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 						_lowercase		:
 lowercase								=  MBartConfig
 lowercase								=  {}
 lowercase								=  'gelu'

 def __init__(					self      :				List[Any]  , snake_case      :				List[str]  , snake_case      :				List[Any]=1_3  , snake_case      :				List[str]=7  , snake_case      :				List[str]=True  , snake_case      :				Optional[Any]=False  , snake_case      :				Dict=9_9  , snake_case      :				str=3_2  , snake_case      :				int=2  , snake_case      :				Any=4  , snake_case      :				List[Any]=3_7  , snake_case      :				List[str]=0.1  , snake_case      :				Union[str, Any]=0.1  , snake_case      :				int=2_0  , snake_case      :				Optional[Any]=2  , snake_case      :				Optional[Any]=1  , snake_case      :				Dict=0  , )  ->		Dict:

  """simple docstring"""





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

 def 		SCREAMING_SNAKE_CASE__  (					self      :				Tuple     )  ->		Dict:

  """simple docstring"""





  UpperCamelCase_   :		Optional[Any]					=   ids_tensor([self.batch_size, self.seq_length - 1]  , self.vocab_size     )
  UpperCamelCase_   :		Union[str, Any]					=   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size     )  , 1     )
  UpperCamelCase_   :		Tuple					=   tf.concat([input_ids, eos_tensor]  , axis=1     )

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

  UpperCamelCase_   :		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  , )
  UpperCamelCase_   :		int					=   prepare_mbart_inputs_dict(_lowerCAmelCase  , _lowerCAmelCase  , _lowerCAmelCase     )
  return config, inputs_dict


 def 		SCREAMING_SNAKE_CASE__  (					self      :				str  , snake_case      :				Tuple  , snake_case      :				int     )  ->		List[str]:

  """simple docstring"""





  UpperCamelCase_   :		Optional[int]					=   TFMBartModel(config=_lowerCAmelCase     ).get_decoder()
  UpperCamelCase_   :		Optional[int]					=   inputs_dict['input_ids']

  UpperCamelCase_   :		Tuple					=   input_ids[:1, :]
  UpperCamelCase_   :		List[Any]					=   inputs_dict['attention_mask'][:1, :]
  UpperCamelCase_   :		Tuple					=   inputs_dict['head_mask']
  UpperCamelCase_   :		Optional[int]					=   1

  # first forward pass
  UpperCamelCase_   :		str					=   model(_lowerCAmelCase  , attention_mask=_lowerCAmelCase  , head_mask=_lowerCAmelCase  , use_cache=_lowerCAmelCase     )

  UpperCamelCase_,					UpperCamelCase_   :		Tuple					=   outputs.to_tuple()
  UpperCamelCase_   :		Optional[int]					=   past_key_values[1]





def   __lowercase	(		lowerCamelCase   :	Optional[Any]    ,    lowerCamelCase   :	Dict    ,    lowerCamelCase   :	Union[str, Any]    ,    lowerCamelCase   :	Dict=None    ,    lowerCamelCase   :	Union[str, Any]=None    ,    lowerCamelCase   :	Optional[int]=None    ,    lowerCamelCase   :	Any=None    ,    lowerCamelCase   :	Optional[Any]=None    ,    ):
 if attention_mask is None:
  UpperCamelCase_   :		Optional[int]					=   tf.cast(tf.math.not_equal(__UpperCAmelCase    ,    config.pad_token_id      )    ,    tf.inta      )
 if decoder_attention_mask is None:
  UpperCamelCase_   :		int					=   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:
  UpperCamelCase_   :		Union[str, Any]					=   tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
 if decoder_head_mask is None:
  UpperCamelCase_   :		Any					=   tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
 if cross_attn_head_mask is None:
  UpperCamelCase_   :		int					=   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 						_lowercase		(   _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, unittest.TestCase			):
 lowercase								=  (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
 lowercase								=  (TFMBartForConditionalGeneration,) if is_tf_available() else ()
 lowercase								=  (
     {
         'conversational': TFMBartForConditionalGeneration,
         'feature-extraction': TFMBartModel,
         'summarization': TFMBartForConditionalGeneration,
         'text2text-generation': TFMBartForConditionalGeneration,
         'translation': TFMBartForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 lowercase								=  True
 lowercase								=  False
 lowercase								=  False

 def 		SCREAMING_SNAKE_CASE__  (					self      :				Optional[Any]  , snake_case      :				Dict  , snake_case      :				Any  , snake_case      :				List[str]  , snake_case      :				Optional[int]  , snake_case      :				str     )  ->		Dict:

  """simple docstring"""





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

  return False

 def 		SCREAMING_SNAKE_CASE__  (					self      :				List[Any]     )  ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_   :		List[Any]					=   TFMBartModelTester(self     )
  UpperCamelCase_   :		Union[str, Any]					=   ConfigTester(self  , config_class=_lowerCAmelCase     )

 def 		SCREAMING_SNAKE_CASE__  (					self      :				Optional[Any]     )  ->		List[str]:

  """simple docstring"""





  self.config_tester.run_common_tests()


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

  """simple docstring"""





  UpperCamelCase_   :		int					=   self.model_tester.prepare_config_and_inputs_for_common()
  self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase     )





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

 @cached_property
 def 		SCREAMING_SNAKE_CASE__  (					self      :				Tuple     )  ->		Optional[int]:

  """simple docstring"""





  return AutoTokenizer.from_pretrained(self.model_name     )

 @cached_property
 def 		SCREAMING_SNAKE_CASE__  (					self      :				Optional[int]     )  ->		List[str]:

  """simple docstring"""





  UpperCamelCase_   :		List[str]					=   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name     )
  return model

 def 		SCREAMING_SNAKE_CASE__  (					self      :				int  , **snake_case      :				Optional[Any]     )  ->		Optional[Any]:

  """simple docstring"""





  UpperCamelCase_   :		int					=   self.translate_src_text(**_lowerCAmelCase     )
  self.assertListEqual(self.expected_text  , _lowerCAmelCase     )

 def 		SCREAMING_SNAKE_CASE__  (					self      :				str  , **snake_case      :				Any     )  ->		Tuple:

  """simple docstring"""





  UpperCamelCase_   :		Optional[Any]					=   self.tokenizer(self.src_text  , **_lowerCAmelCase  , return_tensors='tf'     )
  UpperCamelCase_   :		Optional[int]					=   self.model.generate(
      model_inputs.input_ids  , attention_mask=model_inputs.attention_mask  , num_beams=2     )
  UpperCamelCase_   :		Optional[int]					=   self.tokenizer.batch_decode(_lowerCAmelCase  , skip_special_tokens=_lowerCAmelCase     )
  return generated_words


 @slow
 def 		SCREAMING_SNAKE_CASE__  (					self      :				Any     )  ->		int:

  """simple docstring"""





  self._assert_generated_batch_equal_expected()









from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__					:  Optional[int]      							=				{'configuration_mmbt': ['MMBTConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Any      							=				['MMBTForClassification', 'MMBTModel', 'ModalEmbeddings']


if TYPE_CHECKING:
   from .configuration_mmbt import MMBTConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
   import sys

   lowerCamelCase__					:  str      							=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)