



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




  def __init__(   self  ,__UpperCAmelCase  ,__UpperCAmelCase=13  ,__UpperCAmelCase=7  ,__UpperCAmelCase=True  ,__UpperCAmelCase=False  ,__UpperCAmelCase=99  ,__UpperCAmelCase=32  ,__UpperCAmelCase=2  ,__UpperCAmelCase=4  ,__UpperCAmelCase=37  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=20  ,__UpperCAmelCase=2  ,__UpperCAmelCase=1  ,__UpperCAmelCase=0  ,)   ->		List[str]:
      lowerCAmelCase__			:  Dict											= parent
      lowerCAmelCase__			:  Dict											= batch_size
      lowerCAmelCase__			:  Any											= seq_length
      lowerCAmelCase__			:  str											= is_training
      lowerCAmelCase__			:  Any											= use_labels
      lowerCAmelCase__			:  Union[str, Any]											= vocab_size
      lowerCAmelCase__			:  Optional[int]											= hidden_size
      lowerCAmelCase__			:  Union[str, Any]											= num_hidden_layers
      lowerCAmelCase__			:  List[str]											= num_attention_heads
      lowerCAmelCase__			:  List[str]											= intermediate_size
      lowerCAmelCase__			:  Tuple											= hidden_dropout_prob
      lowerCAmelCase__			:  List[Any]											= attention_probs_dropout_prob
      lowerCAmelCase__			:  Any											= max_position_embeddings
      lowerCAmelCase__			:  int											= eos_token_id
      lowerCAmelCase__			:  Tuple											= pad_token_id
      lowerCAmelCase__			:  List[Any]											= bos_token_id




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

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

      lowerCAmelCase__			:  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  ,)
      lowerCAmelCase__			:  Optional[int]											= prepare_mbart_inputs_dict(UpperCamelCase_  ,UpperCamelCase_  ,UpperCamelCase_				)
      return config, inputs_dict






  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		Optional[Any]:
      lowerCAmelCase__			:  int											= TFMBartModel(config=UpperCamelCase_				).get_decoder()
      lowerCAmelCase__			:  List[Any]											= inputs_dict["""input_ids"""]

      lowerCAmelCase__			:  Tuple											= input_ids[:1, :]
      lowerCAmelCase__			:  Optional[int]											= inputs_dict["""attention_mask"""][:1, :]
      lowerCAmelCase__			:  List[str]											= inputs_dict["""head_mask"""]
      lowerCAmelCase__			:  int											= 1

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

      lowerCAmelCase__				,       lowerCAmelCase__			:  List[Any]											= outputs.to_tuple()
      lowerCAmelCase__			:  Dict											= past_key_values[1]







def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase=None							,   UpperCamelCase=None							,   UpperCamelCase=None							,   UpperCamelCase=None							,   UpperCamelCase=None							,   ):



    """simple docstring"""





    if attention_mask is None:
        lowerCAmelCase__			:  Union[str, Any]											= tf.cast(tf.math.not_equal(UpperCamelCase							,   config.pad_token_id      )							,   tf.inta      )
    if decoder_attention_mask is None:
        lowerCAmelCase__			:  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:
        lowerCAmelCase__			:  Optional[Any]											= tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
    if decoder_head_mask is None:
        lowerCAmelCase__			:  Optional[Any]											= tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
    if cross_attn_head_mask is None:
        lowerCAmelCase__			:  Union[str, 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_(    UpperCamelCase__						,			UpperCamelCase__						,			unittest.TestCase					):
  '''simple docstring'''




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




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		int:







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

      return False




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  List[str]											= TFMBartModelTester(self				)
      lowerCAmelCase__			:  int											= ConfigTester(self  ,config_class=UpperCamelCase_				)




  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      self.config_tester.run_common_tests()






  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      lowerCAmelCase__			:  Union[str, Any]											= self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase_				)







@require_sentencepiece
@require_tokenizers
@require_tf
class 	lowerCAmelCase_(    unittest.TestCase					):
  '''simple docstring'''




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




  @cached_property
  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      return AutoTokenizer.from_pretrained(self.model_name				)




  @cached_property
  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      lowerCAmelCase__			:  List[Any]											= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
      return model




  def 							UpperCAmelCase_     (   self  ,**__UpperCAmelCase				)   ->		Tuple:
      lowerCAmelCase__			:  int											= self.translate_src_text(**UpperCamelCase_				)
      self.assertListEqual(self.expected_text  ,UpperCamelCase_				)




  def 							UpperCAmelCase_     (   self  ,**__UpperCAmelCase				)   ->		Tuple:
      lowerCAmelCase__			:  List[Any]											= self.tokenizer(self.src_text  ,**UpperCamelCase_  ,return_tensors="""tf"""				)
      lowerCAmelCase__			:  Union[str, Any]											= self.model.generate(
          model_inputs.input_ids  ,attention_mask=model_inputs.attention_mask  ,num_beams=2				)
      lowerCAmelCase__			:  Union[str, Any]											= self.tokenizer.batch_decode(UpperCamelCase_  ,skip_special_tokens=UpperCamelCase_				)
      return generated_words






  @slow
  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      self._assert_generated_batch_equal_expected()







'''simple docstring'''





import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				Optional[Any]           =				VQModel
  __lowercase							:				Tuple           =				'''sample'''




  @property
  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase=(32, 32)				)   ->		List[Any]:
      lowerCAmelCase__			:  Dict											= 4
      lowerCAmelCase__			:  List[str]											= 3

      lowerCAmelCase__			:  Dict											= floats_tensor((batch_size, num_channels) + sizes				).to(__UpperCAmelCase				)

      return {"sample": image}




  @property
  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      return (3, 32, 32)




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      return (3, 32, 32)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      lowerCAmelCase__			:  Dict											= {
          """block_out_channels""": [32, 64],
          """in_channels""": 3,
          """out_channels""": 3,
          """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
          """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
          """latent_channels""": 3,
      }
      lowerCAmelCase__			:  Any											= self.dummy_input
      return init_dict, inputs_dict




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      pass




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      pass




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__				,       lowerCAmelCase__			:  Union[str, Any]											= VQModel.from_pretrained("""fusing/vqgan-dummy"""  ,output_loading_info=__UpperCAmelCase				)
      self.assertIsNotNone(__UpperCAmelCase				)
      self.assertEqual(len(loading_info["""missing_keys"""]				)  ,0				)

      model.to(__UpperCAmelCase				)
      lowerCAmelCase__			:  List[Any]											= model(**self.dummy_input				)

      assert image is not None, "Make sure output is not None"






  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      lowerCAmelCase__			:  Tuple											= VQModel.from_pretrained("""fusing/vqgan-dummy"""				)
      model.to(__UpperCAmelCase				).eval()

      torch.manual_seed(0				)
      if torch.cuda.is_available():
          torch.cuda.manual_seed_all(0				)

      lowerCAmelCase__			:  List[str]											= torch.randn(1  ,model.config.in_channels  ,model.config.sample_size  ,model.config.sample_size				)
      lowerCAmelCase__			:  Optional[Any]											= image.to(__UpperCAmelCase				)
      with torch.no_grad():
          lowerCAmelCase__			:  Optional[Any]											= model(__UpperCAmelCase				).sample

      lowerCAmelCase__			:  Tuple											= output[0, -1, -3:, -3:].flatten().cpu()
      # fmt: off
      lowerCAmelCase__			:  Tuple											= torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3]				)
      # fmt: on
      self.assertTrue(torch.allclose(__UpperCAmelCase  ,__UpperCAmelCase  ,atol=1E-3				)				)


