

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 					lowercase		:



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       MarianConfig
 __SCREAMING_SNAKE_CASE      							=       {}
 __SCREAMING_SNAKE_CASE      							=       'gelu'




 def __init__(self , __a , __a=13 , __a=7 , __a=True , __a=False , __a=99 , __a=32 , __a=2 , __a=4 , __a=37 , __a=0.1 , __a=0.1 , __a=20 , __a=2 , __a=1 , __a=0 , )				->      List[str]:






  """simple docstring"""





  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 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  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_marian_inputs_dict(__a , __a , __a				)
  return config, inputs_dict




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






  """simple docstring"""





  UpperCAmelCase__           =       TFMarianModel(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__           =       outputs.to_tuple()

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

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

  UpperCAmelCase__           =       model(__a , attention_mask=__a				)[0]
  UpperCAmelCase__           =       model(__a , attention_mask=__a , past_key_values=__a				)[0]

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

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

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







def 		UpperCamelCase_(    snake_case__:						List[Any]						,					snake_case__:						List[Any]						,					snake_case__:						int						,					snake_case__:						int=None						,					snake_case__:						Any=None						,					snake_case__:						Tuple=None						,					snake_case__:						Union[str, Any]=None						,					snake_case__:						Dict=None						,					) ->				List[str]:
 if attention_mask is None:
  UpperCAmelCase__           =       tf.cast(tf.math.not_equal(lowerCAmelCase__						,					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 					lowercase		( snake_case__				,       snake_case__				,       unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
 __SCREAMING_SNAKE_CASE      							=       (TFMarianMTModel,) if is_tf_available() else ()
 __SCREAMING_SNAKE_CASE      							=       (
     {
         'conversational': TFMarianMTModel,
         'feature-extraction': TFMarianModel,
         'summarization': TFMarianMTModel,
         'text2text-generation': TFMarianMTModel,
         'translation': TFMarianMTModel,
     }
     if is_tf_available()
     else {}
 )
 __SCREAMING_SNAKE_CASE      							=       True
 __SCREAMING_SNAKE_CASE      							=       False
 __SCREAMING_SNAKE_CASE      							=       False




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       TFMarianModelTester(self				)
  UpperCAmelCase__           =       ConfigTester(self , config_class=__a				)




 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  self.config_tester.run_common_tests()




 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__           =       self.model_tester.prepare_config_and_inputs_for_common()
  self.model_tester.check_decoder_model_past_large_inputs(*__a				)







@require_tf
class 					lowercase		( unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       1000  # show more chars for failing integration tests




 @classmethod
 def 						UpperCamelCase__							(cls				)				->      str:






  """simple docstring"""





  UpperCAmelCase__           =       F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
  return cls




 @cached_property
 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  return AutoTokenizer.from_pretrained(self.model_name				)




 @property
 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  return self.tokenizer.eos_token_id




 @cached_property
 def 						UpperCamelCase__							(self				)				->      str:






  """simple docstring"""





  warnings.simplefilter('error'				)
  UpperCAmelCase__           =       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
  assert isinstance(__a , __a				)
  UpperCAmelCase__           =       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 						UpperCamelCase__							(self , **__a				)				->      Dict:






  """simple docstring"""





  UpperCAmelCase__           =       self.translate_src_text(**__a				)
  self.assertListEqual(self.expected_text , __a				)




 def 						UpperCamelCase__							(self , **__a				)				->      Optional[Any]:






  """simple docstring"""





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







@require_sentencepiece
@require_tokenizers
@require_tf
class 					lowercase		( snake_case__      ):



 '''simple docstring'''


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




 @unittest.skip('Skipping until #12647 is resolved.'				)
 @slow
 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 					lowercase		( snake_case__      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       'en'
 __SCREAMING_SNAKE_CASE      							=       'zh'
 __SCREAMING_SNAKE_CASE      							=       ['My name is Wolfgang and I live in Berlin']
 __SCREAMING_SNAKE_CASE      							=       ['我叫沃尔夫冈 我住在柏林']




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






  """simple docstring"""





  self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 					lowercase		( snake_case__      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       'en'
 __SCREAMING_SNAKE_CASE      							=       'ROMANCE'
 __SCREAMING_SNAKE_CASE      							=       [
     '>>fr<< Don\'t spend so much time watching TV.',
     '>>pt<< Your message has been sent.',
     '>>es<< He\'s two years older than me.',
 ]
 __SCREAMING_SNAKE_CASE      							=       [
     '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 						UpperCamelCase__							(self				)				->      str:






