKE_CASE					:       Optional[int]=None	,	SCREAMING_SNAKE_CASE					:       Dict=None	,	SCREAMING_SNAKE_CASE					:       List[str]=None	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]=None	,	):





  """simple docstring"""



  if attention_mask is None:
    a__     : Union[str, Any]  =tf.cast(tf.math.not_equal(__a	,	config.pad_token_id						)	,	tf.inta						)
  if decoder_attention_mask is None:
    a__     : Tuple  =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:
    a__     : str  =tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
  if decoder_head_mask is None:
    a__     : str  =tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
  if cross_attn_head_mask is None:
    a__     : Optional[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     __lowerCAmelCase	(   lowercase__						,    lowercase__						,    unittest.TestCase):






      """simple docstring"""




      _lowercase    : str            =      (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
      _lowercase    : Tuple            =      (TFMBartForConditionalGeneration,) if is_tf_available() else ()
      _lowercase    : Any            =      (
          {
              '''conversational''': TFMBartForConditionalGeneration,
              '''feature-extraction''': TFMBartModel,
              '''summarization''': TFMBartForConditionalGeneration,
              '''text2text-generation''': TFMBartForConditionalGeneration,
              '''translation''': TFMBartForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      _lowercase    : Tuple            =      True
      _lowercase    : Optional[Any]            =      False
      _lowercase    : Dict            =      False







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Tuple:



        '''simple docstring'''




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

        return False







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        a__     : Tuple  =TFMBartModelTester(self							)
        a__     : List[Any]  =ConfigTester(self		,				config_class=_a							)







      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        self.config_tester.run_common_tests()







      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        a__     : str  =self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*_a							)







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






      """simple docstring"""




      _lowercase    : int            =      [
          ''' UN Chief Says There Is No Military Solution in Syria''',
      ]
      _lowercase    : Union[str, Any]            =      [
          '''Şeful ONU declară că nu există o soluţie militară în Siria''',
      ]
      _lowercase    : Dict            =      '''facebook/mbart-large-en-ro'''







      @cached_property
      def        _lowercase  (   self							)       ->						List[Any]:



        '''simple docstring'''




        return AutoTokenizer.from_pretrained(self.model_name							)







      @cached_property
      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : List[Any]  =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
        return model







      def        _lowercase  (   self		,				**lowerCAmelCase__							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : int  =self.translate_src_text(**_a							)
        self.assertListEqual(self.expected_text		,				_a							)







      def        _lowercase  (   self		,				**lowerCAmelCase__							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : Dict  =self.tokenizer(self.src_text		,				**_a		,				return_tensors="tf"							)
        a__     : Dict  =self.model.generate(
            model_inputs.input_ids		,				attention_mask=model_inputs.attention_mask		,				num_beams=2							)
        a__     : Optional[int]  =self.tokenizer.batch_decode(_a		,				skip_special_tokens=_a							)
        return generated_words







      @slow
      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        self._assert_generated_batch_equal_expected()













from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class     __lowerCAmelCase	(   UpperCamelCase__):







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						int:



        '''simple docstring'''




        super().__init__(
            lowerCAmelCase__		,				split=lowerCAmelCase__		,				features=lowerCAmelCase__		,				cache_dir=lowerCAmelCase__		,				keep_in_memory=lowerCAmelCase__		,				streaming=lowerCAmelCase__		,				num_proc=lowerCAmelCase__		,				**lowerCAmelCase__		,				)
        a__     : Tuple  =path_or_paths if isinstance(lowerCAmelCase__		,				lowerCAmelCase__							) else {self.split: path_or_paths}
        a__     : List[str]  =Text(
            cache_dir=lowerCAmelCase__		,				data_files=lowerCAmelCase__		,				features=lowerCAmelCase__		,				**lowerCAmelCase__		,				)







      def        _lowercase  (   self							)       ->						List[Any]:



        '''simple docstring'''




        if self.streaming:
          a__     : str  =self.builder.as_streaming_dataset(split=self.split							)
        # Build regular (map-style) dataset
        else:
          a__     : Dict  =None
          a__     : Optional[Any]  =None
          a__     : Union[str, Any]  =None
          a__     : Tuple  =None

          self.builder.download_and_prepare(
              download_config=lowerCAmelCase__		,				download_mode=lowerCAmelCase__		,				verification_mode=lowerCAmelCase__		,				base_path=lowerCAmelCase__		,				num_proc=self.num_proc		,				)
          a__     : Tuple  =self.builder.as_dataset(
              split=self.split		,				verification_mode=lowerCAmelCase__		,				in_memory=self.keep_in_memory							)
        return dataset






