






'''simple docstring'''




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class   _SCREAMING_SNAKE_CASE   (   SCREAMING_SNAKE_CASE__     ):
 __SCREAMING_SNAKE_CASE					:List[str]      	=					(UnCLIPScheduler,)



 def 			snake_case__  (   self		:   str			,				**a__		:   Optional[int]      ):
      __magic_name__						       =    {
          '''num_train_timesteps''': 1000,
          '''variance_type''': '''fixed_small_log''',
          '''clip_sample''': True,
          '''clip_sample_range''': 1.0,
          '''prediction_type''': '''epsilon''',
      }

      config.update(**a_      )
      return config



 def 			snake_case__  (   self		:   int      ):



      for timesteps in [1, 5, 100, 1000]:
           self.check_over_configs(num_train_timesteps=a_      )



 def 			snake_case__  (   self		:   int      ):



      for variance in ["fixed_small_log", "learned_range"]:
           self.check_over_configs(variance_type=a_      )



 def 			snake_case__  (   self		:   Optional[Any]      ):



      for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=a_      )



 def 			snake_case__  (   self		:   Optional[Any]      ):



      for clip_sample_range in [1, 5, 10, 20]:
           self.check_over_configs(clip_sample_range=a_      )



 def 			snake_case__  (   self		:   Union[str, Any]      ):



      for prediction_type in ["epsilon", "sample"]:
           self.check_over_configs(prediction_type=a_      )



 def 			snake_case__  (   self		:   Optional[Any]      ):



      for time_step in [0, 500, 999]:
           for prev_timestep in [None, 5, 100, 250, 500, 750]:
                if prev_timestep is not None and prev_timestep >= time_step:
                     continue

                self.check_over_forward(time_step=a_			,				prev_timestep=a_      )



 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    self.scheduler_classes[0]
      __magic_name__						       =    self.get_scheduler_config(variance_type='''fixed_small_log'''      )
      __magic_name__						       =    scheduler_class(**a_      )

      assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 1.0000E-10      )      ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.0_549_625      )      ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.9_994_987      )      ) < 1E-5



 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    self.scheduler_classes[0]
      __magic_name__						       =    self.get_scheduler_config(variance_type='''learned_range'''      )
      __magic_name__						       =    scheduler_class(**a_      )

      __magic_name__						       =    0.5

      assert scheduler._get_variance(1			,				predicted_variance=a_      ) - -10.1_712_790 < 1E-5
      assert scheduler._get_variance(487			,				predicted_variance=a_      ) - -5.7_998_052 < 1E-5
      assert scheduler._get_variance(999			,				predicted_variance=a_      ) - -0.0_010_011 < 1E-5



 def 			snake_case__  (   self		:   str      ):
      __magic_name__						       =    self.scheduler_classes[0]
      __magic_name__						       =    self.get_scheduler_config()
      __magic_name__						       =    scheduler_class(**a_      )

      __magic_name__						       =    scheduler.timesteps

      __magic_name__						       =    self.dummy_model()
      __magic_name__						       =    self.dummy_sample_deter
      __magic_name__						       =    torch.manual_seed(0      )

      for i, t in enumerate(a_      ):
           # 1. predict noise residual
           __magic_name__						       =    model(a_			,				a_      )

           # 2. predict previous mean of sample x_t-1
           __magic_name__						       =    scheduler.step(a_			,				a_			,				a_			,				generator=a_      ).prev_sample

           __magic_name__						       =    pred_prev_sample

      __magic_name__						       =    torch.sum(torch.abs(a_      )      )
      __magic_name__						       =    torch.mean(torch.abs(a_      )      )

      assert abs(result_sum.item() - 252.2_682_495      ) < 1E-2
      assert abs(result_mean.item() - 0.3_284_743      ) < 1E-3



 def 			snake_case__  (   self		:   List[Any]      ):
      __magic_name__						       =    self.scheduler_classes[0]
      __magic_name__						       =    self.get_scheduler_config()
      __magic_name__						       =    scheduler_class(**a_      )

      scheduler.set_timesteps(25      )

      __magic_name__						       =    scheduler.timesteps

      __magic_name__						       =    self.dummy_model()
      __magic_name__						       =    self.dummy_sample_deter
      __magic_name__						       =    torch.manual_seed(0      )

      for i, t in enumerate(a_      ):
           # 1. predict noise residual
           __magic_name__						       =    model(a_			,				a_      )

           if i + 1 == timesteps.shape[0]:
                __magic_name__						       =    None
           else:
                __magic_name__						       =    timesteps[i + 1]

           # 2. predict previous mean of sample x_t-1
           __magic_name__						       =    scheduler.step(
               a_			,				a_			,				a_			,				prev_timestep=a_			,				generator=a_      ).prev_sample