  """simple docstring"""





  self._assert_generated_batch_equal_expected()




 @unittest.skip('Skipping until #12647 is resolved.'				)
 @slow
 def 						UpperCamelCase__							(self				)				->      str:






  """simple docstring"""





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






import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, slow, torch_device

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin



class 					lowercase		( _UpperCamelCase				,       _UpperCamelCase				,       unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       UNetaDModel
 __SCREAMING_SNAKE_CASE      							=       """sample"""




 @property
 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  UpperCAmelCase__           =       4
  UpperCAmelCase__           =       14
  UpperCAmelCase__           =       16

  UpperCAmelCase__           =       floats_tensor((batch_size, num_features, seq_len)				).to(__a				)
  UpperCAmelCase__           =       torch.tensor([10] * batch_size				).to(__a				)

  return {"sample": noise, "timestep": time_step}




 @property
 def 						UpperCamelCase__							(self				)				->      Union[str, Any]:






  """simple docstring"""





  return (4, 14, 16)




 @property
 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  return (4, 14, 16)




 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  pass




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  pass




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  super().test_determinism()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  super().test_outputs_equivalence()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  super().test_from_save_pretrained()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  super().test_from_save_pretrained_variant()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  super().test_model_from_pretrained()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  super().test_output()




 def 						UpperCamelCase__							(self				)				->      List[str]:






  """simple docstring"""





  UpperCAmelCase__           =       {
      'block_out_channels': (32, 64, 128, 256),
      'in_channels': 14,
      'out_channels': 14,
      'time_embedding_type': 'positional',
      'use_timestep_embedding': True,
      'flip_sin_to_cos': False,
      'freq_shift': 1.0,
      'out_block_type': 'OutConv1DBlock',
      'mid_block_type': 'MidResTemporalBlock1D',
      'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
      'up_block_types': ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D'),
      'act_fn': 'mish',
  }
  UpperCAmelCase__           =       self.dummy_input
  return init_dict, inputs_dict




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       UNetaDModel.from_pretrained(
      'bglick13/hopper-medium-v2-value-function-hor32' , output_loading_info=__a , subfolder='unet'				)
  self.assertIsNotNone(__a				)
  self.assertEqual(len(loading_info['missing_keys']				) , 0				)

  model.to(__a				)
  UpperCAmelCase__           =       model(**self.dummy_input				)

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




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       UNetaDModel.from_pretrained('bglick13/hopper-medium-v2-value-function-hor32' , subfolder='unet'				)
  torch.manual_seed(0				)
  if torch.cuda.is_available():
   torch.cuda.manual_seed_all(0				)

  UpperCAmelCase__           =       model.config.in_channels
  UpperCAmelCase__           =       16
  UpperCAmelCase__           =       torch.randn((1, seq_len, num_features)				).permute(
      0 , 2 , 1				)  # match original, we can update values and remove
  UpperCAmelCase__           =       torch.full((num_features,) , 0				)

  with torch.no_grad():
   UpperCAmelCase__           =       model(__a , __a				).sample.permute(0 , 2 , 1				)

  UpperCAmelCase__           =       output[0, -3:, -3:].flatten()
  # fmt: off
  UpperCAmelCase__           =       torch.tensor([-2.13_71_72, 1.1_42_60_16, 0.3_68_86_87, -0.76_69_22, 0.7_30_31_46, 0.11_03_88_64, -0.4_76_06_33, 0.13_27_01_72, 0.02_59_13_48]				)
  # fmt: on
  self.assertTrue(torch.allclose(__a , __a , rtol=1E-3				)				)




 def 						UpperCamelCase__							(self				)				->      List[Any]:






  """simple docstring"""





  pass




 @slow
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       'harmonai/maestro-150k'
  UpperCAmelCase__           =       UNetaDModel.from_pretrained(__a , subfolder='unet'				)
  model.to(__a				)

  UpperCAmelCase__           =       65536
  UpperCAmelCase__           =       torch.sin(torch.arange(__a				)[None, None, :].repeat(1 , 2 , 1				)				).to(__a				)
  UpperCAmelCase__           =       torch.tensor([1]				).to(__a				)

  with torch.no_grad():
   UpperCAmelCase__           =       model(__a , __a				).sample

