





'''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      snake_case     :




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[Any]					=MBartConfig
 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					={}
 SCREAMING_SNAKE_CASE_	:					List[str]					="gelu"






 def __init__(       self       :							Optional[int]							,	__A       :							List[Any]							,	__A       :							int=1_3							,	__A       :							Optional[Any]=7							,	__A       :							Dict=True							,	__A       :							List[Any]=False							,	__A       :							Dict=9_9							,	__A       :							Optional[Any]=3_2							,	__A       :							Union[str, Any]=2							,	__A       :							int=4							,	__A       :							List[str]=3_7							,	__A       :							Dict=0.1							,	__A       :							Any=0.1							,	__A       :							str=2_0							,	__A       :							Tuple=2							,	__A       :							Tuple=1							,	__A       :							Optional[int]=0							,	):
        __UpperCamelCase       =  parent
        __UpperCamelCase       =  batch_size
        __UpperCamelCase       =  seq_length
        __UpperCamelCase       =  is_training
        __UpperCamelCase       =  use_labels
        __UpperCamelCase       =  vocab_size
        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  num_hidden_layers
        __UpperCamelCase       =  num_attention_heads
        __UpperCamelCase       =  intermediate_size
        __UpperCamelCase       =  hidden_dropout_prob
        __UpperCamelCase       =  attention_probs_dropout_prob
        __UpperCamelCase       =  max_position_embeddings
        __UpperCamelCase       =  eos_token_id
        __UpperCamelCase       =  pad_token_id
        __UpperCamelCase       =  bos_token_id






 def        _lowerCamelCase					(       self       :							int	):
        __UpperCamelCase       =  ids_tensor([self.batch_size, self.seq_length - 1]							,	self.vocab_size	)
        __UpperCamelCase       =  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)							,	1	)
        __UpperCamelCase       =  tf.concat([input_ids, eos_tensor]							,	axis=1	)

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

        __UpperCamelCase       =  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       =  prepare_mbart_inputs_dict(__A							,	__A							,	__A	)
        return config, inputs_dict






 def        _lowerCamelCase					(       self       :							int							,	__A       :							Dict							,	__A       :							Optional[Any]	):
        __UpperCamelCase       =  TFMBartModel(config=__A	).get_decoder()
        __UpperCamelCase       =  inputs_dict['input_ids']

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

        # first forward pass
        __UpperCamelCase       =  model(__A							,	attention_mask=__A							,	head_mask=__A							,	use_cache=__A	)

        __UpperCamelCase       ,						__UpperCamelCase       =  outputs.to_tuple()
        __UpperCamelCase       =  past_key_values[1]






def 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  List[Any]				,  __lowercase						:  Optional[int]				,  __lowercase						:  Union[str, Any]=None				,  __lowercase						:  List[str]=None				,  __lowercase						:  Dict=None				,  __lowercase						:  Dict=None				,  __lowercase						:  List[Any]=None				,  ) ->      Dict:



       """simple docstring"""






       if attention_mask is None:
              __UpperCamelCase       =  tf.cast(tf.math.not_equal(__lowercase				,  config.pad_token_id     )				,  tf.inta     )
       if decoder_attention_mask is None:
              __UpperCamelCase       =  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       =  tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
       if decoder_head_mask is None:
              __UpperCamelCase       =  tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
       if cross_attn_head_mask is None:
              __UpperCamelCase       =  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     (    __lowerCamelCase			,       __lowerCamelCase			,       unittest.TestCase       ):




 """simple docstring"""



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






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

        return False






 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  TFMBartModelTester(self	)
        __UpperCamelCase       =  ConfigTester(self							,	config_class=__A	)






 def        _lowerCamelCase					(       self       :							Tuple	):
        self.config_tester.run_common_tests()






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  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       ):




 """simple docstring"""



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






 @cached_property
 def        _lowerCamelCase					(       self       :							Optional[int]	):
        return AutoTokenizer.from_pretrained(self.model_name	)






 @cached_property
 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
        return model






 def        _lowerCamelCase					(       self       :							List[Any]							,	**__A       :							Dict	):
        __UpperCamelCase       =  self.translate_src_text(**__A	)
        self.assertListEqual(self.expected_text							,	__A	)






 def        _lowerCamelCase					(       self       :							Dict							,	**__A       :							Union[str, Any]	):
        __UpperCamelCase       =  self.tokenizer(self.src_text							,	**__A							,	return_tensors='tf'	)
        __UpperCamelCase       =  self.model.generate(
            model_inputs.input_ids							,	attention_mask=model_inputs.attention_mask							,	num_beams=2	)
        __UpperCamelCase       =  self.tokenizer.batch_decode(__A							,	skip_special_tokens=__A	)
        return generated_words






 @slow
 def        _lowerCamelCase					(       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     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      UNetaDModel
							A_							:      ScoreSdeVeScheduler






							def __init__(    self    ,					__snake_case    ,					__snake_case  ):
											super().__init__()
											self.register_modules(unet=__snake_case    ,					scheduler=__snake_case  )






							@torch.no_grad()
							def __call__(    self    ,					__snake_case = 1    ,					__snake_case = 2000    ,					__snake_case = None    ,					__snake_case = "pil"    ,					__snake_case = True    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.unet.config.sample_size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      (batch_size, 3, img_size, img_size)

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.unet

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      randn_tensor(__snake_case    ,					generator=__snake_case  ) * self.scheduler.init_noise_sigma
											_SCREAMING_SNAKE_CASE :       Any     		=      sample.to(self.device  )

											self.scheduler.set_timesteps(__snake_case  )
											self.scheduler.set_sigmas(__snake_case  )

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

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

															# prediction step
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      model(__snake_case    ,					__snake_case  ).sample
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.scheduler.step_pred(__snake_case    ,					__snake_case    ,					__snake_case    ,					generator=__snake_case  )

															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       int     		=      output.prev_sample, output.prev_sample_mean

											_SCREAMING_SNAKE_CASE :       Tuple     		=      sample_mean.clamp(0    ,					1  )
											_SCREAMING_SNAKE_CASE :       str     		=      sample.cpu().permute(0    ,					2    ,					3    ,					1  ).numpy()
											if output_type == "pil":
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.numpy_to_pil(__snake_case  )

											if not return_dict:
															return (sample,)

											return ImagePipelineOutput(images=__snake_case  )




