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_														=							MBartConfig
   lowerCAmelCase_														=							{}
   lowerCAmelCase_														=							"gelu"




   def __init__(   self      :    List[str]	,     _lowercase      :    Optional[int]	,     _lowercase      :    List[Any]=13	,     _lowercase      :    List[Any]=7	,     _lowercase      :    Dict=True	,     _lowercase      :    Tuple=False	,     _lowercase      :    Optional[Any]=99	,     _lowercase      :    Dict=32	,     _lowercase      :    str=2	,     _lowercase      :    str=4	,     _lowercase      :    Tuple=37	,     _lowercase      :    Tuple=0.1	,     _lowercase      :    Union[str, Any]=0.1	,     _lowercase      :    Optional[int]=20	,     _lowercase      :    Dict=2	,     _lowercase      :    List[str]=1	,     _lowercase      :    Union[str, Any]=0	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				parent
     SCREAMING_SNAKE_CASE__      							=				batch_size
     SCREAMING_SNAKE_CASE__      							=				seq_length
     SCREAMING_SNAKE_CASE__      							=				is_training
     SCREAMING_SNAKE_CASE__      							=				use_labels
     SCREAMING_SNAKE_CASE__      							=				vocab_size
     SCREAMING_SNAKE_CASE__      							=				hidden_size
     SCREAMING_SNAKE_CASE__      							=				num_hidden_layers
     SCREAMING_SNAKE_CASE__      							=				num_attention_heads
     SCREAMING_SNAKE_CASE__      							=				intermediate_size
     SCREAMING_SNAKE_CASE__      							=				hidden_dropout_prob
     SCREAMING_SNAKE_CASE__      							=				attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__      							=				max_position_embeddings
     SCREAMING_SNAKE_CASE__      							=				eos_token_id
     SCREAMING_SNAKE_CASE__      							=				pad_token_id
     SCREAMING_SNAKE_CASE__      							=				bos_token_id




   def      __a						(   self      :    List[str]				):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__      							=				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	,     )
     SCREAMING_SNAKE_CASE__      							=				prepare_mbart_inputs_dict(_A	,     _A	,     _A				)
     return config, inputs_dict




   def      __a						(   self      :    Optional[Any]	,     _lowercase      :    Optional[Any]	,     _lowercase      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				TFMBartModel(config=_A				).get_decoder()
     SCREAMING_SNAKE_CASE__      							=				inputs_dict['input_ids']

     SCREAMING_SNAKE_CASE__      							=				input_ids[:1, :]
     SCREAMING_SNAKE_CASE__      							=				inputs_dict['attention_mask'][:1, :]
     SCREAMING_SNAKE_CASE__      							=				inputs_dict['head_mask']
     SCREAMING_SNAKE_CASE__      							=				1

     # first forward pass
     SCREAMING_SNAKE_CASE__      							=				model(_A	,     attention_mask=_A	,     head_mask=_A	,     use_cache=_A				)

     SCREAMING_SNAKE_CASE__      							=				outputs.to_tuple()
     SCREAMING_SNAKE_CASE__      							=				past_key_values[1]





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Any			,					__UpperCamelCase   :	Any			,					__UpperCamelCase   :	int			,					__UpperCamelCase   :	str=None			,					__UpperCamelCase   :	Any=None			,					__UpperCamelCase   :	str=None			,					__UpperCamelCase   :	List[Any]=None			,					__UpperCamelCase   :	Optional[Any]=None			,					)							-> Dict:







  """simple docstring"""





  if attention_mask is None:
    SCREAMING_SNAKE_CASE__      							=				tf.cast(tf.math.not_equal(__a			,					config.pad_token_id     )			,					tf.inta     )
  if decoder_attention_mask is None:
    SCREAMING_SNAKE_CASE__      							=				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:
    SCREAMING_SNAKE_CASE__      							=				tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
  if decoder_head_mask is None:
    SCREAMING_SNAKE_CASE__      							=				tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
  if cross_attn_head_mask is None:
    SCREAMING_SNAKE_CASE__      							=				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    (						snake_case_       ,		snake_case_       ,		unittest.TestCase		):
   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      __a						(   self      :    Any	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    List[Any]	,     _lowercase      :    str	,     _lowercase      :    int	,     _lowercase      :    Dict				):



     """simple docstring"""






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

     return False




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				TFMBartModelTester(self				)
     SCREAMING_SNAKE_CASE__      							=				ConfigTester(self	,     config_class=_A				)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     self.config_tester.run_common_tests()




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				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 __snake_case    (						unittest.TestCase		):
   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      __a						(   self      :    str				):



     """simple docstring"""






     return AutoTokenizer.from_pretrained(self.model_name				)




   @cached_property
   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
     return model




   def      __a						(   self      :    Optional[int]	,     **_lowercase      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.translate_src_text(**_A				)
     self.assertListEqual(self.expected_text	,     _A				)




   def      __a						(   self      :    Union[str, Any]	,     **_lowercase      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.tokenizer(self.src_text	,     **_A	,     return_tensors="""tf"""				)
     SCREAMING_SNAKE_CASE__      							=				self.model.generate(
         model_inputs.input_ids	,     attention_mask=model_inputs.attention_mask	,     num_beams=2				)
     SCREAMING_SNAKE_CASE__      							=				self.tokenizer.batch_decode(_A	,     skip_special_tokens=_A				)
     return generated_words




   @slow
   def      __a						(   self      :    str				):



     """simple docstring"""






     self._assert_generated_batch_equal_expected()




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case    (						unittest.TestCase		):




   @slow
   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""	,     return_dict=_lowercase				).to(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""google/mt5-small"""				)

     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hello there"""	,     return_tensors="""pt"""				).input_ids
     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hi I am"""	,     return_tensors="""pt"""				).input_ids

     SCREAMING_SNAKE_CASE__      							=				model(input_ids.to(_lowercase				)	,     labels=labels.to(_lowercase				)				).loss
     SCREAMING_SNAKE_CASE__      							=				-(labels.shape[-1] * loss.item())

     SCREAMING_SNAKE_CASE__      							=				-84.91_27
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE				) < 1E-4				)