  UpperCAmelCase__           =       output.abs().sum()
  UpperCAmelCase__           =       output.abs().max()

  assert (output_sum - 2_24.08_96).abs() < 0.5
  assert (output_max - 0.06_07).abs() < 4E-4







class 					lowercase		( _UpperCamelCase				,       _UpperCamelCase				,       unittest.TestCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       UNetaDModel
 __SCREAMING_SNAKE_CASE      							=       """sample"""




 @property
 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  UpperCAmelCase__           =       4
  UpperCAmelCase__           =       14
  UpperCAmelCase__           =       16

  UpperCAmelCase__           =       floats_tensor((batch_size, num_features, seq_len)				).to(__a				)
  UpperCAmelCase__           =       torch.tensor([10] * batch_size				).to(__a				)

  return {"sample": noise, "timestep": time_step}




 @property
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  return (4, 14, 16)




 @property
 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  return (4, 14, 1)




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  super().test_determinism()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  super().test_outputs_equivalence()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  super().test_from_save_pretrained()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  super().test_from_save_pretrained_variant()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  super().test_model_from_pretrained()




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       self.prepare_init_args_and_inputs_for_common()
  UpperCAmelCase__           =       self.model_class(**__a				)
  model.to(__a				)
  model.eval()

  with torch.no_grad():
   UpperCAmelCase__           =       model(**__a				)

   if isinstance(__a , __a				):
    UpperCAmelCase__           =       output.sample

  self.assertIsNotNone(__a				)
  UpperCAmelCase__           =       torch.Size((inputs_dict['sample'].shape[0], 1)				)
  self.assertEqual(output.shape , __a , 'Input and output shapes do not match'				)




 def 						UpperCamelCase__							(self				)				->      Optional[int]:






  """simple docstring"""





  pass




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  pass




 def 						UpperCamelCase__							(self				)				->      Any:






  """simple docstring"""





  UpperCAmelCase__           =       {
      'in_channels': 14,
      'out_channels': 14,
      'down_block_types': ['DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'],
      'up_block_types': [],
      'out_block_type': 'ValueFunction',
      'mid_block_type': 'ValueFunctionMidBlock1D',
      'block_out_channels': [32, 64, 128, 256],
      'layers_per_block': 1,
      'downsample_each_block': True,
      'use_timestep_embedding': True,
      'freq_shift': 1.0,
      'flip_sin_to_cos': False,
      'time_embedding_type': 'positional',
      'act_fn': 'mish',
  }
  UpperCAmelCase__           =       self.dummy_input
  return init_dict, inputs_dict




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      Union[str, Any]:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       UNetaDModel.from_pretrained(
      'bglick13/hopper-medium-v2-value-function-hor32' , output_loading_info=__a , subfolder='value_function'				)
  self.assertIsNotNone(__a				)
  self.assertEqual(len(vf_loading_info['missing_keys']				) , 0				)

  value_function.to(__a				)
  UpperCAmelCase__           =       value_function(**self.dummy_input				)

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




 @unittest.skipIf(torch_device == 'mps' , 'mish op not supported in MPS'				)
 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  UpperCAmelCase__       ,					UpperCAmelCase__           =       UNetaDModel.from_pretrained(
      'bglick13/hopper-medium-v2-value-function-hor32' , output_loading_info=__a , subfolder='value_function'				)
  torch.manual_seed(0				)
  if torch.cuda.is_available():
   torch.cuda.manual_seed_all(0				)

  UpperCAmelCase__           =       value_function.config.in_channels
  UpperCAmelCase__           =       14
  UpperCAmelCase__           =       torch.randn((1, seq_len, num_features)				).permute(
      0 , 2 , 1				)  # match original, we can update values and remove
  UpperCAmelCase__           =       torch.full((num_features,) , 0				)

  with torch.no_grad():
   UpperCAmelCase__           =       value_function(__a , __a				).sample

  # fmt: off
  UpperCAmelCase__           =       torch.tensor([1_65.25] * seq_len				)
  # fmt: on
  self.assertTrue(torch.allclose(__a , __a , rtol=1E-3				)				)




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  pass



