


"""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 						_A  :







    """simple docstring"""




    UpperCAmelCase       :							Dict	=       MBartConfig
    UpperCAmelCase       :							List[str]	=       {}
    UpperCAmelCase       :							Any	=       """gelu"""






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






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

      a						:		Union[str, Any]      							=			ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size)

      a						:		str      							=			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						:		Dict      							=			prepare_mbart_inputs_dict(__UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase)
      return config, inputs_dict






    def 	__snake_case							(					self				:    Optional[int]				, __UpperCAmelCase				:    List[str]				, __UpperCAmelCase				:    Optional[int]):
      a						:		str      							=			TFMBartModel(config=__UpperCAmelCase).get_decoder()
      a						:		str      							=			inputs_dict["input_ids"]

      a						:		Union[str, Any]      							=			input_ids[:1, :]
      a						:		List[str]      							=			inputs_dict["attention_mask"][:1, :]
      a						:		int      							=			inputs_dict["head_mask"]
      a						:		List[str]      							=			1

      # first forward pass
      a						:		Any      							=			model(__UpperCAmelCase				, attention_mask=__UpperCAmelCase				, head_mask=__UpperCAmelCase				, use_cache=__UpperCAmelCase)

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






def      lowercase			(				A_  ,   A_  ,   A_  ,   A_=None  ,   A_=None  ,   A_=None  ,   A_=None  ,   A_=None  ,   )->      Optional[int]:
  '''simple docstring'''







  if attention_mask is None:
    a						:		Optional[Any]      							=			tf.cast(tf.math.not_equal(UpperCAmelCase_  ,   config.pad_token_id       )  ,   tf.inta       )
  if decoder_attention_mask is None:
    a						:		Optional[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:
    a						:		Optional[int]      							=			tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
  if decoder_head_mask is None:
    a						:		Union[str, Any]      							=			tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
  if cross_attn_head_mask is None:
    a						:		str      							=			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 						_A  (  _a    ,_a    ,unittest.TestCase  ):







    """simple docstring"""




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






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

      return False






    def 	__snake_case							(					self				:    Optional[Any]):
      a						:		Dict      							=			TFMBartModelTester(self)
      a						:		Union[str, Any]      							=			ConfigTester(self				, config_class=__UpperCAmelCase)






    def 	__snake_case							(					self				:    Dict):
      self.config_tester.run_common_tests()






    def 	__snake_case							(					self				:    int):
      a						:		Optional[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 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							List[Any]	=       [
        """ UN Chief Says There Is No Military Solution in Syria""",
    ]
    UpperCAmelCase       :							Optional[int]	=       [
        """Şeful ONU declară că nu există o soluţie militară în Siria""",
    ]
    UpperCAmelCase       :							Tuple	=       """facebook/mbart-large-en-ro"""






    @cached_property
    def 	__snake_case							(					self				:    Union[str, Any]):
      return AutoTokenizer.from_pretrained(self.model_name)






    @cached_property
    def 	__snake_case							(					self				:    List[str]):
      a						:		Dict      							=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
      return model






    def 	__snake_case							(					self				:    Dict				, **__UpperCAmelCase				:    Optional[int]):
      a						:		int      							=			self.translate_src_text(**__UpperCAmelCase)
      self.assertListEqual(self.expected_text				, __UpperCAmelCase)






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






    @slow
    def 	__snake_case							(					self				:    Tuple):
      self._assert_generated_batch_equal_expected()










'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  42
  _lowerCamelCase		    =  42






  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   super().__init__()
   self.register_modules(unet=UpperCamelCase			,  scheduler=UpperCamelCase     )







  @torch.no_grad()
  def __call__(    self			,  UpperCamelCase = 1			,  UpperCamelCase = 2000			,  UpperCamelCase = None			,  UpperCamelCase = "pil"			,  UpperCamelCase = True			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  self.unet.config.sample_size
   lowerCamelCase_      =  (batch_size, 3, img_size, img_size)

   lowerCamelCase_      =  self.unet

   lowerCamelCase_      =  randn_tensor(UpperCamelCase			,  generator=UpperCamelCase     ) * self.scheduler.init_noise_sigma
   lowerCamelCase_      =  sample.to(self.device     )

   self.scheduler.set_timesteps(UpperCamelCase     )
   self.scheduler.set_sigmas(UpperCamelCase     )

   for i, t in enumerate(self.progress_bar(self.scheduler.timesteps     )     ):
    lowerCamelCase_      =  self.scheduler.sigmas[i] * torch.ones(shape[0]			,  device=self.device     )

    # correction step
    for _ in range(self.scheduler.config.correct_steps     ):
     lowerCamelCase_      =  self.unet(UpperCamelCase			,  UpperCamelCase     ).sample
     lowerCamelCase_      =  self.scheduler.step_correct(UpperCamelCase			,  UpperCamelCase			,  generator=UpperCamelCase     ).prev_sample

    # prediction step
    lowerCamelCase_      =  model(UpperCamelCase			,  UpperCamelCase     ).sample
    lowerCamelCase_      =  self.scheduler.step_pred(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  generator=UpperCamelCase     )

    lowerCamelCase_ ,lowerCamelCase_      =  output.prev_sample, output.prev_sample_mean

   lowerCamelCase_      =  sample_mean.clamp(0			,  1     )
   lowerCamelCase_      =  sample.cpu().permute(0			,  2			,  3			,  1     ).numpy()
   if output_type == "pil":
    lowerCamelCase_      =  self.numpy_to_pil(UpperCamelCase     )

   if not return_dict:
    return (sample,)

   return ImagePipelineOutput(images=UpperCamelCase     )