           __magic_name__						       =    pred_prev_sample

      __magic_name__						       =    torch.sum(torch.abs(a_      )      )
      __magic_name__						       =    torch.mean(torch.abs(a_      )      )

      assert abs(result_sum.item() - 258.2_044_983      ) < 1E-2
      assert abs(result_mean.item() - 0.3_362_038      ) < 1E-3



 def 			snake_case__  (   self		:   Optional[Any]      ):
      pass



 def 			snake_case__  (   self		:   Tuple      ):
      pass









'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
      import torch






@is_pipeline_test
class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:List[Any]      	=					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
 __SCREAMING_SNAKE_CASE					:Any      	=					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



 def 			snake_case__  (   self		:   Tuple			,				a__		:   Tuple			,				a__		:   int			,				a__		:   int      ):
      __magic_name__						       =    TextaTextGenerationPipeline(model=a__			,				tokenizer=a__      )
      return generator, ["Something to write", "Something else"]



 def 			snake_case__  (   self		:   List[str]			,				a__		:   List[Any]			,				a__		:   List[str]      ):
      __magic_name__						       =    generator('''Something there'''      )
      self.assertEqual(a__			,				[{'''generated_text''': ANY(a__      )}]      )
      # These are encoder decoder, they don't just append to incoming string
      self.assertFalse(outputs[0]['''generated_text'''].startswith('''Something there'''      )      )

      __magic_name__						       =    generator(['''This is great !''', '''Something else''']			,				num_return_sequences=2			,				do_sample=a__      )
      self.assertEqual(
          a__			,				[
              [{'''generated_text''': ANY(a__      )}, {'''generated_text''': ANY(a__      )}],
              [{'''generated_text''': ANY(a__      )}, {'''generated_text''': ANY(a__      )}],
          ]			,				)

      __magic_name__						       =    generator(
          ['''This is great !''', '''Something else''']			,				num_return_sequences=2			,				batch_size=2			,				do_sample=a__      )
      self.assertEqual(
          a__			,				[
              [{'''generated_text''': ANY(a__      )}, {'''generated_text''': ANY(a__      )}],
              [{'''generated_text''': ANY(a__      )}, {'''generated_text''': ANY(a__      )}],
          ]			,				)

      with self.assertRaises(a__      ):
           generator(4      )



 @require_torch
 def 			snake_case__  (   self		:   Any      ):
      __magic_name__						       =    pipeline('''text2text-generation'''			,				model='''patrickvonplaten/t5-tiny-random'''			,				framework='''pt'''      )
      # do_sample=False necessary for reproducibility
      __magic_name__						       =    generator('''Something there'''			,				do_sample=a__      )
      self.assertEqual(a__			,				[{'''generated_text''': ''''''}]      )

      __magic_name__						       =    3
      __magic_name__						       =    generator(
          '''Something there'''			,				num_return_sequences=a__			,				num_beams=a__			,				)
      __magic_name__						       =    [
          {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide Beide'''},
          {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide'''},
          {'''generated_text''': ''''''},
      ]
      self.assertEqual(a__			,				a__      )

      __magic_name__						       =    generator('''This is a test'''			,				do_sample=a__			,				num_return_sequences=2			,				return_tensors=a__      )
      self.assertEqual(
          a__			,				[
              {'''generated_token_ids''': ANY(torch.Tensor      )},
              {'''generated_token_ids''': ANY(torch.Tensor      )},
          ]			,				)
      __magic_name__						       =    generator.model.config.eos_token_id
      __magic_name__						       =    '''<pad>'''
      __magic_name__						       =    generator(
          ['''This is a test''', '''This is a second test''']			,				do_sample=a__			,				num_return_sequences=2			,				batch_size=2			,				return_tensors=a__			,				)
      self.assertEqual(
          a__			,				[
              [
                  {'''generated_token_ids''': ANY(torch.Tensor      )},
                  {'''generated_token_ids''': ANY(torch.Tensor      )},
              ],
              [
                  {'''generated_token_ids''': ANY(torch.Tensor      )},
                  {'''generated_token_ids''': ANY(torch.Tensor      )},
              ],
          ]			,				)



 @require_tf
 def 			snake_case__  (   self		:   int      ):
      __magic_name__						       =    pipeline('''text2text-generation'''			,				model='''patrickvonplaten/t5-tiny-random'''			,				framework='''tf'''      )
      # do_sample=False necessary for reproducibility
      __magic_name__						       =    generator('''Something there'''			,				do_sample=a__      )
      self.assertEqual(a__			,				[{'''generated_text''': ''''''}]      )






