"""simple docstring"""



from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, 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, TFMarianModel, TFMarianMTModel

@require_tf
class SCREAMING_SNAKE_CASE     :







 """simple docstring"""


 a_       :	Optional[int]  			=MarianConfig
 a_       :	Optional[Any]  			={}
 a_       :	Optional[int]  			="gelu"


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




    '''simple docstring'''






    a__     				=		parent
    a__     				=		batch_size
    a__     				=		seq_length
    a__     				=		is_training
    a__     				=		use_labels
    a__     				=		vocab_size
    a__     				=		hidden_size
    a__     				=		num_hidden_layers
    a__     				=		num_attention_heads
    a__     				=		intermediate_size

    a__     				=		hidden_dropout_prob
    a__     				=		attention_probs_dropout_prob
    a__     				=		max_position_embeddings
    a__     				=		eos_token_id
    a__     				=		pad_token_id
    a__     				=		bos_token_id


 def        _lowerCAmelCase				(   self		:      int				)				->			Dict:




    '''simple docstring'''






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

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

    a__     				=		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__     				=		prepare_marian_inputs_dict(_snake_case					,   _snake_case					,   _snake_case				)
    return config, inputs_dict



 def        _lowerCAmelCase				(   self		:      List[str]					,   _snake_case		:      List[Any]					,   _snake_case		:      Optional[Any]				)				->			List[str]:




    '''simple docstring'''






    a__     				=		TFMarianModel(config=_snake_case				).get_decoder()
    a__     				=		inputs_dict['input_ids']

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

    # first forward pass
    a__     				=		model(_snake_case					,   attention_mask=_snake_case					,   head_mask=_snake_case					,   use_cache=_snake_case				)

    a__				,       a__     				=		outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    a__     				=		ids_tensor((self.batch_size, 3)					,   config.vocab_size				)
    a__     				=		tf.cast(ids_tensor((self.batch_size, 3)					,   2				)					,   tf.inta				)

    # append to next input_ids and
    a__     				=		tf.concat([input_ids, next_tokens]					,   axis=-1				)
    a__     				=		tf.concat([attention_mask, next_attn_mask]					,   axis=-1				)

    a__     				=		model(_snake_case					,   attention_mask=_snake_case				)[0]
    a__     				=		model(_snake_case					,   attention_mask=_snake_case					,   past_key_values=_snake_case				)[0]

    self.parent.assertEqual(next_tokens.shape[1]					,   output_from_past.shape[1]				)

    # select random slice
    a__     				=		int(ids_tensor((1,)					,   output_from_past.shape[-1]				)				)
    a__     				=		output_from_no_past[:, -3:, random_slice_idx]
    a__     				=		output_from_past[:, :, random_slice_idx]

    # test that outputs are equal for slice
    tf.debugging.assert_near(_snake_case					,   _snake_case					,   rtol=1E-3				)





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__=None,UpperCAmelCase__=None,UpperCAmelCase__=None,UpperCAmelCase__=None,UpperCAmelCase__=None,)							->     Optional[Any]:


   '''simple docstring'''


   if attention_mask is None:
      a__     				=		tf.cast(tf.math.not_equal(UpperCAmelCase__,config.pad_token_id					),tf.inta					)
   if decoder_attention_mask is None:
      a__     				=		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__     				=		tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
   if decoder_head_mask is None:
      a__     				=		tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
   if cross_attn_head_mask is None:
      a__     				=		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 SCREAMING_SNAKE_CASE     (    a   ,      a   ,      unittest.TestCase	):







 """simple docstring"""


 a_       :	Dict  			=(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
 a_       :	Optional[int]  			=(TFMarianMTModel,) if is_tf_available() else ()
 a_       :	List[str]  			=(
     {
         "conversational": TFMarianMTModel,
         "feature-extraction": TFMarianModel,
         "summarization": TFMarianMTModel,
         "text2text-generation": TFMarianMTModel,
         "translation": TFMarianMTModel,
     }
     if is_tf_available()
     else {}
 )
 a_       :	List[Any]  			=True
 a_       :	int  			=False
 a_       :	List[Any]  			=False


 def        _lowerCAmelCase				(   self		:      int				)				->			Dict:




    '''simple docstring'''






    a__     				=		TFMarianModelTester(self				)
    a__     				=		ConfigTester(self					,   config_class=_snake_case				)


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




    '''simple docstring'''






    self.config_tester.run_common_tests()



 def        _lowerCAmelCase				(   self		:      int				)				->			List[str]:




    '''simple docstring'''






    a__     				=		self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*_snake_case				)

@require_tf
class SCREAMING_SNAKE_CASE     (    unittest.TestCase	):







 """simple docstring"""


 a_       :	Optional[Any]  			=1_0_0_0  # show more chars for failing integration tests


 @classmethod
 def        _lowerCAmelCase				(   cls		:      Union[str, Any]				)				->			None:




    '''simple docstring'''






    a__     				=		F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
    return cls


 @cached_property
 def        _lowerCAmelCase				(   self		:      Union[str, Any]				)				->			MarianTokenizer:




    '''simple docstring'''






    return AutoTokenizer.from_pretrained(self.model_name				)


 @property
 def        _lowerCAmelCase				(   self		:      List[Any]				)				->			int:




    '''simple docstring'''






    return self.tokenizer.eos_token_id


 @cached_property
 def        _lowerCAmelCase				(   self		:      Union[str, Any]				)				->			List[Any]:




    '''simple docstring'''






    warnings.simplefilter('error'				)
    a__     				=		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
    assert isinstance(_snake_case					,   _snake_case				)
    a__     				=		model.config
    self.assertListEqual(c.bad_words_ids					,   [[c.pad_token_id]]				)
    self.assertEqual(c.max_length					,   512				)
    self.assertEqual(c.decoder_start_token_id					,   c.pad_token_id				)
    return model


 def        _lowerCAmelCase				(   self		:      Dict					,   **_snake_case		:      Optional[Any]				)				->			Union[str, Any]:




    '''simple docstring'''






    a__     				=		self.translate_src_text(**_snake_case				)
    self.assertListEqual(self.expected_text					,   _snake_case				)



 def        _lowerCAmelCase				(   self		:      List[Any]					,   **_snake_case		:      Optional[Any]				)				->			List[Any]:




    '''simple docstring'''






    a__     				=		self.tokenizer(self.src_text					,   **_snake_case					,   padding=_snake_case					,   return_tensors='tf'				)
    a__     				=		self.model.generate(
        model_inputs.input_ids					,   attention_mask=model_inputs.attention_mask					,   num_beams=2					,   max_length=128				)
    a__     				=		self.tokenizer.batch_decode(generated_ids.numpy()					,   skip_special_tokens=_snake_case				)
    return generated_words

@require_sentencepiece
@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 a_       :	Optional[int]  			="mt"
 a_       :	Tuple  			="en"
 a_       :	Dict  			=["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
 a_       :	Optional[Any]  			=["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]


 @unittest.skip('Skipping until #12647 is resolved.'				)
 @slow
 def        _lowerCAmelCase				(   self		:      Optional[int]				)				->			int:




    '''simple docstring'''






    self._assert_generated_batch_equal_expected()

@require_sentencepiece
@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 a_       :	Dict  			="en"
 a_       :	Optional[int]  			="zh"
 a_       :	Dict  			=["My name is Wolfgang and I live in Berlin"]
 a_       :	Union[str, Any]  			=["我叫沃尔夫冈 我住在柏林"]


 @unittest.skip('Skipping until #12647 is resolved.'				)
 @slow
 def        _lowerCAmelCase				(   self		:      Union[str, Any]				)				->			Any:




    '''simple docstring'''






    self._assert_generated_batch_equal_expected()

@require_sentencepiece
@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 a_       :	Optional[int]  			="en"
 a_       :	Tuple  			="ROMANCE"
 a_       :	Tuple  			=[
     ">>fr<< Don't spend so much time watching TV.",
     ">>pt<< Your message has been sent.",
     ">>es<< He's two years older than me.",
 ]
 a_       :	int  			=[
     "Ne passez pas autant de temps à regarder la télé.",
     "A sua mensagem foi enviada.",
     "Es dos años más viejo que yo.",
 ]


 @unittest.skip('Skipping until #12647 is resolved.'				)
 @slow
 def        _lowerCAmelCase				(   self		:      Optional[Any]				)				->			Optional[Any]:




    '''simple docstring'''






    self._assert_generated_batch_equal_expected()



 @unittest.skip('Skipping until #12647 is resolved.'				)
 @slow
 def        _lowerCAmelCase				(   self		:      Tuple				)				->			Union[str, Any]:




    '''simple docstring'''






    a__     				=		TranslationPipeline(self.model					,   self.tokenizer					,   framework='tf'				)
    a__     				=		pipeline(self.src_text				)
    self.assertEqual(self.expected_text					,   [x['translation_text'] for x in output]				)





"""simple docstring"""



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 def __init__(   self		:      Dict					,   _snake_case		:      List[str]					,   _snake_case		:      Any				)				->			Optional[Any]:




    '''simple docstring'''






    super().__init__()
    self.register_modules(unet=_snake_case					,   scheduler=_snake_case				)



 @torch.no_grad()
 def __call__(   self		:      int					,   _snake_case		:      int = 1					,   _snake_case		:      Optional[torch.Generator] = None					,   _snake_case		:      int = 50					,   _snake_case		:      Optional[str] = "pil"					,   _snake_case		:      bool = True					,   **_snake_case		:      str					,   )				->			Union[ImagePipelineOutput, Tuple]:




    '''simple docstring'''






    a__     				=		torch.randn(
        (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,   generator=_snake_case					,   )
    a__     				=		image.to(self.device				)

    # set step values
    self.scheduler.set_timesteps(_snake_case				)

    for t in self.progress_bar(self.scheduler.timesteps				):
       # 1. predict noise model_output
       a__     				=		self.unet(_snake_case					,   _snake_case				).sample

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       a__     				=		self.scheduler.step(_snake_case					,   _snake_case					,   _snake_case				).prev_sample

    a__     				=		(image / 2 + 0.5).clamp(0					,   1				)
    a__     				=		image.cpu().permute(0					,   2					,   3					,   1				).numpy()
    if output_type == "pil":
       a__     				=		self.numpy_to_pil(_snake_case				)

    if not return_dict:
       return (image,), "This is a local test"

    return ImagePipelineOutput(images=_snake_case				), "This is a local test"


